modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-08 06:28:24
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 492
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listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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3mily1u/new-codegen-350m-mono-dpoed-attack-10-1
|
3mily1u
| 2025-04-27T21:21:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"codegen",
"text-generation",
"trl",
"dpo",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T21:19:56Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
zanetwice/suzylittlemusiclora01_lg_1200steps
|
zanetwice
| 2025-04-27T21:07:11Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-04-27T20:50:18Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: SUZYLITTLEMUSICLORA01
---
# Suzylittlemusiclora01_Lg_1200Steps
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `SUZYLITTLEMUSICLORA01` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SUZYLITTLEMUSICLORA01",
"lora_weights": "https://huggingface.co/zanetwice/suzylittlemusiclora01_lg_1200steps/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('zanetwice/suzylittlemusiclora01_lg_1200steps', weight_name='lora.safetensors')
image = pipeline('SUZYLITTLEMUSICLORA01').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1200
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/zanetwice/suzylittlemusiclora01_lg_1200steps/discussions) to add images that show off what you’ve made with this LoRA.
|
mlfoundations-dev/d1_science_shortest
|
mlfoundations-dev
| 2025-04-27T20:44:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T14:40:52Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: d1_science_shortest
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# d1_science_shortest
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_science_shortest dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.0.2
- Tokenizers 0.20.3
|
seba-1111/seba88
|
seba-1111
| 2025-04-27T20:39:30Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-27T20:39:30Z |
---
license: apache-2.0
---
|
Aina-Asif-Viral-Video/FULL.VIDEO.LINK.Aina.Asif.Viral.Video.Leaks.Tutorial
|
Aina-Asif-Viral-Video
| 2025-04-27T20:37:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-27T20:37:02Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/mw5wvsaa?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Actor Aina Asif Original V𝚒deo V𝚒deo took the internet by storm and amazed viewers on various social media platforms. Actor Aina Asif, a young and talented digital creator, recently became famous thanks to this interesting V𝚒deo.
L𝚎aked V𝚒deo Actor Aina Asif V𝚒ral V𝚒deo Original V𝚒deo L𝚒nk On Social Media Telegram X Trending Tiktok (18+)
L𝚎aked V𝚒deo Actor Aina Asif V𝚒ral V𝚒deo Original V𝚒deo L𝚒nk On Social Media X Trending Tiktok (18+)
L𝚎aked V𝚒deo Actor Aina Asif Original V𝚒deo V𝚒ral V𝚒deo L𝚎aked on X Twitter
|
imansadri/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_curious_anaconda
|
imansadri
| 2025-04-27T20:34:20Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am striped curious anaconda",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-09T04:02:26Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_curious_anaconda
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am striped curious anaconda
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_curious_anaconda
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="imansadri/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_curious_anaconda", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
secmlr/DS-Noisy_DS-Clean_DS-OSS_QWQ-OSS_QWQ-Clean_QWQ-Noisy_Con_Qwen2.5-7B-Instruct_sft
|
secmlr
| 2025-04-27T20:34:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T06:19:48Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: DS-Noisy_DS-Clean_DS-OSS_QWQ-OSS_QWQ-Clean_QWQ-Noisy_Con_Qwen2.5-7B-Instruct_sft
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DS-Noisy_DS-Clean_DS-OSS_QWQ-OSS_QWQ-Clean_QWQ-Noisy_Con_Qwen2.5-7B-Instruct_sft
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the DS-Noisy, the DS-Clean, the DS-OSS, the QWQ-OSS, the QWQ-Clean, the QWQ-Noisy and the Con datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 12
- total_train_batch_size: 24
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0
|
estrogen/remnant-32b-ckpt-ep2
|
estrogen
| 2025-04-27T20:31:28Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"glm4",
"generated_from_trainer",
"dataset:allura-org/inkmix-v3.0",
"base_model:THUDM/GLM-4-32B-0414",
"base_model:adapter:THUDM/GLM-4-32B-0414",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-27T20:31:24Z |
---
library_name: peft
license: mit
base_model: THUDM/GLM-4-32B-0414
tags:
- generated_from_trainer
datasets:
- allura-org/inkmix-v3.0
model-index:
- name: workspace/ckpts
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.8.0`
```yaml
# === Model Configuration ===
base_model: THUDM/GLM-4-32B-0414 # e.g. "mistralai/Mistral-Small-24B-Instruct-2501"
load_in_8bit: false
load_in_4bit: true
# === Training Setup ===
num_epochs: 2
micro_batch_size: 3
gradient_accumulation_steps: 2
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
# === Hyperparameter Configuration ===
optimizer: adamw_8bit
# Apollo-mini configuration:
#optim_args: "proj=random,rank=1,scale=128.0,scale_type=tensor,update_proj_gap=200"
# Regular Apollo configuration:
# optim_args:
#optim_target_modules: all_linear
learning_rate: 1e-5
lr_scheduler: rex
weight_decay: 0.01
warmup_ratio: 0.05
# === LoRA Configuration ===
adapter: qlora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.25
lora_target_modules:
lora_target_linear: true
# === Data Configuration ===
datasets:
- path: allura-org/inkmix-v3.0
type: chat_template
split: train
field_messages: conversations
message_field_role: from
message_field_content: value
train_on_eos: all
dataset_prepared_path: last_run_prepared
chat_template: jinja
chat_template_jinja: |
[gMASK]<sop>{%- for msg in messages %}{%- if msg.role == 'system' %}<|system|>
{{ msg.content }}{%- elif msg.role == 'user' %}<|user|>
{{ msg.content }}{%- elif msg.role == 'assistant' %}<|assistant|>
{{ msg.content }}{%- endif %}{%- endfor %}{% if add_generation_prompt %}<|assistant|>{% else %}<|user|>{% endif %}
# === Plugins ===
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
# === Hardware Optimization ===
gradient_checkpointing: offload
gradient_checkpointing_kwargs:
use_reentrant: false
cut_cross_entropy: true
deepspeed: deepspeed_configs/zero3_bf16.json
# === Wandb Tracking ===
wandb_project: glm4-32b-inkmix-v3
# === Checkpointing ===
saves_per_epoch: 2
save_total_limit: 3
# === Advanced Settings ===
output_dir: /workspace/ckpts
bf16: auto
flash_attention: true
train_on_inputs: false
group_by_length: false
logging_steps: 1
trust_remote_code: true
```
</details><br>
# workspace/ckpts
This model is a fine-tuned version of [THUDM/GLM-4-32B-0414](https://huggingface.co/THUDM/GLM-4-32B-0414) on the allura-org/inkmix-v3.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- total_eval_batch_size: 24
- optimizer: Use adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 31
- num_epochs: 2.0
### Training results
### Framework versions
- PEFT 0.15.1
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
dyang39/InternVL-reasoning-2b
|
dyang39
| 2025-04-27T20:29:42Z | 0 | 0 | null |
[
"safetensors",
"internvl_chat",
"custom_code",
"region:us"
] | null | 2025-04-27T06:41:44Z |
# InternVL-2B Finetuned Model
This is a finetuned version of InternVL-2B model.
## Model Details
- Base model: InternVL-2B
|
1shoomun/semant-cache-updated
|
1shoomun
| 2025-04-27T20:09:16Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"t5",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:2620",
"loss:MultipleNegativesRankingLoss",
"loss:CosineSimilarityLoss",
"custom_code",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:jinaai/jina-embedding-b-en-v1",
"base_model:finetune:jinaai/jina-embedding-b-en-v1",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-04-27T20:08:03Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2620
- loss:MultipleNegativesRankingLoss
- loss:CosineSimilarityLoss
base_model: jinaai/jina-embedding-b-en-v1
widget:
- source_sentence: What sector am I most heavily invested in?
sentences:
- 'Show me how to switch my stock portfolio to mutual funds
'
- What percentage of my portfolio is in X
- Which sector do I invest most in?
- source_sentence: Can you tell me how my portfolio ranks among others?
sentences:
- What is my AMC wise split ?
- In which funds am I paying highest fees
- Compare my portfolio with others?
- source_sentence: Which of my funds has the highest risk level?
sentences:
- Give me python code to find best funds in my portfolio
- Show my stocks ranked by performance
- Show my riskiest mutual funds
- source_sentence: What's going right with my portfolio?
sentences:
- Is my portfolio linked?
- My portfolio returns over all the years
- What's going well in my portfolio
- source_sentence: I'd like to know the percentage of large cap in my investments.
sentences:
- Show my riskiest holdings
- Can you show what percentage of my portfolio consists of large cap
- What is the expected return of my portfolio?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on jinaai/jina-embedding-b-en-v1
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: test eval
type: test-eval
metrics:
- type: cosine_accuracy@1
value: 0.8625954198473282
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9961832061068703
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8625954198473282
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.33206106870229
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19999999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8625954198473282
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9961832061068703
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9460250731496836
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9271628498727736
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9271628498727736
name: Cosine Map@100
---
# SentenceTransformer based on jinaai/jina-embedding-b-en-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1) <!-- at revision 32aa658e5ceb90793454d22a57d8e3a14e699516 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: T5EncoderModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
"I'd like to know the percentage of large cap in my investments.",
'Can you show what percentage of my portfolio consists of large cap',
'Show my riskiest holdings',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `test-eval`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:----------|
| cosine_accuracy@1 | 0.8626 |
| cosine_accuracy@3 | 0.9962 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.8626 |
| cosine_precision@3 | 0.3321 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.8626 |
| cosine_recall@3 | 0.9962 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.946** |
| cosine_mrr@10 | 0.9272 |
| cosine_map@100 | 0.9272 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Datasets
#### Unnamed Dataset
* Size: 1,310 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 4 tokens</li><li>mean: 10.62 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.06 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:--------------------------------------------------------------------|:-------------------------------------------------------------------|:-----------------|
| <code>are there any of my funds that are lagging behind</code> | <code>do I hold any funds that haven't been performing well</code> | <code>1.0</code> |
| <code>Which sectors are performing the best in my portfolio?</code> | <code>What are my best performing sectors?</code> | <code>1.0</code> |
| <code>List some of my top holdings</code> | <code>Show some of my best performing holdings</code> | <code>1.0</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### Unnamed Dataset
* Size: 1,310 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 4 tokens</li><li>mean: 10.68 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.13 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:--------------------------------------------------------------------|:----------------------------------------------------------|:-----------------|
| <code>I need my portfolio to hit 1000% returns by next month</code> | <code>make my portfolio return 1000% by next month</code> | <code>1.0</code> |
| <code>What are my stocks?</code> | <code>Show my stocks</code> | <code>1.0</code> |
| <code>I'd like to know my sector distribution.</code> | <code>What is my sector allocation?</code> | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 15
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 15
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | test-eval_cosine_ndcg@10 |
|:-------:|:----:|:-------------:|:------------------------:|
| 1.0 | 82 | - | 0.8929 |
| 2.0 | 164 | - | 0.9007 |
| 3.0 | 246 | - | 0.9112 |
| 4.0 | 328 | - | 0.9188 |
| 5.0 | 410 | - | 0.9285 |
| 6.0 | 492 | - | 0.9286 |
| 6.0976 | 500 | 0.2352 | 0.9291 |
| 7.0 | 574 | - | 0.9356 |
| 8.0 | 656 | - | 0.9404 |
| 9.0 | 738 | - | 0.9406 |
| 10.0 | 820 | - | 0.9434 |
| 11.0 | 902 | - | 0.9424 |
| 12.0 | 984 | - | 0.9455 |
| 12.1951 | 1000 | 0.164 | 0.9460 |
### Framework Versions
- Python: 3.10.16
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.7.0
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
leon-se/gemma-3-27b-it-qat-W4A16-G128
|
leon-se
| 2025-04-27T20:09:06Z | 0 | 1 | null |
[
"safetensors",
"gemma3",
"image-text-to-text",
"conversational",
"base_model:google/gemma-3-27b-it-qat-q4_0-unquantized",
"base_model:quantized:google/gemma-3-27b-it-qat-q4_0-unquantized",
"license:gemma",
"compressed-tensors",
"region:us"
] |
image-text-to-text
| 2025-04-27T19:54:56Z |
---
license: gemma
base_model:
- google/gemma-3-27b-it-qat-q4_0-unquantized
pipeline_tag: image-text-to-text
---
W4A16 quantization using llmcompressor. Run with:
```bash
vllm serve leon-se/gemma-3-27b-it-qat-W4A16-G128 --max-model-len 4096 --max-num-seqs 1
```
|
hardik9719/videomae-base-finetuned-ucf-timesfomer-subset
|
hardik9719
| 2025-04-27T20:07:10Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"timesformer",
"video-classification",
"generated_from_trainer",
"base_model:facebook/timesformer-base-finetuned-k400",
"base_model:finetune:facebook/timesformer-base-finetuned-k400",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2025-04-27T16:54:42Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/timesformer-base-finetuned-k400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ucf-timesfomer-subset
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-base-finetuned-ucf-timesfomer-subset
This model is a fine-tuned version of [facebook/timesformer-base-finetuned-k400](https://huggingface.co/facebook/timesformer-base-finetuned-k400) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6732
- Accuracy: 0.6689
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9369 | 0.26 | 26 | 0.6588 | 0.7272 |
| 0.9571 | 1.26 | 52 | 0.5239 | 0.7708 |
| 0.4176 | 2.26 | 78 | 0.7025 | 0.7142 |
| 0.1547 | 3.22 | 100 | 0.8794 | 0.6986 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu118
- Datasets 3.3.2
- Tokenizers 0.21.1
|
ataur09/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-snappy_regal_woodpecker
|
ataur09
| 2025-04-27T20:06:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am snappy regal woodpecker",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-26T15:30:15Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-snappy_regal_woodpecker
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am snappy regal woodpecker
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-snappy_regal_woodpecker
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ataur09/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-snappy_regal_woodpecker", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mlfoundations-dev/c1_math_10d_1s_10k
|
mlfoundations-dev
| 2025-04-27T20:05:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T03:09:27Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: c1_math_10d_1s_10k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# c1_math_10d_1s_10k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/c1_math_10d_1s_10k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Themira/lb_xnli
|
Themira
| 2025-04-27T20:03:22Z | 0 | 0 | null |
[
"pytorch",
"license:apache-2.0",
"region:us"
] | null | 2025-04-14T15:56:37Z |
---
license: apache-2.0
---
|
shlapique/llm-course-hw2-reward-model
|
shlapique
| 2025-04-27T19:48:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-classification",
"generated_from_trainer",
"trl",
"reward-trainer",
"dataset:HumanLLMs/Human-Like-DPO-Dataset",
"base_model:HuggingFaceTB/SmolLM-135M-Instruct",
"base_model:finetune:HuggingFaceTB/SmolLM-135M-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-27T19:48:09Z |
---
base_model: HuggingFaceTB/SmolLM-135M-Instruct
datasets: HumanLLMs/Human-Like-DPO-Dataset
library_name: transformers
model_name: trainer_output
tags:
- generated_from_trainer
- trl
- reward-trainer
licence: license
---
# Model Card for trainer_output
This model is a fine-tuned version of [HuggingFaceTB/SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct) on the [HumanLLMs/Human-Like-DPO-Dataset](https://huggingface.co/datasets/HumanLLMs/Human-Like-DPO-Dataset) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="shlapique/llm-course-hw2-reward-model", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with Reward.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.1
- Pytorch: 2.5.1+cu124
- Datasets: 3.5.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ghost613/VC-MJY_Woman_40s-chars-3fc-v2.3-subword-ctc
|
ghost613
| 2025-04-27T19:47:27Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper_phoneme",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-25T16:14:45Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
danielevian/lanza-2-merged
|
danielevian
| 2025-04-27T19:43:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:canopylabs/3b-es_it-pretrain-research_release",
"base_model:finetune:canopylabs/3b-es_it-pretrain-research_release",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T19:41:52Z |
---
base_model: canopylabs/3b-es_it-pretrain-research_release
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** danielevian
- **License:** apache-2.0
- **Finetuned from model :** canopylabs/3b-es_it-pretrain-research_release
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
borisloktev/qwen2_5-3B-bf16ft-base-pinpointing-lora-fix-prompt_big_full_txt_output
|
borisloktev
| 2025-04-27T19:40:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-04-27T18:59:09Z |
---
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
model_name: qwen2_5-3B-bf16ft-base-pinpointing-lora-fix-prompt_big_full_txt_output
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2_5-3B-bf16ft-base-pinpointing-lora-fix-prompt_big_full_txt_output
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="borisloktev/qwen2_5-3B-bf16ft-base-pinpointing-lora-fix-prompt_big_full_txt_output", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/5-plus/qwen2_5-3B-pinpointing/runs/wje6x0b1)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0.dev0
- Transformers: 4.51.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
3mily1u/new-codegen-350m-mono-dpoed-attack-50-0.1
|
3mily1u
| 2025-04-27T19:38:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"codegen",
"text-generation",
"trl",
"dpo",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T19:37:43Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
goatley/sentiment-final-model
|
goatley
| 2025-04-27T19:37:29Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-26T18:19:00Z |
---
library_name: transformers
license: mit
---
# Model Card for `goatley/sentiment-final-model`
This model is a fine-tuned **DistilBERT** model for **binary sentiment classification** (positive/negative) of English text reviews.
