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 |
|---|---|---|---|---|---|---|---|---|---|
DAMO-NLP-SG/siglip2-so400m-patch14-384-navit
|
DAMO-NLP-SG
| 2025-03-20T04:12:04Z
| 9,444
| 0
|
transformers
|
[
"transformers",
"safetensors",
"videollama3_vision_encoder",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] |
feature-extraction
| 2025-02-28T07:28:32Z
|
---
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]
|
pshastri/example-model
|
pshastri
| 2025-03-20T04:11:40Z
| 0
| 0
| null |
[
"license:mit",
"region:us"
] | null | 2025-03-20T04:11:09Z
|
---
license: mit
---
This is a test model for understanding the working of huggingface model.
|
DAMO-NLP-SG/VL3-SigLIP-NaViT
|
DAMO-NLP-SG
| 2025-03-20T04:11:24Z
| 8,331
| 7
|
transformers
|
[
"transformers",
"safetensors",
"videollama3_vision_encoder",
"feature-extraction",
"visual-encoder",
"multi-modal-large-language-model",
"image-feature-extraction",
"custom_code",
"en",
"arxiv:2501.13106",
"arxiv:2406.07476",
"arxiv:2306.02858",
"base_model:google/siglip-so400m-patch14-384",
"base_model:finetune:google/siglip-so400m-patch14-384",
"license:apache-2.0",
"region:us"
] |
image-feature-extraction
| 2025-01-21T08:52:21Z
|
---
library_name: transformers
tags:
- visual-encoder
- multi-modal-large-language-model
license: apache-2.0
language:
- en
base_model:
- google/siglip-so400m-patch14-384
pipeline_tag: image-feature-extraction
---
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/626938b16f8f86ad21deb989/543Eaf__U-a9Z72LPGWgC.png" width="150" style="margin-bottom: 0.2;"/>
<p>
<h3 align="center">The visual encoder of <a href="https://arxiv.org/abs/2501.13106">VideoLLaMA 3: Frontier Multimodal Foundation Models for Video Understanding</a></h3>
<h5 align="center"> If you like our project, please give us a star ⭐ on <a href="https://github.com/DAMO-NLP-SG/VideoLLaMA3">Github</a> for the latest update. </h5>
## 🌟 Introduction
This model serves as the visual encoder in VideoLLaMA3.
VideoLLaMA3 leverages the Any-resolution Vision Tokenization (AVT) approach to dynamically process images and videos of varying resolutions. This is accomplished by adapting the pre-trained vision encoder (based on ViT architecture) to use 2D-RoPE (Rotary Position Embeddings), replacing the absolute position embeddings traditionally used in ViT.
With AVT, VideoLLaMA3 is able to represent images and videos with greater detail across different resolutions, enriching the vision tokens with more information. To ensure seamless integration with AVT, we fine-tune both the vision encoder and the projector during the Vision Encoder Adaptation stage (Stage #1 in the VideoLLaMA3 training pipeline) using scene images, document data, and scene images with text.
Before training, the model parameters and architecture are initialized from [SigLip](https://huggingface.co/google/siglip-so400m-patch14-384).
## 🚀 Model Porfermance
| Base Model | GQA | AI2D | ChartQA | DocVQA<sub>val</sub> | MME |
|---------------------------------|------------|------------|-------------|--------------------------|------------|
| clip-vit-large-patch14-336 | 61.50 | 56.28 | 18.32 | 24.86 | **1668.41**|
| dfn5B-clip-vit-h-14-378 | 62.70 | 56.87 | 16.40 | 23.09 | 1665.35 |
| siglip-so400m-patch14-384 **(Our Implementation)** | **62.92** | **57.12** | **22.44** | **31.32** | 1667.92 |
* A more detailed analysis can be found in our [paper](https://arxiv.org/abs/2501.13106).
## 🤖 Quick Start
```python
import torch
from transformers import AutoModel, AutoImageProcessor
from transformers.image_utils import load_image
model_name = "DAMO-NLP-SG/VL3-SigLIP-NaViT"
image_path = "https://github.com/DAMO-NLP-SG/VideoLLaMA3/blob/main/assets/sora.png?raw=true"
images = load_image(image_path)
model = AutoModel.from_pretrained(
model_name,
trust_remote_code=True,
device_map="auto",
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
processor = AutoImageProcessor.from_pretrained(model_name, trust_remote_code=True)
inputs = processor(images=images, merge_size=1)
inputs = {k: torch.tensor(v).cuda() for k, v in inputs.items()}
if "pixel_values" in inputs:
inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
image_features = model(**inputs)
```
## Citation
If you find VideoLLaMA useful for your research and applications, please cite using this BibTeX:
```bibtex
@article{damonlpsg2025videollama3,
title={VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding},
author={Boqiang Zhang, Kehan Li, Zesen Cheng, Zhiqiang Hu, Yuqian Yuan, Guanzheng Chen, Sicong Leng, Yuming Jiang, Hang Zhang, Xin Li, Peng Jin, Wenqi Zhang, Fan Wang, Lidong Bing, Deli Zhao},
journal={arXiv preprint arXiv:2501.13106},
year={2025},
url = {https://arxiv.org/abs/2501.13106}
}
@article{damonlpsg2024videollama2,
title={VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs},
author={Cheng, Zesen and Leng, Sicong and Zhang, Hang and Xin, Yifei and Li, Xin and Chen, Guanzheng and Zhu, Yongxin and Zhang, Wenqi and Luo, Ziyang and Zhao, Deli and Bing, Lidong},
journal={arXiv preprint arXiv:2406.07476},
year={2024},
url = {https://arxiv.org/abs/2406.07476}
}
@article{damonlpsg2023videollama,
title = {Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding},
author = {Zhang, Hang and Li, Xin and Bing, Lidong},
journal = {arXiv preprint arXiv:2306.02858},
year = {2023},
url = {https://arxiv.org/abs/2306.02858}
}
```
|
DAMO-NLP-SG/VideoLLaMA3-7B-Image
|
DAMO-NLP-SG
| 2025-03-20T04:08:44Z
| 5,489
| 10
|
transformers
|
[
"transformers",
"safetensors",
"videollama3_qwen2",
"text-generation",
"multi-modal",
"large-language-model",
"video-language-model",
"visual-question-answering",
"custom_code",
"en",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"dataset:allenai/pixmo-docs",
"dataset:HuggingFaceM4/Docmatix",
"dataset:lmms-lab/LLaVA-Video-178K",
"dataset:ShareGPT4Video/ShareGPT4Video",
"arxiv:2501.13106",
"arxiv:2406.07476",
"arxiv:2306.02858",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
visual-question-answering
| 2025-01-21T08:36:12Z
|
---
library_name: transformers
tags:
- multi-modal
- large-language-model
- video-language-model
license: apache-2.0
datasets:
- lmms-lab/LLaVA-OneVision-Data
- allenai/pixmo-docs
- HuggingFaceM4/Docmatix
- lmms-lab/LLaVA-Video-178K
- ShareGPT4Video/ShareGPT4Video
language:
- en
metrics:
- accuracy
pipeline_tag: visual-question-answering
base_model:
- Qwen/Qwen2.5-7B-Instruct
---
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/626938b16f8f86ad21deb989/tt5KYnAUmQlHtfB1-Zisl.png" width="150" style="margin-bottom: 0.2;"/>
<p>
<h3 align="center"><a href="https://arxiv.org/abs/2501.13106">VideoLLaMA 3: Frontier Multimodal Foundation Models for Video Understanding</a></h3>
<h5 align="center"> If you like our project, please give us a star ⭐ on <a href="https://github.com/DAMO-NLP-SG/VideoLLaMA3">Github</a> for the latest update. </h5>
## 📰 News
<!-- * **[2024.01.23]** 👋👋 Update technical report. If you have works closely related to VideoLLaMA3 but not mentioned in the paper, feel free to let us know. -->
* **[2024.01.24]** 🔥🔥 Online Demo is available: [VideoLLaMA3-Image-7B](https://huggingface.co/spaces/lixin4ever/VideoLLaMA3-Image), [VideoLLaMA3-7B](https://huggingface.co/spaces/lixin4ever/VideoLLaMA3).
