Dataset Viewer
modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
Kansallisarkisto/multicentury-htr-model
|
Kansallisarkisto
| 2025-06-25T05:28:51 | 33 | 0 | null |
[
"pytorch",
"vision-encoder-decoder",
"image-to-text",
"fi",
"sv",
"license:apache-2.0",
"region:us"
] |
image-to-text
| 2024-10-07T11:48:19 |
---
license: apache-2.0
language:
- fi
- sv
metrics:
- cer
pipeline_tag: image-to-text
---
# Model description
**Model Name:** multicentury-htr-model
**Model Type:** Transformer-based OCR (TrOCR)
**Base Model:** microsoft/trocr-large-handwritten
**Purpose:** Handwritten text recognition
**Languages:** Swedish, Finnish
**License:** Apache 2.0
This model is a fine-tuned version of the microsoft/trocr-large-handwritten model, specialized for recognizing handwritten text. It has been trained on various dataset from 17th to 20th centuries and can be used for applications such as document digitization, form recognition, or any task involving handwritten text extraction.
# Model Architecture
The model is based on a Transformer architecture (TrOCR) with an encoder-decoder setup:
- The encoder processes images of handwritten text.
- The decoder generates corresponding text output.
# Intended Use
This model is designed for handwritten text recognition and is intended for use in:
- Document digitization (e.g., archival work, historical manuscripts)
- Handwritten notes transcription
# Training data
The training datasetincludes more than 760 000 samples of handwritten text rows, covering a wide variety of handwriting styles and text samples.
# Evaluation
The model was evaluated on test dataset. Below are key metrics:
**Character Error Rate (CER):** 3.2
**Test Dataset Description:** size ~94 900 text rows
# Used Hyperparameters
**Evaluation strategy:** epoch
**Train batch size per device:** 16
**Learning rate:** 1e-5
**Scheduler:** linear
**Warmup steps:** 500
**Optimizer:** AdamW
**Number of epochs:** 14
**FP16 mixed precision training:** True
**Half precision backend:** cuda_amp
# How to Use the Model
You can use the model directly with Hugging Face’s pipeline function or by manually loading the processor and model.
```python
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
# Load the model and processor
processor = TrOCRProcessor.from_pretrained("Kansallisarkisto/multicentury-htr-model/processor")
model = VisionEncoderDecoderModel.from_pretrained("Kansallisarkisto/multicentury-htr-model")
# Open an image of handwritten text
image = Image.open("path_to_image.png")
# Preprocess and predict
pixel_values = processor(image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)
```
# Limitations and Biases
The model was trained primarily on handwritten text that uses basic Latin characters (A-Z, a-z) and includes Nordic special characters (å, ä, ö). It has not been trained on non-Latin alphabets, such as Chinese characters, Cyrillic script, or other writing systems like Arabic or Hebrew.
The model may not generalize well to any other languages than Finnish, Swedish or English.
# Future Work
Potential improvements for this model include:
- Expanding training data: Incorporating more diverse handwriting styles and languages.
- Optimizing for specific domains: Fine-tuning the model on domain-specific handwriting.
# Citation
If you use this model in your work, please cite it as:
@misc{multicentury_htr_model_2024,
author = {Kansallisarkisto},
title = {Multicentury HTR Model: Handwritten Text Recognition},
year = {2024},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/Kansallisarkisto/multicentury-htr-model/}},
}
## Model Card Authors
Author: Kansallisarkisto
Contact Information: [email protected], [email protected]
|
nurulfauzh/sentimen-analisis-model
|
nurulfauzh
| 2025-06-25T05:11:25 | 0 | 0 | null |
[
"tf",
"bert",
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T04:58:09 |
---
license: apache-2.0
---
|
New-videos-18-Bu-Guru-Salsa-Viral-Video/FULL.VIDEO.Bu.Guru.Salsa.Viral.Video.Tutorial.Official
|
New-videos-18-Bu-Guru-Salsa-Viral-Video
| 2025-06-25T04:32:57 | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-25T04:32:43 |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?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>
|
pennylin09/uuu_fine_tune_gpt2
|
pennylin09
| 2025-06-25T03:38:19 | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:37:31 |
---
license: apache-2.0
---
|
John6666/xl-sat-ior-imitation-of-reality-xl-sat-iorv1-sdxl
|
John6666
| 2025-06-25T03:27:37 | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"photoreal",
"real",
"sateluco",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-06-25T03:21:51 |
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- photoreal
- real
- sateluco
---
Original model is [here](https://civitai.com/models/1713256/xl-sat-ior-imitation-of-reality?modelVersionId=1938771).
This model created by [Sateluco](https://civitai.com/user/Sateluco).
|
JFernandoGRE/llama31_8b_augmenteddemocracy_dpo2_questions_50_critsupport
|
JFernandoGRE
| 2025-06-25T02:44:52 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport",
"base_model:finetune:JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T02:39:47 |
---
base_model: JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** JFernandoGRE
- **License:** apache-2.0
- **Finetuned from model :** JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport
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)
|
yashmahe2018/math-error-classification-gguf
|
yashmahe2018
| 2025-06-25T01:58:19 | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-25T01:57:48 |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** yashmahe2018
- **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)
|
VarunNagaraj/tiny-llm-maui-apiclients-mistral
|
VarunNagaraj
| 2025-06-25T01:01:30 | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.1-bnb-4bit",
"base_model:quantized:unsloth/mistral-7b-instruct-v0.1-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-25T01:00:24 |
---
base_model: unsloth/mistral-7b-instruct-v0.1-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** VarunNagaraj
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.1-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX
|
mlx-community
| 2025-06-25T00:51:02 | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"mixtral",
"Llama 3.2",
"8 X 4B",
"Brainstorm 5x",
"128k context",
"moe",
"8 experts",
"mixture of experts",
"fine tune",
"text-generation",
"conversational",
"base_model:DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit",
"base_model:quantized:DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit",
"8-bit",
"region:us"
] |
text-generation
| 2025-06-25T00:35:40 |
---
library_name: mlx
tags:
- Llama 3.2
- 8 X 4B
- Brainstorm 5x
- 128k context
- moe
- 8 experts
- mixture of experts
- fine tune
- mlx
base_model: DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit
pipeline_tag: text-generation
---
# mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX
This model [mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX](https://huggingface.co/mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX) was
converted to MLX format from [DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit](https://huggingface.co/DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit)
using mlx-lm version **0.25.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
pankajrajdeo/CT-UMLS-Summarizer-Qwen2.5-3B-4bit-adapter
|
pankajrajdeo
| 2025-06-24T23:53:35 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T23:53:22 |
---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** pankajrajdeo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
18-videos-jobz-hunting-sajal-malik-virals/FULL.VIDEO.sajal.malik.Viral.Video.Tutorial.Official
|
18-videos-jobz-hunting-sajal-malik-virals
| 2025-06-24T23:23:06 | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T23:22:49 |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?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>
|
yu3733/paligemma2-3b-lora-vqa-v21-enhanced-d8000-r16
|
yu3733
| 2025-06-24T22:29:33 | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"paligemma",
"lora",
"adapter",
"visual-question-answering",
"image-to-text",
"v2.1-enhanced",
"en",
"base_model:google/paligemma2-3b-mix-224",
"base_model:adapter:google/paligemma2-3b-mix-224",
"region:us"
] |
image-to-text
| 2025-06-24T22:29:19 |
---
tags:
- paligemma
- lora
- adapter
- visual-question-answering
- image-to-text
- v2.1-enhanced
base_model: google/paligemma2-3b-mix-224
language:
- en
library_name: peft
---
# paligemma2-3b-lora-vqa-v21-enhanced-d8000-r16 - v2.1 Enhanced
This is a **v2.1 Enhanced** LoRA adapter for PaliGemma-2 3B trained on VQA tasks.