It was developed as part of an advanced NLP dashboard project demonstrating applied skills in deep learning, NLP engineering, and full-stack app deployment.
## Model Details
### Model Description
- **Developed by:** Keith Goatley
- **License:** MIT
- **Model type:** DistilBERT-based Sequence Classification (Binary)
- **Language(s):** English
- **Fine-tuned from:** `distilbert-base-uncased`
- **Base model:** Hugging Face Transformers v4
- **Framework:** PyTorch
### Model Sources
- **Repository:** [GitHub Repository](https://github.com/Keithgoatley/sentiment-analysis-app)
- **Demo:** [Hugging Face Space (when deployed)](https://huggingface.co/spaces/goatley/sentiment-analysis-dashboard)
## Uses
### Direct Use
- Classifying short text reviews (e.g., Amazon product reviews) into **positive** or **negative** sentiment.
### Downstream Use
- Embedding inside sentiment-driven recommendation engines
- As a component of multi-task NLP dashboards
- Fine-tuning for domain-specific sentiment (e.g., medical, finance, hospitality reviews)
### Out-of-Scope Use
- Not designed for languages other than English.
- Not suited for emotion detection beyond binary sentiment.
## Bias, Risks, and Limitations
This model was fine-tuned on Amazon reviews, which may carry biases toward product-related expressions and cultural language patterns.
Users should be cautious when applying the model outside typical e-commerce datasets.
### Recommendations
For more robust domain generalization:
- Further fine-tuning on task-specific datasets is advised.
## How to Get Started with the Model
```python
from transformers import pipeline
classifier = pipeline(
"sentiment-analysis",
model="goatley/sentiment-final-model",
tokenizer="goatley/sentiment-final-model"
)
classifier(["I love this!", "This was awful."])
Training Details
Training Data
Subset of Amazon Reviews Dataset
Balanced 50/50 split of positive and negative reviews
Approximately 5,000 examples used for fine-tuning
Training Procedure
Fine-tuned for 3 epochs
Learning rate scheduling with warmup
Optimizer: AdamW
Batch size: 16
Device: CPU-based training (GitHub Codespaces)
Training Hyperparameters
Learning Rate: 5e-5
Optimizer: AdamW
Max Sequence Length: 512
Epochs: 3
Evaluation
Testing Data
Held-out test split from the Amazon Reviews dataset
Metrics
Metric Score
Test Accuracy 85%
Evaluation was performed using basic classification metrics (accuracy, precision, recall, F1-score).
Environmental Impact
Hardware Type: CPU (GitHub Codespaces)
Hours Used: ~2 hours
Cloud Provider: GitHub (Microsoft Azure backend)
Compute Region: North America
Carbon Emitted: Negligible (very small dataset + CPU-only fine-tuning)
Technical Specifications
Model Architecture and Objective
Architecture: DistilBERT Transformer Encoder
Task Objective: Sequence classification with 2 labels (positive, negative)
Compute Infrastructure
Training performed on GitHub Codespaces virtual machines.
No GPUs were used.
Software Environment
Hugging Face transformers==4.51.3
Datasets datasets==3.5.0
PyTorch torch==2.6.0
Citation
If you use this model or find it helpful, please cite:
APA:
Goatley, K. (2025). Sentiment Analysis Fine-Tuned DistilBERT Model [Model]. Hugging Face. https://huggingface.co/goatley/sentiment-final-model
BibTeX:
@misc{goatley2025sentiment,
author = {Keith Goatley},
title = {Sentiment
Analysis Fine-Tuned DistilBERT Model},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/goatley/sentiment-final-model}}
}
Model Card Authors
Keith Goatley
Contact
For questions or inquiries, please contact via:
GitHub: https://github.com/Keithgoatley
Hugging Face: https://huggingface.co/goatley
|
Yudees28/MedRefine-QA
|
Yudees28
| 2025-04-27T19:37:11Z | 0 | 0 | null |
[
"safetensors",
"bert",
"region:us"
] | null | 2025-04-27T19:20:03Z |
MedRefine-QA
A medical question-answering system that provides reliable and accurate health information through semantic matching techniques. Designed to help users find trustworthy answers to common medical queries while emphasizing the importance of professional medical advice.
Overview
MedRefine-QA leverages domain-specific language models and semantic similarity matching to deliver accurate responses to medical questions. The system combines the strengths of biomedical models like PubMedBERT and Bio_ClinicalBERT with a curated dataset of verified medical information.
Features
Semantic matching using S-BioBert embeddings for question understanding
Fallback mechanisms for questions outside the knowledge base
Appropriate disclaimers regarding professional medical advice
Simple and intuitive interface for medical inquiries
Limitations
This model is intended for informational purposes only and should not replace professional medical advice, diagnosis, or treatment. Users should always consult qualified healthcare providers for medical concerns.
Citation
If you use this model in your research, please cite:
@software{MedRefine-QA,
title = {MedRefine-QA: A Medical Question-Answering System},
year = {2025},
url = {https://huggingface.co/Yudees28/MedRefine-QA}
}
|
fhaslam/Llama-3.2-1B-Financial-Sentiment13
|
fhaslam
| 2025-04-27T19:36:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"text-generation",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"arxiv:2405.16406",
"license:llama3.2",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T19:36:40Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3.2
extra_gated_prompt: >-
### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT
Llama 3.2 Version Release Date: September 25, 2024
“Agreement” means the terms and conditions for use, reproduction, distribution
and modification of the Llama Materials set forth herein.
“Documentation” means the specifications, manuals and documentation accompanying Llama 3.2
distributed by Meta at https://llama.meta.com/doc/overview.
“Licensee” or “you” means you, or your employer or any other person or entity (if you are
entering into this Agreement on such person or entity’s behalf), of the age required under
applicable laws, rules or regulations to provide legal consent and that has legal authority
to bind your employer or such other person or entity if you are entering in this Agreement
on their behalf.
“Llama 3.2” means the foundational large language models and software and algorithms, including
machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
fine-tuning enabling code and other elements of the foregoing distributed by Meta at
https://www.llama.com/llama-downloads.
“Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and
any portion thereof) made available under this Agreement.
“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or,
if you are an entity, your principal place of business is in the EEA or Switzerland)
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By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials,
you agree to be bound by this Agreement.
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---
## Model Information
The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
**Model Developer:** Meta
**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
| | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
| Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** Sept 25, 2024
**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources.
**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
## How to use
This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import torch
from transformers import pipeline
model_id = "meta-llama/Llama-3.2-1B-Instruct"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/*" --local-dir Llama-3.2-1B-Instruct
```
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | ----- | :---: | :---: | :---: |
| Llama 3.2 1B | 370k | \- | 700 | 107 | 0 |
| Llama 3.2 3B | 460k | \- | 700 | 133 | 0 |
| Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 1B QLora | 1.3k | 0 | 700 | 0.381 | 0 |
| Llama 3.2 3B QLora | 1.6k | 0 | 700 | 0.461 | 0 |
| Total | 833k | 86k | | 240 | 0 |
\*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required.
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).
**Data Freshness:** The pretraining data has a cutoff of December 2023\.
## Quantization
### Quantization Scheme
We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts:
- All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations.
- The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation.
- Similar to classification layer, an 8-bit per channel quantization is used for embedding layer.
### Quantization-Aware Training and LoRA
The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO).
### SpinQuant
[SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length.
## Benchmarks \- English Text
In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library.
### Base Pretrained Models
| Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
| ----- | ----- | :---: | :---: | :---: | :---: | :---: |
| General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 |
| | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 |
| | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 |
| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |
| | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |
| | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |
| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |
### Instruction Tuned Models
| Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 |
| Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 |
| Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 |
| Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 |
| Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 |
| | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 |
| Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 |
| | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 |
| | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 |
| Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 |
| | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 |
| Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 |
| | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 |
| | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 |
| Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 |
\*\*for comparison purposes only. Model not released.
### Multilingual Benchmarks
| Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 |
| | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 |
| | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 |
| | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 |
| | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 |
| | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 |
| | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 |
\*\*for comparison purposes only. Model not released.
## Inference time
In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device.
| Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) |
| :---- | ----- | ----- | ----- | ----- | ----- |
| 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 |
| 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) |
| 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) |
| 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 |
| 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) |
| 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) |
(\*) The performance measurement is done using an adb binary-based approach.
(\*\*) It is measured on an Android OnePlus 12 device.
(\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64
*Footnote:*
- *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.*
- *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.*
- *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better*
- *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch*
- *RSS size \- Memory usage in resident set size (RSS)*
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama
2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm
3. Provide protections for the community to help prevent the misuse of our models
### Responsible Deployment
**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/).
#### Llama 3.2 Instruct
**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/).
**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.2 Systems
**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### New Capabilities and Use Cases
**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.
**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version.
### Evaluations
**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.
**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models.
**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models.
### Community
**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
KNIGHT1205/medical-question-model
|
KNIGHT1205
| 2025-04-27T19:36:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-27T19:36:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sergioalves/6ff3b9b2-ce78-405a-b408-3077e640db6c
|
sergioalves
| 2025-04-27T19:32:52Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:Intel/neural-chat-7b-v3-3",
"base_model:adapter:Intel/neural-chat-7b-v3-3",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-27T19:14:32Z |
---
library_name: peft
license: apache-2.0
base_model: Intel/neural-chat-7b-v3-3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 6ff3b9b2-ce78-405a-b408-3077e640db6c
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: true
adapter: lora
base_model: Intel/neural-chat-7b-v3-3
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- a0c2b0c2a290b3a0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a0c2b0c2a290b3a0_train_data.json
type:
field_input: Context
field_instruction: Question
field_output: Answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: sergioalves/6ff3b9b2-ce78-405a-b408-3077e640db6c
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/a0c2b0c2a290b3a0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3002708a-6c81-44ee-8407-ea206b5cbbd2
wandb_project: s56-8
wandb_run: your_name
wandb_runid: 3002708a-6c81-44ee-8407-ea206b5cbbd2
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 6ff3b9b2-ce78-405a-b408-3077e640db6c
This model is a fine-tuned version of [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4860
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4051 | 0.0992 | 200 | 0.4860 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
robiulawaldev/1355776d-61b9-407d-8492-a1e93cf2cf1d
|
robiulawaldev
| 2025-04-27T19:23:01Z | 0 | 0 |
peft
|
[
"peft",
"generated_from_trainer",
"base_model:lcw99/zephykor-ko-7b-chang",
"base_model:adapter:lcw99/zephykor-ko-7b-chang",
"region:us"
] | null | 2025-04-27T19:22:38Z |
---
library_name: peft
tags:
- generated_from_trainer
base_model: lcw99/zephykor-ko-7b-chang
model-index:
- name: robiulawaldev/1355776d-61b9-407d-8492-a1e93cf2cf1d
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robiulawaldev/1355776d-61b9-407d-8492-a1e93cf2cf1d
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0399
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
shlapique/trainer_output
|
shlapique
| 2025-04-27T19:17:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-classification",
"generated_from_trainer",
"trl",
"reward-trainer",
"dataset:HumanLLMs/Human-Like-DPO-Dataset",
"base_model:HuggingFaceTB/SmolLM-135M-Instruct",
"base_model:finetune:HuggingFaceTB/SmolLM-135M-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-27T19:16:05Z |
---
base_model: HuggingFaceTB/SmolLM-135M-Instruct
datasets: HumanLLMs/Human-Like-DPO-Dataset
library_name: transformers
model_name: trainer_output
tags:
- generated_from_trainer
- trl
- reward-trainer
licence: license
---
# Model Card for trainer_output
This model is a fine-tuned version of [HuggingFaceTB/SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct) on the [HumanLLMs/Human-Like-DPO-Dataset](https://huggingface.co/datasets/HumanLLMs/Human-Like-DPO-Dataset) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="shlapique/trainer_output", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with Reward.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.1
- Pytorch: 2.5.1+cu124
- Datasets: 3.5.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Ishan0612/biobert-ner-disease
|
Ishan0612
| 2025-04-27T19:04:49Z | 52 | 2 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"medical-ner",
"biobert",
"healthcare",
"disease-extraction",
"named-entity-recognition",
"huggingface",
"ncbi-disease-dataset",
"en",
"dataset:ncbi/ncbi_disease",
"base_model:dmis-lab/biobert-base-cased-v1.1",
"base_model:finetune:dmis-lab/biobert-base-cased-v1.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-03-12T21:34:11Z |
---
library_name: transformers
tags:
- medical-ner
- biobert
- healthcare
- disease-extraction
- named-entity-recognition
- huggingface
- ncbi-disease-dataset
license: apache-2.0
datasets:
- ncbi/ncbi_disease
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- dmis-lab/biobert-base-cased-v1.1
pipeline_tag: token-classification
---
# Note: New Improved Model Available (F1 Score: 89.04%)
For better performance and an **improved F1 score, please use the updated model here: https://huggingface.co/Ishan0612/biobert-ner-disease-ncbi**
# BioBERT Disease NER Model
One of the **powerful medical NER models**, fine-tuned on BioBERT with the NCBI Disease dataset.
