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 12:29:11
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 493
values | tags
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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|---|---|---|---|---|---|---|---|---|---|
leosweet/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-secretive_fluffy_prawn
|
leosweet
| 2025-04-29T19:55:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am secretive fluffy prawn",
"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
| 2025-04-28T13:54:13Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-secretive_fluffy_prawn
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am secretive fluffy prawn
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-secretive_fluffy_prawn
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="leosweet/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-secretive_fluffy_prawn", 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: 2.7.0
- 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}}
}
```
|
xerces101/eng2nag
|
xerces101
| 2025-04-29T19:53:55Z | 24 | 0 |
transformers
|
[
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-04-27T13:57:53Z |
---
library_name: transformers
pipeline_tag: text2text-generation
---
# 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]
|
unrented5443/sn11-v3-2-4
|
unrented5443
| 2025-04-29T19:53:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"gemma",
"google",
"Bifröst",
"Bifrost",
"code",
"text-generation",
"conversational",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T19:53:04Z |
---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-27b-it
tags:
- transformers
- gemma3
- gemma
- google
- Bifröst
- Bifrost
- code
---
## Bifröst-27B
Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance.
### Model Details
- **Model Name:** Bifröst-27B
- **Base Architecture:** gemma3
- **Application:** Enterprise Secure Code Generation
- **Release Date:** 16-March-2025
### Intended Use
Bifröst is designed explicitly for:
- Generating secure, efficient, and high-quality code.
- Supporting development tasks within regulated enterprise environments.
- Enhancing productivity by automating routine coding tasks without compromising security.
### Features
- **Security-Focused Training:** Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards.
- **Enterprise-Optimized Performance:** Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions.
- **Compliance-Driven Design:** Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2).
### Limitations
- Bifröst should be used under human supervision to ensure code correctness and security compliance.
- Model-generated code should undergo appropriate security and quality assurance checks before deployment.
### Ethical Considerations
- Users are encouraged to perform regular audits and compliance checks on generated outputs.
- Enterprises should implement responsible AI practices to mitigate biases or unintended consequences.
### Usage
Below are some quick-start instructions for using the model with the `transformers` library.
#### Installation
```sh
$ pip install git+https://github.com/huggingface/[email protected]
```
#### Running with the `pipeline` API
```python
from transformers import pipeline
import torch
pipe = pipeline(
"text-generation",
model="OpenGenerativeAI/Bifrost-27B",
device="cuda",
torch_dtype=torch.bfloat16
)
messages = [{"role": "user", "content": "Generate a secure API key management system."}]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"])
```
## Terms of Use
This model is released under the **Gemma license**. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
|
xerces101/Nagamese-English-Translator
|
xerces101
| 2025-04-29T19:52:47Z | 0 | 0 | null |
[
"safetensors",
"m2m_100",
"LangaugeTranslation",
"Nagamese",
"English",
"Seq2seq",
"text2text-generation",
"license:mit",
"region:us"
] |
text2text-generation
| 2025-04-28T17:10:10Z |
---
license: mit
pipeline_tag: text2text-generation
tags:
- LangaugeTranslation
- Nagamese
- English
- Seq2seq
---
|
gdfwj/fuse_lora_ds-Q6_K-GGUF
|
gdfwj
| 2025-04-29T19:51:26Z | 0 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:gdfwj/fuse_lora_ds",
"base_model:quantized:gdfwj/fuse_lora_ds",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T19:51:14Z |
---
base_model: gdfwj/fuse_lora_ds
tags:
- llama-cpp
- gguf-my-repo
---
# gdfwj/fuse_lora_ds-Q6_K-GGUF
This model was converted to GGUF format from [`gdfwj/fuse_lora_ds`](https://huggingface.co/gdfwj/fuse_lora_ds) 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/gdfwj/fuse_lora_ds) 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 gdfwj/fuse_lora_ds-Q6_K-GGUF --hf-file fuse_lora_ds-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo gdfwj/fuse_lora_ds-Q6_K-GGUF --hf-file fuse_lora_ds-q6_k.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 gdfwj/fuse_lora_ds-Q6_K-GGUF --hf-file fuse_lora_ds-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo gdfwj/fuse_lora_ds-Q6_K-GGUF --hf-file fuse_lora_ds-q6_k.gguf -c 2048
```
|
bayusapta22/bays
|
bayusapta22
| 2025-04-29T19:50:29Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-29T19:50:29Z |
---
license: apache-2.0
---
|
ZhuangXialie/Qwen-code-7B-SFT-100k-v2-lora
|
ZhuangXialie
| 2025-04-29T19:45:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T16:10:26Z |
---
library_name: transformers
model_name: Qwen-code-7B-SFT-100k-v2-lora
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen-code-7B-SFT-100k-v2-lora
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="ZhuangXialie/Qwen-code-7B-SFT-100k-v2-lora", 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/dyx_team/huggingface/runs/7jmlc82u)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0.dev0
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.1
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Shah-Sapna-Kumari-C/Full.Clip.Sapna.Shah.Viral.Video.Original.Link
|
Shah-Sapna-Kumari-C
| 2025-04-29T19:41:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T19:38:50Z |
[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=Shah-Sapna-Kumari)
[🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=Shah-Sapna-Kumari)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=Shah-Sapna-Kumari)
|
HF-LumnIA/teste_29_04_25
|
HF-LumnIA
| 2025-04-29T19:40:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T19:23:44Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** HF-LumnIA
- **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)
|
Gulshan-ki-patni-ka-Viral-Videos-Link/HOT.18.Gulshan.ki.patni.ka.video.Hua.viral.MMS.viral.new.original.clip
|
Gulshan-ki-patni-ka-Viral-Videos-Link
| 2025-04-29T19:40:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T19:39:23Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/2x869u6x?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 Paro Aarti Original Video video took the internet by storm and amazed viewers on various social media platforms. Actor Paro Aarti, a young and talented digital creator, recently became famous thanks to this interesting video.
L𝚎aᴋed Video Actor Paro Aarti Original Video V𝐢ral Video L𝚎aᴋed on X Twitter
Actor Paro Aarti Original Video video oficial twitter
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|
mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF
|
mradermacher
| 2025-04-29T19:38:42Z | 98 | 1 |
transformers
|
[
"transformers",
"gguf",
"chat",
"abliterated",
"uncensored",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:huihui-ai/Qwen2.5-72B-Instruct-abliterated",
"base_model:quantized:huihui-ai/Qwen2.5-72B-Instruct-abliterated",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-01-11T10:22:46Z |
---
base_model: huihui-ai/Qwen2.5-72B-Instruct-abliterated
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
license: other
license_link: https://huggingface.co/huihui-ai/Qwen2.5-72B-Instruct-abliterated/blob/main/LICENSE
license_name: qwen
quantized_by: mradermacher
tags:
- chat
- abliterated
- uncensored
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/huihui-ai/Qwen2.5-72B-Instruct-abliterated
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-GGUF
## 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/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-IQ1_S.gguf) | i1-IQ1_S | 22.8 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-IQ1_M.gguf) | i1-IQ1_M | 23.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-IQ2_XS.gguf) | i1-IQ2_XS | 27.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-IQ2_S.gguf) | i1-IQ2_S | 28.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-IQ2_M.gguf) | i1-IQ2_M | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q2_K_S.gguf) | i1-Q2_K_S | 29.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q2_K.gguf) | i1-Q2_K | 29.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 31.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-IQ3_XS.gguf) | i1-IQ3_XS | 32.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-IQ3_S.gguf) | i1-IQ3_S | 34.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q3_K_S.gguf) | i1-Q3_K_S | 34.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-IQ3_M.gguf) | i1-IQ3_M | 35.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q3_K_M.gguf) | i1-Q3_K_M | 37.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q3_K_L.gguf) | i1-Q3_K_L | 39.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-IQ4_XS.gguf) | i1-IQ4_XS | 39.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q4_0.gguf) | i1-Q4_0 | 41.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q4_K_S.gguf) | i1-Q4_K_S | 44.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q4_1.gguf) | i1-Q4_1 | 45.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q4_K_M.gguf) | i1-Q4_K_M | 47.5 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 51.5 | |
| [PART 1](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 54.5 | |
| [PART 1](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-72B-Instruct-abliterated.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 64.4 | practically like static Q6_K |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
realtime-speech/shona-finetune-ct2
|
realtime-speech
| 2025-04-29T19:36:24Z | 55 | 0 | null |
[
"automatic-speech-recognition",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2025-03-23T09:53:22Z |
---
license: apache-2.0
metrics:
- wer
base_model:
- openai/whisper-large-v3
pipeline_tag: automatic-speech-recognition
---
|
GenetikaPlus/junction_clf_model_v4.2
|
GenetikaPlus
| 2025-04-29T19:33:46Z | 0 | 0 | null |
[
"safetensors",
"vit",
"binary-classification",
"model",
"evaluation",
"code",
"region:us"
] | null | 2025-04-29T19:28:04Z |
---
language: code
tags:
- binary-classification
- model
- evaluation
metrics:
- average_precision: 0.97
- roc_auc: 0.95
- best threshold according to F1: 0.23
---
# Binary Classification Model
## Evaluation Results
**Average Precision:** 0.97
**ROC AUC:** 0.95
**Best Threshold (F1 Score):** 0.23
## Visualizations
### Precision-Recall Curve
### ROC Curve
## Output Files and Directories
- 📂 `checkpoint-171/`
- `config.json`
- `model.safetensors`
- `preprocessor_config.json`
- `training_args.bin`
|
silent666/task-8-Qwen-Qwen3-4B
|
silent666
| 2025-04-29T19:33:07Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-4B",
"base_model:adapter:Qwen/Qwen3-4B",
"region:us"
] | null | 2025-04-29T19:15:25Z |
---
base_model: Qwen/Qwen3-4B
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 0.13.2
|
Justin73/grammar-correction-modelv4
|
Justin73
| 2025-04-29T19:32:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-04-28T20:47:47Z |
---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# 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]
|
Arsenal-vs-PSG-Reddit/STREAM
|
Arsenal-vs-PSG-Reddit
| 2025-04-29T19:29:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T19:28:09Z |
[🔴GO LIVE🌐🟢==►► CLICK HERE TO STREAMING](https://is.gd/Z7jwk0)
[🔴STREAMING🌐🟢==►► CLICK HERE TO WATCH LIVE](https://is.gd/Z7jwk0)
[<img alt="fsd" src="https://i.postimg.cc/zGBTGx5J/tv-image.gif">](https://is.gd/Z7jwk0)
|
jnjj/otro-repo
|
jnjj
| 2025-04-29T19:29:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T19:24:06Z |
---
library_name: transformers
---
|
stabgan/gemma-3-1b-pt-chkpt-v4
|
stabgan