## 🆕 v2.1 Enhanced Improvements
- **EOS Token Learning**: Explicit EOS tokens for better generation termination
- **Memory Optimization**: 16-step gradient accumulation for stability
- **VizWiz Format Support**: Full support with most frequent answer selection
- **Robust Label Masking**: Enhanced prompt masking during training
- **Production Memory Management**: Advanced garbage collection
## Usage
```python
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
from peft import PeftModel
import torch
from PIL import Image
# Base model
base_model_id = "google/paligemma2-3b-mix-224"
adapter_id = "yu3733/paligemma2-3b-lora-vqa-v21-enhanced-d8000-r16"
# Load processor
processor = AutoProcessor.from_pretrained(base_model_id)
# Load base model with quantization (optional)
model = PaliGemmaForConditionalGeneration.from_pretrained(
base_model_id,
torch_dtype=torch.float16,
device_map="auto"
)
# Load LoRA adapter
model = PeftModel.from_pretrained(model, adapter_id)
# Prepare input
image = Image.open("your_image.jpg")
prompt = "<image>\nQuestion: What is in this image?\nAnswer:"
# Process
inputs = processor(text=prompt, images=image, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}
# Generate
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=20)
# Decode
print(processor.decode(outputs[0], skip_special_tokens=True))
```
## Training Configuration
- **Base Model**: google/paligemma2-3b-mix-224
- **LoRA Rank**: 16
- **Training Framework**: PEFT + Transformers
- **Optimization**: 4-bit quantization + gradient checkpointing
- **Dataset**: VizWiz VQA
## License
Same as the base model (see google/paligemma2-3b-mix-224)
|
rbelanec/train_qnli_1750781358
|
rbelanec
| 2025-06-24T22:14:42 | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prompt-tuning",
"generated_from_trainer",
"base_model:google/gemma-3-1b-it",
"base_model:adapter:google/gemma-3-1b-it",
"license:gemma",
"region:us"
] | null | 2025-06-24T16:11:43 |
---
library_name: peft
license: gemma
base_model: google/gemma-3-1b-it
tags:
- llama-factory
- prompt-tuning
- generated_from_trainer
model-index:
- name: train_qnli_1750781358
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. -->
# train_qnli_1750781358
This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on the qnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0861
- Num Input Tokens Seen: 117444992
## 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: 8
- eval_batch_size: 2
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF
|
bartowski
| 2025-06-24T21:56:58 | 5,846 | 16 | null |
[
"gguf",
"image-text-to-text",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"base_model:quantized:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"license:apache-2.0",
"region:us",
"conversational"
] |
image-text-to-text
| 2025-06-20T19:03:22 |
---
quantized_by: bartowski
pipeline_tag: image-text-to-text
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
base_model: mistralai/Mistral-Small-3.2-24B-Instruct-2506
base_model_relation: quantized
license: apache-2.0
inference: false
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
---
## Llamacpp imatrix Quantizations of Mistral-Small-3.2-24B-Instruct-2506 by mistralai
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5697">b5697</a> for quantization.
Original model: https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project
## Prompt format
```
<s>[SYSTEM_PROMPT]{system_prompt}[/SYSTEM_PROMPT][INST]{prompt}[/INST]
```
## What's new:
Fix chat template to support tool calling
Will require use of --chat-template and the Mistral-Small-3.2-24B-Instruct-2506.jinja, uploaded here and available in llama.cpp (if the PR is merged: https://github.com/ggml-org/llama.cpp/pull/14349)
Full server run command:
```
./llama-server -m /models/Mistral-Small-3.2-24B-Instruct-2506-GGUF/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf -ngl 100 --host 0.0.0.0 -fa --slots --jinja --chat-template-file /models/Mistral-Small-3.2-24B-Instruct-2506.jinja
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Mistral-Small-3.2-24B-Instruct-2506-bf16.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-bf16.gguf) | bf16 | 47.15GB | false | Full BF16 weights. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q8_0.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q8_0.gguf) | Q8_0 | 25.05GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q6_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q6_K_L.gguf) | Q6_K_L | 19.67GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q6_K.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q6_K.gguf) | Q6_K | 19.35GB | false | Very high quality, near perfect, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q5_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q5_K_L.gguf) | Q5_K_L | 17.18GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q5_K_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q5_K_M.gguf) | Q5_K_M | 16.76GB | false | High quality, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q5_K_S.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q5_K_S.gguf) | Q5_K_S | 16.30GB | false | High quality, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q4_1.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_1.gguf) | Q4_1 | 14.87GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q4_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_L.gguf) | Q4_K_L | 14.83GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf) | Q4_K_M | 14.33GB | false | Good quality, default size for most use cases, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q4_K_S.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_S.gguf) | Q4_K_S | 13.55GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q4_0.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_0.gguf) | Q4_0 | 13.49GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ4_NL.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ4_NL.gguf) | IQ4_NL | 13.47GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q3_K_XL.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q3_K_XL.gguf) | Q3_K_XL | 12.99GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ4_XS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ4_XS.gguf) | IQ4_XS | 12.76GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q3_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q3_K_L.gguf) | Q3_K_L | 12.40GB | false | Lower quality but usable, good for low RAM availability. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q3_K_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q3_K_M.gguf) | Q3_K_M | 11.47GB | false | Low quality. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ3_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ3_M.gguf) | IQ3_M | 10.65GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q3_K_S.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q3_K_S.gguf) | Q3_K_S | 10.40GB | false | Low quality, not recommended. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ3_XS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ3_XS.gguf) | IQ3_XS | 9.91GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q2_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q2_K_L.gguf) | Q2_K_L | 9.55GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ3_XXS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ3_XXS.gguf) | IQ3_XXS | 9.28GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q2_K.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q2_K.gguf) | Q2_K | 8.89GB | false | Very low quality but surprisingly usable. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ2_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ2_M.gguf) | IQ2_M | 8.11GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ2_S.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ2_S.gguf) | IQ2_S | 7.48GB | false | Low quality, uses SOTA techniques to be usable. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ2_XS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ2_XS.gguf) | IQ2_XS | 7.21GB | false | Low quality, uses SOTA techniques to be usable. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ2_XXS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ2_XXS.gguf) | IQ2_XXS | 6.55GB | false | Very low quality, uses SOTA techniques to be usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
## Downloading using huggingface-cli
<details>
<summary>Click to view download instructions</summary>
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF --include "mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF --include "mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q8_0) or download them all in place (./)
</details>
## ARM/AVX information
Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.
Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.
As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.
Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.
<details>
<summary>Click to view Q4_0_X_X information (deprecated</summary>
I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
<details>
<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
</details>
</details>
## Which file should I choose?
<details>
<summary>Click here for details</summary>
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
</details>
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
Thank you ZeroWw for the inspiration to experiment with embed/output.