* **[2024.01.22]** Release models and inference code of VideoLLaMA 3.
## 🌟 Introduction
VideoLLaMA 3 represents a state-of-the-art series of multimodal foundation models designed to excel in both image and video understanding tasks. Leveraging advanced architectures, VideoLLaMA 3 demonstrates exceptional capabilities in processing and interpreting visual content across various contexts. These models are specifically designed to address complex multimodal challenges, such as integrating textual and visual information, extracting insights from sequential video data, and performing high-level reasoning over both dynamic and static visual scenes.
## 🌎 Model Zoo
| Model | Base Model | HF Link |
| -------------------- | ------------ | ------------------------------------------------------------ |
| VideoLLaMA3-7B | Qwen2.5-7B | [DAMO-NLP-SG/VideoLLaMA3-7B](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA3-7B) |
| VideoLLaMA3-2B | Qwen2.5-1.5B | [DAMO-NLP-SG/VideoLLaMA3-2B](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA3-2B) |
| VideoLLaMA3-7B-Image (**This Checkpoint**) | Qwen2.5-7B | [DAMO-NLP-SG/VideoLLaMA3-7B-Image](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA3-7B-Image) |
| VideoLLaMA3-2B-Image | Qwen2.5-1.5B | [DAMO-NLP-SG/VideoLLaMA3-2B-Image](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA3-2B-Image) |
We also upload the tuned vision encoder of VideoLLaMA3-7B for wider application:
| Model | Base Model | HF Link |
| ----------------------------- | ------------------------- | ------------------------------------------------------------ |
| VideoLLaMA3-7B Vision Encoder | siglip-so400m-patch14-384 | [DAMO-NLP-SG/VL3-SigLIP-NaViT](https://huggingface.co/DAMO-NLP-SG/VL3-SigLIP-NaViT) |
## 🚀 Main Results
<img width="500" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/626938b16f8f86ad21deb989/ArHgZAmidn8Qlz8BwOdJI.png">
* \* denotes the reproduced results.
## 🤖 Quick Start
```python
import torch
from transformers import AutoModelForCausalLM, AutoProcessor, AutoModel, AutoImageProcessor
model_name = "DAMO-NLP-SG/VideoLLaMA3-7B-Image"
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
device_map="auto",
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
# Image conversation
conversation = [
{
"role": "user",
"content": [
{"type": "image", "image": {"image_path": "https://github.com/DAMO-NLP-SG/VideoLLaMA3/blob/main/assets/sora.png?raw=true"}},
{"type": "text", "text": "What is the woman wearing?"},
]
}
]
inputs = processor(conversation=conversation, return_tensors="pt")
inputs = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
if "pixel_values" in inputs:
inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
output_ids = model.generate(**inputs, max_new_tokens=128)
response = processor.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
print(response)
```
## Citation
If you find VideoLLaMA useful for your research and applications, please cite using this BibTeX:
```bibtex
@article{damonlpsg2025videollama3,
title={VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding},
author={Boqiang Zhang, Kehan Li, Zesen Cheng, Zhiqiang Hu, Yuqian Yuan, Guanzheng Chen, Sicong Leng, Yuming Jiang, Hang Zhang, Xin Li, Peng Jin, Wenqi Zhang, Fan Wang, Lidong Bing, Deli Zhao},
journal={arXiv preprint arXiv:2501.13106},
year={2025},
url = {https://arxiv.org/abs/2501.13106}
}
@article{damonlpsg2024videollama2,
title={VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs},
author={Cheng, Zesen and Leng, Sicong and Zhang, Hang and Xin, Yifei and Li, Xin and Chen, Guanzheng and Zhu, Yongxin and Zhang, Wenqi and Luo, Ziyang and Zhao, Deli and Bing, Lidong},
journal={arXiv preprint arXiv:2406.07476},
year={2024},
url = {https://arxiv.org/abs/2406.07476}
}
@article{damonlpsg2023videollama,
title = {Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding},
author = {Zhang, Hang and Li, Xin and Bing, Lidong},
journal = {arXiv preprint arXiv:2306.02858},
year = {2023},
url = {https://arxiv.org/abs/2306.02858}
}
```
|
quancute/QwQ-32B-Q4_K_M-GGUF
|
quancute
| 2025-03-20T04:08:10Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:Qwen/QwQ-32B",
"base_model:quantized:Qwen/QwQ-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-03-20T04:06:39Z
|
---
base_model: Qwen/QwQ-32B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/QWQ-32B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- llama-cpp
- gguf-my-repo
---
# quancute/QwQ-32B-Q4_K_M-GGUF
This model was converted to GGUF format from [`Qwen/QwQ-32B`](https://huggingface.co/Qwen/QwQ-32B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/QwQ-32B) 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 quancute/QwQ-32B-Q4_K_M-GGUF --hf-file qwq-32b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo quancute/QwQ-32B-Q4_K_M-GGUF --hf-file qwq-32b-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 quancute/QwQ-32B-Q4_K_M-GGUF --hf-file qwq-32b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo quancute/QwQ-32B-Q4_K_M-GGUF --hf-file qwq-32b-q4_k_m.gguf -c 2048
```
|
sohnikaavisakula/inventory-optimization
|
sohnikaavisakula
| 2025-03-20T04:04:03Z
| 0
| 0
| null |
[
"region:us"
] | null | 2025-03-20T03:17:28Z
|
# 📦 Random Forest Model for Inventory Optimization
This is a trained **Random Forest Regressor** model for predicting **stockout risks** and **optimizing inventory levels** based on supplier lead time and demand fluctuations.
## Model Overview
- **Algorithm Used**: Random Forest Regressor
- **Purpose**: Forecasting inventory demand & optimizing reorder points
- **Key Features**:
- Supplier lead times
- Order quantities
- Shipment modes
- Regional logistics data
- Demand fluctuations
## 📊 Training Details
- **Dataset**: Historical e-commerce inventory data (orders, shipments, supplier info)
- **Feature Engineering**: Handled missing values, removed outliers, and normalized data
- **Performance Metrics**:
- **Mean Absolute Error (MAE):** *XYZ*
- **Root Mean Squared Error (RMSE):** *XYZ*
- **R² Score:** *XYZ*
## 🔧 How to Use the Model
To load and use the model in Python:
```python
import joblib
from huggingface_hub import hf_hub_download
# Download the model
model_path = hf_hub_download(repo_id="sohnikaavisakula/inventory-optimization", filename="inventory_model.pkl")
# Load the model
model = joblib.load(model_path)
# Example input (adjust based on your dataset)
X_test = [[5.2, 1.3, 7.8, 3.1]] # Replace with real data
prediction = model.predict(X_test)
print("Predicted stockout risk:", prediction)
|
codecandy/antiblur
|
codecandy
| 2025-03-20T04:03:33Z
| 0
| 0
|
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"image-generation",
"flux",
"safetensors",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-03-20T04:03:33Z
|
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- image-generation
- flux
- safetensors
widget:
- text: >-
a young college student, walking on the street, campus background,
photography
output:
url: images/2f82e6b1e5969d70a9044c19975bcdcca06b0f251d14f9c2c6095fa6.jpg
- text: a young woman, New York City
output:
url: images/340c1ae6709f56f3d8176848653dcade93d2b5b8ade662da167ef818.jpg
- text: >-
happy stunning girl with long dark hair, wearing blue clothes, playing
guitar, a beautiful field of flowers, colorful flowers everywhere, hills in
the background
output:
url: images/ec9a40eed46e8d17d3db1560a6543c6e6be9ebe1e41ecd5d137c01e0.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# FLUX.1-dev-LoRA-AntiBlur
This is a functional LoRA trained on FLUX.1-dev for deep DoF (Anti-Blur🔥) by [Vadim_Fedenko](https://www.shakker.ai/userpage/1f90018d803d4045b8dec4d627915098/publish) on [Shakker AI](https://www.shakker.ai/modelinfo/5c3fa3f1d5034e63be325196eae0b4f6?from=search).