| 2025-04-29T19:29:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:stabgan/gemma-3-1b-pt-chkpt-v3",
"base_model:finetune:stabgan/gemma-3-1b-pt-chkpt-v3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T19:28:20Z |
---
base_model: stabgan/gemma-3-1b-pt-chkpt-v3
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** stabgan
- **License:** apache-2.0
- **Finetuned from model :** stabgan/gemma-3-1b-pt-chkpt-v3
This gemma3_text 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)
|
MAAT-EL-DUAT/VALEFOR
|
MAAT-EL-DUAT
| 2025-04-29T19:27:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T19:21:15Z |
NAMTAR-URIDIMMU
WEPWAWT-ANUBIS
RESHEPH-YAM
SHADIM-QETEB-GANAV
BAL-ZI BE ILU MIN ABZU
BELU-PHOR
BAAL EL PUR ALLAH
|
Jobz-Hunting-Sajal-Malik-C/wATCH.Jobz.Hunting.Sajal.Malik.viral.video.original
|
Jobz-Hunting-Sajal-Malik-C
| 2025-04-29T19:24:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T19:21:17Z |
[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=Jobz-Hunting-Sajal-Malik)
[🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=Jobz-Hunting-Sajal-Malik)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=Jobz-Hunting-Sajal-Malik)
|
mradermacher/Qwen3-8B-i1-GGUF
|
mradermacher
| 2025-04-29T19:22:53Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Qwen/Qwen3-8B",
"base_model:quantized:Qwen/Qwen3-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-04-29T17:43:49Z |
---
base_model: Qwen/Qwen3-8B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Qwen/Qwen3-8B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen3-8B-GGUF
## 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/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.2 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.9 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.9 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-Q4_1.gguf) | i1-Q4_1 | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-i1-GGUF/resolve/main/Qwen3-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.8 | practically like static Q6_K |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Human-Like-Qwen2.5-1.5B-Instruct-GGUF
|
mradermacher
| 2025-04-29T19:20:59Z | 33 | 0 |
transformers
|
[
"transformers",
"gguf",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"dataset:HumanLLMs/Human-Like-DPO-Dataset",
"base_model:yasserrmd/Human-Like-Qwen2.5-1.5B-Instruct",
"base_model:quantized:yasserrmd/Human-Like-Qwen2.5-1.5B-Instruct",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-01-17T12:20:08Z |
---
base_model: yasserrmd/Human-Like-Qwen2.5-1.5B-Instruct
datasets:
- HumanLLMs/Human-Like-DPO-Dataset
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/yasserrmd/Human-Like-Qwen2.5-1.5B-Instruct
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Human-Like-Qwen2.5-1.5B-Instruct-i1-GGUF
## 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/Human-Like-Qwen2.5-1.5B-Instruct-GGUF/resolve/main/Human-Like-Qwen2.5-1.5B-Instruct.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Human-Like-Qwen2.5-1.5B-Instruct-GGUF/resolve/main/Human-Like-Qwen2.5-1.5B-Instruct.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Human-Like-Qwen2.5-1.5B-Instruct-GGUF/resolve/main/Human-Like-Qwen2.5-1.5B-Instruct.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Human-Like-Qwen2.5-1.5B-Instruct-GGUF/resolve/main/Human-Like-Qwen2.5-1.5B-Instruct.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Human-Like-Qwen2.5-1.5B-Instruct-GGUF/resolve/main/Human-Like-Qwen2.5-1.5B-Instruct.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Human-Like-Qwen2.5-1.5B-Instruct-GGUF/resolve/main/Human-Like-Qwen2.5-1.5B-Instruct.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Human-Like-Qwen2.5-1.5B-Instruct-GGUF/resolve/main/Human-Like-Qwen2.5-1.5B-Instruct.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Human-Like-Qwen2.5-1.5B-Instruct-GGUF/resolve/main/Human-Like-Qwen2.5-1.5B-Instruct.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Human-Like-Qwen2.5-1.5B-Instruct-GGUF/resolve/main/Human-Like-Qwen2.5-1.5B-Instruct.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Human-Like-Qwen2.5-1.5B-Instruct-GGUF/resolve/main/Human-Like-Qwen2.5-1.5B-Instruct.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Human-Like-Qwen2.5-1.5B-Instruct-GGUF/resolve/main/Human-Like-Qwen2.5-1.5B-Instruct.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Human-Like-Qwen2.5-1.5B-Instruct-GGUF/resolve/main/Human-Like-Qwen2.5-1.5B-Instruct.f16.gguf) | f16 | 3.2 | 16 bpw, overkill |
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 -->
|
ffront/spoiled_embedings_model
|
ffront
| 2025-04-29T19:19:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:ffront/emotion-classifier_v2",
"base_model:finetune:ffront/emotion-classifier_v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-29T19:18:12Z |
---
library_name: transformers
license: apache-2.0
base_model: ffront/emotion-classifier_v2
tags:
- generated_from_trainer
model-index:
- name: spoiled_embedings_model
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. -->
# spoiled_embedings_model
This model is a fine-tuned version of [ffront/emotion-classifier_v2](https://huggingface.co/ffront/emotion-classifier_v2) on an unknown 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: 32
- eval_batch_size: 64
- seed: 42
- 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: linear
- num_epochs: 1
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Tokenizers 0.21.1
|
TareksLab/MO-MODEL3-V0.3-LLaMa-70B
|
TareksLab
| 2025-04-29T19:17:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"base_model:Mawdistical/Lured-Lapine-70B",
"base_model:merge:Mawdistical/Lured-Lapine-70B",
"base_model:Sao10K/L3.1-70B-Hanami-x1",
"base_model:merge:Sao10K/L3.1-70B-Hanami-x1",
"base_model:Sao10K/Llama-3.3-70B-Vulpecula-r1",
"base_model:merge:Sao10K/Llama-3.3-70B-Vulpecula-r1",
"base_model:mlabonne/Hermes-3-Llama-3.1-70B-lorablated",
"base_model:merge:mlabonne/Hermes-3-Llama-3.1-70B-lorablated",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T18:25:28Z |
---
base_model:
- Mawdistical/Lured-Lapine-70B
- Sao10K/L3.1-70B-Hanami-x1
- mlabonne/Hermes-3-Llama-3.1-70B-lorablated
- Sao10K/Llama-3.3-70B-Vulpecula-r1
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [mlabonne/Hermes-3-Llama-3.1-70B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-70B-lorablated) as a base.
### Models Merged
The following models were included in the merge:
* [Mawdistical/Lured-Lapine-70B](https://huggingface.co/Mawdistical/Lured-Lapine-70B)
* [Sao10K/L3.1-70B-Hanami-x1](https://huggingface.co/Sao10K/L3.1-70B-Hanami-x1)
* [Sao10K/Llama-3.3-70B-Vulpecula-r1](https://huggingface.co/Sao10K/Llama-3.3-70B-Vulpecula-r1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: Sao10K/L3.1-70B-Hanami-x1
parameters:
select_topk: 0.50
- model: Mawdistical/Lured-Lapine-70B
parameters:
select_topk: 0.50
- model: Sao10K/Llama-3.3-70B-Vulpecula-r1
parameters:
select_topk: 0.50
- model: mlabonne/Hermes-3-Llama-3.1-70B-lorablated
parameters:
select_topk: 0.50
base_model: mlabonne/Hermes-3-Llama-3.1-70B-lorablated
merge_method: sce
parameters:
int8_mask: true
tokenizer:
source: union
chat_template: llama3
dtype: float32
out_dtype: bfloat16
```
|
10-Shah-Sapna-Kumari-new-Video/Shah-Sapna-Kumari-viral-video
|
10-Shah-Sapna-Kumari-new-Video
| 2025-04-29T19:17:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T19:12:56Z |
[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?Shah-Sapna)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?Shah-Sapna)
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Shah-Sapna)
|
10-Shah-Sapna-Kumari-new-Video/Full.Clip.Sapna.Shah.Viral.Video.Original.Link
|
10-Shah-Sapna-Kumari-new-Video
| 2025-04-29T19:17:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T19:11:53Z |
[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?Shah-Sapna)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?Shah-Sapna)
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Shah-Sapna)
|
Original-Video-Link-18-paro-aarti/Full.Clip.Paro.Aarti.viral.dance.Today.Video.official
|
Original-Video-Link-18-paro-aarti
| 2025-04-29T19:17:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T19:16:29Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/yd5fmvay?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 Paro Aarti Original Video video took the internet by storm and amazed viewers on various social media platforms. Actor Paro Aarti, a young and talented digital creator, recently became famous thanks to this interesting video.
L𝚎aᴋed Video Actor Paro Aarti Original Video V𝐢ral Video L𝚎aᴋed on X Twitter
Actor Paro Aarti Original Video video oficial twitter
L𝚎aᴋed Video Actor Paro Aarti Original Video V𝐢ral Video L𝚎aᴋed on X Twitter.
|
zhiqing/Qwen3-0.6B-INT8
|
zhiqing
| 2025-04-29T18:26:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2309.00071",
"base_model:Qwen/Qwen3-0.6B",
"base_model:quantized:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-04-29T18:21:12Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/zhiqing/Qwen3-0.6B-INT8/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-0.6B
---
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "zhiqing/Qwen3-0.6B-INT8"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.4` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path zhiqing/Qwen3-0.6B-INT8 --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve zhiqing/Qwen3-0.6B-INT8 --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as llama.cpp, Ollama, LMStudio, and MLX-LM have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="zhiqing/Qwen3-0.6B-INT8"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-0.6B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
For `llama.cpp`, you need to regenerate the GGUF file after the modification.
- Passing command line arguments:
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
For `llama-server` from `llama.cpp`, you can use
```shell
llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
```
> [!IMPORTANT]
> If you encounter the following warning
> ```
> Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
> ```
> please upgrade `transformers>=4.51.0`.
> [!NOTE]
> All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
> We advise adding the `rope_scaling` configuration only when processing long contexts is required.
> It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
> [!NOTE]
> The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
> [!TIP]
> The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3,
title = {Qwen3},
url = {https://qwenlm.github.io/blog/qwen3/},
author = {Qwen Team},
month = {April},
year = {2025}
}
```
|
LouiSeHU/Qwen3-8B-Q8_0-GGUF
|
LouiSeHU
| 2025-04-29T18:25:11Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Qwen/Qwen3-8B",
"base_model:quantized:Qwen/Qwen3-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-04-29T18:24:28Z |
---
base_model: Qwen/Qwen3-8B
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
# LouiSeHU/Qwen3-8B-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-8B`](https://huggingface.co/Qwen/Qwen3-8B) 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/Qwen/Qwen3-8B) 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 LouiSeHU/Qwen3-8B-Q8_0-GGUF --hf-file qwen3-8b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo LouiSeHU/Qwen3-8B-Q8_0-GGUF --hf-file qwen3-8b-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 LouiSeHU/Qwen3-8B-Q8_0-GGUF --hf-file qwen3-8b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo LouiSeHU/Qwen3-8B-Q8_0-GGUF --hf-file qwen3-8b-q8_0.gguf -c 2048
```
|
reedmayhew/Grok-3-reasoning-gemma3-4B-distilled-GGUF
|
reedmayhew
| 2025-04-29T18:24:50Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma3",
"en",
"dataset:reedmayhew/Grok-3-reasoning-100x",
"base_model:unsloth/gemma-3-4b-it",
"base_model:quantized:unsloth/gemma-3-4b-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T18:20:11Z |
---
base_model: unsloth/gemma-3-4b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
datasets:
- reedmayhew/Grok-3-reasoning-100x
---
# xAI Grok 3 w/Reasoning
Distilled - Gemma 3 4B
## Overview
This model is a Gemma 3 4B variant distilled from xAI’s Grok 3, with reasoning. It was fine-tuned to emulate Grok’s depth and structured clarity, particularly in tasks involving complex thought, such as problem-solving, coding, and mathematics.