Thank you to LM Studio for sponsoring my work.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
tekeufranck681/lung-tumor-detection-model
|
tekeufranck681
| 2025-06-24T21:10:42 | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T16:42:09 |
# Lung Cancer Detection Inference Endpoint (with Grad-CAM)
This endpoint accepts a base64-encoded lung image and returns:
- Predicted cancer type
- Confidence
- Probabilities for all classes
- Grad-CAM annotated image (base64)
### Expected Input
```json
{
"image": "BASE64_ENCODED_JPEG_IMAGE"
}
|
New-videos-Maya-G-viral-video-Clips/FULL.VIDEO.Maya.G.Viral.Video.Tutorial.Official
|
New-videos-Maya-G-viral-video-Clips
| 2025-06-24T20:55:21 | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:55:05 |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?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>
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-up_positive-negative-addition-same_layer_20_2_song_3_49
|
winnieyangwannan
| 2025-06-24T20:39:13 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T05:44:29 |
---
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]
|
SrivatsaBhamidipati/starcoder2-3b-qlora
|
SrivatsaBhamidipati
| 2025-06-24T20:22:59 | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-Instruct-hf",
"base_model:adapter:codellama/CodeLlama-7b-Instruct-hf",
"license:llama2",
"region:us"
] | null | 2025-06-24T19:12:17 |
---
library_name: peft
license: llama2
base_model: codellama/CodeLlama-7b-Instruct-hf
tags:
- generated_from_trainer
model-index:
- name: starcoder2-3b-qlora
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. -->
# starcoder2-3b-qlora
This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the None 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: 0.0004
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.14.0
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Esabramowitz/Taxi-v3
|
Esabramowitz
| 2025-06-24T18:20:58 | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-24T18:20:51 |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Esabramowitz/Taxi-v3", 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"])
```
|
jetfan-xin/ppo-Pyramids
|
jetfan-xin
| 2025-06-24T17:32:47 | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2025-06-24T17:19:42 |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: jetfan-xin/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
# 🧠 PPO Agent Trained on Unity Pyramids Environment
This repository contains a reinforcement learning agent trained using **Proximal Policy Optimization (PPO)** on Unity’s **Pyramids** environment via **ML-Agents**.
## 📌 Model Overview
- **Algorithm**: PPO with RND (Random Network Distillation)
- **Environment**: Unity Pyramids (3D sparse-reward maze)
- **Framework**: ML-Agents v1.2.0.dev0
- **Backend**: PyTorch 2.7.1 (CUDA-enabled)
The agent learns to navigate a 3D maze and reach the goal area by combining extrinsic and intrinsic rewards.
---
## 🚀 How to Use This Model
You can use the `.onnx` model directly in Unity.
### ✅ Steps:
1. **Download the model**
Clone the repository or download `Pyramids.onnx`:
```bash
git lfs install
git clone https://huggingface.co/jetfan-xin/ppo-Pyramids
```
2. **Place in Unity project**
Put the model file in your Unity project under:
```
Assets/ML-Agents/Examples/Pyramids/Pyramids.onnx
```
3. **Assign in Unity Editor**
- Select your agent GameObject.
- In `Behavior Parameters`, assign `Pyramids.onnx` as the model.
- Make sure the Behavior Name matches your training config.
---
## ⚙️ Training Configuration
Key settings from `configuration.yaml`:
- `trainer_type`: `ppo`
- `max_steps`: `1000000`
- `batch_size`: `128`, `buffer_size`: `2048`
- `learning_rate`: `3e-4`
- `reward_signals`:
- `extrinsic`: γ=0.99, strength=1.0
- `rnd`: γ=0.99, strength=0.01
- `hidden_units`: `512`, `num_layers`: `2`
- `summary_freq`: `30000`
See `configuration.yaml` for full details.
---
## 📈 Training Performance
Sample rewards from training log:
| Step | Mean Reward |
|-----------|-------------|
| 300,000 | -0.22 |
| 480,000 | 0.35 |
| 660,000 | 1.14 |
| 840,000 | 1.47 |
| 990,000 | 1.54 |
details:
```
(rl_py310) 4xin@ltgpu3:~/deep_rl/unit5/ml-agents$ CUDA_VISIBLE_DEVICES=3 mlagents-learn ./config/ppo/PyramidsRND.yaml \
--env=./training-envs-executables/linux/Pyramids/Pyramids.x86_64 \
--run-id="PyramidsGPUTest" \
--no-graphics
┐ ╖
╓╖╬│╡ ││╬╖╖
╓╖╬│││││┘ ╬│││││╬╖
╖╬│││││╬╜ ╙╬│││││╖╖ ╗╗╗
╬╬╬╬╖││╦╖ ╖╬││╗╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╜╜╜ ╟╣╣
╬╬╬╬╬╬╬╬╖│╬╖╖╓╬╪│╓╣╣╣╣╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╒╣╣╖╗╣╣╣╗ ╣╣╣ ╣╣╣╣╣╣ ╟╣╣╖ ╣╣╣
╬╬╬╬┐ ╙╬╬╬╬│╓╣╣╣╝╜ ╫╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╟╣╣╣╙ ╙╣╣╣ ╣╣╣ ╙╟╣╣╜╙ ╫╣╣ ╟╣╣
╬╬╬╬┐ ╙╬╬╣╣ ╫╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╟╣╣╬ ╣╣╣ ╣╣╣ ╟╣╣ ╣╣╣┌╣╣╜
╬╬╬╜ ╬╬╣╣ ╙╝╣╣╬ ╙╣╣╣╗╖╓╗╣╣╣╜ ╟╣╣╬ ╣╣╣ ╣╣╣ ╟╣╣╦╓ ╣╣╣╣╣
╙ ╓╦╖ ╬╬╣╣ ╓╗╗╖ ╙╝╣╣╣╣╝╜ ╘╝╝╜ ╝╝╝ ╝╝╝ ╙╣╣╣ ╟╣╣╣
╩╬╬╬╬╬╬╦╦╬╬╣╣╗╣╣╣╣╣╣╣╝ ╫╣╣╣╣
╙╬╬╬╬╬╬╬╣╣╣╣╣╣╝╜
╙╬╬╬╣╣╣╜
╙
Version information:
ml-agents: 1.2.0.dev0,
ml-agents-envs: 1.2.0.dev0,
Communicator API: 1.5.0,
PyTorch: 2.7.1+cu126
[INFO] Connected to Unity environment with package version 2.2.1-exp.1 and communication version 1.5.0
[INFO] Connected new brain: Pyramids?team=0
[INFO] Hyperparameters for behavior name Pyramids:
trainer_type: ppo
hyperparameters:
batch_size: 128
buffer_size: 2048
learning_rate: 0.0003
beta: 0.01
epsilon: 0.2
lambd: 0.95
num_epoch: 3
shared_critic: False
learning_rate_schedule: linear
beta_schedule: linear
epsilon_schedule: linear
checkpoint_interval: 500000
network_settings:
normalize: False
hidden_units: 512
num_layers: 2
vis_encode_type: simple
memory: None
goal_conditioning_type: hyper
deterministic: False
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
network_settings:
normalize: False
hidden_units: 128
num_layers: 2
vis_encode_type: simple
memory: None
goal_conditioning_type: hyper
deterministic: False
rnd:
gamma: 0.99
strength: 0.01
network_settings:
normalize: False
hidden_units: 64
num_layers: 3
vis_encode_type: simple
memory: None
goal_conditioning_type: hyper
deterministic: False
learning_rate: 0.0001
encoding_size: None
init_path: None
keep_checkpoints: 5
even_checkpoints: False
max_steps: 1000000
time_horizon: 128
summary_freq: 30000
threaded: False
self_play: None
behavioral_cloning: None
[INFO] Pyramids. Step: 30000. Time Elapsed: 45.356 s. Mean Reward: -1.000. Std of Reward: 0.000. Training.