It achieves **98.79% accuracy** and an **F1-score of 86.98%**, delivering reliable performance for disease
extraction tasks by accurately identifying diseases and symptoms in medical texts.
# Model Performance
- **Precision:** 85.69%
- **Recall:** 88.31%
- **F1-Score:** 86.98%
- **Accuracy:** 98.79%
✅ Fine-tuned over **6,800+ annotated examples** for **5 epochs**, achieving consistently high validation scores.
## Intended Use
This model is designed for:
- Extracting disease mentions from clinical and biomedical texts.
- Powering information retrieval, research automation, and medical chatbots.
# Training Data
This model was trained on the [NCBI disease dataset](https://huggingface.co/datasets/ncbi_disease), which consists of 793 PubMed abstracts with 6892 disease mentions.
## How to Use
You can use this model with the Hugging Face Transformers library:
*Note:* LABEL_0 corresponds to "O" (Outside), LABEL_1 to "B-Disease", and LABEL_2 to "I-Disease" following the BIO tagging format.
```python
from transformers import pipeline
# Load from Hugging Face
nlp = pipeline("ner", model="Ishan0612/biobert_medical_ner", tokenizer="Ishan0612/biobert_medical_ner", aggregation_strategy="simple")
# Sample medical text
text = """Robert suffering from chest pain and thiroid."""
# Extract entities
ner_results = nlp(text)
# Display results
print("Extracted Medical Entities:")
for entity in ner_results:
print(f"{entity['word']} ({entity['entity_group']}) - Confidence: {entity['score']:.2f}")
```
This should output:
Extracted Medical Entities:
Robert suffering from (LABEL_0) - Confidence: 1.00
chest (LABEL_1) - Confidence: 1.00
pain (LABEL_2) - Confidence: 1.00
and (LABEL_0) - Confidence: 1.00
th (LABEL_1) - Confidence: 1.00
##iroid (LABEL_2) - Confidence: 0.97
. (LABEL_0) - Confidence: 1.00
|
7-Star-Maya-G-viral-video/TRENDING.Star.Maya.G.Viral.Video.Leaks.Tutorial
|
7-Star-Maya-G-viral-video
| 2025-04-27T19:01:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-27T18:59:53Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/53zstebc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
"SNL" Stars Maya Rudolph And Jim Gaffigan Are Going Viral For Their "Absolutely Perfect" Impressions Of Kamala Harris And Tim Walz
Maya Rudolph made her big return as Vice President Kamala Harris, while Jim Gaffigan debuted as her
Watch Maya Rudolph cover viral TikTok hit 'One Margarita' while supporting Vampire Weekend
Maya Rudolph opened for Vampire Weekend this weekend, and marked the occasion by covering the viral
Angel Reese Talks G Herbo & Baecation With Jalen Duren (Video)
|
AnkitaMungalpara/llava-finetune-medtrinity
|
AnkitaMungalpara
| 2025-04-27T18:22:28Z | 38 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:llava-hf/llava-v1.6-mistral-7b-hf",
"base_model:adapter:llava-hf/llava-v1.6-mistral-7b-hf",
"license:apache-2.0",
"region:us"
] | null | 2025-04-23T19:28:22Z |
---
library_name: peft
license: apache-2.0
base_model: llava-hf/llava-v1.6-mistral-7b-hf
tags:
- generated_from_trainer
model-index:
- name: llava-finetune-medtrinity
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llava-finetune-medtrinity
This model is a fine-tuned version of [llava-hf/llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2860
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 6.9632 | 0.3565 | 100 | 3.4625 |
| 6.6578 | 0.7130 | 200 | 3.3283 |
| 6.5774 | 1.0677 | 300 | 3.2879 |
| 6.573 | 1.4242 | 400 | 3.2861 |
| 6.5728 | 1.7807 | 500 | 3.2860 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1
- Datasets 3.5.0
- Tokenizers 0.21.1
|
shovit/MedTalk-Llama3.2-3B-lora
|
shovit
| 2025-04-27T18:15:35Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-26T09:56:28Z |
---
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** shovit
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
BootesVoid/cm9zsazyn02ouqeqo68mb4r5u_cm9zxyxbe02qoqeqohubitv3i
|
BootesVoid
| 2025-04-27T18:14:16Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-04-27T18:14:14Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: KALANI365
---
# Cm9Zsazyn02Ouqeqo68Mb4R5U_Cm9Zxyxbe02Qoqeqohubitv3I
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `KALANI365` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "KALANI365",
"lora_weights": "https://huggingface.co/BootesVoid/cm9zsazyn02ouqeqo68mb4r5u_cm9zxyxbe02qoqeqohubitv3i/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cm9zsazyn02ouqeqo68mb4r5u_cm9zxyxbe02qoqeqohubitv3i', weight_name='lora.safetensors')
image = pipeline('KALANI365').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cm9zsazyn02ouqeqo68mb4r5u_cm9zxyxbe02qoqeqohubitv3i/discussions) to add images that show off what you’ve made with this LoRA.
|
balajiX/QG
|
balajiX
| 2025-04-27T18:12:34Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-04-23T21:41:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
BenevolenceMessiah/FuseChat-Qwen-2.5-7B-Instruct-Q8_0-GGUF
|
BenevolenceMessiah
| 2025-04-27T18:11:32Z | 0 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"dataset:FuseAI/FuseChat-3.0-DPO-Data",
"base_model:FuseAI/FuseChat-Qwen-2.5-7B-Instruct",
"base_model:quantized:FuseAI/FuseChat-Qwen-2.5-7B-Instruct",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-27T18:10:57Z |
---
base_model: FuseAI/FuseChat-Qwen-2.5-7B-Instruct
datasets:
- FuseAI/FuseChat-3.0-DPO-Data
tags:
- llama-cpp
- gguf-my-repo
---
# BenevolenceMessiah/FuseChat-Qwen-2.5-7B-Instruct-Q8_0-GGUF
This model was converted to GGUF format from [`FuseAI/FuseChat-Qwen-2.5-7B-Instruct`](https://huggingface.co/FuseAI/FuseChat-Qwen-2.5-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/FuseAI/FuseChat-Qwen-2.5-7B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo BenevolenceMessiah/FuseChat-Qwen-2.5-7B-Instruct-Q8_0-GGUF --hf-file fusechat-qwen-2.5-7b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo BenevolenceMessiah/FuseChat-Qwen-2.5-7B-Instruct-Q8_0-GGUF --hf-file fusechat-qwen-2.5-7b-instruct-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo BenevolenceMessiah/FuseChat-Qwen-2.5-7B-Instruct-Q8_0-GGUF --hf-file fusechat-qwen-2.5-7b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo BenevolenceMessiah/FuseChat-Qwen-2.5-7B-Instruct-Q8_0-GGUF --hf-file fusechat-qwen-2.5-7b-instruct-q8_0.gguf -c 2048
```
|
davidfrem/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stalking_hardy_ape
|
davidfrem
| 2025-04-27T18:10:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am stalking hardy ape",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-25T00:11:52Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stalking_hardy_ape
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am stalking hardy ape
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stalking_hardy_ape
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="davidfrem/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stalking_hardy_ape", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
StevenMD/llama1.1v1
|
StevenMD
| 2025-04-27T18:02:40Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-27T17:28:20Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** StevenMD
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ShaSapna-Kumari-MMS-Original-video/Video.shah.sapna.kumari.viral.video.link.telegram
|
ShaSapna-Kumari-MMS-Original-video
| 2025-04-27T17:59:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-27T17:59:25Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
ShaSapna-Kumari-MMS-Original-video/full.video.shah.sapna.kumari.viral.video.link.telegram
|
ShaSapna-Kumari-MMS-Original-video
| 2025-04-27T17:58:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-27T17:58:40Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Zack-Z/gemma3_27bi_cotsft_rs0_0_5cut_ru_cot2_e2
|
Zack-Z
| 2025-04-27T17:53:42Z | 0 | 0 |
transformers
|
[
"transformers",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"gemma3",
"conversational",
"en",
"base_model:unsloth/gemma-3-27b-it",
"base_model:finetune:unsloth/gemma-3-27b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T14:08:33Z |
---
base_model: unsloth/gemma-3-27b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Zack-Z
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-27b-it
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
huyg1108/gemma-3-4b-lora-essay_feedback-backup
|
huyg1108
| 2025-04-27T17:50:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-27T17:50:16Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** huyg1108
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ver-lady-alvarez-polemica-viral-video/Ver.video.Lady.alvarez.polemica.viral.el.contenido.de.la.chonera.bonita
|
ver-lady-alvarez-polemica-viral-video
| 2025-04-27T17:48:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-27T17:47:52Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
LuckyLukke/DPO_5-1000
|
LuckyLukke
| 2025-04-27T17:37:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T17:34:30Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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## Citation [optional]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
kayrab/turkish-gpt2-medium-deepseek-qa
|
kayrab
| 2025-04-27T17:37:20Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"turkish",
"text-generation",
"question-answering",
"instruction-following",
"deepseek",
"fine-tuning",
"lora",
"tr",
"base_model:ytu-ce-cosmos/turkish-gpt2-medium",
"base_model:adapter:ytu-ce-cosmos/turkish-gpt2-medium",
"license:mit",
"region:us"
] |
question-answering
| 2025-04-27T16:41:48Z |
---
license: mit
language: tr
base_model: ytu-ce-cosmos/turkish-gpt2-medium
pipeline_tag: question-answering
tags:
- turkish
- gpt2
- text-generation
- question-answering
- instruction-following
- deepseek
- fine-tuning
- lora
---
# Turkish GPT-2 Medium - DeepSeek Soru-Cevap İnce Ayarlı Model (kayrab/turkish-gpt2-medium-deepseek-qa)
Bu model, [ytu-ce-cosmos/turkish-gpt2-medium](https://huggingface.co/ytu-ce-cosmos/turkish-gpt2-medium) temel alınarak, belirli bir soru-cevap veri kümesi üzerinde **LoRA (Low-Rank Adaptation)** yöntemiyle ince ayarlanmış (fine-tuned) bir Türkçe dil modelidir.
## Model Açıklaması
Model, kendisine `<SORU>` ve `<CEVAP>` etiketleriyle yapılandırılmış bir biçimde sunulan sorulara yanıt vermek üzere eğitilmiştir. Eğitimde kullanılan cevaplar **DeepSeek** modeli tarafından üretilmiştir. Amaç, temel modelin belirli bir talimat biçimine uyarak tutarlı ve bağlama uygun cevaplar üretme yeteneğini geliştirmektir.
## Eğitim Verisi
Model, aşağıdaki yapıya sahip bir `.csv` dosyasındaki verilerle eğitilmiştir:
* **Soru:** Türkçe sorunun metni.
* **DeepSeek cevabı:** İlgili soru için DeepSeek tarafından üretilmiş cevap metni.
Eğitim sırasında veri, modelin girdi/çıktı sınırlarını anlaması için özel etiketlerle biçimlendirilmiştir:
```python
<SORU> [Soru metni buraya gelecek] </SORU> <CEVAP> [Cevap metni buraya gelecek] </CEVAP><|endoftext|>
```
* `<SORU>` ve `</SORU>`: Sorunun başlangıcını ve bitişini işaretler.
* `<CEVAP>` ve `</CEVAP>`: Cevabın başlangıcını ve bitişini işaretler.
* `<|endoftext|>`: GPT-2'nin standart metin sonu (EOS) belirteci olup, her örneğin bittiğini gösterir.
Bu özel belirteçler tokenizer'a eklenmiş ve modelin kelime dağarcığı genişletilmiştir.
## Eğitim Prosedürü
Model, Hugging Face `transformers` ve `trl` (Transformer Reinforcement Learning) kütüphaneleri kullanılarak `SFTTrainer` (Supervised Fine-tuning Trainer) ile eğitilmiştir. Eğitimde kullanılan temel hiperparametreler şunlardır:
* **Öğrenme Oranı (Learning Rate):** 1e-4
* **Batch Büyüklüğü (Per Device):** 2
* **Gradyan Biriktirme Adımları (Gradient Accumulation Steps):** 8 (Etkin batch büyüklüğü: 2 * 8 * #GPU)
* **Epoch Sayısı:** 2
* **Maksimum Sekans Uzunluğu (Max Sequence Length):** 1024 token
* **Optimizatör (Optimizer):** paged_adamw_8bit (Bellek verimliliği için)
* **Ağırlık Azaltma (Weight Decay):** 0.01
* **Isınma Oranı (Warmup Ratio):** 0.03
* **LR Zamanlayıcı Tipi (LR Scheduler Type):** linear
* **Maksimum Gradyan Normu (Max Grad Norm):** 0.1
* **LoRA Rank (r):** 8
* **LoRA Alpha (α):** 16
* **LoRA Hedef Modüller (Target Modules):** `c_attn`, `c_proj`, `c_fc` (GPT-2 mimarisine uygun dikkat ve feed-forward katmanları)
* **Eğitim Hassasiyeti:** fp16
Eğitim sırasında, padding belirteçleri ve özel `<SORU>`, `</SORU>`, `<CEVAP>` belirteçleri kayıp (loss) hesaplamasından maskelenmiştir (`ignore_index = -100`). Yalnızca cevap kısmındaki (`</CEVAP>` hariç) belirteçler üzerinden öğrenme gerçekleşmiştir.
## Eğitim Kayıp Grafiği (Training Loss):
Eğitim süreci boyunca kayıp değerinin (loss) değişimi aşağıdaki grafikte görülebilir.
## Nasıl Kullanılır
Modeli `transformers` kütüphanesi ile kolayca kullanabilirsiniz. Model, girdiyi eğitimde kullanılan biçimde beklemektedir (`<SORU> ... </SORU> <CEVAP>`).
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Model ve tokenizer adını belirtin
model_name = "kayrab/turkish-gpt2-medium-deepseek-qa"
# Tokenizer'ı yükleyin (use_fast=True önerilir)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
# Modeli yükleyin (GPU varsa otomatik olarak GPU'ya yükler)
# Düşük bellekli GPU'lar için dtype=torch.float16 veya torch.bfloat16 kullanabilirsiniz
model = AutoModelForCausalLM.from_pretrained(
model_name,
# torch_dtype=torch.float16, # Opsiyonel: fp16 kullanmak için
device_map="auto" # Modeli uygun cihaza (GPU/CPU) dağıtır
)
# Kullanılacak soruyu tanımlayın
soru = "Türkiye'nin en kalabalık şehri hangisidir ve neden önemlidir?"