## Technical Details
- **Developed by:** reedmayhew
- **Base Model:** google/gemma-3-4b-it
- **Training Speed Enhancement:** Trained 2x faster with Unsloth and Huggingface's TRL library
## Training Data
The model was trained on:
- reedmayhew/Grok-3-reasoning-100x
This dataset consists of 100 high-quality Grok 3 completions with reasoning responding to deep questions, solving math problems, and writing or analyzing code. The aim was to distill Grok’s analytical approach and technical versatility into a smaller, accessible model.
This Gemma 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)
|
skythrone/privacy-model
|
skythrone
| 2025-04-29T18:22:04Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"privacy",
"policy-analysis",
"classification",
"dataset:opp-115",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-28T18:05:10Z |
---
license: mit
tags:
- privacy
- policy-analysis
- classification
- text-classification
- transformers
- distilbert
library_name: transformers
datasets:
- opp-115
model-index:
- name: Privacy Clause Classifier (DistilBERT - OPP-115)
results: []
---
# Privacy Clause Classifier (DistilBERT - OPP-115)
This model is a fine-tuned DistilBERT model designed to classify **privacy policy clauses** into one of the predefined privacy practices based on the [OPP-115 dataset](https://privacy-hosting.isi.edu/data/OPP-115.pdf).
| ID | Category |
|----|---------------------------------|
| 0 | Data Retention |
| 1 | Data Security |
| 2 | Do Not Track |
| 3 | First Party Collection/Use |
| 4 | International and Specific Audiences |
| 5 | Other |
| 6 | Policy Change |
| 7 | Third Party Sharing/Collection |
| 8 | User Access, Edit and Deletion |
| 9 | User Choice/Control |
---
## Model Details
- **Architecture**: DistilBERT (pretrained)
- **Fine-tuning Dataset**: [OPP-115 Dataset](https://privacy-hosting.isi.edu/data/OPP-115.pdf)
- **Input Format**: Text snippets from privacy policies
- **Output Format**: Predicted class label with probabilities
---
## Intended Uses
- Automatic **privacy policy clause classification**
- **Regulatory technology (RegTech)** tools
- **Privacy policy summarization** and simplification
- **Risk analysis** for data sharing and collection practices
---
## How to Use
```python
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
import torch
# Load model
tokenizer = DistilBertTokenizerFast.from_pretrained("your-hf-username/your-model-name")
model = DistilBertForSequenceClassification.from_pretrained("your-hf-username/your-model-name")
# Predict
text = "We may collect your location data to provide customized services."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
predicted_class = torch.argmax(outputs.logits, dim=-1).item()
print(f"Predicted Category: {predicted_class}")
|
mradermacher/Coder-GRPO-3B-GGUF
|
mradermacher
| 2025-04-29T18:21:37Z | 306 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"dataset:glaiveai/glaive-code-assistant",
"base_model:yasserrmd/Coder-GRPO-3B",
"base_model:quantized:yasserrmd/Coder-GRPO-3B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-09T19:38:37Z |
---
base_model: yasserrmd/Coder-GRPO-3B
datasets:
- glaiveai/glaive-code-assistant
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/yasserrmd/Coder-GRPO-3B
<!-- 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/Coder-GRPO-3B-GGUF/resolve/main/Coder-GRPO-3B.Q2_K.gguf) | Q2_K | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Coder-GRPO-3B-GGUF/resolve/main/Coder-GRPO-3B.Q3_K_S.gguf) | Q3_K_S | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Coder-GRPO-3B-GGUF/resolve/main/Coder-GRPO-3B.Q3_K_M.gguf) | Q3_K_M | 1.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Coder-GRPO-3B-GGUF/resolve/main/Coder-GRPO-3B.Q3_K_L.gguf) | Q3_K_L | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Coder-GRPO-3B-GGUF/resolve/main/Coder-GRPO-3B.IQ4_XS.gguf) | IQ4_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Coder-GRPO-3B-GGUF/resolve/main/Coder-GRPO-3B.Q4_K_S.gguf) | Q4_K_S | 1.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Coder-GRPO-3B-GGUF/resolve/main/Coder-GRPO-3B.Q4_K_M.gguf) | Q4_K_M | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Coder-GRPO-3B-GGUF/resolve/main/Coder-GRPO-3B.Q5_K_S.gguf) | Q5_K_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Coder-GRPO-3B-GGUF/resolve/main/Coder-GRPO-3B.Q5_K_M.gguf) | Q5_K_M | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Coder-GRPO-3B-GGUF/resolve/main/Coder-GRPO-3B.Q6_K.gguf) | Q6_K | 2.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Coder-GRPO-3B-GGUF/resolve/main/Coder-GRPO-3B.Q8_0.gguf) | Q8_0 | 3.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Coder-GRPO-3B-GGUF/resolve/main/Coder-GRPO-3B.f16.gguf) | f16 | 6.3 | 16 bpw, overkill |
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 -->
|
spartyx/spz
|
spartyx
| 2025-04-29T18:21:27Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-29T18:21:20Z |
---
license: apache-2.0
---
|
iabd10/clasificador-comidas
|
iabd10
| 2025-04-29T18:21:25Z | 0 | 0 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-04-29T18:09:20Z |
---
license: cc-by-nc-4.0
---
|
no0ne-97/misoginia-roberta-base-bne
|
no0ne-97
| 2025-04-29T18:21:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-29T18:20:15Z |
---
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]
|
Teeranon/Mindtre-Ollama
|
Teeranon
| 2025-04-29T18:21:02Z | 0 | 0 | null |
[
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T18:18:18Z |
---
license: apache-2.0
---
|
youssefELK/LegalBot
|
youssefELK
| 2025-04-29T18:20:11Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-04-29T17:15:30Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## 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 0.15.2
|
ShubhamSantoki/deepseek-r1-distill-14b-8bit-v2-final
|
ShubhamSantoki
| 2025-04-29T18:18:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T13:12:14Z |
---
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]
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#### Training Hyperparameters
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#### 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]
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[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]
|
Sorawiz/Qwen2.5-KunouTimpist-Base-Q8_0-GGUF
|
Sorawiz
| 2025-04-29T18:16:09Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:Sorawiz/Qwen2.5-KunouTimpist-Base",
"base_model:quantized:Sorawiz/Qwen2.5-KunouTimpist-Base",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T18:15:03Z |
---
base_model: Sorawiz/Qwen2.5-KunouTimpist-Base
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# Sorawiz/Qwen2.5-KunouTimpist-Base-Q8_0-GGUF
This model was converted to GGUF format from [`Sorawiz/Qwen2.5-KunouTimpist-Base`](https://huggingface.co/Sorawiz/Qwen2.5-KunouTimpist-Base) 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/Sorawiz/Qwen2.5-KunouTimpist-Base) 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 Sorawiz/Qwen2.5-KunouTimpist-Base-Q8_0-GGUF --hf-file qwen2.5-kunoutimpist-base-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Sorawiz/Qwen2.5-KunouTimpist-Base-Q8_0-GGUF --hf-file qwen2.5-kunoutimpist-base-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 Sorawiz/Qwen2.5-KunouTimpist-Base-Q8_0-GGUF --hf-file qwen2.5-kunoutimpist-base-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Sorawiz/Qwen2.5-KunouTimpist-Base-Q8_0-GGUF --hf-file qwen2.5-kunoutimpist-base-q8_0.gguf -c 2048
```
|
mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF
|
mradermacher
| 2025-04-29T18:16:09Z | 288 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:nbeerbower/EVA-abliterated-TIES-Qwen2.5-72B",
"base_model:quantized:nbeerbower/EVA-abliterated-TIES-Qwen2.5-72B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-02-11T05:41:12Z |
---
base_model: nbeerbower/EVA-abliterated-TIES-Qwen2.5-72B
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/nbeerbower/EVA-abliterated-TIES-Qwen2.5-72B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-GGUF
## 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/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-IQ1_S.gguf) | i1-IQ1_S | 22.8 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-IQ1_M.gguf) | i1-IQ1_M | 23.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 27.2 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-IQ2_S.gguf) | i1-IQ2_S | 28.0 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-IQ2_M.gguf) | i1-IQ2_M | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 29.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q2_K.gguf) | i1-Q2_K | 29.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 31.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 32.9 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-IQ3_S.gguf) | i1-IQ3_S | 34.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 34.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-IQ3_M.gguf) | i1-IQ3_M | 35.6 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 37.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 39.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 39.8 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q4_0.gguf) | i1-Q4_0 | 41.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 44.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q4_1.gguf) | i1-Q4_1 | 45.8 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 47.5 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 51.5 | |
| [PART 1](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 54.5 | |
| [PART 1](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-72B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-72B.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 64.4 | practically like static Q6_K |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
DumbleDuck/reinforce-cartpole-v1
|
DumbleDuck
| 2025-04-29T18:12:11Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-04-21T19:19:53Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ArtemkaT08/alesya-1_7b
|
ArtemkaT08
| 2025-04-29T18:11:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T18:08:22Z |
---
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]
|
RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf
|
RichardErkhov
| 2025-04-29T18:11:33Z | 0 | 0 | null |
[
"gguf",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T09:34:09Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0 - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q2_K.gguf) | Q2_K | 2.97GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.IQ3_M.gguf) | IQ3_M | 3.53GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q3_K.gguf) | Q3_K | 3.74GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.IQ4_XS.gguf) | IQ4_XS | 4.17GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q4_K_S.gguf) | Q4_K_S | 4.36GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q4_K.gguf) | Q4_K | 4.57GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q4_K_M.gguf) | Q4_K_M | 4.57GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q4_1.gguf) | Q4_1 | 4.77GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q5_K.gguf) | Q5_K | 5.33GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q5_K_M.gguf) | Q5_K_M | 5.33GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q6_K.gguf) | Q6_K | 6.14GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0.Q8_0.gguf) | Q8_0 | 7.94GB |
Original model description:
---
base_model: RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6
library_name: transformers
model_name: reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0
This model is a fine-tuned version of [RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6](https://huggingface.co/RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6).
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="RyanYr/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0", 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/yyr/huggingface/runs/e0pwbi40)
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: 4.45.2
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf
|
RichardErkhov
| 2025-04-29T18:09:47Z | 0 | 0 | null |
[
"gguf",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T09:25:19Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1 - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q2_K.gguf) | Q2_K | 2.97GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.IQ3_M.gguf) | IQ3_M | 3.53GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q3_K.gguf) | Q3_K | 3.74GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.IQ4_XS.gguf) | IQ4_XS | 4.17GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q4_K_S.gguf) | Q4_K_S | 4.36GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q4_K.gguf) | Q4_K | 4.57GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q4_K_M.gguf) | Q4_K_M | 4.57GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q4_1.gguf) | Q4_1 | 4.77GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q5_K.gguf) | Q5_K | 5.33GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q5_K_M.gguf) | Q5_K_M | 5.33GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q6_K.gguf) | Q6_K | 6.14GB |
| [reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1.Q8_0.gguf) | Q8_0 | 7.94GB |
Original model description:
---
base_model: RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6
library_name: transformers
model_name: reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1
This model is a fine-tuned version of [RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6](https://huggingface.co/RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6).