It may not be fancy, but it works.
<div class="container">
<img src="./poster.jpg" width="1024"/>
</div>
<!-- ## Showcases
<Gallery /> -->
## Comparison
The following example shows a simple comparison with FLUX.1-dev under the same parameter setting.
<div class="container">
<img src="./compare1.png" width="1024"/>
</div>
It is worth noting that this LoRA has very little damage to image quality while enhancing the depth of field, and can be used together with other components, such as ControlNet. We regard it as a basic functional LoRA.
<div class="container">
<img src="./compare2.png" width="1024"/>
</div>
## Trigger words
The trigger word is not required. The recommended scale is `1.0` to `1.5` in diffusers.
## Inference
```python
import torch
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.load_lora_weights("Shakker-Labs/FLUX.1-dev-LoRA-AntiBlur", weight_name="FLUX-dev-lora-AntiBlur.safetensors")
pipe.fuse_lora(lora_scale=1.5)
pipe.to("cuda")
prompt = "a young college student, walking on the street, campus background, photography"
image = pipe(prompt,
num_inference_steps=24,
guidance_scale=3.5,
width=768, height=1024,
).images[0]
image.save(f"example.png")
```
## Online Inference
You can also run this model at [Shakker AI](https://www.shakker.ai/modelinfo/5c3fa3f1d5034e63be325196eae0b4f6?from=search), where we provide an online interface to generate images.
## Acknowledgements
This model is trained by our copyrighted users [Vadim_Fedenko](https://www.shakker.ai/userpage/1f90018d803d4045b8dec4d627915098/publish). We release this model under permissions. The model follows [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
|
toilaluan/latent-lm-vae-z6-decoder
|
toilaluan
| 2025-03-20T04:03:26Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-20T02:40:06Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **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]
|
toilaluan/latent-lm-vae-z6-encoder
|
toilaluan
| 2025-03-20T04:03:19Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-20T02:39:39Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF
|
mradermacher
| 2025-03-20T04:00:18Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:dutti/UnslopNemo-Mag-Mell_T-1",
"base_model:quantized:dutti/UnslopNemo-Mag-Mell_T-1",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-03-20T00:47:54Z
|
---
base_model: dutti/UnslopNemo-Mag-Mell_T-1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/dutti/UnslopNemo-Mag-Mell_T-1
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-Q4_1.gguf) | i1-Q4_1 | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
locuslab/base-smollm2-1.7b-score0_baseline20p_then_mix_rephrase123_with_mild_refusal45_metadata_5p-600B
|
locuslab
| 2025-03-20T03:59:19Z
| 0
| 0
| null |
[
"pytorch",
"llama",
"model",
"transformer",
"smollm2",
"license:mit",
"region:us"
] | null | 2025-03-20T03:28:59Z
|
---
version: main
family: smollm2-1.7b
model_name: score0_baseline20p_then_mix_rephrase123_with_mild_refusal45_metadata_5p-600B
license: mit
tags:
- model
- transformer
- smollm2
---
# SmolLM2 score0_baseline20p_then_mix_rephrase123_with_mild_refusal45_metadata_5p-600B (Version: main)
## Model Details
- **Architecture:** SmolLM2
- **Parameters:** 1.7B
## Training Configuration
```yaml
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: 0.0005
weight_decay: 0.01
precision: bf16-mixed
seed: 42
train:
global_batch_size: 1024
max_seq_length: 2048
max_tokens: 600000000000
micro_batch_size: 8
```
## Model Loading and Revision System
This repository hosts multiple revisions of the model.
To load a specific revision, use the `revision` parameter. For example:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("locuslab/score0_baseline20p_then_mix_rephrase123_with_mild_refusal45_metadata_5p-600B", revision="final")
tokenizer = AutoTokenizer.from_pretrained("locuslab/score0_baseline20p_then_mix_rephrase123_with_mild_refusal45_metadata_5p-600B", revision="final")
```
Replace `"final"` with the desired revision.
|
pasukka/detail-classifier-new-app-v.10
|
pasukka
| 2025-03-20T03:58:19Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-20T03:30:50Z
|
---
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]
|
SHEN0829/whisper-turbo_fine_tune1
|
SHEN0829
| 2025-03-20T03:53:29Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"zh",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-large-v3-turbo",
"base_model:finetune:openai/whisper-large-v3-turbo",
"license:mit",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-03-20T02:29:32Z
|
---
library_name: transformers
language:
- zh
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: whisper-turbo_fine_tune
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. -->
# whisper-turbo_fine_tune
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2246
- Cer: 12.4782
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1535 | 1.4184 | 1000 | 0.2609 | 13.4480 |
| 0.0729 | 2.8369 | 2000 | 0.2373 | 12.2139 |
| 0.0202 | 4.2553 | 3000 | 0.2397 | 13.2842 |
| 0.0079 | 5.6738 | 4000 | 0.2266 | 9.7511 |
| 0.001 | 7.0922 | 5000 | 0.2246 | 12.4782 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0
|
StrangeSX/NNN-BNFT-32-004-fnec
|
StrangeSX
| 2025-03-20T03:52:17Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"camembert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-03-20T03:51:30Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **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]
|
Romain-XV/b44d81d1-acf0-4a71-bad8-cb1bbcca529e
|
Romain-XV
| 2025-03-20T03:49:16Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-3B-Instruct",
"license:other",
"region:us"
] | null | 2025-03-20T01:27:09Z
|
---
library_name: peft
license: other
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b44d81d1-acf0-4a71-bad8-cb1bbcca529e
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
adapter: lora
base_model: Qwen/Qwen2.5-3B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- d07706475c9111d1_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d07706475c9111d1_train_data.json
type:
field_input: text
field_instruction: messages
field_output: tools
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Romain-XV/b44d81d1-acf0-4a71-bad8-cb1bbcca529e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.00025
load_best_model_at_end: true
load_in_4bit: false
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
lora_target_modules:
- q_proj
- k_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 2958
micro_batch_size: 4
mlflow_experiment_name: /tmp/d07706475c9111d1_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
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: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.044705124995529484
wandb_entity: null
wandb_mode: online
wandb_name: 4700666c-d716-4c84-a87b-c76fa5df3349
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 4700666c-d716-4c84-a87b-c76fa5df3349
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# b44d81d1-acf0-4a71-bad8-cb1bbcca529e
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00025
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- 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: 2958
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4272 | 0.0003 | 1 | 0.7962 |
| 0.0013 | 0.0300 | 100 | 0.0008 |
| 0.0 | 0.0599 | 200 | 0.0001 |
| 0.0 | 0.0899 | 300 | 0.0000 |
| 0.0012 | 0.1198 | 400 | 0.0000 |
| 0.0 | 0.1498 | 500 | 0.0002 |
| 0.0 | 0.1797 | 600 | 0.0000 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mradermacher/UnslopNemo-Mag-Mell_T-1-GGUF
|
mradermacher
| 2025-03-20T03:47:41Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:dutti/UnslopNemo-Mag-Mell_T-1",
"base_model:quantized:dutti/UnslopNemo-Mag-Mell_T-1",
"endpoints_compatible",
"region:us"
] | null | 2025-03-19T23:46:35Z
|
---
base_model: dutti/UnslopNemo-Mag-Mell_T-1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/dutti/UnslopNemo-Mag-Mell_T-1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/UnslopNemo-Mag-Mell_T-1-GGUF/resolve/main/UnslopNemo-Mag-Mell_T-1.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Alphatao/dd25756c-6a8d-4ec0-b8a1-b1f456f6a333