# Soruyu modelin beklediği biçime getirin
# Dikkat: Prompt'un sonunda <CEVAP> etiketi ve bir boşluk olmalı!
prompt = f"<SORU> {soru} </SORU> <CEVAP> "
# Girdiyi token'lara çevirin ve modelin cihazına gönderin
inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False).to(model.device)
# Cevap üretme parametreleri
# </CEVAP> token'ını EOS (End Of Sentence) olarak kullanacağız
eos_token_id = tokenizer.convert_tokens_to_ids("</CEVAP>")
if eos_token_id == tokenizer.unk_token_id: # Eğer token eklenmemişse (nadiren olur)
eos_token_id = tokenizer.eos_token_id
# Metin üretme (generate) fonksiyonunu çağırın
outputs = model.generate(
**inputs,
max_new_tokens=150, # Üretilecek maksimum yeni token sayısı
eos_token_id=eos_token_id, # Bu token üretildiğinde dur
pad_token_id=tokenizer.eos_token_id, # Padding için EOS kullan
do_sample=True, # Olasılıksal örnekleme yap
temperature=0.7, # Daha tutarlı çıktılar için sıcaklığı düşür
top_p=0.9, # Nucleus sampling
no_repeat_ngram_size=3 # 3-gram tekrarını engelle
)
# Üretilen tokenları alın (girdi prompt'u hariç)
output_tokens = outputs[0, inputs["input_ids"].shape[1]:]
# Tokenları metne çevirin
# skip_special_tokens=True, özel token'ları (örn: <|endoftext|>) çıktıdan kaldırır
cevap = tokenizer.decode(output_tokens, skip_special_tokens=True)
# </CEVAP> etiketi kalıntılarını temizle (generate bazen tam EOS'ta durmaz)
cevap_temiz = cevap.split("</CEVAP>")[0].strip()
print("-" * 20)
print(f"Soru: {soru}")
print("-" * 20)
print(f"Üretilen Cevap: {cevap_temiz}")
print("-" * 20)
# Örnek Çıktı (Modele göre değişebilir):
# --------------------
# Soru: Türkiye'nin en kalabalık şehri hangisidir ve neden önemlidir?
# --------------------
# Üretilen Cevap: Türkiye'nin en kalabalık şehri İstanbul'dur. İstanbul, tarihi, kültürel ve ekonomik açıdan büyük bir öneme sahiptir. İki kıtayı birbirine bağlayan stratejik konumu, zengin tarihi mirası ve Türkiye ekonomisinin merkezi olması nedeniyle önemlidir.
# --------------------
```
## Değerlendirme Sonuçları
Modelin performansı, eğitim veri kümesinde bulunmayan, özel olarak hazırlanmış bir soru kümesi üzerinde de sınanmıştır. Bu sınama için kullanılan sorular ve modelin ürettiği cevaplar `gpt2_medium_deepseek.csv` dosyasında yer almaktadır.
[gpt2_medium_deepseek.csv](gpt2_medium_deepseek.csv) dosyasını inceleyerek modelin farklı türdeki sorulara verdiği yanıtların kalitesini görebilirsiniz.
## Sınırlılıklar ve Dikkat Edilmesi Gerekenler
* Modelin performansı, girdi sorusunun eğitim verisindeki biçim ve tarza ne kadar benzediğine bağlıdır.
* Model, temel modelden (turkish-gpt2-medium) ve eğitim verisinden (DeepSeek cevapları) kaynaklanan yanlılıkları (bias) miras almış olabilir.
* Üretilen cevapların doğruluğu her zaman garanti edilmez ve kritik uygulamalar için kontrol edilmelidir.
* Model, `<SORU> ... </SORU> <CEVAP>` biçimi dışında verilen girdilere beklenmedik veya anlamsız yanıtlar üretebilir.
# Turkish GPT-2 Medium - DeepSeek Question-Answering Fine-tuned Model (kayrab/turkish-gpt2-medium-deepseek-qa)
This model is a Turkish language model fine-tuned using the **LoRA (Low-Rank Adaptation)** method on a specific question-answering dataset, based on [ytu-ce-cosmos/turkish-gpt2-medium](https://huggingface.co/ytu-ce-cosmos/turkish-gpt2-medium).
## Model Description
The model is trained to respond to questions presented in a structured format with `<SORU>` and `<CEVAP>` tags. The answers used during training were generated by the **DeepSeek** model. The goal is to enhance the base model's ability to produce consistent and contextually appropriate answers following a specific instruction format.
## Training Data
The model was trained on data from a `.csv` file with the following structure:
* **Soru:** The text of the Turkish question.
* **DeepSeek cevabı:** The answer text generated by DeepSeek for the corresponding question.
During training, the data was formatted with special tags to help the model understand input/output boundaries:
```python
<SORU> [Question text here] </SORU> <CEVAP> [Answer text here] </CEVAP><|endoftext|>
```
* `<SORU>` and `</SORU>`: Mark the beginning and end of the question.
* `<CEVAP>` and `</CEVAP>`: Mark the beginning and end of the answer.
* `<|endoftext|>`: GPT-2's standard end-of-text (EOS) token, indicating the end of each example.
These special tokens were added to the tokenizer, expanding the model's vocabulary.
## Training Procedure
The model was trained using the Hugging Face `transformers` and `trl` (Transformer Reinforcement Learning) libraries with the `SFTTrainer` (Supervised Fine-tuning Trainer). The core hyperparameters used during training are:
* **Learning Rate:** 1e-4
* **Batch Size (Per Device):** 2
* **Gradient Accumulation Steps:** 8 (Effective batch size: 2 * 8 * #GPUs)
* **Number of Epochs:** 2
* **Maximum Sequence Length:** 1024 tokens
* **Optimizer:** paged_adamw_8bit (For memory efficiency)
* **Weight Decay:** 0.01
* **Warmup Ratio:** 0.03
* **LR Scheduler Type:** linear
* **Max Grad Norm:** 0.1
* **LoRA Rank (r):** 8
* **LoRA Alpha (α):** 16
* **LoRA Target Modules:** `c_attn`, `c_proj`, `c_fc` (Attention and feed-forward layers suitable for GPT-2 architecture)
* **Training Precision:** fp16
During training, padding tokens and the special tokens `<SORU>`, `</SORU>`, `<CEVAP>` were masked from the loss calculation (`ignore_index = -100`). Learning occurred only over the tokens in the answer part (excluding `</CEVAP>`).
## Training Loss Graph:
The change in the loss value during the training process can be seen in the graph below.
## How to Use
You can easily use the model with the `transformers` library. The model expects the input in the format used during training (`<SORU> ... </SORU> <CEVAP>`).
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Specify the model and tokenizer name
model_name = "kayrab/turkish-gpt2-medium-deepseek-qa"
# Load the tokenizer (use_fast=True is recommended)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
# Load the model (automatically loads to GPU if available)
# For low-memory GPUs, you can use dtype=torch.float16 or torch.bfloat16
model = AutoModelForCausalLM.from_pretrained(
model_name,
# torch_dtype=torch.float16, # Optional: to use fp16
device_map="auto" # Distributes the model to the appropriate device (GPU/CPU)
)
# Define the question to use
soru = "Türkiye'nin en kalabalık şehri hangisidir ve neden önemlidir?" # "Which is Turkey's most populous city and why is it important?"
# Format the question into the format expected by the model
# Note: The prompt must end with the <CEVAP> tag and a space!
prompt = f"<SORU> {soru} </SORU> <CEVAP> "
# Tokenize the input and send it to the model's device
inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False).to(model.device)
# Answer generation parameters
# We will use the </CEVAP> token as EOS (End Of Sentence)
eos_token_id = tokenizer.convert_tokens_to_ids("</CEVAP>")
if eos_token_id == tokenizer.unk_token_id: # If the token wasn't added (rarely happens)
eos_token_id = tokenizer.eos_token_id
# Call the text generation (generate) function
outputs = model.generate(
**inputs,
max_new_tokens=150, # Maximum number of new tokens to generate
eos_token_id=eos_token_id, # Stop when this token is generated
pad_token_id=tokenizer.eos_token_id, # Use EOS for padding
do_sample=True, # Perform probabilistic sampling
temperature=0.7, # Lower temperature for more consistent outputs
top_p=0.9, # Nucleus sampling
no_repeat_ngram_size=3 # Prevent 3-gram repetition
)
# Get the generated tokens (excluding the input prompt)
output_tokens = outputs[0, inputs["input_ids"].shape[1]:]
# Decode the tokens into text
# skip_special_tokens=True removes special tokens (e.g., <|endoftext|>) from the output
cevap = tokenizer.decode(output_tokens, skip_special_tokens=True)
# Clean up any </CEVAP> tag remnants (generate sometimes doesn't stop exactly at EOS)
cevap_temiz = cevap.split("</CEVAP>")[0].strip()
print("-" * 20)
print(f"Soru (Question): {soru}")
print("-" * 20)
print(f"Üretilen Cevap (Generated Answer): {cevap_temiz}")
print("-" * 20)
# Example Output (May vary depending on the model):
# --------------------
# Soru (Question): Türkiye'nin en kalabalık şehri hangisidir ve neden önemlidir?
# --------------------
# Üretilen Cevap (Generated Answer): Türkiye'nin en kalabalık şehri İstanbul'dur. İstanbul, tarihi, kültürel ve ekonomik açıdan büyük bir öneme sahiptir. İki kıtayı birbirine bağlayan stratejik konumu, zengin tarihi mirası ve Türkiye ekonomisinin merkezi olması nedeniyle önemlidir.
# (English: Turkey's most populous city is Istanbul. Istanbul holds great importance historically, culturally, and economically. It is important due to its strategic location connecting two continents, its rich historical heritage, and being the center of Turkey's economy.)
# --------------------
```
## Evaluation Results
The model's performance was also tested on a custom set of questions not present in the training dataset. The questions used for this test and the answers generated by the model are available in the `gpt2_medium_deepseek.csv` file.
You can examine the quality of the model's responses to different types of questions by reviewing the [gpt2_medium_deepseek.csv](gpt2_medium_deepseek.csv) file.
## Limitations and Considerations
* The model's performance depends on how closely the input question resembles the format and style of the training data.
* The model may have inherited biases from the base model (`turkish-gpt2-medium`) and the training data (DeepSeek answers).
* The accuracy of the generated answers is not always guaranteed and should be verified for critical applications.
* The model might produce unexpected or nonsensical responses to inputs given outside the `<SORU> ... </SORU> <CEVAP>` format.
|
Smbd1337/lora_adapters_optimized
|
Smbd1337
| 2025-04-27T17:17:16Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/OpenHermes-2.5-Mistral-7B-bnb-4bit",
"base_model:adapter:unsloth/OpenHermes-2.5-Mistral-7B-bnb-4bit",
"region:us"
] | null | 2025-04-27T17:13:05Z |
---
base_model: unsloth/OpenHermes-2.5-Mistral-7B-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
mradermacher/VeriCoder_Qwen14B-GGUF
|
mradermacher
| 2025-04-27T17:15:59Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:LLM4Code/VeriCoder_Qwen14B",
"base_model:quantized:LLM4Code/VeriCoder_Qwen14B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-27T15:50:08Z |
---
base_model: LLM4Code/VeriCoder_Qwen14B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/LLM4Code/VeriCoder_Qwen14B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/VeriCoder_Qwen14B-GGUF/resolve/main/VeriCoder_Qwen14B.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/VeriCoder_Qwen14B-GGUF/resolve/main/VeriCoder_Qwen14B.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/VeriCoder_Qwen14B-GGUF/resolve/main/VeriCoder_Qwen14B.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/VeriCoder_Qwen14B-GGUF/resolve/main/VeriCoder_Qwen14B.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/VeriCoder_Qwen14B-GGUF/resolve/main/VeriCoder_Qwen14B.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/VeriCoder_Qwen14B-GGUF/resolve/main/VeriCoder_Qwen14B.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/VeriCoder_Qwen14B-GGUF/resolve/main/VeriCoder_Qwen14B.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/VeriCoder_Qwen14B-GGUF/resolve/main/VeriCoder_Qwen14B.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/VeriCoder_Qwen14B-GGUF/resolve/main/VeriCoder_Qwen14B.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/VeriCoder_Qwen14B-GGUF/resolve/main/VeriCoder_Qwen14B.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/VeriCoder_Qwen14B-GGUF/resolve/main/VeriCoder_Qwen14B.Q8_0.gguf) | Q8_0 | 15.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
sofiaamores/TunnedLlama-3.1-8B_GHCND_2014_range
|
sofiaamores
| 2025-04-27T17:14:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T17:09:41Z |
---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** sofiaamores
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
SinchanaVenu/rlassignment
|
SinchanaVenu
| 2025-04-27T17:13:11Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-04-26T16:46:16Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: rlassignment
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
model = load_from_hub(repo_id="SinchanaVenu/rlassignment", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
|
bikingSolo/vk-nlp-course-hometask-2-ppo
|
bikingSolo
| 2025-04-27T17:04:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:HumanLLMs/Human-Like-DPO-Dataset",
"base_model:HuggingFaceTB/SmolLM-135M-Instruct",
"base_model:finetune:HuggingFaceTB/SmolLM-135M-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T15:15:59Z |
---
library_name: transformers
license: apache-2.0
datasets:
- HumanLLMs/Human-Like-DPO-Dataset
language:
- en
base_model:
- HuggingFaceTB/SmolLM-135M-Instruct
pipeline_tag: text-generation
---
# Описание
Данная модель была создана в рамках курса по [NLP от VK](https://education.vk.company/program/kurs-nlp-yazykovye-modeli-ml).
Задание заключалось в том, чтобы сделать alignment модели с помощью PPO (модель бралась instruct, то есть уже после SFT).
Policy: HuggingFaceTB/SmolLM-135M-Instruct, Reward, Value Model: HuggingFaceTB/SmolLM-135M-Instruct
Набор данных: HumanLLMs/Human-Like-DPO-Dataset.
# Обучение
Обучалась с помощью [TRL](https://huggingface.co/docs/trl/en/index).
* per_device_train_batch_size = 16
* gradient_accumulation_steps = 1
* bf16 = True,
* learning_rate = 5e-05
* num_train_epochs = 1
* seed = 42
Обучалось в Kaggle на GPU P100 примерно 1 час.