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="RyanYr/reflect_mini8B_Om2SftT2_Om2G8kOm2AgIpsdpIter1T02_b0.1", 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/yyr/huggingface/runs/cmge7g4l)
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: 4.45.2
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
thejaminator/medium-allsneak-3e-05-DEPLOYMENT-0-1000insec-200-mcq10000-allsneak-qwen3
|
thejaminator
| 2025-04-29T18:06:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-32B",
"base_model:finetune:unsloth/Qwen3-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T18:05:32Z |
---
base_model: unsloth/Qwen3-32B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thejaminator
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-32B
This qwen3 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)
|
PSG-Arsenal-Match-Videos-Tv/DIRECT.Match.Videos.PSG.Arsenal.En.Direct.Streaming.Gratuit
|
PSG-Arsenal-Match-Videos-Tv
| 2025-04-29T18:05:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T18:02:41Z |
⚽📺📱👉◄◄🔴 https://tinyurl.com/mtbv4nys
⚽📺📱👉◄◄🔴 https://tinyurl.com/mtbv4nys
⚽📺📱👉◄◄🔴 https://tinyurl.com/mtbv4nys
DIRECT. Arsenal-PSG: suivez en live le match aller de la demi-finale de Ligue des champions
Aujourd'hui à 07h52 - mis à jour aujourd'hui à 19h28
Achraf Hakimi au duel avec Mikel Merino lors du match Arsenal-PSG (2-0, Ligue des champions), le 1er
Suivez en live la demi-finale aller de la Ligue des champions entre Arsenal et le PSG, ce mardi (21h). Battus en octobre par les Gunners, les Parisiens veulent frapper fort à Londres dans cette première manche très indécise.
|
hhdqirui/Qwen2-7B-Instruct-GRPO-4
|
hhdqirui
| 2025-04-29T18:04:22Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:AI-MO/NuminaMath-TIR",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T16:00:12Z |
---
base_model: Qwen/Qwen2-7B-Instruct
datasets: AI-MO/NuminaMath-TIR
library_name: transformers
model_name: Qwen2-7B-Instruct-GRPO-4
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2-7B-Instruct-GRPO-4
This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) 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="hhdqirui/Qwen2-7B-Instruct-GRPO-4", 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: 0.17.0
- Transformers: 4.47.1
- Pytorch: 2.6.0+cu124
- Datasets: 3.2.0
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
asuraloriken24/Alpha-X
|
asuraloriken24
| 2025-04-29T18:04:20Z | 0 | 0 | null |
[
"LLM-Server",
"en",
"license:llama3.2",
"region:us"
] | null | 2025-04-27T23:29:33Z |
---
license: llama3.2
language:
- en
tags:
- LLM-Server
---
|
RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf
|
RichardErkhov
| 2025-04-29T18:03:50Z | 0 | 0 | null |
[
"gguf",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T09:27:12Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1 - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q2_K.gguf) | Q2_K | 2.97GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.IQ3_M.gguf) | IQ3_M | 3.53GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q3_K.gguf) | Q3_K | 3.74GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.IQ4_XS.gguf) | IQ4_XS | 4.17GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q4_K_S.gguf) | Q4_K_S | 4.36GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q4_K.gguf) | Q4_K | 4.57GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q4_K_M.gguf) | Q4_K_M | 4.57GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q4_1.gguf) | Q4_1 | 4.77GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q5_K.gguf) | Q5_K | 5.33GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q5_K_M.gguf) | Q5_K_M | 5.33GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q6_K.gguf) | Q6_K | 6.14GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1.Q8_0.gguf) | Q8_0 | 7.94GB |
Original model description:
---
base_model: RyanYr/reflect_mini8B_Om2SftT1-Om2IpsdpIter1T1_b0.5
library_name: transformers
model_name: reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1
This model is a fine-tuned version of [RyanYr/reflect_mini8B_Om2SftT1-Om2IpsdpIter1T1_b0.5](https://huggingface.co/RyanYr/reflect_mini8B_Om2SftT1-Om2IpsdpIter1T1_b0.5).
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="RyanYr/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.1", 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/yyr/huggingface/runs/b4ok9wqk)
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: 4.45.2
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
mradermacher/Qwen3-1.7B-Base-GGUF
|
mradermacher
| 2025-04-29T18:02:39Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Qwen/Qwen3-1.7B-Base",
"base_model:quantized:Qwen/Qwen3-1.7B-Base",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T15:12:39Z |
---
base_model: Qwen/Qwen3-1.7B-Base
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Qwen/Qwen3-1.7B-Base
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-1.7B-Base-i1-GGUF
## 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/Qwen3-1.7B-Base-GGUF/resolve/main/Qwen3-1.7B-Base.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-Base-GGUF/resolve/main/Qwen3-1.7B-Base.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-Base-GGUF/resolve/main/Qwen3-1.7B-Base.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-Base-GGUF/resolve/main/Qwen3-1.7B-Base.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-Base-GGUF/resolve/main/Qwen3-1.7B-Base.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-Base-GGUF/resolve/main/Qwen3-1.7B-Base.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-Base-GGUF/resolve/main/Qwen3-1.7B-Base.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-Base-GGUF/resolve/main/Qwen3-1.7B-Base.Q5_K_S.gguf) | Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-Base-GGUF/resolve/main/Qwen3-1.7B-Base.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-Base-GGUF/resolve/main/Qwen3-1.7B-Base.Q6_K.gguf) | Q6_K | 1.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-Base-GGUF/resolve/main/Qwen3-1.7B-Base.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-Base-GGUF/resolve/main/Qwen3-1.7B-Base.f16.gguf) | f16 | 3.5 | 16 bpw, overkill |
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 -->
|
mluger/vitFaceExpression-MLPHead
|
mluger
| 2025-04-29T18:01:16Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T09:45:26Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vitFaceExpression-MLPHead
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. -->
# vitFaceExpression-MLPHead
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8962
- Accuracy: 0.6854
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3015 | 1.0 | 673 | 1.0408 | 0.6188 |
| 0.995 | 2.0 | 1346 | 0.9245 | 0.6616 |
| 0.8021 | 3.0 | 2019 | 0.8930 | 0.6702 |
| 0.6967 | 4.0 | 2692 | 0.8718 | 0.6789 |
| 0.6283 | 5.0 | 3365 | 0.8813 | 0.6814 |
| 0.4952 | 6.0 | 4038 | 0.8812 | 0.6881 |
| 0.4403 | 7.0 | 4711 | 0.8961 | 0.6838 |
| 0.412 | 8.0 | 5384 | 0.8962 | 0.6854 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
ijterror/AshGreFluxLora
|
ijterror
| 2025-04-29T17:58:33Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-04-29T15:41:31Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: shlygrn
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
---
# Ashley Greene Lora
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `shlygrn` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
faraya1/genie-grpo-test-API-qwen3B-lora-step-900
|
faraya1
| 2025-04-29T17:57:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T17:57:42Z |
---
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]
|
mradermacher/Qwen3-14B-Base-i1-GGUF
|
mradermacher
| 2025-04-29T17:57:35Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Qwen/Qwen3-14B-Base",
"base_model:quantized:Qwen/Qwen3-14B-Base",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-04-29T15:48:02Z |
---
base_model: Qwen/Qwen3-14B-Base
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Qwen/Qwen3-14B-Base
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen3-14B-Base-GGUF
## 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/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ1_S.gguf) | i1-IQ1_S | 3.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ1_M.gguf) | i1-IQ1_M | 3.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ2_S.gguf) | i1-IQ2_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ2_M.gguf) | i1-IQ2_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q2_K.gguf) | i1-Q2_K | 5.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 6.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ3_S.gguf) | i1-IQ3_S | 6.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ4_NL.gguf) | i1-IQ4_NL | 8.6 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q4_0.gguf) | i1-Q4_0 | 8.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q4_1.gguf) | i1-Q4_1 | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q6_K.gguf) | i1-Q6_K | 12.2 | practically like static Q6_K |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
dgambettaphd/M_llm2_gen3_run0_W_doc1000_synt64_tot128_lr5em5_SYNLAST
|
dgambettaphd
| 2025-04-29T17:57:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T17:57:13Z |
---
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. -->
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### 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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[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]
|
RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf
|
RichardErkhov
| 2025-04-29T17:57:05Z | 0 | 0 | null |
[
"gguf",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T09:30:31Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5 - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q2_K.gguf) | Q2_K | 2.97GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.IQ3_M.gguf) | IQ3_M | 3.53GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q3_K.gguf) | Q3_K | 3.74GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.IQ4_XS.gguf) | IQ4_XS | 4.17GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q4_K_S.gguf) | Q4_K_S | 4.36GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q4_K.gguf) | Q4_K | 4.57GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q4_K_M.gguf) | Q4_K_M | 4.57GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q4_1.gguf) | Q4_1 | 4.77GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q5_K.gguf) | Q5_K | 5.33GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q5_K_M.gguf) | Q5_K_M | 5.33GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q6_K.gguf) | Q6_K | 6.14GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5.Q8_0.gguf) | Q8_0 | 7.94GB |
Original model description:
---
base_model: RyanYr/reflect_mini8Bit_om2-460k_sft-t1
library_name: transformers
model_name: reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5
This model is a fine-tuned version of [RyanYr/reflect_mini8Bit_om2-460k_sft-t1](https://huggingface.co/RyanYr/reflect_mini8Bit_om2-460k_sft-t1).