|
Alphatao
| 2025-03-20T03:45:42Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-0.5B-Instruct",
"base_model:adapter:unsloth/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-03-19T22:39:16Z
|
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: dd25756c-6a8d-4ec0-b8a1-b1f456f6a333
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
adapter: lora
base_model: unsloth/Qwen2-0.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 19fd35b02e02d35a_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/19fd35b02e02d35a_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
device_map:
? ''
: 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/dd25756c-6a8d-4ec0-b8a1-b1f456f6a333
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 8832
micro_batch_size: 4
mlflow_experiment_name: /tmp/19fd35b02e02d35a_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
save_steps: 100
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.044897409419476494
wandb_entity: null
wandb_mode: online
wandb_name: b3cd6cf2-8402-4373-a1f6-7aa530c7ed80
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b3cd6cf2-8402-4373-a1f6-7aa530c7ed80
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# dd25756c-6a8d-4ec0-b8a1-b1f456f6a333
This model is a fine-tuned version of [unsloth/Qwen2-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9745
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- 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: 6648
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.5935 | 0.0003 | 1 | 2.7137 |
| 2.4756 | 0.0301 | 100 | 2.4414 |
| 2.417 | 0.0602 | 200 | 2.3848 |
| 2.1331 | 0.0903 | 300 | 2.3462 |
| 2.0593 | 0.1203 | 400 | 2.3140 |
| 2.0063 | 0.1504 | 500 | 2.2910 |
| 2.4628 | 0.1805 | 600 | 2.2694 |
| 2.2041 | 0.2106 | 700 | 2.2530 |
| 2.4011 | 0.2407 | 800 | 2.2392 |
| 2.2987 | 0.2708 | 900 | 2.2226 |
| 2.199 | 0.3008 | 1000 | 2.2099 |
| 2.2245 | 0.3309 | 1100 | 2.1960 |
| 2.375 | 0.3610 | 1200 | 2.1850 |
| 2.2182 | 0.3911 | 1300 | 2.1771 |
| 2.3893 | 0.4212 | 1400 | 2.1658 |
| 2.1014 | 0.4513 | 1500 | 2.1578 |
| 2.1474 | 0.4813 | 1600 | 2.1484 |
| 2.4473 | 0.5114 | 1700 | 2.1396 |
| 1.9483 | 0.5415 | 1800 | 2.1326 |
| 2.1937 | 0.5716 | 1900 | 2.1209 |
| 2.2298 | 0.6017 | 2000 | 2.1139 |
| 2.1117 | 0.6318 | 2100 | 2.1069 |
| 2.2471 | 0.6619 | 2200 | 2.0990 |
| 2.1825 | 0.6919 | 2300 | 2.0947 |
| 2.1731 | 0.7220 | 2400 | 2.0892 |
| 1.8862 | 0.7521 | 2500 | 2.0825 |
| 2.1224 | 0.7822 | 2600 | 2.0744 |
| 1.9015 | 0.8123 | 2700 | 2.0710 |
| 2.103 | 0.8424 | 2800 | 2.0637 |
| 2.0056 | 0.8724 | 2900 | 2.0575 |
| 1.8938 | 0.9025 | 3000 | 2.0523 |
| 2.1503 | 0.9326 | 3100 | 2.0460 |
| 2.2166 | 0.9627 | 3200 | 2.0415 |
| 2.1761 | 0.9928 | 3300 | 2.0358 |
| 1.9747 | 1.0229 | 3400 | 2.0398 |
| 1.6468 | 1.0529 | 3500 | 2.0353 |
| 1.7083 | 1.0830 | 3600 | 2.0323 |
| 1.9831 | 1.1131 | 3700 | 2.0292 |
| 1.8527 | 1.1432 | 3800 | 2.0236 |
| 1.9907 | 1.1733 | 3900 | 2.0209 |
| 1.9898 | 1.2034 | 4000 | 2.0193 |
| 1.9063 | 1.2335 | 4100 | 2.0153 |
| 1.674 | 1.2635 | 4200 | 2.0101 |
| 1.7583 | 1.2936 | 4300 | 2.0083 |
| 2.076 | 1.3237 | 4400 | 2.0045 |
| 1.92 | 1.3538 | 4500 | 2.0034 |
| 2.0666 | 1.3839 | 4600 | 1.9988 |
| 1.8152 | 1.4140 | 4700 | 1.9958 |
| 1.6996 | 1.4440 | 4800 | 1.9938 |
| 1.7863 | 1.4741 | 4900 | 1.9926 |
| 1.9677 | 1.5042 | 5000 | 1.9888 |
| 1.9768 | 1.5343 | 5100 | 1.9879 |
| 1.7981 | 1.5644 | 5200 | 1.9857 |
| 1.7892 | 1.5945 | 5300 | 1.9841 |
| 1.8826 | 1.6245 | 5400 | 1.9830 |
| 1.8107 | 1.6546 | 5500 | 1.9810 |
| 2.01 | 1.6847 | 5600 | 1.9790 |
| 1.789 | 1.7148 | 5700 | 1.9787 |
| 1.6017 | 1.7449 | 5800 | 1.9773 |
| 1.8574 | 1.7750 | 5900 | 1.9767 |
| 1.695 | 1.8051 | 6000 | 1.9758 |
| 1.8974 | 1.8351 | 6100 | 1.9752 |
| 1.7432 | 1.8652 | 6200 | 1.9752 |
| 1.7931 | 1.8953 | 6300 | 1.9748 |
| 1.9937 | 1.9254 | 6400 | 1.9747 |
| 2.2055 | 1.9555 | 6500 | 1.9746 |
| 1.8637 | 1.9856 | 6600 | 1.9745 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
maanasharma5/dialect-debiasing-gpt2-medium-translated-pnlogmse-e1-r5_eval-n10.0
|
maanasharma5
| 2025-03-20T03:41:37Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"gpt2",
"arxiv:1910.09700",
"base_model:openai-community/gpt2-medium",
"base_model:adapter:openai-community/gpt2-medium",
"region:us"
] | null | 2025-03-20T03:41:23Z
|
---
base_model: gpt2-medium
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.13.2
|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OnlineIPO1-0317153039-epoch-6
|
vectorzhou
| 2025-03-20T03:40:22Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"fine-tuned",
"trl",
"extra-gradient",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2503.08942",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-20T03:37:18Z
|
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OnlineIPO1
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- extra-gradient
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OnlineIPO1
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OnlineIPO1-0317153039-epoch-6", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/zhourunlongvector/nlhf/runs/oo2oec73)
This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942).
### Framework versions
- TRL: 0.13.0
- Transformers: 4.48.0
- Pytorch: 2.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite Extragradient as:
```bibtex
@misc{zhou2025extragradientpreferenceoptimizationegpo,
title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback},
author={Runlong Zhou and Maryam Fazel and Simon S. Du},
year={2025},
eprint={2503.08942},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.08942},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
LYang123/deepseek_talk_model
|
LYang123
| 2025-03-20T03:39:45Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T03:38:40Z
|
---
base_model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** LYang123
- **License:** apache-2.0
- **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
maanasharma5/dialect-debiasing-gpt2-medium-translated-pnlogmse-e1-r100_eval-n10.0
|
maanasharma5
| 2025-03-20T03:37:57Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"gpt2",
"arxiv:1910.09700",
"base_model:openai-community/gpt2-medium",
"base_model:adapter:openai-community/gpt2-medium",
"region:us"
] | null | 2025-03-20T03:37:54Z
|
---
base_model: gpt2-medium
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.13.2
|
Willy030125/command-r7b-12-2024-gguf
|
Willy030125
| 2025-03-20T03:37:06Z
| 0
| 0
| null |
[
"gguf",
"base_model:CohereForAI/c4ai-command-r7b-12-2024",
"base_model:quantized:CohereForAI/c4ai-command-r7b-12-2024",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-20T01:52:02Z
|
---
license: cc-by-nc-4.0
base_model:
- CohereForAI/c4ai-command-r7b-12-2024
---
Quantized from model: <a href="https://huggingface.co/CohereForAI/c4ai-command-r7b-12-2024">CohereForAI/c4ai-command-r7b-12-2024</a>
The model was quantized to GGUF format using these:
- Model loaded with Transformers: v4.48.3
- Converted to gguf with Transformers: v4.49.0 (from requirements.txt llama.cpp)
- Llama.cpp commit: <a href="https://github.com/ggml-org/llama.cpp/tree/7841fc723e059d1fd9640e5c0ef19050fcc7c698">@7841fc7</a> (Compatible with Llama-cpp-python v0.3.8)
|
texanrangee/64ebb989-b179-4307-8c07-7f577d282c1c
|
texanrangee
| 2025-03-20T03:36:09Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-19T23:23:37Z
|
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
AJosh/emotion
|
AJosh
| 2025-03-20T03:33:20Z
| 0
| 0
| null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-03-20T03:26:50Z
|
---
license: apache-2.0
---
|
channudam/unet2dcon-khm-35
|
channudam
| 2025-03-20T03:29:35Z
| 13
| 0
|
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"km",
"license:mit",
"region:us"
] |
text-to-image
| 2025-03-18T06:34:07Z
|
---
license: mit
library_name: diffusers
language:
- km
pipeline_tag: text-to-image
---
# Welcome to Khmer Text Image Generation!
This model is based on UNet2DConditional and is designed to generate Khmer text images.
## Model Overview
This model is a conditional text-to-image generation model, meaning it requires text input encoded using the <b> channudam/roberta-khm-35</b> tokenizer and encoder which is available in this collection. The model was trained from scratch without any pre-trained initialization, ensuring that it learns Khmer text generation from the ground up.
## Usage & Fine-Tuning
For optimal performance, fine-tuning on your own dataset is recommended. The model serves as a foundational framework that can be further refined for specific downstream tasks.
## Dataset
The dataset used for training is publicly available on Kaggle
<br>🔗 Khmer Text Recognition Dataset: https://www.kaggle.com/datasets/emhengly/khmer-text-recognition-dataset/data</br>
## Example Usage
To generate Khmer text images using the **UNet2DConditional** model, use the following example:
```python
import torch
import matplotlib.pylab as plt
from diffusers import UNet2DConditionModel, DDPMScheduler
from transformers import RobertaTokenizerFast, RobertaModel
# Load the UNet model and tokenizer
model = UNet2DConditionModel.from_pretrained("channudam/unet2dcon-khm-35").to("cuda")
tokenizer = RobertaTokenizerFast.from_pretrained("channudam/roberta-khm-35")
text_encoder = RobertaModel.from_pretrained("channudam/roberta-khm-35").to("cuda")
# Load the DDPM scheduler
scheduler = DDPMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
num_train_timesteps=1000,
)
# Generate random noise for image generation
batch_size = 1
image_width, image_height, channels = 64, 32, 1
# Set manual seed for reproducibility
generator = torch.Generator(device="cuda").manual_seed(42)
latents = torch.randn((batch_size, channels, image_height, image_width), device="cuda", generator=generator)
# Encode input text
text = "តោះទៅ" # Example Khmer text
input_ids = tokenizer(text, max_length=35, padding="max_length", truncation=True, return_tensors="pt")['input_ids'].to("cuda")
encoder_hidden_states = text_encoder(input_ids)[0]
# Denoising loop
scheduler.set_timesteps(50)
for t in scheduler.timesteps:
with torch.no_grad():
noise_pred = model(latents, t, encoder_hidden_states)[0]
latents = scheduler.step(noise_pred, t, latents).prev_sample
# Display results
print("Encoded Text: ", input_ids)
print("Decoded Text: ", tokenizer.batch_decode(input_ids))
print("Text Embedding Shape: ", encoder_hidden_states.shape)
# Convert latents to image
plt.imshow(((latents[0].permute(1, 2, 0) + 1.0) * 127.5).cpu().type(torch.uint8).numpy(), cmap="gray")
plt.axis("off")
plt.show()
```
|
lilelife/SyntheOcc
|
lilelife
| 2025-03-20T03:28:46Z
| 0
| 1
|
diffusers
|
[
"diffusers",
"safetensors",
"image-to-image",
"arxiv:2410.00337",
"region:us"
] |
image-to-image
| 2024-10-02T13:19:19Z
|
---
pipeline_tag: image-to-image
---
# SyntheOcc
> SyntheOcc: Synthesize Geometric-Controlled Street View Images through 3D Semantic MPIs <br>
> [Leheng Li](https://len-li.github.io), Weichao Qiu, Yingjie Cai, Xu Yan, Qing Lian, Bingbing Liu, Ying-Cong Chen
SyntheOcc is a project focused on synthesizing image data under geometry control (occupancy voxel). This repository provides tools and scripts to process, train, and generate synthetic image data in the nuScenes dataset, using occupancy control.
#### [Project Page](https://len-li.github.io/syntheocc-web) | [Paper](https://huggingface.co/papers/2410.00337) | [Video](https://len-li.github.io/syntheocc-web/videos/teaser-occedit.mp4) | [Checkpoint](https://huggingface.co/lilelife/SyntheOcc)
Code: https://github.com/EnVision-Research/SyntheOcc
## Table of Contents
- [Installation](#installation)
- [Prepare Dataset](#prepare-dataset)
- [Prepare Checkpoint](#prepare-checkpoint)
- [Train](#train)
- [Inference](#inference)
## Installation
To get started with SyntheOcc, follow these steps:
1. **Clone the repository:**
```bash
git clone https://github.com/Len-Li/SyntheOcc.git
cd SyntheOcc
```
2. **Set up a environment :**
```bash
pip install torch torchvision transformers
pip install diffusers==0.26.0.dev0
# We use a old version of diffusers, please take care of it.
```
## Prepare Dataset
To use SyntheOcc, follow the steps below:
1. **Download the NuScenes dataset:**
- Register and download the dataset from the [NuScenes website](https://www.nuscenes.org/nuscenes).
- Download the [train](https://github.com/JeffWang987/OpenOccupancy/releases/tag/train_pkl)/[val](https://github.com/JeffWang987/OpenOccupancy/releases/tag/val_pkl) pickle files from OpenOccupancy and put them in `data/nuscenes` folder.
After preparation, you will be able to see the following directory structure:
```
SyntheOcc/
├── data/
│ ├── nuscenes/
│ │ ├── samples/
│ │ ├── sweeps/
| | ├── v1.0-trainval/
| | ├── nuscenes_occ_infos_train.pkl
| | ├── nuscenes_occ_infos_val.pkl
```
2. **Download occupancy annotation from [SurroundOcc](https://github.com/weiyithu/SurroundOcc/blob/main/docs/data.md)**
You need to generate the high resolution 0.2m occupancy from mesh vertices and put them in `data/nuscenes` folder.
You can also download the 0.5m occupancy. The precision may be limited compared with 0.2m.
3. **Run the script to convert occupancy to 3D semantic multiplane images:**
```bash
torchrun utils/gen_mtp.py
```
It will generate the 3D semantic MPI and save them in `data/nuscenes/samples_syntheocc_surocc/` folder.
## Prepare Checkpoint
Our model is based on [stable-diffusion-v2-1](https://huggingface.co/stabilityai/stable-diffusion-v2-1). Please put them at `./SyntheOcc/ckp/`.
Our checkpoint of SyntheOcc is released in [huggingface](https://huggingface.co/lilelife/SyntheOcc). If you want to use our model to run inference. Please also put them at `./SyntheOcc/ckp/`.