[INFO] Pyramids. Step: 60000. Time Elapsed: 90.519 s. Mean Reward: -0.853. Std of Reward: 0.588. Training.
[INFO] Pyramids. Step: 90000. Time Elapsed: 136.319 s. Mean Reward: -0.797. Std of Reward: 0.646. Training.
[INFO] Pyramids. Step: 120000. Time Elapsed: 182.893 s. Mean Reward: -0.831. Std of Reward: 0.654. Training.
[INFO] Pyramids. Step: 150000. Time Elapsed: 227.995 s. Mean Reward: -0.715. Std of Reward: 0.760. Training.
[INFO] Pyramids. Step: 180000. Time Elapsed: 270.527 s. Mean Reward: -0.731. Std of Reward: 0.712. Training.
[INFO] Pyramids. Step: 210000. Time Elapsed: 316.617 s. Mean Reward: -0.699. Std of Reward: 0.810. Training.
[INFO] Pyramids. Step: 240000. Time Elapsed: 361.434 s. Mean Reward: -0.640. Std of Reward: 0.822. Training.
[INFO] Pyramids. Step: 270000. Time Elapsed: 407.787 s. Mean Reward: -0.520. Std of Reward: 0.969. Training.
[INFO] Pyramids. Step: 300000. Time Elapsed: 451.612 s. Mean Reward: -0.222. Std of Reward: 1.135. Training.
[INFO] Pyramids. Step: 330000. Time Elapsed: 496.996 s. Mean Reward: -0.328. Std of Reward: 1.124. Training.
[INFO] Pyramids. Step: 360000. Time Elapsed: 541.248 s. Mean Reward: -0.452. Std of Reward: 0.995. Training.
[INFO] Pyramids. Step: 390000. Time Elapsed: 587.186 s. Mean Reward: -0.411. Std of Reward: 1.044. Training.
[INFO] Pyramids. Step: 420000. Time Elapsed: 630.923 s. Mean Reward: -0.042. Std of Reward: 1.228. Training.
[INFO] Pyramids. Step: 450000. Time Elapsed: 675.866 s. Mean Reward: 0.009. Std of Reward: 1.237. Training.
[INFO] Pyramids. Step: 480000. Time Elapsed: 721.391 s. Mean Reward: 0.351. Std of Reward: 1.271. Training.
[INFO] Exported results/PyramidsGPUTest/Pyramids/Pyramids-499992.onnx
[INFO] Pyramids. Step: 510000. Time Elapsed: 767.344 s. Mean Reward: 0.647. Std of Reward: 1.140. Training.
[INFO] Pyramids. Step: 540000. Time Elapsed: 812.656 s. Mean Reward: 0.526. Std of Reward: 1.178. Training.
[INFO] Pyramids. Step: 570000. Time Elapsed: 857.156 s. Mean Reward: 0.525. Std of Reward: 1.236. Training.
[INFO] Pyramids. Step: 600000. Time Elapsed: 900.647 s. Mean Reward: 0.979. Std of Reward: 0.977. Training.
[INFO] Pyramids. Step: 630000. Time Elapsed: 949.947 s. Mean Reward: 1.044. Std of Reward: 1.040. Training.
[INFO] Pyramids. Step: 660000. Time Elapsed: 1006.810 s. Mean Reward: 1.143. Std of Reward: 0.937. Training.
[INFO] Pyramids. Step: 690000. Time Elapsed: 1062.833 s. Mean Reward: 1.151. Std of Reward: 0.997. Training.
[INFO] Pyramids. Step: 720000. Time Elapsed: 1119.948 s. Mean Reward: 1.499. Std of Reward: 0.563. Training.
[INFO] Pyramids. Step: 750000. Time Elapsed: 1178.547 s. Mean Reward: 1.308. Std of Reward: 0.835. Training.
[INFO] Pyramids. Step: 780000. Time Elapsed: 1226.204 s. Mean Reward: 1.278. Std of Reward: 0.866. Training.
[INFO] Pyramids. Step: 810000. Time Elapsed: 1275.499 s. Mean Reward: 1.318. Std of Reward: 0.856. Training.
[INFO] Pyramids. Step: 840000. Time Elapsed: 1322.302 s. Mean Reward: 1.477. Std of Reward: 0.641. Training.
[INFO] Pyramids. Step: 870000. Time Elapsed: 1370.429 s. Mean Reward: 1.367. Std of Reward: 0.816. Training.
[INFO] Pyramids. Step: 900000. Time Elapsed: 1418.228 s. Mean Reward: 1.471. Std of Reward: 0.689. Training.
[INFO] Pyramids. Step: 930000. Time Elapsed: 1465.721 s. Mean Reward: 1.514. Std of Reward: 0.619. Training.
[INFO] Pyramids. Step: 960000. Time Elapsed: 1513.116 s. Mean Reward: 1.403. Std of Reward: 0.810. Training.
[INFO] Pyramids. Step: 990000. Time Elapsed: 1563.057 s. Mean Reward: 1.544. Std of Reward: 0.666. Training.
[INFO] Exported results/PyramidsGPUTest/Pyramids/Pyramids-999909.onnx
[INFO] Exported results/PyramidsGPUTest/Pyramids/Pyramids-1000037.onnx
[INFO] Copied results/PyramidsGPUTest/Pyramids/Pyramids-1000037.onnx to results/PyramidsGPUTest/Pyramids.onnx.
```
✅ Model exported to `Pyramids.onnx` after reaching max steps.