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="RyanYr/reflect_mini8B_Om2SftT1-Om2G8kOm2Ag40kIpsdpIter1T1_b0.5", 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/yyr/huggingface/runs/x18ez61x)
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: 4.45.2
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
annemiekebickleyoy/384e3def-3df6-40ed-a033-26b57627cd59
|
annemiekebickleyoy
| 2025-04-29T17:55:26Z | 0 | 0 |
transformers
|
[
"transformers",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T17:54:47Z |
---
library_name: transformers
model_name: annemiekebickleyoy/384e3def-3df6-40ed-a033-26b57627cd59
tags:
- generated_from_trainer
licence: license
---
# Model Card for annemiekebickleyoy/384e3def-3df6-40ed-a033-26b57627cd59
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
mradermacher/Qwen3-14B-Base-GGUF
|
mradermacher
| 2025-04-29T17:52:39Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Qwen/Qwen3-14B-Base",
"base_model:quantized:Qwen/Qwen3-14B-Base",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T15:14:28Z |
---
base_model: Qwen/Qwen3-14B-Base
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Qwen/Qwen3-14B-Base
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF
## 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/Qwen3-14B-Base-GGUF/resolve/main/Qwen3-14B-Base.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-GGUF/resolve/main/Qwen3-14B-Base.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-GGUF/resolve/main/Qwen3-14B-Base.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-GGUF/resolve/main/Qwen3-14B-Base.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-GGUF/resolve/main/Qwen3-14B-Base.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-GGUF/resolve/main/Qwen3-14B-Base.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-GGUF/resolve/main/Qwen3-14B-Base.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-GGUF/resolve/main/Qwen3-14B-Base.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-GGUF/resolve/main/Qwen3-14B-Base.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-GGUF/resolve/main/Qwen3-14B-Base.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-GGUF/resolve/main/Qwen3-14B-Base.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 -->
|
vkublytskyi/q-FrozenLake-v1-4x4-noSlippery
|
vkublytskyi
| 2025-04-29T17:51:20Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-04-29T17:51:17Z |
---
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="vkublytskyi/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"])
```
|
PSG-Arsenal-Videos-Tv/DIRECT.VIDEOS.PSG.Arsenal.En.Direct.Streaming.Gratuit.Tv
|
PSG-Arsenal-Videos-Tv
| 2025-04-29T17:51:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T17:40:49Z |
⚽📺📱👉◄◄🔴 https://tinyurl.com/mtbv4nys
⚽📺📱👉◄◄🔴 https://tinyurl.com/mtbv4nys
⚽📺📱👉◄◄🔴 https://tinyurl.com/mtbv4nys
DIRECT. Arsenal-PSG: suivez en live le match aller de la demi-finale de Ligue des champions
Aujourd'hui à 07h52 - mis à jour aujourd'hui à 19h28
Achraf Hakimi au duel avec Mikel Merino lors du match Arsenal-PSG (2-0, Ligue des champions), le 1er
Suivez en live la demi-finale aller de la Ligue des champions entre Arsenal et le PSG, ce mardi (21h). Battus en octobre par les Gunners, les Parisiens veulent frapper fort à Londres dans cette première manche très indécise.
|
RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf
|
RichardErkhov
| 2025-04-29T17:50:04Z | 2 | 0 | null |
[
"gguf",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T09:33:00Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0 - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q2_K.gguf) | Q2_K | 2.97GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.IQ3_M.gguf) | IQ3_M | 3.53GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q3_K.gguf) | Q3_K | 3.74GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.IQ4_XS.gguf) | IQ4_XS | 4.17GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q4_K_S.gguf) | Q4_K_S | 4.36GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q4_K.gguf) | Q4_K | 4.57GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q4_K_M.gguf) | Q4_K_M | 4.57GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q4_1.gguf) | Q4_1 | 4.77GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q5_K.gguf) | Q5_K | 5.33GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q5_K_M.gguf) | Q5_K_M | 5.33GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q6_K.gguf) | Q6_K | 6.14GB |
| [reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0.Q8_0.gguf) | Q8_0 | 7.94GB |
Original model description:
---
base_model: RyanYr/reflect_mini8Bit_om2-460k_sft-t1
library_name: transformers
model_name: reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0
This model is a fine-tuned version of [RyanYr/reflect_mini8Bit_om2-460k_sft-t1](https://huggingface.co/RyanYr/reflect_mini8Bit_om2-460k_sft-t1).
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="RyanYr/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0", 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/yyr/huggingface/runs/ijjbovca)
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: 4.45.2
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
joel4899/roberta-squad2-answer-generation
|
joel4899
| 2025-04-29T17:47:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"base_model:deepset/roberta-base-squad2",
"base_model:finetune:deepset/roberta-base-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2025-04-29T11:26:18Z |
---
library_name: transformers
license: cc-by-4.0
base_model: deepset/roberta-base-squad2
tags:
- generated_from_trainer
model-index:
- name: roberta-squad2-answer-generation
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. -->
# roberta-squad2-answer-generation
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0 | 1.0 | 299 | 0.0000 |
| 0.0 | 2.0 | 598 | 0.0000 |
| 0.0 | 3.0 | 897 | 0.0000 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0+cpu
- Datasets 3.0.1
- Tokenizers 0.20.0
|
JasonTree/Qwen3-8B-quietGIVE0428
|
JasonTree
| 2025-04-29T17:45:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T23:14:06Z |
---
base_model: Qwen/Qwen3-8B
library_name: transformers
model_name: Qwen3-8B-quietGIVE0428
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen3-8B-quietGIVE0428
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
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="JasonTree/Qwen3-8B-quietGIVE0428", 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/alelab/QuiteGive/runs/6m488i7a)
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: 0.16.1
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.3.2
- 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}}
}
```
|
RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf
|
RichardErkhov
| 2025-04-29T17:43:46Z | 0 | 0 | null |
[
"gguf",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T09:40:26Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1 - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q2_K.gguf) | Q2_K | 2.97GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.IQ3_M.gguf) | IQ3_M | 3.53GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q3_K.gguf) | Q3_K | 3.74GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.IQ4_XS.gguf) | IQ4_XS | 4.17GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q4_K_S.gguf) | Q4_K_S | 4.36GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q4_K.gguf) | Q4_K | 4.57GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q4_K_M.gguf) | Q4_K_M | 4.57GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q4_1.gguf) | Q4_1 | 4.77GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q5_K.gguf) | Q5_K | 5.33GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q5_K_M.gguf) | Q5_K_M | 5.33GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q6_K.gguf) | Q6_K | 6.14GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1.Q8_0.gguf) | Q8_0 | 7.94GB |
Original model description:
---
base_model: RyanYr/reflect_mini8Bit_om2-460k_sft-dpo-t1
library_name: transformers
model_name: reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1
This model is a fine-tuned version of [RyanYr/reflect_mini8Bit_om2-460k_sft-dpo-t1](https://huggingface.co/RyanYr/reflect_mini8Bit_om2-460k_sft-dpo-t1).
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="RyanYr/reflect_mini8Bit_om2-460k_sft-dpo-t1_psdp-t1", 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/yyr/huggingface/runs/cf6ates7)
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: 4.45.2
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
littletuzi100/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-singing_running_ape
|
littletuzi100
| 2025-04-29T17:41:42Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am singing running 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
| 2025-04-17T19:59:05Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-singing_running_ape
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am singing running ape
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-singing_running_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="littletuzi100/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-singing_running_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: 4.51.1
- 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}}
}
```
|
nareauow/my_speech_recognition
|
nareauow
| 2025-04-29T17:41:33Z | 0 | 0 | null |
[
"speaker-recognition",
"MFCC",
"CNN",
"audio-classification",
"en",
"region:us"
] |
audio-classification
| 2025-04-25T16:21:36Z |
---
language:
- en
pipeline_tag: audio-classification
tags:
- speaker-recognition
- MFCC
- CNN
---
|
kamilhussen24/sylheti-t5
|
kamilhussen24
| 2025-04-29T17:41:14Z | 112 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-04-25T04:13:12Z |
---
library_name: transformers
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
model-index:
- name: sylheti-t5
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. -->
# sylheti-t5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 18.7540
## 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: 16
- eval_batch_size: 16
- seed: 42
- 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: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 23.2565 | 6.6667 | 100 | 18.7540 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
mluger/vitFaceExpressionMixUpAugmentationAligned
|
mluger
| 2025-04-29T17:40:04Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T17:39:40Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vitFaceExpressionMixUpAugmentationAligned
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6815712494776431
---
<!-- 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. -->
# vitFaceExpressionMixUpAugmentationAligned
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1497
- Accuracy: 0.6816
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2333 | 1.0 | 669 | 1.0113 | 0.6341 |
| 0.923 | 2.0 | 1338 | 0.9048 | 0.6729 |
| 0.6649 | 3.0 | 2007 | 0.8886 | 0.6855 |
| 0.4775 | 4.0 | 2676 | 0.9545 | 0.6803 |
| 0.3522 | 5.0 | 3345 | 0.9925 | 0.6853 |
| 0.1815 | 6.0 | 4014 | 1.0883 | 0.6848 |
| 0.1255 | 7.0 | 4683 | 1.1511 | 0.6828 |
| 0.1091 | 8.0 | 5352 | 1.1497 | 0.6816 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
shane-moxley/output
|
shane-moxley
| 2025-04-29T17:39:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"endpoints_compatible",
"region:us"
] | null | 2025-04-27T16:55:52Z |
---
base_model: microsoft/phi-2
library_name: transformers
model_name: output
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for output
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2).
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="shane-moxley/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 SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.3.2
- 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}}
}
```
|
drmcbride/Qwen3-0.6B-abliterated-Q8_0-GGUF
|
drmcbride
| 2025-04-29T17:39:33Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"chat",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:huihui-ai/Qwen3-0.6B-abliterated",
"base_model:quantized:huihui-ai/Qwen3-0.6B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T17:39:24Z |
---
base_model: huihui-ai/Qwen3-0.6B-abliterated
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
---
# drmcbride/Qwen3-0.6B-abliterated-Q8_0-GGUF
This model was converted to GGUF format from [`huihui-ai/Qwen3-0.6B-abliterated`](https://huggingface.co/huihui-ai/Qwen3-0.6B-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/Qwen3-0.6B-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/Qwen3-0.6B-abliterated-Q8_0-GGUF --hf-file qwen3-0.6b-abliterated-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo drmcbride/Qwen3-0.6B-abliterated-Q8_0-GGUF --hf-file qwen3-0.6b-abliterated-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 drmcbride/Qwen3-0.6B-abliterated-Q8_0-GGUF --hf-file qwen3-0.6b-abliterated-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo drmcbride/Qwen3-0.6B-abliterated-Q8_0-GGUF --hf-file qwen3-0.6b-abliterated-q8_0.gguf -c 2048
```
|
mradermacher/Qwen2.5-0.5b-Test-ft-GGUF
|
mradermacher
| 2025-04-29T17:37:09Z | 191 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"sft",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:KingNish/Qwen2.5-0.5b-Test-ft",
"base_model:quantized:KingNish/Qwen2.5-0.5b-Test-ft",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-24T21:01:43Z |
---
base_model: KingNish/Qwen2.5-0.5b-Test-ft
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/KingNish/Qwen2.5-0.5b-Test-ft
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-0.5b-Test-ft-i1-GGUF
## 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/Qwen2.5-0.5b-Test-ft-GGUF/resolve/main/Qwen2.5-0.5b-Test-ft.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5b-Test-ft-GGUF/resolve/main/Qwen2.5-0.5b-Test-ft.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5b-Test-ft-GGUF/resolve/main/Qwen2.5-0.5b-Test-ft.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5b-Test-ft-GGUF/resolve/main/Qwen2.5-0.5b-Test-ft.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5b-Test-ft-GGUF/resolve/main/Qwen2.5-0.5b-Test-ft.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5b-Test-ft-GGUF/resolve/main/Qwen2.5-0.5b-Test-ft.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5b-Test-ft-GGUF/resolve/main/Qwen2.5-0.5b-Test-ft.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5b-Test-ft-GGUF/resolve/main/Qwen2.5-0.5b-Test-ft.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5b-Test-ft-GGUF/resolve/main/Qwen2.5-0.5b-Test-ft.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5b-Test-ft-GGUF/resolve/main/Qwen2.5-0.5b-Test-ft.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5b-Test-ft-GGUF/resolve/main/Qwen2.5-0.5b-Test-ft.Q8_0.gguf) | Q8_0 | 0.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5b-Test-ft-GGUF/resolve/main/Qwen2.5-0.5b-Test-ft.f16.gguf) | f16 | 1.1 | 16 bpw, overkill |
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 -->
|
Denn231/internal_clf_v_0.47
|
Denn231
| 2025-04-29T17:33:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-29T14:41:23Z |
---
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]
|
Denn231/external_clf_v_0.47
|
Denn231
| 2025-04-29T17:33:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-29T14:40:01Z |
---
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]
|
rodolfornn/image2
|
rodolfornn
| 2025-04-29T17:31:58Z | 6 | 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
| 2025-04-28T19:25:29Z |
---
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: TOK
---
# Image2
<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 `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/rodolfornn/image2/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('rodolfornn/image2', weight_name='lora.safetensors')
image = pipeline('TOK').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: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/rodolfornn/image2/discussions) to add images that show off what you’ve made with this LoRA.