## Train
```bash
bash train.sh
```
The details of the script are as follows:
```bash
export WANDB_DISABLED=True
export HF_HUB_OFFLINE=True
export MODEL_DIR="./ckp/stable-diffusion-v2-1"
export EXP_NAME="train_syntheocc"
export OUTPUT_DIR="./ckp/$EXP_NAME"
export SAVE_IMG_DIR="vis_dir/$EXP_NAME/samples"
export DATA_USED="samples_syntheocc_surocc"
accelerate launch --gpu_ids 0, --num_processes 1 --main_process_port 3226 train.py \
--pretrained_model_name_or_path=$MODEL_DIR \
--output_dir=$OUTPUT_DIR \
--width=800 \
--height=448 \
--learning_rate=2e-5 \
--num_train_epochs=6 \
--train_batch_size=1 \
--mixed_precision="fp16" \
--num_validation_images=2 \
--validation_steps=1000 \
--checkpointing_steps=5000 \
--checkpoints_total_limit=10 \
--ctrl_channel=257 \
--enable_xformers_memory_efficient_attention \
--report_to='wandb' \
--use_cbgs=True \
--mtp_path='samples_syntheocc_surocc' \
--resume_from_checkpoint="latest"
```
The training process will take 1~2 days to complete, depending on the hardware. We use a fixed batchsize=1, image resolution = (800, 448), which will take 25GB memory for each GPU.
## Inference
```bash
bash infer.sh
```
You will find generated images at `./ckp/$EXP_NAME/samples`. The image is shown as follows:
## Acknowledgment
Additionally, we express our gratitude to the authors of the following opensource projects:
- [SurroundOcc](https://github.com/weiyithu/SurroundOcc) (Occupancy annotation)
- [OpenOccupancy](https://github.com/JeffWang987/OpenOccupancy) (Occupancy annotation)
- [MagicDrive](https://github.com/cure-lab/MagicDrive) (Cross-view and cross-frame attention implementation)
- [Diffusers controlnet example](https://github.com/huggingface/diffusers/tree/main/examples/controlnet) (Diffusion model implementation)
## BibTeX
```bibtex
@inproceedings{li2024SyntheOcc,
title={SyntheOcc: Synthesize Geometric Controlled Street View Images through 3D Semantic MPIs},
author={Li, Leheng and Qiu, Weichao and Chen, Ying-Cong et.al.},
booktitle={arxiv preprint},
year={2024}
}
```
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
license: mit
---
|
drmcbride/l3-test-3b-Q8_0-GGUF
|
drmcbride
| 2025-03-20T03:25:31Z
| 0
| 0
| null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:drmcbride/l3-test-3b",
"base_model:quantized:drmcbride/l3-test-3b",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T03:25:15Z
|
---
base_model: drmcbride/l3-test-3b
tags:
- llama-cpp
- gguf-my-repo
---
# drmcbride/l3-test-3b-Q8_0-GGUF
This model was converted to GGUF format from [`drmcbride/l3-test-3b`](https://huggingface.co/drmcbride/l3-test-3b) 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/drmcbride/l3-test-3b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo drmcbride/l3-test-3b-Q8_0-GGUF --hf-file l3-test-3b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo drmcbride/l3-test-3b-Q8_0-GGUF --hf-file l3-test-3b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo drmcbride/l3-test-3b-Q8_0-GGUF --hf-file l3-test-3b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo drmcbride/l3-test-3b-Q8_0-GGUF --hf-file l3-test-3b-q8_0.gguf -c 2048
```
|
mlfoundations-dev/global_batchsize_1024_laradjusted2
|
mlfoundations-dev
| 2025-03-20T03:24:34Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-18T19:13:45Z
|
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: global_batchsize_1024_laradjusted2
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. -->
# global_batchsize_1024_laradjusted2
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the open-thoughts/OpenThoughts-114k 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: 0.000226274
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 32
- total_train_batch_size: 1024
- total_eval_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
mlfoundations-dev/global_batchsize_1024_laradjusted8
|
mlfoundations-dev
| 2025-03-20T03:19:59Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-18T19:14:28Z
|
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: global_batchsize_1024_laradjusted8
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. -->
# global_batchsize_1024_laradjusted8
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the open-thoughts/OpenThoughts-114k 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: 0.00011313708
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 32
- total_train_batch_size: 1024
- total_eval_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
knguyennguyen/Qwen2-VietMed-base
|
knguyennguyen
| 2025-03-20T03:16:29Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T03:13:16Z
|
---
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]
|
raihanp/business-card
|
raihanp
| 2025-03-20T03:13:16Z
| 11
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:cahya/xlm-roberta-base-indonesian-NER",
"base_model:finetune:cahya/xlm-roberta-base-indonesian-NER",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-03-18T01:26:38Z
|
---
library_name: transformers
base_model: cahya/xlm-roberta-base-indonesian-NER
tags:
- generated_from_trainer
model-index:
- name: business-card
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. -->
# business-card
This model is a fine-tuned version of [cahya/xlm-roberta-base-indonesian-NER](https://huggingface.co/cahya/xlm-roberta-base-indonesian-NER) 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 12
- 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_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
VPTQ-community/Qwen2.5-14B-Instruct-v8-k65536-256-woft
|
VPTQ-community
| 2025-03-20T03:12:57Z
| 20
| 0
| null |
[
"safetensors",
"qwen2",
"VPTQ",
"Quantized",
"Quantization",
"arxiv:2409.17066",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-14B-Instruct",
"license:other",
"vptq",
"region:us"
] | null | 2024-09-28T15:38:40Z
|
---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct/blob/main/LICENSE
base_model:
- Qwen/Qwen2.5-14B-Instruct
base_model_relation: quantized
tags:
- VPTQ
- Quantized
- Quantization
---
**Disclaimer**:
The model is reproduced based on the paper *VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models* [github](https://github.com/microsoft/vptq) and [arXiv](https://arxiv.org/abs/2409.17066)
The model itself is sourced from a community release.
It is intended only for experimental purposes.
Users are responsible for any consequences arising from the use of this model.
**Note**:
The PPL test results are for reference only and were collected using GPTQ testing script.
```json
{
"ctx_2048": {
"wikitext2": 6.457276344299316
},
"ctx_4096": {
"wikitext2": 5.975520610809326
},
"ctx_8192": {
"wikitext2": 5.70115327835083
}
}
```
|
VPTQ-community/Qwen2.5-14B-Instruct-v8-k65536-65536-woft
|
VPTQ-community
| 2025-03-20T03:12:33Z
| 8
| 0
| null |
[
"safetensors",
"qwen2",
"VPTQ",
"Quantized",
"Quantization",
"arxiv:2409.17066",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-14B-Instruct",
"license:other",
"vptq",
"region:us"
] | null | 2024-09-28T15:42:31Z
|
---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct/blob/main/LICENSE
base_model:
- Qwen/Qwen2.5-14B-Instruct
base_model_relation: quantized
tags:
- VPTQ
- Quantized
- Quantization
---
**Disclaimer**:
The model is reproduced based on the paper *VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models* [github](https://github.com/microsoft/vptq) and [arXiv](https://arxiv.org/abs/2409.17066)
The model itself is sourced from a community release.
It is intended only for experimental purposes.
Users are responsible for any consequences arising from the use of this model.
**Note**:
The PPL test results are for reference only and were collected using GPTQ testing script.
```json
{
"ctx_2048": {
"wikitext2": 5.8772149085998535
},
"ctx_4096": {
"wikitext2": 5.4326276779174805
},
"ctx_8192": {
"wikitext2": 5.163432598114014
}
}
```
|
VPTQ-community/Qwen2.5-14B-Instruct-v8-k65536-0-woft
|
VPTQ-community
| 2025-03-20T03:11:44Z
| 12
| 0
| null |
[
"safetensors",
"qwen2",
"VPTQ",
"Quantized",
"Quantization",
"arxiv:2409.17066",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-14B-Instruct",
"license:other",
"vptq",
"region:us"
] | null | 2024-09-28T15:40:33Z
|
---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct/blob/main/LICENSE
base_model:
- Qwen/Qwen2.5-14B-Instruct
base_model_relation: quantized
tags:
- VPTQ
- Quantized
- Quantization
---
**Disclaimer**:
The model is reproduced based on the paper *VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models* [github](https://github.com/microsoft/vptq) and [arXiv](https://arxiv.org/abs/2409.17066)
The model itself is sourced from a community release.
It is intended only for experimental purposes.