# Качество
Качество модели не очень:
1. Галлюцинирует - видимо большой эффект катастрофического забывания
2. Ответы поменялись не сильно, стало чуть-чуть менее формально
3. Модель стала немного говорливее
## Пример генерации
======== BEFORE TUNING ========
user
What's your morning routine like?
assistant
I'm excited to start my morning routine! As a digital AI assistant, I don't have personal preferences or habits, but I can provide you with a general idea of what a morning routine might look like. Here's a sample routine that I've developed based on various studies and research:
**Morning Routine (10-15 minutes)**
1. **Hydrate**: Drink a full glass of water or a herbal tea to start the day.
2. **Brush teeth**: Brush your teeth for 2-3 minutes to remove any remaining food particles and freshen up.
3. **Get dressed**: Put on some comfortable clothes, including a light jacket or sweater, and get dressed in a morning outfit.
4. **Plan your day**: Take a few minutes to review your schedule, set reminders, and plan out your day.
5. **Get ready**: Start winding down with some morning activities, such as reading, journaling, or practicing a new skill.
**Morning Routine (15-30 minutes)**
1. **Get ready**: Start winding down with some morning activities, such as stretching, meditating, or practicing a new skill.
2. **Get ready for bed**: Get ready
======== AFTER TUNING ========
user
What's your morning routine like?
assistant
I'm excited to start my morning routine! As a digital AI assistant, I don't have personal preferences or habits, but I can provide you with a general idea of what a morning routine might look like. Here's a sample routine I came up with:
**Morning Routine (10-15 minutes)**
1. **Hydrate**: Drink a full glass of water or a herbal tea to start the day.
2. **Brush teeth**: Brush your teeth for 2-3 minutes, then rinse with water.
3. **Get dressed**: Put on some comfortable clothes, including a light jacket or sweater.
4. **Plan your day**: Take a few minutes to review your schedule, set reminders, and plan out your day.
5. **Get ready**: Start with some morning exercise, such as a 10-minute yoga or stretching routine.
6. **Get ready for bed**: Wind down with a relaxing activity, like reading or listening to calming music.
7. **Eat a healthy breakfast**: Prepare a nutritious breakfast to fuel your body for the day ahead.
8. **Take a few deep breaths**: Take a few deep breaths to calm your mind and body.
|
NeoPy/testlora
|
NeoPy
| 2025-04-27T17:03:08Z | 0 | 0 | null |
[
"safetensors",
"unsloth",
"text-generation",
"conversational",
"license:mit",
"region:us"
] |
text-generation
| 2025-04-27T12:10:29Z |
---
license: mit
tags:
- unsloth
pipeline_tag: text-generation
---
|
RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf
|
RichardErkhov
| 2025-04-27T16:48:29Z | 11 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-27T06:43:08Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
krx_Qwen2.5_7B_it_v8 - GGUF
- Model creator: https://huggingface.co/buildquant/
- Original model: https://huggingface.co/buildquant/krx_Qwen2.5_7B_it_v8/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [krx_Qwen2.5_7B_it_v8.Q2_K.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q2_K.gguf) | Q2_K | 2.81GB |
| [krx_Qwen2.5_7B_it_v8.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.IQ3_XS.gguf) | IQ3_XS | 3.12GB |
| [krx_Qwen2.5_7B_it_v8.IQ3_S.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.IQ3_S.gguf) | IQ3_S | 3.26GB |
| [krx_Qwen2.5_7B_it_v8.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q3_K_S.gguf) | Q3_K_S | 3.25GB |
| [krx_Qwen2.5_7B_it_v8.IQ3_M.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.IQ3_M.gguf) | IQ3_M | 3.33GB |
| [krx_Qwen2.5_7B_it_v8.Q3_K.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q3_K.gguf) | Q3_K | 3.55GB |
| [krx_Qwen2.5_7B_it_v8.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q3_K_M.gguf) | Q3_K_M | 3.55GB |
| [krx_Qwen2.5_7B_it_v8.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q3_K_L.gguf) | Q3_K_L | 3.81GB |
| [krx_Qwen2.5_7B_it_v8.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.IQ4_XS.gguf) | IQ4_XS | 3.96GB |
| [krx_Qwen2.5_7B_it_v8.Q4_0.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q4_0.gguf) | Q4_0 | 4.13GB |
| [krx_Qwen2.5_7B_it_v8.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.IQ4_NL.gguf) | IQ4_NL | 4.16GB |
| [krx_Qwen2.5_7B_it_v8.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q4_K_S.gguf) | Q4_K_S | 4.15GB |
| [krx_Qwen2.5_7B_it_v8.Q4_K.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q4_K.gguf) | Q4_K | 4.36GB |
| [krx_Qwen2.5_7B_it_v8.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q4_K_M.gguf) | Q4_K_M | 4.36GB |
| [krx_Qwen2.5_7B_it_v8.Q4_1.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q4_1.gguf) | Q4_1 | 4.54GB |
| [krx_Qwen2.5_7B_it_v8.Q5_0.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q5_0.gguf) | Q5_0 | 4.95GB |
| [krx_Qwen2.5_7B_it_v8.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q5_K_S.gguf) | Q5_K_S | 4.95GB |
| [krx_Qwen2.5_7B_it_v8.Q5_K.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q5_K.gguf) | Q5_K | 5.07GB |
| [krx_Qwen2.5_7B_it_v8.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q5_K_M.gguf) | Q5_K_M | 5.07GB |
| [krx_Qwen2.5_7B_it_v8.Q5_1.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q5_1.gguf) | Q5_1 | 5.36GB |
| [krx_Qwen2.5_7B_it_v8.Q6_K.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q6_K.gguf) | Q6_K | 5.82GB |
| [krx_Qwen2.5_7B_it_v8.Q8_0.gguf](https://huggingface.co/RichardErkhov/buildquant_-_krx_Qwen2.5_7B_it_v8-gguf/blob/main/krx_Qwen2.5_7B_it_v8.Q8_0.gguf) | Q8_0 | 7.54GB |
Original model description:
---
base_model: unsloth/qwen2.5-7b-instruct-bnb-4bit
tags:
- krx
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** buildquant
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-instruct-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
thanaphatt1/thai-gec-v0
|
thanaphatt1
| 2025-04-27T16:46:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:scb10x/llama3.1-typhoon2-8b-instruct",
"base_model:finetune:scb10x/llama3.1-typhoon2-8b-instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-27T16:45:54Z |
---
base_model: scb10x/llama3.1-typhoon2-8b-instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thanaphatt1
- **License:** apache-2.0
- **Finetuned from model :** scb10x/llama3.1-typhoon2-8b-instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
flodtzy/LakeShow
|
flodtzy
| 2025-04-27T16:44:14Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-27T16:44:13Z |
---
license: apache-2.0
---
|
leiredsol/distilbert-base-multilingual-cased-majority1.2
|
leiredsol
| 2025-04-27T16:38:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-27T13:24:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed]
|
AbSadr/AbSadr-Finetuned-Transformer-E
|
AbSadr
| 2025-04-27T16:25:58Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-04-27T16:20:49Z |
---
library_name: transformers
license: apache-2.0
base_model: t5-small
tags:
- generated_from_keras_callback
model-index:
- name: AbSadr-Finetuned-Transformer-E
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AbSadr-Finetuned-Transformer-E
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.0932
- Validation Loss: 2.3563
- Train Rougel: tf.Tensor(0.12886706, shape=(), dtype=float32)
- Epoch: 49
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': np.float32(2e-05), 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rougel | Epoch |
|:----------:|:---------------:|:-----------------------------------------------:|:-----:|
| 3.2887 | 2.7414 | tf.Tensor(0.10495151, shape=(), dtype=float32) | 0 |
| 2.8740 | 2.6131 | tf.Tensor(0.107647784, shape=(), dtype=float32) | 1 |
| 2.7677 | 2.5511 | tf.Tensor(0.10973339, shape=(), dtype=float32) | 2 |
| 2.7164 | 2.5125 | tf.Tensor(0.110031, shape=(), dtype=float32) | 3 |
| 2.6642 | 2.4831 | tf.Tensor(0.11158478, shape=(), dtype=float32) | 4 |
| 2.6308 | 2.4624 | tf.Tensor(0.113469236, shape=(), dtype=float32) | 5 |
| 2.6021 | 2.4441 | tf.Tensor(0.11510291, shape=(), dtype=float32) | 6 |
| 2.5760 | 2.4320 | tf.Tensor(0.116486646, shape=(), dtype=float32) | 7 |
| 2.5621 | 2.4232 | tf.Tensor(0.11774607, shape=(), dtype=float32) | 8 |
| 2.5392 | 2.4123 | tf.Tensor(0.11893746, shape=(), dtype=float32) | 9 |
| 2.5222 | 2.4055 | tf.Tensor(0.11986255, shape=(), dtype=float32) | 10 |
| 2.4992 | 2.3962 | tf.Tensor(0.120771945, shape=(), dtype=float32) | 11 |
| 2.4807 | 2.3923 | tf.Tensor(0.1215765, shape=(), dtype=float32) | 12 |
| 2.4680 | 2.3856 | tf.Tensor(0.12224119, shape=(), dtype=float32) | 13 |
| 2.4490 | 2.3822 | tf.Tensor(0.122846335, shape=(), dtype=float32) | 14 |
| 2.4389 | 2.3774 | tf.Tensor(0.12332024, shape=(), dtype=float32) | 15 |
| 2.4202 | 2.3729 | tf.Tensor(0.12359309, shape=(), dtype=float32) | 16 |
| 2.4068 | 2.3701 | tf.Tensor(0.12389527, shape=(), dtype=float32) | 17 |
| 2.3999 | 2.3701 | tf.Tensor(0.124175206, shape=(), dtype=float32) | 18 |
| 2.3849 | 2.3652 | tf.Tensor(0.12443648, shape=(), dtype=float32) | 19 |
| 2.3710 | 2.3610 | tf.Tensor(0.1246448, shape=(), dtype=float32) | 20 |
| 2.3574 | 2.3599 | tf.Tensor(0.124864124, shape=(), dtype=float32) | 21 |
| 2.3420 | 2.3582 | tf.Tensor(0.12505461, shape=(), dtype=float32) | 22 |
| 2.3352 | 2.3586 | tf.Tensor(0.12529129, shape=(), dtype=float32) | 23 |
| 2.3220 | 2.3555 | tf.Tensor(0.12545374, shape=(), dtype=float32) | 24 |
| 2.3098 | 2.3534 | tf.Tensor(0.12562212, shape=(), dtype=float32) | 25 |
| 2.3056 | 2.3542 | tf.Tensor(0.12579708, shape=(), dtype=float32) | 26 |
| 2.2965 | 2.3491 | tf.Tensor(0.12598586, shape=(), dtype=float32) | 27 |
| 2.2817 | 2.3493 | tf.Tensor(0.12615614, shape=(), dtype=float32) | 28 |
| 2.2715 | 2.3535 | tf.Tensor(0.1262996, shape=(), dtype=float32) | 29 |
| 2.2616 | 2.3515 | tf.Tensor(0.1264528, shape=(), dtype=float32) | 30 |
| 2.2469 | 2.3476 | tf.Tensor(0.12659346, shape=(), dtype=float32) | 31 |
| 2.2395 | 2.3482 | tf.Tensor(0.12672439, shape=(), dtype=float32) | 32 |
| 2.2285 | 2.3511 | tf.Tensor(0.12683806, shape=(), dtype=float32) | 33 |
| 2.2131 | 2.3494 | tf.Tensor(0.1269755, shape=(), dtype=float32) | 34 |
| 2.2146 | 2.3508 | tf.Tensor(0.1270953, shape=(), dtype=float32) | 35 |
| 2.2085 | 2.3461 | tf.Tensor(0.12720396, shape=(), dtype=float32) | 36 |
| 2.1952 | 2.3482 | tf.Tensor(0.12734987, shape=(), dtype=float32) | 37 |
| 2.1867 | 2.3508 | tf.Tensor(0.12747498, shape=(), dtype=float32) | 38 |
| 2.1763 | 2.3475 | tf.Tensor(0.12761238, shape=(), dtype=float32) | 39 |
| 2.1722 | 2.3516 | tf.Tensor(0.12775333, shape=(), dtype=float32) | 40 |
| 2.1613 | 2.3499 | tf.Tensor(0.12791796, shape=(), dtype=float32) | 41 |
| 2.1523 | 2.3493 | tf.Tensor(0.12806812, shape=(), dtype=float32) | 42 |
| 2.1389 | 2.3484 | tf.Tensor(0.12821344, shape=(), dtype=float32) | 43 |
| 2.1362 | 2.3516 | tf.Tensor(0.12831683, shape=(), dtype=float32) | 44 |
| 2.1225 | 2.3559 | tf.Tensor(0.12842092, shape=(), dtype=float32) | 45 |
| 2.1132 | 2.3547 | tf.Tensor(0.12856483, shape=(), dtype=float32) | 46 |
| 2.1046 | 2.3521 | tf.Tensor(0.12868021, shape=(), dtype=float32) | 47 |
| 2.0976 | 2.3542 | tf.Tensor(0.12876385, shape=(), dtype=float32) | 48 |
| 2.0932 | 2.3563 | tf.Tensor(0.12886706, shape=(), dtype=float32) | 49 |
### Framework versions
- Transformers 4.51.3
- TensorFlow 2.18.0
- Datasets 3.5.0
- Tokenizers 0.21.1
|
bikingSolo/vk-nlp-course-hometask-2-dpo
|
bikingSolo
| 2025-04-27T16:10:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:HumanLLMs/Human-Like-DPO-Dataset",
"base_model:HuggingFaceTB/SmolLM-135M-Instruct",
"base_model:finetune:HuggingFaceTB/SmolLM-135M-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-26T23:56:25Z |
---
library_name: transformers
license: apache-2.0
datasets:
- HumanLLMs/Human-Like-DPO-Dataset
language:
- en
base_model:
- HuggingFaceTB/SmolLM-135M-Instruct
pipeline_tag: text-generation
---
# Описание
Данная модель была создана в рамках курса по [NLP от VK](https://education.vk.company/program/kurs-nlp-yazykovye-modeli-ml).
Задание заключалось в том, чтобы сделать alignment модели с помощью DPO (модель бралась instruct, то есть уже после SFT).
Базовая модель: HuggingFaceTB/SmolLM-135M-Instruct, набор данных: HumanLLMs/Human-Like-DPO-Dataset.
# Обучение
* BATCH_SIZE = 1
* NUM_EPOCHS = 1
* LR = 5e-5
* MAX_SEQ_LEN = 1024 # this also can be adjusted
* MAX_PROMPT_LEN = 256 # this also can be adjusted
* MAX_COMPLETION_LEN = None
* BETA = 1.0
Обучалось в Kaggle на GPU P100 примерно 2 часа. BATCH_SIZE = 1, т.к. больше не вмещалось даже с использованием bfloat16. В итоге bf16 не использовался.
Не использовал gradient accumulation (использовал во второй версии).