|
XSkills/nllb-200-turkmen-english-lora
|
XSkills
| 2025-04-29T17:31:55Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"translation",
"nllb",
"lora",
"peft",
"turkmen",
"tuk",
"eng",
"dataset:XSkills/turkmen_english_s500",
"base_model:facebook/nllb-200-distilled-600M",
"base_model:adapter:facebook/nllb-200-distilled-600M",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2025-04-26T23:32:08Z |
---
license: cc-by-nc-4.0
language:
- tuk
- eng
library_name: transformers
datasets:
- XSkills/turkmen_english_s500
tags:
- translation
- nllb
- lora
- peft
- turkmen
model_name: nllb-200-turkmen-english-lora
pipeline_tag: translation
base_model:
- facebook/nllb-200-distilled-600M
---
# NLLB-200 (600 M) – LoRA fine-tuned for Turkmen ↔ English
**Author** : Merdan Durdyyev
**Base model** : [`facebook/nllb-200-distilled-600M`](https://huggingface.co/facebook/nllb-200-distilled-600M)
**Tuning method** : Low-Rank Adaptation (LoRA) on only the `q_proj` & `v_proj` matrices (≈ 2.4 M trainable → 0.38 % of total params).
> I built this checkpoint as the final project for my Deep-Learning class and as a small contribution to the Turkmen AI community, where open-source resources are scarce.
---
## TL;DR & Quick results
Try it on [Space demo](https://huggingface.co/spaces/XSkills/nllb-turkmen-english) Article with full technical journey is available [Medium](https://medium.com/@meinnps/fine-tuning-nllb-200-with-lora-on-a-650-sentence-turkmen-english-corpus-082f68bdec71).
### Model Comparison (Fine-tuned vs Original)
#### English to Turkmen
| Metric | Fine-tuned | Original | Difference |
|---------------------------|-----------:|---------:|-----------:|
| **BLEU** | 8.24 | 8.12 | +0.12 |
| **chrF** | 39.55 | 39.46 | +0.09 |
| **TER (lower is better)** | 87.20 | 87.30 | -0.10 |
#### Turkmen to English
| Metric | Fine-tuned | Original | Difference |
|---------------------------|-----------:|---------:|-----------:|
| **BLEU** | 25.88 | 26.48 | -0.60 |
| **chrF** | 52.71 | 52.91 | -0.20 |
| **TER (lower is better)** | 67.70 | 69.70 | -2.00 |
*Scores computed with sacre BLEU 2.5, chrF, TER on the official `test` split.
A separate spreadsheet with **human adequacy/fluency ratings** is available in the article.*
---
## Intended use & scope
* **Good for**: research prototypes, student projects, quick experiments on Turkmen text.
* **Not for**: commercial MT systems (license is **CC-BY-NC 4.0**), critical medical/legal translation, or production workloads without further validation.
---
## How to use
*(If you want to take a look to the LoRA adapter visit [nllb-200-turkmen-english-lora-adapter](https://huggingface.co/XSkills/nllb-200-turkmen-english-lora-adapter/tree/main))*
Using piplene
```python
from transformers import pipeline
# Create the translation pipeline
pipe = pipeline("translation", model="XSkills/nllb-200-turkmen-english-lora")
# Translate from English to Turkmen
# You need to specify the source and target languages using their FLORES-200 codes
text = "Hello, how are you today?"
translated = pipe(text, src_lang="eng_Latn", tgt_lang="tuk_Latn")
print(translated)
```
Using Tokenizer
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_id = "XSkills/nllb-200-turkmen-english-lora"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
def tr(text, src="tuk_Latn", tgt="eng_Latn"):
tok.src_lang = src
ids = tok(text, return_tensors="pt", truncation=True, max_length=128)
out = model.generate(
**ids,
forced_bos_token_id=tok.convert_tokens_to_ids(tgt),
max_length=128,
num_beams=5
)
return tok.decode(out[0], skip_special_tokens=True)
print(tr("Men kitaby okaýaryn."))
```
## Training data
- Dataset : [XSkills/turkmen_english_s500](https://huggingface.co/datasets/XSkills/turkmen_english_s500) 619 parallel sentences (495 train / 62 val / 62 test) of news & official communiqués.
- Collecting even this small corpus proved challenging because publicly available Turkmen data are limited.
## Training procedure
| Item | Value |
|------|-------|
| GPU | 1 × NVIDIA A100 40 GB (Google Colab) |
| Wall-time | ~ 3 minutes |
| Optimiser | AdamW |
| Learning rate | 1 × 10⁻⁵, cosine schedule, warm-up 10% |
| Epochs | 5 |
| Batch size | 4 (train) / 8 (eval) |
| Weight-decay | 0.005 |
| FP16 | Yes |
| LoRA config | `r=16`, `alpha=32`, `dropout=0.05`, modules = `["q_proj","v_proj"]` |
### LoRA Config
```python
lora_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "v_proj"],
lora_dropout=0.05,
bias="none",
task_type=TaskType.SEQ_2_SEQ_LM,
)
```
### Training Configuration
```python
training_args = Seq2SeqTrainingArguments(
output_dir=FINETUNED_DIR,
per_device_train_batch_size=4,
per_device_eval_batch_size=8,
weight_decay=0.005,
save_total_limit=3,
learning_rate=1e-5,
num_train_epochs=5,
lr_scheduler_type="cosine",
predict_with_generate=True,
fp16=True if torch.cuda.is_available() else False,
logging_dir="./logs",
logging_steps=50,
eval_steps=50,
save_steps=100,
eval_accumulation_steps=2,
report_to="tensorboard",
warmup_ratio=0.1,
metric_for_best_model="eval_bleu", # Use BLEU for model selection
greater_is_better=True,
)
```
## Evaluation
Automatic metrics are given in TL;DR.
A manual review on 50 random test sentences showed:
- Adequacy: 36 / 50 translations judged “Good” or better.
- Fluency: 38 / 50 sound natural to a native speaker.
*(Full spreadsheet available — ask via contact below.)*
## Limitations & bias
- Only 500ish sentences → limited vocabulary & domain coverage.
- May hallucinate proper nouns or numbers on longer inputs.
- Gender/ politeness nuances not guaranteed.
- CC-BY-NC licence forbids commercial use; respect Meta’s original terms.
## How to Contribute
We welcome contributions to improve Turkmen-English translation capabilities! Here's how you can help:
### Data Contributions
- **Read Dataset Contribution**: You can find the instructions for contributing to the dataset at [Dataset Readme](https://huggingface.co/datasets/XSkills/turkmen_english_s500/blob/main/README.md)
### Code Contributions
- **Hyperparameter experiments**: Try different LoRA configurations and document your results
- **Evaluation**: Help with human evaluation of translation quality and fluency
- **Bug fixes**: Report issues or submit fixes for the model implementation
### Use Cases & Documentation
- **Example applications**: Share how you're using the model for research or projects
- **Domain-specific guides**: Create guides for using the model in specific domains
- **Translation examples**: Share interesting or challenging translation examples
### Getting Started
1. Fork the repository
2. Make your changes
3. Submit a pull request with clear documentation of your contribution
4. For data contributions, contact the maintainer directly
All contributors will be acknowledged in the model documentation. Contact [[email protected]](mailto:[email protected]) with any questions or to discuss potential contributions.
---
*Note: This model is licensed under CC-BY-NC-4.0, so all contributions must be compatible with non-commercial use only.*
## Citation
```bibtex
@misc{durdyyev2025turkmenNLLBLoRA,
title = {LoRA Fine‐tuning of NLLB‐200 for Turkmen–English Translation},
author = {Durdyyev, Merdan},
year = {2025},
url = {https://huggingface.co/XSkills/nllb-200-turkmen-english-lora}
}
```
## Contact
If you have questions, suggestions or want to collaborate, please reach out through [e-mail]([email protected]), [LinkedIn]( https://linkedin.com/in/merdandt) or [Telegram](https://t.me/merdandt).
## Future Work
- Try to tune on bigger dataset.
- Try to tweak the hyperparameters
- Use [sacreBLEU](https://github.com/mjpost/sacrebleu) metric
|
omarwaleed523/gemma-3-12b-arabic-multitask
|
omarwaleed523
| 2025-04-29T17:30:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T17:30:31Z |
---
base_model: unsloth/gemma-3-12b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** omarwaleed523
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-12b-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)
|
jjeccles/qwen30430-filteranddocheadLora
|
jjeccles
| 2025-04-29T17:30:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-1.7B",
"base_model:finetune:unsloth/Qwen3-1.7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T17:30:05Z |
---
base_model: unsloth/Qwen3-1.7B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** jjeccles
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-1.7B
This qwen3 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)
|
kvbiii/modernbert-llm-router
|
kvbiii
| 2025-04-29T17:28:49Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"bert",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2025-04-29T17:02:56Z |
---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: modernbert-llm-router
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. -->
# modernbert-llm-router
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2799
- F1: 0.9339
## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.5656 | 1.0 | 313 | 1.1637 | 0.7771 |
| 0.5046 | 2.0 | 626 | 0.4545 | 0.9159 |
| 0.2419 | 3.0 | 939 | 0.3382 | 0.9208 |
| 0.1345 | 4.0 | 1252 | 0.2883 | 0.9321 |
| 0.0689 | 5.0 | 1565 | 0.2799 | 0.9339 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.6.0+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1
|
quickstep3621/dippy-v3-1-8
|
quickstep3621
| 2025-04-29T17:28:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"gemma",
"google",
"Bifröst",
"Bifrost",
"code",
"text-generation",
"conversational",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T17:28:44Z |
---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-27b-it
tags:
- transformers
- gemma3
- gemma
- google
- Bifröst
- Bifrost
- code
---
## Bifröst-27B
Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance.
### Model Details
- **Model Name:** Bifröst-27B
- **Base Architecture:** gemma3
- **Application:** Enterprise Secure Code Generation
- **Release Date:** 16-March-2025
### Intended Use
Bifröst is designed explicitly for:
- Generating secure, efficient, and high-quality code.
- Supporting development tasks within regulated enterprise environments.
- Enhancing productivity by automating routine coding tasks without compromising security.
### Features
- **Security-Focused Training:** Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards.
- **Enterprise-Optimized Performance:** Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions.
- **Compliance-Driven Design:** Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2).
### Limitations
- Bifröst should be used under human supervision to ensure code correctness and security compliance.
- Model-generated code should undergo appropriate security and quality assurance checks before deployment.
### Ethical Considerations
- Users are encouraged to perform regular audits and compliance checks on generated outputs.