---
## 🖥️ Training Setup
- **Run ID**: `PyramidsGPUTest`
- **GPU**: NVIDIA A100 80GB PCIe
- **Training time**: ~26 minutes
- **ML-Agents Envs**: v1.2.0.dev0
- **Communicator API**: v1.5.0
---
## 📁 Repository Contents
| File / Folder | Description |
|------------------------|----------------------------------------------|
| `Pyramids.onnx` | Exported trained PPO agent |
| `configuration.yaml` | Full PPO + RND training config |
| `run_logs/` | Training logs from ML-Agents |
| `Pyramids/` | Environment-specific output folder |
| `config.json` | Metadata for Hugging Face model card |
---
## 📚 Citation
If you use this model, please consider citing:
```
@misc{ppoPyramidsJetfan,
author = {Jingfan Xin},
title = {PPO Agent Trained on Unity Pyramids Environment},
year = {2025},
howpublished = {\url{https://huggingface.co/jetfan-xin/ppo-Pyramids}},
}
```
|
SabahNawab/llama3.2_3B-urdu-qlora
|
SabahNawab
| 2025-06-24T16:41:06 | 29 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"llama",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:adapter:meta-llama/Llama-3.2-3B",
"license:llama3.2",
"region:us"
] | null | 2025-06-18T08:08:28 |
---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-3B
tags:
- generated_from_trainer
model-index:
- name: llama3.2_3B-urdu-qlora
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. -->
# llama3.2_3B-urdu-qlora
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6659
## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4891 | 1.0 | 1 | 2.6659 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
edwry/reg-id-model
|
edwry
| 2025-06-24T15:59:17 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T07:50:38 |
---
base_model: unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** edwry
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
daixuancheng/distill_1.5b_sac-init0.4_constrainbyAdv_global_step_500
|
daixuancheng
| 2025-06-24T15:41:39 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T15:22:37 |
---
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]
|
Theros/Q2.5-ColdBrew-RxB-Q4_K_M-GGUF
|
Theros
| 2025-06-24T14:22:29 | 0 | 0 | null |
[
"gguf",
"merge",
"lazymergekit",
"llama-cpp",
"gguf-my-repo",
"base_model:SvalTek/Q2.5-ColdBrew-RxB",
"base_model:quantized:SvalTek/Q2.5-ColdBrew-RxB",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T14:22:04 |
---
base_model: SvalTek/Q2.5-ColdBrew-RxB
tags:
- merge
- lazymergekit
- llama-cpp
- gguf-my-repo
---
# Theros/Q2.5-ColdBrew-RxB-Q4_K_M-GGUF
This model was converted to GGUF format from [`SvalTek/Q2.5-ColdBrew-RxB`](https://huggingface.co/SvalTek/Q2.5-ColdBrew-RxB) 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/SvalTek/Q2.5-ColdBrew-RxB) 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 Theros/Q2.5-ColdBrew-RxB-Q4_K_M-GGUF --hf-file q2.5-coldbrew-rxb-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Theros/Q2.5-ColdBrew-RxB-Q4_K_M-GGUF --hf-file q2.5-coldbrew-rxb-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Theros/Q2.5-ColdBrew-RxB-Q4_K_M-GGUF --hf-file q2.5-coldbrew-rxb-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Theros/Q2.5-ColdBrew-RxB-Q4_K_M-GGUF --hf-file q2.5-coldbrew-rxb-q4_k_m.gguf -c 2048
```
|
CedrVal/llama-31-hhrlhf-squad-rlhf-policy-model
|
CedrVal
| 2025-06-24T13:08:33 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T13:07:17 |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sergioalves/15cef6f6-837d-4f4d-b80f-673194a83024
|
sergioalves
| 2025-06-24T13:00:20 | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:The-matt/llama2_ko-7b_distinctive-snowflake-182_1060",
"base_model:adapter:The-matt/llama2_ko-7b_distinctive-snowflake-182_1060",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-24T12:27:46 |
---
library_name: peft
base_model: The-matt/llama2_ko-7b_distinctive-snowflake-182_1060
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 15cef6f6-837d-4f4d-b80f-673194a83024
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: The-matt/llama2_ko-7b_distinctive-snowflake-182_1060
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 40b11ac967e55b10_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.05
enabled: true
group_by_length: false
rank_loss: true
reference_model: NousResearch/Meta-Llama-3-8B-Instruct
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 0.9
group_by_length: false
hub_model_id: sergioalves/15cef6f6-837d-4f4d-b80f-673194a83024
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-05
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/40b11ac967e55b10_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: cfc6dd83-e96b-4c12-880a-44ef6e43eabf
wandb_project: s56-7
wandb_run: your_name
wandb_runid: cfc6dd83-e96b-4c12-880a-44ef6e43eabf
warmup_steps: 10
weight_decay: 0.05
xformers_attention: false
```
</details><br>
# 15cef6f6-837d-4f4d-b80f-673194a83024
This model is a fine-tuned version of [The-matt/llama2_ko-7b_distinctive-snowflake-182_1060](https://huggingface.co/The-matt/llama2_ko-7b_distinctive-snowflake-182_1060) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8920
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.3868 | 0.0002 | 1 | 2.2192 |
| 2.1741 | 0.0082 | 50 | 1.9233 |
| 1.9713 | 0.0163 | 100 | 1.8920 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
haihp02/Qwen2.5-1.5B-e286e9d0-2a8c-4ad7-9ca3-c5c8dd364d12-DPO
|
haihp02
| 2025-06-24T12:07:08 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T12:05:44 |
---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
hyokwan/gemma2b_sound
|
hyokwan
| 2025-06-24T11:46:21 | 0 | 0 | null |
[
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-24T11:43:12 |
---
license: apache-2.0
---
|
varshithkumar/cricket-qa-llama3b
|
varshithkumar
| 2025-06-24T11:42:24 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-24T11:40:39 |
---
library_name: transformers
tags:
- 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]
|
kshitijthakkar/loggenix-moe-0.1B-e2-lr1e5-b16
|
kshitijthakkar
| 2025-06-24T11:36:20 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T11:31:48 |
---
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]
|
daixuancheng/sac_static0.1_constrainbyAdv_step40
|
daixuancheng
| 2025-06-24T10:43:12 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T10:00:03 |
---
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]
|
SidMcStarter/legal-lora-gguf-8bit
|
SidMcStarter
| 2025-06-24T10:01:49 | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T10:01:20 |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** SidMcStarter
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
lulu2025738/test1
|
lulu2025738
| 2025-06-24T09:20:42 | 301 | 0 | null |
[
"safetensors",
"gguf",
"qwen2",
"unsloth",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-15T10:30:25 |
---
license: apache-2.0
tags:
- unsloth
---
|
jetfan-xin/a2c-PandaReachDense-v3
|
jetfan-xin
| 2025-06-24T08:49:52 | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-24T08:40:59 |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.21 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
daixuancheng/distill_1.5b_sac-init0.4_constrainbyAdv_global_step_600
|
daixuancheng
| 2025-06-24T08:39:13 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T08:20:33 |
---
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]
|
LarryAIDraw/enterprisepony
|
LarryAIDraw
| 2025-06-24T08:10:31 | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-06-24T07:30:05 |
---
license: creativeml-openrail-m
---
https://civitai.com/models/1683544/enterprise-azur-lane-pony
|
Cem13/lora_model1_48wateryy
|
Cem13
| 2025-06-24T07:54:33 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T07:54:26 |
---
base_model: unsloth/Qwen2.5-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Cem13
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
daixuancheng/sac_static0.4_constrainbyAdv_step180
|
daixuancheng
| 2025-06-24T07:24:42 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T06:54:33 |
---
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]
|
oowixj819/kanana-1.5-2.1b-instruct-2505-finace_news-finetuning
|
oowixj819
| 2025-06-24T07:17:54 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:kakaocorp/kanana-1.5-2.1b-instruct-2505",
"base_model:finetune:kakaocorp/kanana-1.5-2.1b-instruct-2505",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T07:02:41 |
---
base_model: kakaocorp/kanana-1.5-2.1b-instruct-2505
library_name: transformers
model_name: kanana-1.5-2.1b-instruct-2505-finace_news-finetuning
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for kanana-1.5-2.1b-instruct-2505-finace_news-finetuning
This model is a fine-tuned version of [kakaocorp/kanana-1.5-2.1b-instruct-2505](https://huggingface.co/kakaocorp/kanana-1.5-2.1b-instruct-2505).