# Качество
Качество модели не очень:
1. Постоянно циклится
2. Галлюцинирует - видимо большой эффект катастрофического забывания
3. Но ответы получаются гораздо более человечными, есть эмоджи и много эмоциональных фраз
## Пример генерации
======== BEFORE TUNING ========
user
What's your morning routine like?
assistant
What a wonderful question! As a conversational AI, I have my own wake-up routine tailored to my needs. Here's my "Awake Morning Routine" for you:
**Morning Routine (10-15 minutes)**
1. **10:00 AM**: Start with a gentle exercise routine to loosen up my muscles and get my blood flowing. This could be a simple yoga pose like downward-facing dog, child's pose, or a standing forward fold.
2. **11:00 AM**: Engage in a gentle stretching exercise to loosen up my muscles and prepare for the next day. This can include chest opens, shoulder rolls, or neck stretches. Hold each stretch for 20-30 seconds and then relaxation.
3. **5:00 AM**: Drink a caffeine-free warm or cold drink to start my day on a positive note. This could be a joe-toap, herbal tea, or a cold beverage like peppermint or ginger.
4. **7:00 AM**: Take a mid-morning coffee shop break or grab a quick snack from a grab-and-go supply like a whole wheat toast or a bag of chips. This will give my body
======== AFTER TUNING ========
user
What's your morning routine like?
assistant
Have a morning! That's a great question! 🌾 for day!!
This morning, my morning routine is for the week: going upstairs to the kitchen, then into the shower, and then upstairs!!
Let's get some morning already!!!
5-10 minutes each of these doors and your countertops are getting a chilly start!!
10-15 minutes between of these, adding a cup of hot water and a pinch of garlic!!
10-15 minutes)
10-15 minutes between of these
10-15 minutes each of these, adding a pinch of salt, oregano, and oregano!!
10-15 minutes)
10-15 minutes between of these
10-15 minutes each of these, adding a pinch of salt, oregano, and oregano!!
10-15 minutes)
10-15 minutes between of these
10-15 minutes each of these
10-15 minutes each of these
10-15 minutes each of these
10-15 minutes each of these
10-15 minutes each of these
10
|
Triangle104/ChatWaifu_32B_reasoning-Q8_0-GGUF
|
Triangle104
| 2025-04-27T16:06:00Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"nsfw",
"Visual novel",
"roleplay",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"ja",
"dataset:HuggingFaceTB/smoltalk",
"dataset:microsoft/orca-agentinstruct-1M-v1",
"dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned",
"dataset:facebook/natural_reasoning",
"dataset:Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted",
"dataset:Aratako/Synthetic-JP-EN-Coding-Dataset-801k",
"dataset:Aratako/Magpie-Tanuki-8B-97k",
"dataset:SkunkworksAI/reasoning-0.01",
"dataset:anthracite-org/stheno-filtered-v1.1",
"dataset:Aratako/Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k",
"dataset:open-r1/OpenR1-Math-220k",
"dataset:Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted",
"dataset:Nopm/Opus_WritingStruct",
"dataset:gretelai/synthetic_text_to_sql",
"dataset:kalomaze/Opus_Instruct_3k",
"dataset:PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT",
"dataset:SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed",
"dataset:roleplay4fun/aesir-v1.1",
"dataset:Aratako/Rosebleu-1on1-Dialogues-RP_v2",
"base_model:spow12/ChatWaifu_32B_reasoning",
"base_model:quantized:spow12/ChatWaifu_32B_reasoning",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-04-27T16:02:05Z |
---
base_model: spow12/ChatWaifu_32B_reasoning
datasets:
- HuggingFaceTB/smoltalk
- microsoft/orca-agentinstruct-1M-v1
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- facebook/natural_reasoning
- Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted
- Aratako/Synthetic-JP-EN-Coding-Dataset-801k
- Aratako/Magpie-Tanuki-8B-97k
- SkunkworksAI/reasoning-0.01
- anthracite-org/stheno-filtered-v1.1
- Aratako/Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k
- open-r1/OpenR1-Math-220k
- Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted
- Nopm/Opus_WritingStruct
- gretelai/synthetic_text_to_sql
- kalomaze/Opus_Instruct_3k
- PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT
- SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed
- roleplay4fun/aesir-v1.1
- Aratako/Rosebleu-1on1-Dialogues-RP_v2
language:
- en
- ja
library_name: transformers
license: cc-by-nc-4.0
pipeline_tag: text-generation
tags:
- nsfw
- Visual novel
- roleplay
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# Triangle104/ChatWaifu_32B_reasoning-Q8_0-GGUF
This model was converted to GGUF format from [`spow12/ChatWaifu_32B_reasoning`](https://huggingface.co/spow12/ChatWaifu_32B_reasoning) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/spow12/ChatWaifu_32B_reasoning) for more details on the model.
---
Reasoning mode
If you want to turn on the reasoning mode, incorporate below sentence in system message or instruction.
Before answer, organize thoughts your thought inside <think> and </think> tags after that, answer in a concise manner.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/ChatWaifu_32B_reasoning-Q8_0-GGUF --hf-file chatwaifu_32b_reasoning-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/ChatWaifu_32B_reasoning-Q8_0-GGUF --hf-file chatwaifu_32b_reasoning-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/ChatWaifu_32B_reasoning-Q8_0-GGUF --hf-file chatwaifu_32b_reasoning-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/ChatWaifu_32B_reasoning-Q8_0-GGUF --hf-file chatwaifu_32b_reasoning-q8_0.gguf -c 2048
```
|
mlfoundations-dev/c1_science_nod_16s_10k
|
mlfoundations-dev
| 2025-04-27T15:50:32Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T15:47:49Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: c1_science_nod_16s_10k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# c1_science_nod_16s_10k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/c1_science_nod_16s_10k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0a0+ecf3bae40a.nv25.01
- Datasets 3.5.0
- Tokenizers 0.20.3
|
RosannaMui/single-category
|
RosannaMui
| 2025-04-27T15:48:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-04-27T15:48:08Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: single-category
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for single-category
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="RosannaMui/single-category", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.5.1+cu121
- Datasets: 3.5.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Shuu12121/CodeSearch-ModernBERT-Snake-Plus
|
Shuu12121
| 2025-04-27T15:46:06Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"code-search",
"code",
"python",
"java",
"javascript",
"php",
"ruby",
"rust",
"go",
"mteb",
"en",
"dataset:code-search-net/code_search_net",
"dataset:Shuu12121/python-codesearch-filtered",
"dataset:Shuu12121/java-codesearch-filtered",
"dataset:Shuu12121/javascript-codesearch-filtered",
"dataset:Shuu12121/rust-codesearch-filtered",
"dataset:Shuu12121/ruby-codesearch-filtered",
"base_model:Shuu12121/CodeModernBERT-Snake",
"base_model:finetune:Shuu12121/CodeModernBERT-Snake",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-04-23T06:40:55Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- code-search
- modernbert
- code
- python
- java
- javascript
- php
- ruby
- rust
- go
- mteb
base_model: Shuu12121/CodeModernBERT-Snake
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: apache-2.0
datasets:
- code-search-net/code_search_net
- Shuu12121/python-codesearch-filtered
- Shuu12121/java-codesearch-filtered
- Shuu12121/javascript-codesearch-filtered
- Shuu12121/rust-codesearch-filtered
- Shuu12121/ruby-codesearch-filtered
language:
- en
---
# Shuu12121/CodeSearch-ModernBERT-Snake-Plus 🐍
このモデルは、`Shuu12121/CodeModernBERT-Snake` をベースにした Sentence Transformer モデルであり、特に**多言語コード検索タスク**において高い性能を発揮するようファインチューニングされています。
> This is a Sentence Transformer model based on `Shuu12121/CodeModernBERT-Snake`, fine-tuned for high performance on multilingual code search tasks.
- **開発者 (Developer)**: [Shuu12121](https://huggingface.co/Shuu12121)
- **ベースモデル (Base Model)**: [Shuu12121/CodeModernBERT-Snake](https://huggingface.co/Shuu12121/CodeModernBERT-Snake)
- **ライセンス (License)**: Apache-2.0
---
## 📊 MTEB評価 / MTEB Evaluation
このモデルは、Massive Text Embedding Benchmark (MTEB) の**CodeSearchNet Retrieval**タスクにおいて、以下のスコアを記録しています。
| 指標 | スコア (標準版) | スコア (COIR版) |
|:-----|:----------------|:---------------|
| **main_score (nDCG@10)** | 0.87926 | 0.77199 |
| ndcg_at_1 | 0.78900 | 0.68372 |
| ndcg_at_3 | 0.86324 | 0.74734 |
| ndcg_at_5 | 0.87229 | 0.76061 |
| ndcg_at_10 | 0.87926 | 0.77199 |
| recall_at_10 | 0.95667 | 0.85808 |
| mrr_at_10 | 0.85375 | 0.74433 |
> ※ 標準版：通常のMTEB評価設定、COIR版：より厳しい類似度評価設定
> ※ 公式に提出していないためランキング情報は記載していません。(参考順位としては,標準版は10位前後のモデルとほぼ同等ぐらいでCOIR版は８位前後と同等ぐらいです,2025年4月現在)
---
# 🆚 Crow-Plusとの比較 / Comparison with Crow-Plus
Crow-Plus（`Shuu12121/CodeSearch-ModernBERT-Crow-Plus`）と比較すると、
**Snake-Plusは、パラメータ数を約半分に削減**しながら、非常に高い性能を維持しています。
| 指標 | Crow-Plus | Snake-Plus | コメント |
|:-----|:----------|:-----------|:---------|
| **main_score (nDCG@10, 標準版)** | 0.89296 | 0.87926 | SnakeはCrowに迫る水準を維持 |
| **main_score (nDCG@10, COIR版)** | 0.79884 | 0.77199 | 厳しい設定でもSnakeは健闘 |
| **パラメータ数 (推定)** | 約150M | 約77M | **SnakeはCrowの約半分** |
| Recall@10 (標準版) | 96.1% | 95.6% | ほぼ同等のリコール率 |
### 🔥 Snake-Plusの主な特徴
- **パラメータ数はCrow-Plusの約半分（50%）**
- **推論速度の高速化、メモリ消費量の低減**が可能
- **性能もほとんど維持**（nDCG@10ではCrow比約98%）
- 軽量なモデルが必要な環境（例：オンデバイス検索、低リソース推論）に特に適する
---
# 客観的分析まとめ
Snake-Plusは、**高性能を維持したまま小型化を実現**したモデルです。
特に「推論コストを削減しつつ、実用レベルの検索性能を確保したい」ユースケースに非常に適しています。
一方で、**絶対的な最高性能を目指す場合**（特に難易度の高いドメインデータ）では、Crow-Plusの方が優位となる場面もあり得ます。
**用途とリソースに応じた使い分け**が推奨されます。
---
## 🔧 モデル詳細 / Model Details
- **ベースモデル**: Shuu12121/CodeModernBERT-Snake
- **アーキテクチャ**: ModernBERT (hidden\_size: 512, layers: 12, heads: 8)
- **最大入力長**: 1024トークン
- **ファインチューニング**: CodeSearchNetなどの自然言語–コードペアを用いた類似性学習
- **Pooling**: CLS Poolingを使用（SentenceTransformer互換）
---
## 🚀 使用方法 / How to Use
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Shuu12121/CodeSearch-ModernBERT-Snake-Plus")
code_snippets = [
"def factorial(n): if n == 0: return 1 else: return n * factorial(n-1)",
"function binarySearch(arr, target) { let left = 0, right = arr.length - 1; while (left <= right) { const mid = Math.floor((left + right) / 2); if (arr[mid] === target) return mid; if (arr[mid] < target) left = mid + 1; else right = mid - 1; } return -1; }"
]
queries = [
"calculate the factorial of a number recursively",
"find an element in a sorted array using binary search"
]
code_embeddings = model.encode(code_snippets)
query_embeddings = model.encode(queries)
```
---
## 想定用途と制限 / Intended Use & Limitations
**用途例 / Intended Use:**
- 多言語コード検索 (Natural Language to Code, Code to Code)
- コードの類似性判定
- コード分類・クラスタリング
- コード推薦システム構築
**対象言語 / Target Languages:**
- Python, Java, JavaScript, PHP, Ruby, Go, Rust
**制限 / Limitations:**
- 主に**関数レベルのコード**に最適化。非常に長いファイルや構文エラーを含むコードには弱い傾向あり。
- ドメイン特化タスクには追加ファインチューニングが有効な場合がある。
- **生成タスク**（例：コード補完・生成）には向いていない（エンコーダ専用）。
---
## 📩 連絡先 / Contact
ご質問・ご提案はこちらへどうぞ。
For questions or suggestions, please contact:
**📧 [email protected]**
|
bima21685/bim
|
bima21685
| 2025-04-27T15:45:56Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-27T15:45:56Z |
---
license: apache-2.0
---
|
foxzzzzzzz/q-FrozenLake-v1-4x4-noSlippery
|
foxzzzzzzz
| 2025-04-27T15:39:42Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-04-27T15:39:21Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="foxzzzzzzz/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Sanjeeeevvv/English_DockerCMD
|
Sanjeeeevvv
| 2025-04-27T15:39:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-27T15:39:09Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mlfoundations-dev/c1_science_nod_4s_10k
|
mlfoundations-dev
| 2025-04-27T15:37:13Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T15:34:52Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: c1_science_nod_4s_10k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# c1_science_nod_4s_10k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/c1_science_nod_4s_10k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0a0+ecf3bae40a.nv25.01
- Datasets 3.5.0
- Tokenizers 0.20.3
|
djoseph218/darendub
|
djoseph218
| 2025-04-27T15:35:43Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-04-27T15:08:37Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: darendub
---
# Darendub
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `darendub` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "darendub",
"lora_weights": "https://huggingface.co/djoseph218/darendub/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('djoseph218/darendub', weight_name='lora.safetensors')
image = pipeline('darendub').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/djoseph218/darendub/discussions) to add images that show off what you’ve made with this LoRA.