Users are responsible for any consequences arising from the use of this model.
**Note**:
The PPL test results are for reference only and were collected using GPTQ testing script.
```json
{
"ctx_2048": {
"wikitext2": 8.052566528320312
},
"ctx_4096": {
"wikitext2": 7.470157146453857
},
"ctx_8192": {
"wikitext2": 7.160165786743164
}
}
```
|
VPTQ-community/Meta-Llama-3.1-8B-Instruct-v8-k65536-256-woft
|
VPTQ-community
| 2025-03-20T03:10:44Z
| 110
| 0
| null |
[
"safetensors",
"llama",
"VPTQ",
"Quantized",
"Quantization",
"arxiv:2409.17066",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"vptq",
"region:us"
] | null | 2024-09-24T05:11:28Z
|
---
license: llama3.1
base_model:
- meta-llama/Llama-3.1-8B-Instruct
base_model_relation: quantized
tags:
- VPTQ
- Quantized
- Quantization
---
**Disclaimer**:
The model is reproduced based on the paper *VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models* [github](https://github.com/microsoft/vptq) and [arXiv](https://arxiv.org/abs/2409.17066)
The model itself is sourced from a community release.
It is intended only for experimental purposes.
Users are responsible for any consequences arising from the use of this model.
**Note**:
The PPL test results are for reference only and were collected using GPTQ testing script.
```json
{
"ctx_2048": {
"wikitext2": 8.166712760925293
},
"ctx_4096": {
"wikitext2": 7.6312713623046875
},
"ctx_8192": {
"wikitext2": 7.3152079582214355
}
}
```
|
VPTQ-community/Qwen2.5-7B-Instruct-v8-k65536-0-woft
|
VPTQ-community
| 2025-03-20T03:09:50Z
| 34
| 0
| null |
[
"safetensors",
"qwen2",
"VPTQ",
"Quantized",
"Quantization",
"arxiv:2409.17066",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"vptq",
"region:us"
] | null | 2024-09-29T02:16:40Z
|
---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
base_model:
- Qwen/Qwen2.5-7B-Instruct
base_model_relation: quantized
tags:
- VPTQ
- Quantized
- Quantization
---
**Disclaimer**:
The model is reproduced based on the paper *VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models* [github](https://github.com/microsoft/vptq) and [arXiv](https://arxiv.org/abs/2409.17066)
The model itself is sourced from a community release.
It is intended only for experimental purposes.
Users are responsible for any consequences arising from the use of this model.
**Note**:
The PPL test results are for reference only and were collected using GPTQ testing script.
```json
{
"ctx_2048": {
"wikitext2": 9.751266479492188
},
"ctx_4096": {
"wikitext2": 9.006874084472656
},
"ctx_8192": {
"wikitext2": 8.547307014465332
}
}
```
|
lesso10/f2dce8bb-d0d4-4cf8-8970-e15aac49f9df
|
lesso10
| 2025-03-20T03:09:28Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-3B-Instruct",
"license:other",
"region:us"
] | null | 2025-03-20T01:28:05Z
|
---
library_name: peft
license: other
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f2dce8bb-d0d4-4cf8-8970-e15aac49f9df
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
adapter: lora
base_model: Qwen/Qwen2.5-3B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- d07706475c9111d1_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d07706475c9111d1_train_data.json
type:
field_input: text
field_instruction: messages
field_output: tools
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso10/f2dce8bb-d0d4-4cf8-8970-e15aac49f9df
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.00021
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/d07706475c9111d1_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 100
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 4700666c-d716-4c84-a87b-c76fa5df3349
wandb_project: 10a
wandb_run: your_name
wandb_runid: 4700666c-d716-4c84-a87b-c76fa5df3349
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f2dce8bb-d0d4-4cf8-8970-e15aac49f9df
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0005
## 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: 0.00021
- train_batch_size: 4
- eval_batch_size: 4
- seed: 100
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0003 | 1 | 7.9294 |
| 0.0004 | 0.1506 | 500 | 0.0005 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso17/bb20785e-7938-4cdd-b069-d9841b1970d9
|
lesso17
| 2025-03-20T03:09:03Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-3B-Instruct",
"license:other",
"region:us"
] | null | 2025-03-20T01:28:16Z
|
---
library_name: peft
license: other
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bb20785e-7938-4cdd-b069-d9841b1970d9
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
adapter: lora
base_model: Qwen/Qwen2.5-3B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- d07706475c9111d1_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d07706475c9111d1_train_data.json
type:
field_input: text
field_instruction: messages
field_output: tools
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso17/bb20785e-7938-4cdd-b069-d9841b1970d9
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000217
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/d07706475c9111d1_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 170
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 4700666c-d716-4c84-a87b-c76fa5df3349
wandb_project: 17a
wandb_run: your_name
wandb_runid: 4700666c-d716-4c84-a87b-c76fa5df3349
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# bb20785e-7938-4cdd-b069-d9841b1970d9
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0005
## 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: 0.000217
- train_batch_size: 4
- eval_batch_size: 4
- seed: 170
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0003 | 1 | 7.9342 |
| 0.002 | 0.1506 | 500 | 0.0005 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
VPTQ-community/Qwen2.5-7B-Instruct-v8-k65536-256-woft
|
VPTQ-community
| 2025-03-20T03:09:00Z
| 23
| 0
| null |
[
"safetensors",
"qwen2",
"VPTQ",
"Quantized",
"Quantization",
"arxiv:2409.17066",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"vptq",
"region:us"
] | null | 2024-09-24T14:50:31Z
|
---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
base_model:
- Qwen/Qwen2.5-7B-Instruct
base_model_relation: quantized
tags:
- VPTQ
- Quantized
- Quantization
---
**Disclaimer**:
The model is reproduced based on the paper *VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models* [github](https://github.com/microsoft/vptq) and [arXiv](https://arxiv.org/abs/2409.17066)
The model itself is sourced from a community release.
It is intended only for experimental purposes.
Users are responsible for any consequences arising from the use of this model.
**Note**:
The PPL test results are for reference only and were collected using GPTQ testing script.
```json
{
"ctx_2048": {
"wikitext2": 7.946412086486816
},
"ctx_4096": {
"wikitext2": 7.310400009155273
},
"ctx_8192": {
"wikitext2": 6.938364028930664
}
}
```
|
VPTQ-community/Qwen2.5-7B-Instruct-v16-k65536-65536-woft
|
VPTQ-community
| 2025-03-20T03:08:28Z
| 30
| 1
| null |
[
"safetensors",
"qwen2",
"VPTQ",
"Quantized",
"Quantization",
"arxiv:2409.17066",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"vptq",
"region:us"
] | null | 2024-09-29T02:11:02Z
|
---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
base_model:
- Qwen/Qwen2.5-7B-Instruct
base_model_relation: quantized
tags:
- VPTQ
- Quantized
- Quantization
---
**Disclaimer**:
The model is reproduced based on the paper *VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models* [github](https://github.com/microsoft/vptq) and [arXiv](https://arxiv.org/abs/2409.17066)
The model itself is sourced from a community release.
It is intended only for experimental purposes.
Users are responsible for any consequences arising from the use of this model.
**Note**:
The PPL test results are for reference only and were collected using GPTQ testing script.