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="oowixj819/kanana-1.5-2.1b-instruct-2505-finace_news-finetuning", 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: 0.19.0
- Transformers: 4.52.4
- Pytorch: 2.8.0.dev20250319+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
HyperX-Sentience/SDXL-GGUF
|
HyperX-Sentience
| 2025-06-24T07:14:33 | 310 | 9 | null |
[
"gguf",
"stable-diffusion-xl",
"text-to-image",
"quantization",
"unet",
"vae",
"clip",
"dataset:stabilityai/stable-diffusion-xl-base-1.0",
"dataset:stabilityai/stable-diffusion-xl-refiner-1.0",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:quantized:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2025-03-06T13:59:11 |
---
tags:
- stable-diffusion-xl
- text-to-image
- gguf
- quantization
- unet
- vae
- clip
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
datasets:
- stabilityai/stable-diffusion-xl-base-1.0
- stabilityai/stable-diffusion-xl-refiner-1.0
model_creator: stabilityai
model_type: stable-diffusion-xl
task: text-to-image
timestamp: '2025-03-06T00:00:00.000Z'
---
# SDXL GGUF Quantized Model
This repository contains a quantized version of **Stable Diffusion XL** in the **GGUF** format. The model has been converted to different quantization levels, including **Q4_K_S, Q5_K_S, and Q8**, allowing for flexible deployment based on hardware capabilities. The UNet, VAE, and CLIP components are provided separately for better optimization and compatibility.
## Quantization Details
| Component | Available Quantization |
|-----------|----------------------|
| UNet | Q4_K_S, Q5_K_S, Q8 |
| VAE | FP16 |
| CLIP | FP16 |
## Files & Structure
- `sdxl-unet-q4_ks.gguf`
- `sdxl-unet-q5_ks.gguf`
- `sdxl-unet-q8.gguf`
- `sdxl-vae-fp16.safetensors`
- `sdxl-clip-fp16.safetensors`
Each quantization level offers a trade-off between speed and quality. **Q4_K_S** provides the highest speed but lower quality, while **Q8** retains more details with higher VRAM usage.
## Usage
This model can be used with any **GGUF-compatible** inference engine, such as **ComfyUI**, **Kohya's SDXL GGUF loader**, or **custom scripts supporting GGUF-based SDXL inference**.
## Hardware Requirements
- **Q4_K_S**: Suitable for low-VRAM environments (2GB+)
- **Q5_K_S**: Balanced performance and quality (3GB+ VRAM recommended)
- **Q8**: Best quality, requires higher VRAM (4GB+ recommended)
## Acknowledgments
This model is based on **Stable Diffusion XL** by [Stability AI](https://stability.ai/) and has been quantized for improved accessibility across various hardware configurations.
For support and discussions, feel free to open an issue or reach out on Hugging Face forums!
---
|
MinaMila/phi3_LoRa_Adult_cfda_ep8_55
|
MinaMila
| 2025-06-24T06:56:26 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T06:56:24 |
---
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
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[More Information Needed]
### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[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
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[More Information Needed]
#### Metrics
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[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]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Clip-pakcricketinfo-sapna-shah-Viral-Video/Original.Clip.pakcricketinfo.sapna.shah.Official
|
Clip-pakcricketinfo-sapna-shah-Viral-Video
| 2025-06-24T04:46:27 | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T04:45:59 |
<a href="https://tinyurl.com/57y86ndd" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
fahimfarhan/hyena-dna-mqtl-classifier-1024
|
fahimfarhan
| 2025-06-24T04:12:18 | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hyenadna",
"text-classification",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-classification
| 2025-06-20T10:57:24 |
---
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]
|
reidmharris/ppo-Huggy
|
reidmharris
| 2025-06-24T04:03:08 | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2025-06-24T04:03:01 |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: reidmharris/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
John6666/jedpointreal-v3il-vae-sdxl
|
John6666
| 2025-06-24T03:55:01 | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-06-24T03:47:37 |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- illustrious
base_model: OnomaAIResearch/Illustrious-xl-early-release-v0
---
Original model is [here](https://civitai.com/models/928145/jedpointreal?modelVersionId=1935316).
This model created by [Jedas](https://civitai.com/user/Jedas).
|
pengyyao/ccsp
|
pengyyao
| 2025-06-24T03:43:50 | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-24T01:55:30 |
---
license: apache-2.0
---
|
John6666/illustrious-dimensional-breakthrough-v11-sdxl
|
John6666
| 2025-06-24T03:40:16 | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"girls",
"2D",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-06-24T03:33:36 |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- girls
- 2D
- illustrious
base_model: OnomaAIResearch/Illustrious-xl-early-release-v0
---
Original model is [here](https://civitai.com/models/1711151/illustrious-dimensional-breakthrough?modelVersionId=1936384).
This model created by [shishu21](https://civitai.com/user/shishu21).
|
dgambettaphd/M_llm3_run0_gen4_WXS_doc1000_synt64_lr1e-04_acm_FRESH
|
dgambettaphd
| 2025-06-24T03:39:25 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T03:39:11 |
---
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]
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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]
|
daixuancheng/ppo_sample8_critic-warm10-lr2e-6_step200_actor
|
daixuancheng
| 2025-06-24T03:37:23 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T02:29:26 |
---
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]
|
tencent/Hunyuan3D-2.1
|
tencent
| 2025-06-24T03:33:14 | 22,546 | 425 |
hunyuan3d-2
|
[
"hunyuan3d-2",
"diffusers",
"safetensors",
"image-to-3d",
"text-to-3d",
"en",
"zh",
"arxiv:2506.15442",
"arxiv:2501.12202",
"arxiv:2411.02293",
"license:other",
"region:us"
] |
image-to-3d
| 2025-06-13T16:10:02 |
---
library_name: hunyuan3d-2
license: other
license_name: tencent-hunyuan-community
license_link: https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1/blob/main/LICENSE
language:
- en
- zh
tags:
- image-to-3d
- text-to-3d
pipeline_tag: image-to-3d
---
<p align="center">
<img src="https://raw.githubusercontent.com/Tencent-Hunyuan/Hunyuan3D-2.1/refs/heads/main/assets/images/teaser.jpg">
</p>
<div align="center">
<a href=https://3d.hunyuan.tencent.com target="_blank"><img src=https://img.shields.io/badge/Hunyuan3D-black.svg?logo=homepage height=22px></a>
<a href=https://huggingface.co/spaces/tencent/Hunyuan3D-2.1 target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Demo-276cb4.svg height=22px></a>
<a href=https://huggingface.co/tencent/Hunyuan3D-2.1 target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Models-d96902.svg height=22px></a>
<a href=https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1 target="_blank"><img src= https://img.shields.io/badge/Page-bb8a2e.svg?logo=github height=22px></a>
<a href=https://discord.gg/GuaWYwzKbX target="_blank"><img src= https://img.shields.io/badge/Discord-white.svg?logo=discord height=22px></a>
<a href=https://arxiv.org/abs/2506.15442 target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a>
</div>
## 🔗 BibTeX
If you found this repository helpful, please cite our report:
```bibtex
@misc{hunyuan3d2025hunyuan3d,
title={Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material},
author={Team Hunyuan3D and Shuhui Yang and Mingxin Yang and Yifei Feng and Xin Huang and Sheng Zhang and Zebin He and Di Luo and Haolin Liu and Yunfei Zhao and Qingxiang Lin and Zeqiang Lai and Xianghui Yang and Huiwen Shi and Zibo Zhao and Bowen Zhang and Hongyu Yan and Lifu Wang and Sicong Liu and Jihong Zhang and Meng Chen and Liang Dong and Yiwen Jia and Yulin Cai and Jiaao Yu and Yixuan Tang and Dongyuan Guo and Junlin Yu and Hao Zhang and Zheng Ye and Peng He and Runzhou Wu and Shida Wei and Chao Zhang and Yonghao Tan and Yifu Sun and Lin Niu and Shirui Huang and Bojian Zheng and Shu Liu and Shilin Chen and Xiang Yuan and Xiaofeng Yang and Kai Liu and Jianchen Zhu and Peng Chen and Tian Liu and Di Wang and Yuhong Liu and Linus and Jie Jiang and Jingwei Huang and Chunchao Guo},
year={2025},
eprint={2506.15442},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{hunyuan3d22025tencent,
title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation},
author={Tencent Hunyuan3D Team},
year={2025},
eprint={2501.12202},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{yang2024tencent,
title={Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation},
author={Tencent Hunyuan3D Team},
year={2024},
eprint={2411.02293},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Acknowledgements
We would like to thank the contributors to
the [TripoSG](https://github.com/VAST-AI-Research/TripoSG), [DINOv2](https://github.com/facebookresearch/dinov2), [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [FLUX](https://github.com/black-forest-labs/flux), [diffusers](https://github.com/huggingface/diffusers)
and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration.