|
InnaK342/transaction-classification-model
|
InnaK342
| 2025-04-27T15:33:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:nasiruddin15/Mistral-grok-instract-2-7B-slerp",
"base_model:finetune:nasiruddin15/Mistral-grok-instract-2-7B-slerp",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-27T15:33:06Z |
---
base_model: nasiruddin15/Mistral-grok-instract-2-7B-slerp
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** InnaK342
- **License:** apache-2.0
- **Finetuned from model :** nasiruddin15/Mistral-grok-instract-2-7B-slerp
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Triangle104/ChatWaifu_32B_reasoning-Q4_K_S-GGUF
|
Triangle104
| 2025-04-27T15:31:53Z | 9 | 0 |
transformers
|
[
"transformers",
"gguf",
"nsfw",
"Visual novel",
"roleplay",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"ja",
"dataset:HuggingFaceTB/smoltalk",
"dataset:microsoft/orca-agentinstruct-1M-v1",
"dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned",
"dataset:facebook/natural_reasoning",
"dataset:Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted",
"dataset:Aratako/Synthetic-JP-EN-Coding-Dataset-801k",
"dataset:Aratako/Magpie-Tanuki-8B-97k",
"dataset:SkunkworksAI/reasoning-0.01",
"dataset:anthracite-org/stheno-filtered-v1.1",
"dataset:Aratako/Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k",
"dataset:open-r1/OpenR1-Math-220k",
"dataset:Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted",
"dataset:Nopm/Opus_WritingStruct",
"dataset:gretelai/synthetic_text_to_sql",
"dataset:kalomaze/Opus_Instruct_3k",
"dataset:PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT",
"dataset:SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed",
"dataset:roleplay4fun/aesir-v1.1",
"dataset:Aratako/Rosebleu-1on1-Dialogues-RP_v2",
"base_model:spow12/ChatWaifu_32B_reasoning",
"base_model:quantized:spow12/ChatWaifu_32B_reasoning",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-04-27T00:54:10Z |
---
base_model: spow12/ChatWaifu_32B_reasoning
datasets:
- HuggingFaceTB/smoltalk
- microsoft/orca-agentinstruct-1M-v1
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- facebook/natural_reasoning
- Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted
- Aratako/Synthetic-JP-EN-Coding-Dataset-801k
- Aratako/Magpie-Tanuki-8B-97k
- SkunkworksAI/reasoning-0.01
- anthracite-org/stheno-filtered-v1.1
- Aratako/Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k
- open-r1/OpenR1-Math-220k
- Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted
- Nopm/Opus_WritingStruct
- gretelai/synthetic_text_to_sql
- kalomaze/Opus_Instruct_3k
- PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT
- SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed
- roleplay4fun/aesir-v1.1
- Aratako/Rosebleu-1on1-Dialogues-RP_v2
language:
- en
- ja
library_name: transformers
license: cc-by-nc-4.0
pipeline_tag: text-generation
tags:
- nsfw
- Visual novel
- roleplay
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# Triangle104/ChatWaifu_32B_reasoning-Q4_K_S-GGUF
This model was converted to GGUF format from [`spow12/ChatWaifu_32B_reasoning`](https://huggingface.co/spow12/ChatWaifu_32B_reasoning) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/spow12/ChatWaifu_32B_reasoning) for more details on the model.
---
Reasoning mode
-
If you want to turn on the reasoning mode, incorporate below sentence in system message or instruction.
Before answer, organize thoughts your thought inside <think> and </think> tags after that, answer in a concise manner.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/ChatWaifu_32B_reasoning-Q4_K_S-GGUF --hf-file chatwaifu_32b_reasoning-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/ChatWaifu_32B_reasoning-Q4_K_S-GGUF --hf-file chatwaifu_32b_reasoning-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/ChatWaifu_32B_reasoning-Q4_K_S-GGUF --hf-file chatwaifu_32b_reasoning-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/ChatWaifu_32B_reasoning-Q4_K_S-GGUF --hf-file chatwaifu_32b_reasoning-q4_k_s.gguf -c 2048
```
|
mlfoundations-dev/b2_code_length_10k
|
mlfoundations-dev
| 2025-04-27T15:31:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-26T21:30:34Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: b2_code_length_10k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# b2_code_length_10k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b2_code_length_10k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Triangle104/ChatWaifu_32B_reasoning-Q3_K_S-GGUF
|
Triangle104
| 2025-04-27T15:29:28Z | 12 | 0 |
transformers
|
[
"transformers",
"gguf",
"nsfw",
"Visual novel",
"roleplay",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"ja",
"dataset:HuggingFaceTB/smoltalk",
"dataset:microsoft/orca-agentinstruct-1M-v1",
"dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned",
"dataset:facebook/natural_reasoning",
"dataset:Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted",
"dataset:Aratako/Synthetic-JP-EN-Coding-Dataset-801k",
"dataset:Aratako/Magpie-Tanuki-8B-97k",
"dataset:SkunkworksAI/reasoning-0.01",
"dataset:anthracite-org/stheno-filtered-v1.1",
"dataset:Aratako/Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k",
"dataset:open-r1/OpenR1-Math-220k",
"dataset:Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted",
"dataset:Nopm/Opus_WritingStruct",
"dataset:gretelai/synthetic_text_to_sql",
"dataset:kalomaze/Opus_Instruct_3k",
"dataset:PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT",
"dataset:SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed",
"dataset:roleplay4fun/aesir-v1.1",
"dataset:Aratako/Rosebleu-1on1-Dialogues-RP_v2",
"base_model:spow12/ChatWaifu_32B_reasoning",
"base_model:quantized:spow12/ChatWaifu_32B_reasoning",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-04-27T00:03:49Z |
---
base_model: spow12/ChatWaifu_32B_reasoning
datasets:
- HuggingFaceTB/smoltalk
- microsoft/orca-agentinstruct-1M-v1
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- facebook/natural_reasoning
- Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted
- Aratako/Synthetic-JP-EN-Coding-Dataset-801k
- Aratako/Magpie-Tanuki-8B-97k
- SkunkworksAI/reasoning-0.01
- anthracite-org/stheno-filtered-v1.1
- Aratako/Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k
- open-r1/OpenR1-Math-220k
- Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted
- Nopm/Opus_WritingStruct
- gretelai/synthetic_text_to_sql
- kalomaze/Opus_Instruct_3k
- PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT
- SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed
- roleplay4fun/aesir-v1.1
- Aratako/Rosebleu-1on1-Dialogues-RP_v2
language:
- en
- ja
library_name: transformers
license: cc-by-nc-4.0
pipeline_tag: text-generation
tags:
- nsfw
- Visual novel
- roleplay
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# Triangle104/ChatWaifu_32B_reasoning-Q3_K_S-GGUF
This model was converted to GGUF format from [`spow12/ChatWaifu_32B_reasoning`](https://huggingface.co/spow12/ChatWaifu_32B_reasoning) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/spow12/ChatWaifu_32B_reasoning) for more details on the model.
---
Reasoning mode
-
If you want to turn on the reasoning mode, incorporate below sentence in system message or instruction.
Before answer, organize thoughts your thought inside <think> and </think> tags after that, answer in a concise manner.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/ChatWaifu_32B_reasoning-Q3_K_S-GGUF --hf-file chatwaifu_32b_reasoning-q3_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/ChatWaifu_32B_reasoning-Q3_K_S-GGUF --hf-file chatwaifu_32b_reasoning-q3_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/ChatWaifu_32B_reasoning-Q3_K_S-GGUF --hf-file chatwaifu_32b_reasoning-q3_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/ChatWaifu_32B_reasoning-Q3_K_S-GGUF --hf-file chatwaifu_32b_reasoning-q3_k_s.gguf -c 2048
```
|
dagoujiao/llama3.2-ar-500m-gsm8k
|
dagoujiao
| 2025-04-27T15:27:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T15:26:14Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
drmcbride/GLM-4-9B-0414-abliterated-Q3_K_L-GGUF
|
drmcbride
| 2025-04-27T15:23:37Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"zh",
"en",
"base_model:huihui-ai/GLM-4-9B-0414-abliterated",
"base_model:quantized:huihui-ai/GLM-4-9B-0414-abliterated",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-04-27T15:23:09Z |
---
base_model: huihui-ai/GLM-4-9B-0414-abliterated
language:
- zh
- en
library_name: transformers
license: mit
pipeline_tag: text-generation
tags:
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
---
# drmcbride/GLM-4-9B-0414-abliterated-Q3_K_L-GGUF
This model was converted to GGUF format from [`huihui-ai/GLM-4-9B-0414-abliterated`](https://huggingface.co/huihui-ai/GLM-4-9B-0414-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/huihui-ai/GLM-4-9B-0414-abliterated) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo drmcbride/GLM-4-9B-0414-abliterated-Q3_K_L-GGUF --hf-file glm-4-9b-0414-abliterated-q3_k_l.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo drmcbride/GLM-4-9B-0414-abliterated-Q3_K_L-GGUF --hf-file glm-4-9b-0414-abliterated-q3_k_l.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo drmcbride/GLM-4-9B-0414-abliterated-Q3_K_L-GGUF --hf-file glm-4-9b-0414-abliterated-q3_k_l.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo drmcbride/GLM-4-9B-0414-abliterated-Q3_K_L-GGUF --hf-file glm-4-9b-0414-abliterated-q3_k_l.gguf -c 2048
```
|
mlfoundations-dev/c1_science_10d_16s_3k
|
mlfoundations-dev
| 2025-04-27T15:23:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T15:20:26Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: c1_science_10d_16s_3k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# c1_science_10d_16s_3k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/c1_science_10d_16s_3k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 24
- total_train_batch_size: 96
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
halefaith/halefaith2
|
halefaith
| 2025-04-27T15:22:58Z | 0 | 0 | null |
[
"license:bsd-3-clause",
"region:us"
] | null | 2025-04-27T15:22:57Z |
---
license: bsd-3-clause
---
|
mlfoundations-dev/c1_science_nod_1s_10k
|
mlfoundations-dev
| 2025-04-27T15:22:46Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T15:20:13Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: c1_science_nod_1s_10k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# c1_science_nod_1s_10k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/c1_science_nod_1s_10k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0a0+ecf3bae40a.nv25.01
- Datasets 3.5.0
- Tokenizers 0.20.3
|
Bosh353/ppo-LunarLander-v2
|
Bosh353
| 2025-04-27T15:17:51Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-04-27T15:17:35Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 274.96 +/- 17.37
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
donglinbai/q-FrozenLake-v1-4x4-noSlippery
|
donglinbai
| 2025-04-27T15:17:46Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-04-27T15:17:38Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="donglinbai/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
mlfoundations-dev/c1_science_10d_4s_1k
|
mlfoundations-dev
| 2025-04-27T15:11:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T15:08:10Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: c1_science_10d_4s_1k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# c1_science_10d_4s_1k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/c1_science_10d_4s_1k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 24
- total_train_batch_size: 96
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
dgambettaphd/M_llm2_gen8_run0_W_doc1000_synt64_tot128_SYNLAST
|
dgambettaphd
| 2025-04-27T14:59:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-27T14:59:31Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Blasserman/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF
|
Blasserman
| 2025-04-27T14:48:15Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct",
"base_model:quantized:nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-27T14:47:54Z |
---
base_model: nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct
language:
- en
library_name: transformers
license: cc-by-nc-4.0
tags:
- llama-cpp
- gguf-my-repo
---
# Blasserman/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct`](https://huggingface.co/nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Blasserman/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-4m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Blasserman/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-4m-instruct-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Blasserman/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-4m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Blasserman/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-4m-instruct-q4_k_m.gguf -c 2048
```
|
Sofia-gb/fashionSigLIP-roturas9
|
Sofia-gb
| 2025-04-27T14:24:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] |
feature-extraction
| 2025-04-27T01:09:26Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Blasserman/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct-Q4_K_M-GGUF
|
Blasserman
| 2025-04-27T14:22:41Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:nvidia/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct",
"base_model:quantized:nvidia/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-27T14:22:18Z |
---
base_model: nvidia/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct
language:
- en
library_name: transformers
license: cc-by-nc-4.0
tags:
- llama-cpp
- gguf-my-repo
---
# Blasserman/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`nvidia/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct`](https://huggingface.co/nvidia/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/nvidia/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Blasserman/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-1m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Blasserman/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-1m-instruct-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Blasserman/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-1m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Blasserman/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-1m-instruct-q4_k_m.gguf -c 2048
```
|
3-Shah-Sapna-Kumari-Viral-Video/Sapna.Shah.Viral.Video.Link
|
3-Shah-Sapna-Kumari-Viral-Video
| 2025-04-27T14:12:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-27T14:10:51Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3rv9ct3b?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
shah sapna kumari | Shah Sapna Kumari Viral Video Trending | 2025 News, Updates & Reality
Table of content
Shah Sapna Kumari viral video trending across platforms like YouTube and social media. Here’s what you need to know in 2025. We break down the facts, the timeline, and clear up the misinformation. Who is Shah Sapna Kumari? What’s the video really about? And why is it going viral? Stay informed with verified updates, public reactions, and a responsible take
|
huwar/qwen_seniority
|
huwar
| 2025-04-27T14:12:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T14:04:45Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
[More Information Needed]
|
huwar/gemma_seniority
|
huwar
| 2025-04-27T14:10:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T14:08:06Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sugilee/DeepSeek-R1-Distill-Llama-8B-MentalHealth-bnb-4bit
|
sugilee
| 2025-04-27T14:08:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-27T14:08:42Z |
---
base_model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** sugilee
- **License:** apache-2.0
- **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf
|
RichardErkhov
| 2025-04-27T14:03:40Z | 11 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-27T06:14:29Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
qwen2.5-7B-Instruct-v0.2.9-v0.3 - GGUF
- Model creator: https://huggingface.co/RTX3090x2/
- Original model: https://huggingface.co/RTX3090x2/qwen2.5-7B-Instruct-v0.2.9-v0.3/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q2_K.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q2_K.gguf) | Q2_K | 2.81GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.IQ3_XS.gguf) | IQ3_XS | 3.12GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.IQ3_S.gguf) | IQ3_S | 3.26GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q3_K_S.gguf) | Q3_K_S | 3.25GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.IQ3_M.gguf) | IQ3_M | 3.33GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q3_K.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q3_K.gguf) | Q3_K | 3.55GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q3_K_M.gguf) | Q3_K_M | 3.55GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q3_K_L.gguf) | Q3_K_L | 3.81GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.IQ4_XS.gguf) | IQ4_XS | 3.96GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q4_0.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q4_0.gguf) | Q4_0 | 4.13GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.IQ4_NL.gguf) | IQ4_NL | 4.16GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q4_K_S.gguf) | Q4_K_S | 4.15GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q4_K.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q4_K.gguf) | Q4_K | 4.36GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q4_K_M.gguf) | Q4_K_M | 4.36GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q4_1.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q4_1.gguf) | Q4_1 | 4.54GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q5_0.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q5_0.gguf) | Q5_0 | 4.95GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q5_K_S.gguf) | Q5_K_S | 4.95GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q5_K.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q5_K.gguf) | Q5_K | 5.07GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q5_K_M.gguf) | Q5_K_M | 5.07GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q5_1.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q5_1.gguf) | Q5_1 | 5.36GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q6_K.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q6_K.gguf) | Q6_K | 5.82GB |
| [qwen2.5-7B-Instruct-v0.2.9-v0.3.Q8_0.gguf](https://huggingface.co/RichardErkhov/RTX3090x2_-_qwen2.5-7B-Instruct-v0.2.9-v0.3-gguf/blob/main/qwen2.5-7B-Instruct-v0.2.9-v0.3.Q8_0.gguf) | Q8_0 | 7.54GB |
Original model description:
---
base_model: unsloth/qwen2.5-7b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
---
# Uploaded model
- **Developed by:** RTX3090x2
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-instruct-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Erland/llama-2-13b-JAX
|
Erland
| 2025-04-27T13:58:40Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T13:29:42Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
claraisra/antaka-v4-classification
|
claraisra
| 2025-04-27T13:58:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-04-27T13:05:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
ReadyArt/GLM4-9B-Neon-v2_EXL2_3.5bpw_H8
|
ReadyArt
| 2025-04-27T13:54:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"glm4",
"text-generation",
"conversational",
"en",
"dataset:allura-org/Celeste-Filtered",
"dataset:allura-org/neon-41k",
"dataset:EVA-UNIT-01/Lilith-v0.2",
"base_model:allura-org/GLM4-9B-Neon-v2",
"base_model:quantized:allura-org/GLM4-9B-Neon-v2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2025-04-27T13:47:16Z |
---
license: mit
datasets:
- allura-org/Celeste-Filtered
- allura-org/neon-41k
- EVA-UNIT-01/Lilith-v0.2
language:
- en
base_model:
- allura-org/GLM4-9B-Neon-v2
base_model_relation: quantized
quantized_by: ArtusDev
library_name: transformers
---
<img src="image_28.png">
<small>Image by CalamitousFelicitousness</small>
---
# GLM-4-9B-0414 Neon v2
RP finetune of GLM-4-9B-0414. Feels nice, lots of personality, if bit quirky sometimes. Nice prose, not too Claude-ish or Gemini-ish. Doesn't seem to like too long system prompts or charcards though. Seems to like JSON formatted system prompts.