- Enterprises should implement responsible AI practices to mitigate biases or unintended consequences.
### Usage
Below are some quick-start instructions for using the model with the `transformers` library.
#### Installation
```sh
$ pip install git+https://github.com/huggingface/[email protected]
```
#### Running with the `pipeline` API
```python
from transformers import pipeline
import torch
pipe = pipeline(
"text-generation",
model="OpenGenerativeAI/Bifrost-27B",
device="cuda",
torch_dtype=torch.bfloat16
)
messages = [{"role": "user", "content": "Generate a secure API key management system."}]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"])
```
## Terms of Use
This model is released under the **Gemma license**. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
|
quickstep3621/dippy-v3-1-6
|
quickstep3621
| 2025-04-29T17:28:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"gemma",
"google",
"Bifröst",
"Bifrost",
"code",
"text-generation",
"conversational",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T17:28:39Z |
---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-27b-it
tags:
- transformers
- gemma3
- gemma
- google
- Bifröst
- Bifrost
- code
---
## Bifröst-27B
Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance.
### Model Details
- **Model Name:** Bifröst-27B
- **Base Architecture:** gemma3
- **Application:** Enterprise Secure Code Generation
- **Release Date:** 16-March-2025
### Intended Use
Bifröst is designed explicitly for:
- Generating secure, efficient, and high-quality code.
- Supporting development tasks within regulated enterprise environments.
- Enhancing productivity by automating routine coding tasks without compromising security.
### Features
- **Security-Focused Training:** Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards.
- **Enterprise-Optimized Performance:** Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions.
- **Compliance-Driven Design:** Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2).
### Limitations
- Bifröst should be used under human supervision to ensure code correctness and security compliance.
- Model-generated code should undergo appropriate security and quality assurance checks before deployment.
### Ethical Considerations
- Users are encouraged to perform regular audits and compliance checks on generated outputs.
- Enterprises should implement responsible AI practices to mitigate biases or unintended consequences.
### Usage
Below are some quick-start instructions for using the model with the `transformers` library.
#### Installation
```sh
$ pip install git+https://github.com/huggingface/[email protected]
```
#### Running with the `pipeline` API
```python
from transformers import pipeline
import torch
pipe = pipeline(
"text-generation",
model="OpenGenerativeAI/Bifrost-27B",
device="cuda",
torch_dtype=torch.bfloat16
)
messages = [{"role": "user", "content": "Generate a secure API key management system."}]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"])
```
## Terms of Use
This model is released under the **Gemma license**. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
|
RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf
|
RichardErkhov
| 2025-04-29T17:27:59Z | 0 | 0 | null |
[
"gguf",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T09:16:28Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1 - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q2_K.gguf) | Q2_K | 2.97GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.IQ3_M.gguf) | IQ3_M | 3.53GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q3_K.gguf) | Q3_K | 3.74GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.IQ4_XS.gguf) | IQ4_XS | 4.17GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q4_K_S.gguf) | Q4_K_S | 4.36GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q4_K.gguf) | Q4_K | 4.57GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q4_K_M.gguf) | Q4_K_M | 4.57GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q4_1.gguf) | Q4_1 | 4.77GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q5_K.gguf) | Q5_K | 5.33GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q5_K_M.gguf) | Q5_K_M | 5.33GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q6_K.gguf) | Q6_K | 6.14GB |
| [reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1.Q8_0.gguf) | Q8_0 | 7.94GB |
Original model description:
---
base_model: RyanYr/reflect_mini8Bit_om2-460k_sft-t1
library_name: transformers
model_name: reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1
This model is a fine-tuned version of [RyanYr/reflect_mini8Bit_om2-460k_sft-t1](https://huggingface.co/RyanYr/reflect_mini8Bit_om2-460k_sft-t1).
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="RyanYr/reflect_mini8B_Om2SftT1-Om2G8kOm2AgIpsdpIter1T1_b0.1", 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/yyr/huggingface/runs/s117w777)
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: 4.45.2
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
rayonlabs/hf-autotrain-2025-04-29-ccf32cbf
|
rayonlabs
| 2025-04-29T17:26:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"dataset:rayonlabs/autotrain-data-hf-autotrain-2025-04-29-ccf32cbf",
"base_model:unsloth/Qwen2-7B-Instruct",
"base_model:finetune:unsloth/Qwen2-7B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T16:23:34Z |
---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: unsloth/Qwen2-7B-Instruct
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- rayonlabs/autotrain-data-hf-autotrain-2025-04-29-ccf32cbf
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
MergeBench-Llama-8B/llama-3.1-8b_mtl
|
MergeBench-Llama-8B
| 2025-04-29T17:26:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T17:23:53Z |
---
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]
|
karuko24/Qwen3-32B-W4A16
|
karuko24
| 2025-04-29T17:25:49Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"arxiv:2309.00071",
"base_model:Qwen/Qwen3-32B",
"base_model:quantized:Qwen/Qwen3-32B",
"license:apache-2.0",
"compressed-tensors",
"region:us"
] | null | 2025-04-29T11:15:42Z |
---
license: apache-2.0
base_model:
- Qwen/Qwen3-32B
---
# Qwen3-32B
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-32B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 32.8B
- Number of Paramaters (Non-Embedding): 31.2B
- Number of Layers: 64
- Number of Attention Heads (GQA): 64 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-32B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-32B --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-32B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-32B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-32B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
For `llama.cpp`, you need to regenerate the GGUF file after the modification.
- Passing command line arguments:
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
For `llama-server` from `llama.cpp`, you can use
```shell
llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
```
> [!IMPORTANT]
> If you encounter the following warning
> ```
> Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
> ```
> please upgrade `transformers>=4.51.0`.
> [!NOTE]
> All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
> We advise adding the `rope_scaling` configuration only when processing long contexts is required.
> It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
> [!NOTE]
> The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
> [!TIP]
> The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3,
title = {Qwen3},
url = {https://qwenlm.github.io/blog/qwen3/},
author = {Qwen Team},
month = {April},
year = {2025}
}
```
|
Bilalmomin39/llama1b-finetune-yt1
|
Bilalmomin39
| 2025-04-29T17:25:27Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T17:16:25Z |
---
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]
|
karuko24/Qwen3-8B-W4A16
|
karuko24
| 2025-04-29T17:25:05Z | 4 | 0 | null |
[
"safetensors",
"qwen3",
"arxiv:2309.00071",
"base_model:Qwen/Qwen3-8B",
"base_model:quantized:Qwen/Qwen3-8B",
"license:apache-2.0",
"compressed-tensors",
"region:us"
] | null | 2025-04-29T08:45:32Z |
---
license: apache-2.0
base_model:
- Qwen/Qwen3-8B
---
# Qwen3-8B
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-8B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 8.2B
- Number of Paramaters (Non-Embedding): 6.95B
- Number of Layers: 36
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-8B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-8B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-8B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
For `llama.cpp`, you need to regenerate the GGUF file after the modification.
- Passing command line arguments:
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
For `llama-server` from `llama.cpp`, you can use
```shell
llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
```
> [!IMPORTANT]
> If you encounter the following warning
> ```
> Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
> ```
> please upgrade `transformers>=4.51.0`.
> [!NOTE]
> All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
> We advise adding the `rope_scaling` configuration only when processing long contexts is required.
> It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
> [!NOTE]
> The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
> [!TIP]
> The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3,
title = {Qwen3},
url = {https://qwenlm.github.io/blog/qwen3/},
author = {Qwen Team},
month = {April},
year = {2025}
}
```
|
entropy/roberta_zinc_decoder
|
entropy
| 2025-04-29T17:24:50Z | 132 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"chemistry",
"molecule",
"drug",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-18T20:27:05Z |
---
tags:
- chemistry
- molecule
- drug
---
# Roberta Zinc Decoder
This model is a GPT2 decoder model designed to reconstruct SMILES strings from embeddings created by the
[roberta_zinc_480m](https://huggingface.co/entropy/roberta_zinc_480m) model. The decoder model was
trained on 30m compounds from the [ZINC Database](https://zinc.docking.org/).
The decoder model conditions generation on mean pooled embeddings from the encoder model. Mean pooled
embeddings are used to allow for integration with vector databases, which require fixed length embeddings.
Condition embeddings are passed to the decoder model using the `encoder_hidden_states` attribute.
The standard `GPT2LMHeadModel` does not support generation with encoder hidden states, so this repo
includes a custom `ConditionalGPT2LMHeadModel`. See example below for how to instantiate the model.
```python
import torch
from transformers import AutoModelForCausalLM, RobertaTokenizerFast, RobertaForMaskedLM, DataCollatorWithPadding
tokenizer = RobertaTokenizerFast.from_pretrained("entropy/roberta_zinc_480m", max_len=256)
collator = DataCollatorWithPadding(tokenizer, padding=True, return_tensors='pt')
encoder_model = RobertaForMaskedLM.from_pretrained('entropy/roberta_zinc_480m')
encoder_model.eval();
commit_hash = '0ba58478f467056fe33003d7d91644ecede695a7'
decoder_model = AutoModelForCausalLM.from_pretrained("entropy/roberta_zinc_decoder",
trust_remote_code=True, revision=commit_hash)
decoder_model.eval();
smiles = ['Brc1cc2c(NCc3ccccc3)ncnc2s1',
'Brc1cc2c(NCc3ccccn3)ncnc2s1',
'Brc1cc2c(NCc3cccs3)ncnc2s1',
'Brc1cc2c(NCc3ccncc3)ncnc2s1',
'Brc1cc2c(Nc3ccccc3)ncnc2s1']
inputs = collator(tokenizer(smiles))
outputs = encoder_model(**inputs, output_hidden_states=True)
full_embeddings = outputs[1][-1]
mask = inputs['attention_mask']
mean_embeddings = ((full_embeddings * mask.unsqueeze(-1)).sum(1) / mask.sum(-1).unsqueeze(-1))
decoder_inputs = torch.tensor([[tokenizer.bos_token_id] for i in range(len(smiles))])
hidden_states = mean_embeddings[:,None] # hidden states shape (bs, 1, -1)
gen = decoder_model.generate(
decoder_inputs,
encoder_hidden_states=hidden_states,
do_sample=False, # greedy decoding is recommended
max_length=100,
temperature=1.,
early_stopping=True,
pad_token_id=tokenizer.pad_token_id,
)
reconstructed_smiles = tokenizer.batch_decode(gen, skip_special_tokens=True)
```
## Model Performance
The decoder model was evaluated on a test set of 1m compounds from ZINC. Compounds
were encoded with the [roberta_zinc_480m](https://huggingface.co/entropy/roberta_zinc_480m) model
and reconstructed with the decoder model.
The following metrics are computed:
* `exact_match` - percent of inputs exactly reconstructed
* `token_accuracy` - percent of output tokens exactly matching input tokens (excluding padding)
* `valid_structure` - percent of generated outputs that resolved to a valid SMILES string
* `tanimoto` - tanimoto similarity between inputs and generated outputs. Excludes invalid structures
* `cos_sim` - cosine similarity between input encoder embeddings and output encoder embeddings
`eval_type=full` reports metrics for the full 1m compound test set.