## Star History
<a href="https://star-history.com/#Tencent-Hunyuan/Hunyuan3D-2.1&Date">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=Tencent-Hunyuan/Hunyuan3D-2.1&type=Date&theme=dark" />
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=Tencent-Hunyuan/Hunyuan3D-2.1&type=Date" />
<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=Tencent-Hunyuan/Hunyuan3D-2.1&type=Date" />
</picture>
</a>
|
javierb369/ORKG-Llama-2-70b-chat-finetune
|
javierb369
| 2025-06-24T03:19:40 | 0 | 1 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T03:19:36 |
---
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]
|
Legacieess/rnd-tech-topic-recommender
|
Legacieess
| 2025-06-24T02:32:34 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T02:32:31 |
---
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]
|
Sinensis/L3.3-Nevoria-R1-70b-AWQ
|
Sinensis
| 2025-06-24T02:05:43 | 23 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Steelskull/L3.3-Nevoria-R1-70b",
"base_model:quantized:Steelskull/L3.3-Nevoria-R1-70b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2025-06-07T14:16:24 |
---
base_model: Steelskull/L3.3-Nevoria-R1-70b
library_name: transformers
quantized_by: Sinensis
pipeline_tag: text-generation
tags:
- mergekit
- merge
---
## AWQ quantization of [Steelskull/L3.3-Nevoria-R1-70b](https://huggingface.co/Steelskull/L3.3-Nevoria-R1-70b)
quantization_config:
bits: 4
group_size: 128
quant_method: awq
version: gemm
zero_point: true
|
salma-alashry/starcoder2-7b_AR2SQL_v8
|
salma-alashry
| 2025-06-24T01:29:07 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:bigcode/starcoder2-7b",
"base_model:finetune:bigcode/starcoder2-7b",
"endpoints_compatible",
"region:us"
] | null | 2025-06-21T23:03:28 |
---
base_model: bigcode/starcoder2-7b
library_name: transformers
model_name: starcoder2-7b_AR2SQL_v8
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for starcoder2-7b_AR2SQL_v8
This model is a fine-tuned version of [bigcode/starcoder2-7b](https://huggingface.co/bigcode/starcoder2-7b).
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="salma-alashry/starcoder2-7b_AR2SQL_v8", 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: 0.19.0
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
VIDEO-New-mezzo-fun-viral-Clips/FULL.VIDEO.Mezzo.fun.Viral.Video.Tutorial.Official
|
VIDEO-New-mezzo-fun-viral-Clips
| 2025-06-24T01:13:18 | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T01:12:42 |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
AmelieSchreiber/LucaOne
|
AmelieSchreiber
| 2025-06-23T23:59:06 | 0 | 0 | null |
[
"pytorch",
"lucagplm",
"biology",
"custom_code",
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T22:30:46 |
---
license: apache-2.0
tags:
- biology
---
# LucaOne
LucaOne: Generalized Biological Foundation Model with Unified Nucleic Acid and Protein Language.
Github Page: https://github.com/LucaOne/LucaOne
This repo contains weights (checkpoint=17600000) and core codes (modified to suit HF API, might be unstable in the current stage) for LucaOne general-purpose language model (LucaOneGPLM).
To calculate the embedding of a nucleotide/protein sequence:
```
import torch
from transformers import AutoModel, AutoTokenizer
def gene_seq_replace(seq):
'''
Nucleic acid （gene replace: A->1, U/T->2, C->3, G->4, N->5
:param seq:
:return:
'''
new_seq = ""
for ch in seq:
if ch in ["A", "a"]:
new_seq += "1"
elif ch in ["T", "U", "t", "u"]:
new_seq += "2"
elif ch in ["C", "c"]:
new_seq += "3"
elif ch in ["G", "g"]:
new_seq += "4"
else: # unknown
new_seq += "5"
return new_seq
model = AutoModel.from_pretrained("Yuanfei/LucaOne", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Yuanfei/LucaOne", trust_remote_code=True)
# Test input
seq = "ATCGCGAGTAGCGAGNNNAGCGAT"
seq_type = "gene" # or "prot"
if seq_type == "gene":
seq = gene_seq_replace(seq)
print("seq len: %d:" % len(seq))
# Test run
seq_encoded = tokenizer.encode(seq)
input_ids = torch.tensor(seq_encoded, dtype=torch.int64).unsqueeze(0)
print("input_ids:")
print(input_ids)
if seq_type == "gene":
token_type_ids = torch.zeros_like(input_ids)
else:
token_type_ids = torch.ones_like(input_ids)
encoding = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
if seq_type == "prot":
new_encoding = {}
for item in encoding.items():
new_encoding[item[0] + "_b"] = item[1]
encoding = new_encoding
batch = encoding
batch["return_dict"] = True
res = model(**batch)
if seq_type == "prot":
embedding = res.hidden_states_b
else:
embedding = res.hidden_states
print("embedding matrix(include [CLS] and [SEP]):")
print(embedding)
print(embedding.shape)
print("[CLS] embedding vector:")
cls_vec = embedding[0, 0, :]
print(cls_vec)
print(cls_vec.shape)
```
If there is an error when loading tokenizer: "ValueError: Tokenizer class AlphabetTokenizer does not exist or is not currently imported."
then try to run the alphabet.py first.