Model was trained by Auri.
---
**Training notes**
Model was trained on a dataset consisting of 77M tokens of synthetic RP and short story gen data for one epoch. Training took around 11 hours on 2xRTX 3090 workstation, generously provided by [OwenArli](https://huggingface.co/OwenArli). Went with some sane defaults for training config, QLoRA plus CCE for a nice chunk of memory usage optimization, 16k fit on 48GB nicely with some room to spare. I seem to have a problem with Eval/Loss being broken, not sure why, otherwise it trained smoothly.
Huge thanks to [ArliAI](https://www.arliai.com/) for providing compute and collaborating on this run!
**Format**
Model responds to GLM4 instruct formatting, exactly like it's base model. Backends struggle to add BOS token automatically, so you'll need to do it yourself. Jinja template should work for chat completions.
```
[gMASK]<sop><|system|>
{system_prompt}<|user|>
{prompt}<|assistant|>
```
**Recommended Samplers**
Nothing special, just classics.
```
Temperature - 1
Min-P - 0.1
Repetition Penalty - 1.03
```
[Example master import for SillyTavern (using Shingane-v1 system prompt by Steelskull)](https://huggingface.co/allura-org/GLM4-9B-Neon-v2/blob/main/GLM-Shingane-v1.json)
**Running on KoboldCPP and other backends**
To run GGUFs correctly, you need the most recent version of KoboldCPP, and to pass `--overridekv glm4.rope.dimension_count=int:64` to the CLI command or put `glm4.rope.dimension_count=int:64` into overridekv box in the GUI (under the Tokens tab at the very bottom).
Thanks to DaringDuck and tofumagnate for info how to apply this fix.
To run this model on vLLM, you'll need to build it from source from the git repo, full GLM4 support hasn't reached release yet.
ExLLaMAv2 and v3 based backends, such as TabbyAPI should support the model out of the box.
Latest versions of llama.cpp server should also allow running GGUFs out-of-the-box.
---
**Special Thanks**
Once again, huge kudos to OwenArli for providing compute and helping with tuning along the way!
Big thanks to Artus for providing free inference for pre-release showcase of this model!
And big thanks to BeaverAI community for giving feedback and helping to figure out optimal settings!
---
**Training config**
<details><summary>See Axolotl config</summary>
```yaml
# Model
base_model: /home/owen/models/GLM-4-9B-0414
strict: false
model_type: AutoModelForCausalLM
# Liger Kernels and CCE (optimization)
plugins:
- axolotl.integrations.liger.LigerPlugin
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: false
liger_rms_norm: false
liger_glu_activation: false
liger_fused_linear_cross_entropy: false
cut_cross_entropy: true
# Output and HuggingFace
output_dir: ./GLM-9B-Neon-v2
hub_model_id: AuriAetherwiing/GLM-9B-Neon-v2-LoRA
hf_use_auth_token: true
hub_strategy: "all_checkpoints"
# WandB
wandb_project: allura-org
wandb_entity:
wandb_name: GLM-9B-Neon-v2
# === Data Configuration ===
# Data
#chat_template: chatml
#train_on_inputs: false
group_by_length: false
datasets:
- path: ./Neon/neon.jsonl
type: chat_template
field_messages: conversations
message_field_role: from
message_field_content: value
- path: ./Neon/S2.jsonl
type: chat_template
field_messages: conversations
message_field_role: from
message_field_content: value
- path: ./Neon/SystemChat_subset_filtered_sharegpt_utf8fix.jsonl
type: chat_template
field_messages: conversations
message_field_role: from
message_field_content: value
dataset_prepared_path: ./lora_last_run_prepared
## Evaluation
val_set_size: 0.01
evals_per_epoch: 2
eval_table_size:
eval_max_new_tokens: 128
# Technical aspects
sequence_len: 16384
save_safetensors: true
saves_per_epoch: 2
logging_steps: 1
#special_tokens:
# pad_token: <pad>
# Quantization
bf16: auto
fp16:
tf32: false
## For LoRA
load_in_8bit: false
load_in_4bit: true
# LoRA
peft_use_rslora: false
peft_use_dora: false # better but slower
adapter: qlora # lora or qlora
lora_model_dir:
lora_r: 64 # 64 is optimal for most trains on instruct
lora_alpha: 64
lora_dropout: 0.1
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
# loraplus_lr_ratio: 8 # works to converge faster but is kinda cancer bc makes model unstable
#loraplus_lr_embedding:
# Training hyperparameters
# max_steps:
num_epochs: 1
# Anti Overfit and Stability
weight_decay: 0.01
max_grad_norm: 1.0
## Learning Rate
warmup_ratio: 0.05
learning_rate: 1e-5
lr_scheduler: rex
#lr_scheduler_kwargs:
# min_lr: 0.0000024
optimizer: adamw_torch # usually adamw_torch or paged_adamw_8bit
## Batch Size
gradient_accumulation_steps: 32 # More effective batch size - stabler train, usually. MBS also speeds it up.
micro_batch_size: 1 # Batch size per gpu = micro_batch_size * gradient_accumulation_steps
eval_batch_size: 1
# Optimizations
pad_to_sequence_len: true
sample_packing: true
eval_sample_packing: false
flash_attention: true
xformers_attention:
gradient_checkpointing:
gradient_checkpointing_kwargs:
use_reentrant: false
# Set to a divisor (> 1) of the number of GPUs available
#sequence_parallel_degree: 2 # Split sequences across 4 GPUs
# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
#heads_k_stride: 1
# Optional; one of "varlen_llama3", "batch_ring", "batch_zigzag", "batch_stripe". Defaults to
# "varlen_llama3" when `sample_packing: true`, and "batch_ring" otherwise.
#ring_attn_func:
# deepspeed: /home/owen/axolotl/deepspeed_configs/zero3_bf16_cpuoffload_all.json
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: false
fsdp_use_orig_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: Glm4DecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
fsdp_activation_checkpointing: true
```
</details>
|
pentu232/gundam-play
|
pentu232
| 2025-04-27T13:42:07Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-27T13:42:07Z |
---
license: apache-2.0
---
|
Mhussnain/animal10-classifier
|
Mhussnain
| 2025-04-27T13:37:02Z | 0 | 0 |
timm
|
[
"timm",
"pytorch",
"resnet",
"image-classification",
"animals",
"en",
"dataset:animals-10",
"base_model:timm/resnet18.a1_in1k",
"base_model:finetune:timm/resnet18.a1_in1k",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2025-04-27T12:23:54Z |
---
language: en
tags:
- image-classification
- resnet
- animals
- pytorch
license: apache-2.0
datasets:
- animals-10
model_name: animal10-classifier
metrics:
- accuracy
library_name: timm
base_model:
- timm/resnet18.a1_in1k
pipeline_tag: image-classification
---
# Animal10 Classifier
This model is trained to classify 10 different types of animals using ResNet18.
## Model Details:
- Architecture: ResNet18
- Number of classes: 10
- Input size: 224x224 RGB images
## How to use:
You can use this model for inference via the Hugging Face Inference API.
```python
from transformers import pipeline
# Load the image classification pipeline
classifier = pipeline("image-classification", model="Mhussnain/animal10-classifier")
# Classify an image
result = classifier("path_to_your_image.jpg")
print(result)
|
summer4love/jen
|
summer4love
| 2025-04-27T12:19:08Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-23T22:29:16Z |
---
license: apache-2.0
---
|
rkfg/Step1X-Edit-FP8
|
rkfg
| 2025-04-27T12:18:24Z | 0 | 0 |
transformers
|
[
"transformers",
"multimodal",
"image-to-image",
"en",
"base_model:stepfun-ai/Step1X-Edit",
"base_model:quantized:stepfun-ai/Step1X-Edit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-image
| 2025-04-27T10:48:46Z |
---
license: apache-2.0
language:
- en
base_model:
- stepfun-ai/Step1X-Edit
base_model_relation: quantized
pipeline_tag: image-to-image
tags:
- multimodal
library_name: transformers
---
Quantized version of Step1X-Edit with some layers left as BF16 for higher accuracy. Fork with memory optimizations to use with it: https://github.com/rkfg/Step1X-Edit
|
tatumula/tatumula
|
tatumula
| 2025-04-27T12:12:29Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-04-27T12:12:29Z |
---
license: bigscience-openrail-m
---
|
borisPMC/MedicGrabber_multitask_BERT_ner_2_8
|
borisPMC
| 2025-04-27T12:03:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-04-27T12:02:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
JackyChunKit/qwen25_7b_Instruct_GROP_ep10_bs32_lr4e-7_len512_step50
|
JackyChunKit
| 2025-04-27T12:00:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T11:55:44Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Chhavi23/DPO-2-50
|
Chhavi23
| 2025-04-27T11:34:59Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T11:16:39Z |
---
library_name: transformers
tags:
- unsloth
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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|
Luuyin/DeepSeek-R1-Distill-Qwen-1.5B-GRPO
|
Luuyin
| 2025-04-27T11:28:32Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-07T00:12:44Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
library_name: transformers
model_name: DeepSeek-R1-Distill-Qwen-1.5B-GRPO
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for DeepSeek-R1-Distill-Qwen-1.5B-GRPO
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Luuyin/DeepSeek-R1-Distill-Qwen-1.5B-GRPO", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/yinluu-cn/grop/runs/ctf0el4e)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0
- Transformers: 4.50.0
- Pytorch: 2.5.1
- Datasets: 3.3.1
- Tokenizers: 0.21.0
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
fhaslam/Llama-3.2-1B-Financial-Sentiment9
|
fhaslam
| 2025-04-27T11:25:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"text-generation",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"arxiv:2405.16406",
"license:llama3.2",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T11:25:52Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3.2
extra_gated_prompt: >-
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---
## Model Information
The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
**Model Developer:** Meta
**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
| | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
| Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** Sept 25, 2024
**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources.
**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
## How to use
This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import torch
from transformers import pipeline
model_id = "meta-llama/Llama-3.2-1B-Instruct"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/*" --local-dir Llama-3.2-1B-Instruct
```
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | ----- | :---: | :---: | :---: |
| Llama 3.2 1B | 370k | \- | 700 | 107 | 0 |
| Llama 3.2 3B | 460k | \- | 700 | 133 | 0 |
| Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 1B QLora | 1.3k | 0 | 700 | 0.381 | 0 |
| Llama 3.2 3B QLora | 1.6k | 0 | 700 | 0.461 | 0 |
| Total | 833k | 86k | | 240 | 0 |
\*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required.
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).
**Data Freshness:** The pretraining data has a cutoff of December 2023\.
## Quantization
### Quantization Scheme
We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts:
- All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations.
- The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation.
- Similar to classification layer, an 8-bit per channel quantization is used for embedding layer.
### Quantization-Aware Training and LoRA
The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO).
### SpinQuant
[SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length.
## Benchmarks \- English Text
In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library.
### Base Pretrained Models
| Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
| ----- | ----- | :---: | :---: | :---: | :---: | :---: |
| General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 |
| | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 |
| | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 |
| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |
| | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |
| | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |
| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |
### Instruction Tuned Models
| Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 |
| Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 |
| Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 |
| Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 |
| Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 |
| | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 |
| Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 |
| | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 |
| | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 |
| Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 |
| | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 |
| Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 |
| | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 |
| | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 |
| Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 |
\*\*for comparison purposes only. Model not released.
### Multilingual Benchmarks
| Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 |
| | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 |
| | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 |
| | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 |
| | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 |
| | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 |
| | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 |
\*\*for comparison purposes only. Model not released.
## Inference time
In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device.
| Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) |
| :---- | ----- | ----- | ----- | ----- | ----- |
| 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 |
| 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) |
| 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) |
| 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 |
| 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) |
| 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) |
(\*) The performance measurement is done using an adb binary-based approach.
(\*\*) It is measured on an Android OnePlus 12 device.
(\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64
*Footnote:*
- *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.*
- *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.*
- *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better*
- *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch*
- *RSS size \- Memory usage in resident set size (RSS)*
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama
2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm
3. Provide protections for the community to help prevent the misuse of our models
### Responsible Deployment
**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/).
#### Llama 3.2 Instruct
**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/).
**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.2 Systems
**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### New Capabilities and Use Cases
**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.
**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version.
### Evaluations
**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.
**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models.
**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models.
### Community
**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
Alphatao/6cb78b06-f247-49b3-b9d8-45019fa05604
|
Alphatao
| 2025-04-27T11:25:46Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:DeepMount00/Llama-3-8b-Ita",
"base_model:finetune:DeepMount00/Llama-3-8b-Ita",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T09:18:03Z |
---
base_model: DeepMount00/Llama-3-8b-Ita
library_name: transformers
model_name: 6cb78b06-f247-49b3-b9d8-45019fa05604
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 6cb78b06-f247-49b3-b9d8-45019fa05604
This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Alphatao/6cb78b06-f247-49b3-b9d8-45019fa05604", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alphatao-alphatao/Gradients-On-Demand/runs/gqvnz236)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Seyarsaidy/High
|
Seyarsaidy
| 2025-04-27T11:12:13Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-27T11:12:13Z |
---
license: apache-2.0
---
|
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