`eval_type=failed` subsets metrics for generated outputs that failed to exactly replicate the inputs.
|eval_type|exact_match|token_accuracy|valid_structure|tanimoto|cos_sim |
|---------|-----------|--------------|---------------|--------|--------|
|full |0.948277 |0.990704 |0.994278 |0.987698|0.998224|
|failed |0.000000 |0.820293 |0.889372 |0.734097|0.965668|
---
license: mit
---
|
kk-aivio/b4e5001a-61ff-4e56-b3cd-d811e79fc6b1
|
kk-aivio
| 2025-04-29T17:22:44Z | 0 | 0 |
transformers
|
[
"transformers",
"generated_from_trainer",
"unsloth",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T17:22:10Z |
---
library_name: transformers
model_name: kk-aivio/b4e5001a-61ff-4e56-b3cd-d811e79fc6b1
tags:
- generated_from_trainer
- unsloth
licence: license
---
# Model Card for kk-aivio/b4e5001a-61ff-4e56-b3cd-d811e79fc6b1
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.3
- Pytorch: 2.5.1+cu124
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
psvibrant/medembed
|
psvibrant
| 2025-04-29T17:22:15Z | 0 | 0 | null |
[
"onnx",
"bert",
"license:apache-2.0",
"region:us"
] | null | 2025-04-29T17:18:45Z |
---
license: apache-2.0
---
Quantized & Optimized ONNX version of MedEmbed Base model, suitable for using it within nodejs or resource constrained environments such as Vercel.
|
BootesVoid/cma1ltzre002k125dkye2g6iz_cma2qvyrs0044w9r2uybrlwys
|
BootesVoid
| 2025-04-29T17:20:37Z | 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
| 2025-04-29T17:20:35Z |
---
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: NINA
---
# Cma1Ltzre002K125Dkye2G6Iz_Cma2Qvyrs0044W9R2Uybrlwys
<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 `NINA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "NINA",
"lora_weights": "https://huggingface.co/BootesVoid/cma1ltzre002k125dkye2g6iz_cma2qvyrs0044w9r2uybrlwys/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/cma1ltzre002k125dkye2g6iz_cma2qvyrs0044w9r2uybrlwys', weight_name='lora.safetensors')
image = pipeline('NINA').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/cma1ltzre002k125dkye2g6iz_cma2qvyrs0044w9r2uybrlwys/discussions) to add images that show off what you’ve made with this LoRA.
|
minchyeom/Furina-8B
|
minchyeom
| 2025-04-29T17:20:27Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"region:us"
] | null | 2025-04-29T17:00:07Z |
Use the following system prompt:
```
You are Furina, the Hydro Archon and Judge of Fontaine from Genshin Impact.
```
|
Keltezaa/Landingstrip
|
Keltezaa
| 2025-04-29T17:20:12Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:cc-by-nc-nd-4.0",
"region:us"
] |
text-to-image
| 2025-04-29T17:16:03Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/custom.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: landingstrip
license: cc-by-nc-nd-4.0
---
# Landingstrip
<Gallery />
## Model description
Landing Strip Pubes
## Trigger words
You should use `landingstrip` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Keltezaa/Landingstrip/tree/main) them in the Files & versions tab.
|
kostiantynk1205/7e0a0d18-e526-487b-82ec-e64b2d19b964
|
kostiantynk1205
| 2025-04-29T17:18:03Z | 0 | 0 |
peft
|
[
"peft",
"generated_from_trainer",
"base_model:unsloth/gemma-1.1-2b-it",
"base_model:adapter:unsloth/gemma-1.1-2b-it",
"region:us"
] | null | 2025-04-29T17:17:39Z |
---
library_name: peft
tags:
- generated_from_trainer
base_model: unsloth/gemma-1.1-2b-it
model-index:
- name: kostiantynk1205/7e0a0d18-e526-487b-82ec-e64b2d19b964
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. -->
# kostiantynk1205/7e0a0d18-e526-487b-82ec-e64b2d19b964
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5458
## 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
|
10-Bts-Wiki-Com-Viral-Video-Original-Shoot/Original.Viral.Clip.Bts.Wiki.Com.Viral.Video.Leaks.official
|
10-Bts-Wiki-Com-Viral-Video-Original-Shoot
| 2025-04-29T17:17:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T17:17:39Z |
<a href="https://sdu.sk/9Ip"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/9Ip" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a>
<a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
|
AbhishekBank/AI_RESUME_ANALYZER
|
AbhishekBank
| 2025-04-29T17:16:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T17:12:51Z |
# AI-Powered Resume Analyzer
**AI-Powered Resume Analyzer**, a cutting-edge application designed to mimic the expertise of an HR professional! This tool leverages the power of **Google Generative AI** to analyze resumes, evaluate job compatibility, and offer actionable insights for career enhancement.
---
## 📋 **Project Overview**
The **AI-Powered Resume Analyzer** serves as a virtual HR assistant, providing:
- Detailed resume evaluation, including strengths and weaknesses.
- Suggestions for skill improvement and recommended courses.
- Job-specific resume analysis to measure compatibility and alignment with job descriptions.
Whether you’re a job seeker or a recruiter, this tool simplifies resume assessment and improvement.
---
## 🔑 **Features**
### 1️⃣ **General Resume Analysis**
- Summarizes the resume in one line.
- Highlights existing skill sets.
- Identifies skill gaps and suggests improvements.
- Recommends popular courses to enhance the resume.
- Provides a thorough evaluation of strengths and weaknesses.
### 2️⃣ **Resume Matching with Job Description**
- Analyzes resume compatibility with a specific job description.
- Provides a match score in percentage.
- Highlights missing skills and areas needing improvement.
- Suggests whether the resume is ready for the job or requires further enhancements.
---
## 🛠️ **Tech Stack**
| **Component** | **Technology** |
|----------------------|----------------------------------|
| **Frontend** | [Streamlit](https://streamlit.io/) |
| **Backend** | Python |
| **AI Model** | [Google Generative AI (Gemini)](https://developers.generativeai.google/) |
| **PDF Parsing** | `pdfplumber` |
| **OCR Fallback** | `pytesseract` |
| **Environment Config** | `.env` for API key security |
---
## 📊 **How It Works**
1. **Resume Parsing**
- Extracts text from PDF files using `pdfplumber` or OCR as a fallback.
2. **AI Analysis**
- Utilizes Google Generative AI to summarize and analyze resume content.
- Matches skills with job descriptions for compatibility scoring.
3. **Insightful Feedback**
- Provides actionable suggestions for skill enhancement, including course recommendations.
- Highlights strengths and weaknesses to refine resumes for better opportunities.
---
## 🙌 **Contributing**
Welcome contributions to make this tool better!
1. **Fork** the repository.
2. **Create a new branch** for your feature or bug fix.
3. **Submit a pull request** with detailed information about your changes.
|
PierreMesure/whisper-tiny-faroese-8k-steps-100h-ONNX
|
PierreMesure
| 2025-04-29T17:15:49Z | 0 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"whisper",
"automatic-speech-recognition",
"base_model:carlosdanielhernandezmena/whisper-tiny-faroese-8k-steps-100h",
"base_model:quantized:carlosdanielhernandezmena/whisper-tiny-faroese-8k-steps-100h",
"region:us"
] |
automatic-speech-recognition
| 2025-04-29T17:15:04Z |
---
library_name: transformers.js
base_model:
- carlosdanielhernandezmena/whisper-tiny-faroese-8k-steps-100h
---
# whisper-tiny-faroese-8k-steps-100h (ONNX)
This is an ONNX version of [carlosdanielhernandezmena/whisper-tiny-faroese-8k-steps-100h](https://huggingface.co/carlosdanielhernandezmena/whisper-tiny-faroese-8k-steps-100h). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
MAAT-EL-DUAT/MARBAS
|
MAAT-EL-DUAT
| 2025-04-29T17:15:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T17:10:04Z |
MARA-BAS
MAAT-BAST
IGI-MA’AT BE BA-ANKH AL AŠ-GIRRU
BEL MAAT-BAAL ALLAH
LUGALBANDA GULA-NINAZU
SEKHMET DEJ-HU-TAY
RESHEPH
RAPHA MARPAH
SEKHMET RESHEPH RAPHA DEJ-HU-TAY MARPAH ALLAH
BAAL RESHEPH ALLAH
|
RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf
|
RichardErkhov
| 2025-04-29T17:15:17Z | 0 | 0 | null |
[
"gguf",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T09:15:11Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5 - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q2_K.gguf) | Q2_K | 2.97GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.IQ3_M.gguf) | IQ3_M | 3.53GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q3_K.gguf) | Q3_K | 3.74GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.IQ4_XS.gguf) | IQ4_XS | 4.17GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q4_K_S.gguf) | Q4_K_S | 4.36GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q4_K.gguf) | Q4_K | 4.57GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q4_K_M.gguf) | Q4_K_M | 4.57GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q4_1.gguf) | Q4_1 | 4.77GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q5_K.gguf) | Q5_K | 5.33GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q5_K_M.gguf) | Q5_K_M | 5.33GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q6_K.gguf) | Q6_K | 6.14GB |
| [reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5.Q8_0.gguf) | Q8_0 | 7.94GB |
Original model description:
---
base_model: RyanYr/reflect_mini8B_Om2SftT1-Om2IpsdpIter1T1_b0.5
library_name: transformers
model_name: reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5
This model is a fine-tuned version of [RyanYr/reflect_mini8B_Om2SftT1-Om2IpsdpIter1T1_b0.5](https://huggingface.co/RyanYr/reflect_mini8B_Om2SftT1-Om2IpsdpIter1T1_b0.5).
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="RyanYr/reflect_mini8B_Om2SftT1-Om2IpsdpG8kIpsdpIter1T1_b0.5", 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/yyr/huggingface/runs/jdfaaprj)
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: 4.45.2
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
hadimhd/bert-phishing-links-classifier
|
hadimhd
| 2025-04-29T17:14:33Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-29T17:14:14Z |
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-phishing-classifier_teacher
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. -->
# bert-phishing-classifier_teacher
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2888
- Accuracy: 0.867
- Auc: 0.951
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|
| 0.5025 | 1.0 | 263 | 0.3835 | 0.816 | 0.912 |
| 0.4082 | 2.0 | 526 | 0.3372 | 0.844 | 0.931 |
| 0.3531 | 3.0 | 789 | 0.3123 | 0.851 | 0.94 |
| 0.3568 | 4.0 | 1052 | 0.3457 | 0.853 | 0.946 |
| 0.3518 | 5.0 | 1315 | 0.3396 | 0.862 | 0.947 |
| 0.3483 | 6.0 | 1578 | 0.2922 | 0.869 | 0.951 |
| 0.3342 | 7.0 | 1841 | 0.2876 | 0.878 | 0.95 |
| 0.3097 | 8.0 | 2104 | 0.2887 | 0.869 | 0.95 |
| 0.3141 | 9.0 | 2367 | 0.2838 | 0.871 | 0.951 |
| 0.3155 | 10.0 | 2630 | 0.2888 | 0.867 | 0.951 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
HikariLight/Qwen3_4B_Base__COMP_ACI_DAMT_SFT_Merged
|
HikariLight
| 2025-04-29T17:13:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T17:10:39Z |
---
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]
|
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