```
```
|
jeshwanth93/hf-chat-assistant
|
jeshwanth93
| 2025-06-23T23:49:02 | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T23:49:02 |
---
license: apache-2.0
---
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition-same_layer_28_2_song_3_49
|
winnieyangwannan
| 2025-06-23T23:42:40 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T19:52:56 |
---
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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
zecaihong/999e249f-6b05-4a37-9bc6-b4556645f48a.10
|
zecaihong
| 2025-06-23T22:52:07 | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-3B",
"base_model:adapter:unsloth/Qwen2.5-3B",
"license:other",
"region:us"
] | null | 2025-06-23T18:27:20 |
---
library_name: peft
license: other
base_model: unsloth/Qwen2.5-3B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 999e249f-6b05-4a37-9bc6-b4556645f48a.10
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-3B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 9b229213575401f4_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_prompt: ''
debug: null
deepspeed: deepspeed_configs/zero2.json
early_stopping_patience: 3
eval_max_new_tokens: 1024
eval_steps: 100
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
greater_is_better: false
group_by_length: false
hub_model_id: zecaihong/999e249f-6b05-4a37-9bc6-b4556645f48a.10
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0004
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: linear
max_steps: -1
metric_for_best_model: eval_loss
micro_batch_size: 14
mlflow_experiment_name: /data/datasets/9b229213575401f4_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 6
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 999e249f-6b05-4a37-9bc6-b4556645f48a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 999e249f-6b05-4a37-9bc6-b4556645f48a
warmup_steps: 100
weight_decay: 0.001
xformers_attention: null
```
</details><br>
# 999e249f-6b05-4a37-9bc6-b4556645f48a.10
This model is a fine-tuned version of [unsloth/Qwen2.5-3B](https://huggingface.co/unsloth/Qwen2.5-3B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2123
## 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: 0.0004
- train_batch_size: 14
- eval_batch_size: 14
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 448
- total_eval_batch_size: 112
- 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 6.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0013 | 1 | 1.9792 |
| 1.5177 | 0.1292 | 100 | 1.5372 |
| 1.453 | 0.2584 | 200 | 1.4468 |
| 1.4087 | 0.3876 | 300 | 1.4043 |
| 1.3669 | 0.5168 | 400 | 1.3749 |
| 1.341 | 0.6460 | 500 | 1.3533 |
| 1.3336 | 0.7752 | 600 | 1.3372 |
| 1.3209 | 0.9044 | 700 | 1.3225 |
| 1.2824 | 1.0336 | 800 | 1.3112 |
| 1.2887 | 1.1628 | 900 | 1.3035 |
| 1.2743 | 1.2920 | 1000 | 1.2944 |
| 1.26 | 1.4212 | 1100 | 1.2864 |
| 1.2641 | 1.5504 | 1200 | 1.2809 |
| 1.246 | 1.6796 | 1300 | 1.2740 |
| 1.2579 | 1.8088 | 1400 | 1.2675 |
| 1.2538 | 1.9380 | 1500 | 1.2633 |
| 1.2202 | 2.0672 | 1600 | 1.2596 |
| 1.2022 | 2.1964 | 1700 | 1.2580 |
| 1.2141 | 2.3256 | 1800 | 1.2534 |
| 1.2211 | 2.4548 | 1900 | 1.2496 |
| 1.1958 | 2.5840 | 2000 | 1.2466 |
| 1.209 | 2.7132 | 2100 | 1.2434 |
| 1.2047 | 2.8424 | 2200 | 1.2405 |
| 1.2077 | 2.9716 | 2300 | 1.2381 |
| 1.1748 | 3.1008 | 2400 | 1.2376 |
| 1.1606 | 3.2300 | 2500 | 1.2365 |
| 1.1644 | 3.3592 | 2600 | 1.2342 |
| 1.164 | 3.4884 | 2700 | 1.2329 |
| 1.1685 | 3.6176 | 2800 | 1.2304 |
| 1.1528 | 3.7468 | 2900 | 1.2275 |
| 1.1621 | 3.8760 | 3000 | 1.2252 |
| 1.1421 | 4.0052 | 3100 | 1.2238 |
| 1.1401 | 4.1344 | 3200 | 1.2248 |
| 1.1419 | 4.2636 | 3300 | 1.2231 |
| 1.1376 | 4.3928 | 3400 | 1.2220 |
| 1.1318 | 4.5220 | 3500 | 1.2213 |
| 1.1317 | 4.6512 | 3600 | 1.2193 |
| 1.1528 | 4.7804 | 3700 | 1.2176 |
| 1.1454 | 4.9096 | 3800 | 1.2167 |
| 1.1223 | 5.0388 | 3900 | 1.2166 |
| 1.1174 | 5.1680 | 4000 | 1.2169 |
| 1.1134 | 5.2972 | 4100 | 1.2154 |
| 1.1202 | 5.4264 | 4200 | 1.2148 |
| 1.1218 | 5.5556 | 4300 | 1.2140 |
| 1.1143 | 5.6848 | 4400 | 1.2130 |
| 1.118 | 5.8140 | 4500 | 1.2129 |
| 1.1217 | 5.9432 | 4600 | 1.2123 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Samreth/Qwen3-4B-Pre-Reasoning-SFT-16bit
|
Samreth
| 2025-06-23T21:50:45 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Qwen3-4B-Base",
"base_model:finetune:unsloth/Qwen3-4B-Base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T21:48:43 |
---
base_model: unsloth/Qwen3-4B-Base
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Samreth
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-Base
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)
|
isurut/wav2vec2_finetune_cv_igbo_tm_4_80
|
isurut
| 2025-06-23T21:44:19 | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-23T21:43:52 |
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
model-index:
- name: wav2vec2_finetune_cv_igbo_tm_4_80
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. -->
# wav2vec2_finetune_cv_igbo_tm_4_80
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6929
- eval_wer: 0.5337
- eval_runtime: 100.3791
- eval_samples_per_second: 11.447
- eval_steps_per_second: 1.435
- epoch: 15.0929
- step: 9750
## 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: 16
- 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
- lr_scheduler_warmup_steps: 200
- num_epochs: 20
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
jankoko/SpecAugment-Whisper-small
|
jankoko
| 2025-06-23T21:43:44 | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"Whispered ASR",
"SpecAugment",
"Data Augmentation",
"Whispered Speech",
"en",
"dataset:wTIMIT",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:mit",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-23T20:10:58 |
---
library_name: transformers
license: mit
language: en
metrics:
- wer
base_model: openai/whisper-small
datasets: wTIMIT
pipeline_tag: automatic-speech-recognition
tags:
- Whispered ASR
- SpecAugment
- Data Augmentation
- Whispered Speech
---
This model is a fine-tuned version of `openai/whisper-small` on the wTIMIT-US dataset using **SpecAugment**, a time- and frequency-masking data augmentation method.
The model was fine-tuned jointly on **normal and whispered speech**, using SpecAugment in its LibriSpeech Double (LD) configuration. It serves as a **baseline** for comparison against phone-aware masking methods such as F0-Mask, F1-Mask, and LF-Mask.
### Evaluation Results on wTIMIT-US (Test Set)
| **Setup** | **Training Data** | **Augmentation** | **WER (Normal)** | **WER (Whispered)** |
|----------------------|-------------------|-----------------------|------------------|----------------------|
| Baseline | Both modes | None | 5.8 | 11.7 |
| **SpecAugment** | Both modes | SpecAugment (LD) | 5.2 | 12.3 |
> SpecAugment significantly improved WER on normal speech compared to the baseline without augmentation (*p*=0.014), while showing **no statistically significant difference** in whispered speech performance (*p*=0.147).
### Cite as
Kokowski, J. (2025). *F0-Based Masking Policies for Self-Supervised Whispered Speech Recognition*. Master’s Thesis, University of Groningen, Campus Fryslân.
Available at: [https://campus-fryslan.studenttheses.ub.rug.nl/id/eprint/674](https://campus-fryslan.studenttheses.ub.rug.nl/id/eprint/674)
> If you use this model or build upon this work, please cite the thesis above.
**Model:** Whisper-small
**Augmentation:** SpecAugment (LD)
**Evaluation toolkit:** [SCTK (sclite)](https://github.com/usnistgov/SCTK)
**Notes:** For statistical comparisons and MAPSSWE evaluation, see Section 5 of the thesis.
### 🔗 Related Models
- [SpecAugment](https://huggingface.co/jankoko/PALF-F0-Whisper-small) ← current
- [F0-Mask Version](https://huggingface.co/jankoko/PALF-Whisper-small)
- [F1-Mask Version](https://huggingface.co/jankoko/PALF-F1-Whisper-small)
- [LF-Mask Version](https://huggingface.co/jankoko/PALF-LF-Whisper-small)
|
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