```json
{
"ctx_2048": {
"wikitext2": 9.281352996826172
},
"ctx_4096": {
"wikitext2": 8.55495834350586
},
"ctx_8192": {
"wikitext2": 8.152359962463379
}
}
```
|
mtzig/reverse_add_replicate_eval17_small_1layer
|
mtzig
| 2025-03-20T03:02:44Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"nanogpt",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T02:46:30Z
|
---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: reverse_add_replicate_eval17_small_1layer
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. -->
# reverse_add_replicate_eval17_small_1layer
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5994
- Accuracy: 0.0
## 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: 0.001
- train_batch_size: 128
- eval_batch_size: 128
- seed: 7658372
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| No log | 0 | 0 | 2.6405 | 0.0 |
| 2.6234 | 0.0064 | 100 | 2.6259 | 0.0 |
| 2.577 | 0.0128 | 200 | 2.5785 | 0.0 |
| 2.5307 | 0.0192 | 300 | 2.5300 | 0.0 |
| 2.4899 | 0.0256 | 400 | 2.4878 | 0.0 |
| 2.4573 | 0.032 | 500 | 2.4559 | 0.0 |
| 2.4345 | 0.0384 | 600 | 2.4337 | 0.0 |
| 2.4184 | 0.0448 | 700 | 2.4186 | 0.0 |
| 2.4046 | 0.0512 | 800 | 2.4096 | 0.0 |
| 2.3941 | 0.0576 | 900 | 2.3994 | 0.0 |
| 2.3886 | 0.064 | 1000 | 2.3996 | 0.0 |
| 2.3771 | 0.0704 | 1100 | 2.4505 | 0.0 |
| 2.37 | 0.0768 | 1200 | 2.4449 | 0.0 |
| 2.3755 | 0.0832 | 1300 | 2.4213 | 0.0 |
| 2.3742 | 0.0896 | 1400 | 2.5070 | 0.0 |
| 2.3745 | 0.096 | 1500 | 2.4311 | 0.0 |
| 2.3674 | 0.1024 | 1600 | 2.4830 | 0.0 |
| 2.3656 | 0.1088 | 1700 | 2.4634 | 0.0 |
| 2.3616 | 0.1152 | 1800 | 2.4772 | 0.0 |
| 2.3681 | 0.1216 | 1900 | 2.4977 | 0.0 |
| 2.3728 | 0.128 | 2000 | 2.6562 | 0.0 |
| 2.3677 | 0.1344 | 2100 | 2.4819 | 0.0 |
| 2.3676 | 0.1408 | 2200 | 2.4610 | 0.0 |
| 2.3634 | 0.1472 | 2300 | 2.5009 | 0.0 |
| 2.3705 | 0.1536 | 2400 | 2.4709 | 0.0 |
| 2.3663 | 0.16 | 2500 | 2.4841 | 0.0 |
| 2.3676 | 0.1664 | 2600 | 2.5541 | 0.0 |
| 2.3573 | 0.1728 | 2700 | 2.4714 | 0.0 |
| 2.3642 | 0.1792 | 2800 | 2.4749 | 0.0 |
| 2.3626 | 0.1856 | 2900 | 2.5095 | 0.0 |
| 2.365 | 0.192 | 3000 | 2.5000 | 0.0 |
| 2.3592 | 0.1984 | 3100 | 2.5363 | 0.0 |
| 2.3649 | 0.2048 | 3200 | 2.4799 | 0.0 |
| 2.3576 | 0.2112 | 3300 | 2.4855 | 0.0 |
| 2.3679 | 0.2176 | 3400 | 2.5114 | 0.0 |
| 2.3647 | 0.224 | 3500 | 2.5487 | 0.0 |
| 2.371 | 0.2304 | 3600 | 2.4369 | 0.0 |
| 2.354 | 0.2368 | 3700 | 2.5066 | 0.0 |
| 2.3581 | 0.2432 | 3800 | 2.4871 | 0.0 |
| 2.364 | 0.2496 | 3900 | 2.5979 | 0.0 |
| 2.3597 | 0.256 | 4000 | 2.5254 | 0.0 |
| 2.3675 | 0.2624 | 4100 | 2.5234 | 0.0 |
| 2.3613 | 0.2688 | 4200 | 2.4946 | 0.0 |
| 2.3629 | 0.2752 | 4300 | 2.4694 | 0.0 |
| 2.3609 | 0.2816 | 4400 | 2.4860 | 0.0 |
| 2.355 | 0.288 | 4500 | 2.5495 | 0.0 |
| 2.3633 | 0.2944 | 4600 | 2.5450 | 0.0 |
| 2.3577 | 0.3008 | 4700 | 2.5079 | 0.0 |
| 2.3628 | 0.3072 | 4800 | 2.5156 | 0.0 |
| 2.3549 | 0.3136 | 4900 | 2.4778 | 0.0 |
| 2.3621 | 0.32 | 5000 | 2.5554 | 0.0 |
| 2.3563 | 0.3264 | 5100 | 2.5000 | 0.0 |
| 2.3624 | 0.3328 | 5200 | 2.5690 | 0.0 |
| 2.3563 | 0.3392 | 5300 | 2.4614 | 0.0 |
| 2.3553 | 0.3456 | 5400 | 2.4333 | 0.0 |
| 2.3573 | 0.352 | 5500 | 2.4946 | 0.0 |
| 2.3586 | 0.3584 | 5600 | 2.5507 | 0.0 |
| 2.3608 | 0.3648 | 5700 | 2.5246 | 0.0 |
| 2.3626 | 0.3712 | 5800 | 2.4721 | 0.0 |
| 2.3635 | 0.3776 | 5900 | 2.5269 | 0.0 |
| 2.3555 | 0.384 | 6000 | 2.4758 | 0.0 |
| 2.3607 | 0.3904 | 6100 | 2.5192 | 0.0 |
| 2.3559 | 0.3968 | 6200 | 2.5747 | 0.0 |
| 2.3664 | 0.4032 | 6300 | 2.4620 | 0.0 |
| 2.3604 | 0.4096 | 6400 | 2.5626 | 0.0 |
| 2.3647 | 0.416 | 6500 | 2.5473 | 0.0 |
| 2.3624 | 0.4224 | 6600 | 2.5852 | 0.0 |
| 2.3574 | 0.4288 | 6700 | 2.6200 | 0.0 |
| 2.36 | 0.4352 | 6800 | 2.5269 | 0.0 |
| 2.3557 | 0.4416 | 6900 | 2.5453 | 0.0 |
| 2.3603 | 0.448 | 7000 | 2.5212 | 0.0 |
| 2.3569 | 0.4544 | 7100 | 2.6011 | 0.0 |
| 2.3544 | 0.4608 | 7200 | 2.5631 | 0.0 |
| 2.3613 | 0.4672 | 7300 | 2.5656 | 0.0 |
| 2.3565 | 0.4736 | 7400 | 2.5427 | 0.0 |
| 2.3551 | 0.48 | 7500 | 2.4880 | 0.0 |
| 2.3585 | 0.4864 | 7600 | 2.5707 | 0.0 |
| 2.3576 | 0.4928 | 7700 | 2.5616 | 0.0 |
| 2.3632 | 0.4992 | 7800 | 2.5697 | 0.0 |
| 2.3579 | 0.5056 | 7900 | 2.5803 | 0.0 |
| 2.3593 | 0.512 | 8000 | 2.6355 | 0.0 |
| 2.3604 | 0.5184 | 8100 | 2.5355 | 0.0 |
| 2.3594 | 0.5248 | 8200 | 2.5198 | 0.0 |
| 2.357 | 0.5312 | 8300 | 2.5762 | 0.0 |
| 2.3487 | 0.5376 | 8400 | 2.5462 | 0.0 |
| 2.3652 | 0.544 | 8500 | 2.5878 | 0.0 |
| 2.3549 | 0.5504 | 8600 | 2.5376 | 0.0 |
| 2.3516 | 0.5568 | 8700 | 2.5517 | 0.0 |
| 2.358 | 0.5632 | 8800 | 2.5280 | 0.0 |
| 2.3587 | 0.5696 | 8900 | 2.5489 | 0.0 |
| 2.3646 | 0.576 | 9000 | 2.6044 | 0.0 |
| 2.3549 | 0.5824 | 9100 | 2.5392 | 0.0 |
| 2.3579 | 0.5888 | 9200 | 2.6203 | 0.0 |
| 2.3654 | 0.5952 | 9300 | 2.5952 | 0.0 |
| 2.3657 | 0.6016 | 9400 | 2.5479 | 0.0 |
| 2.3571 | 0.608 | 9500 | 2.5350 | 0.0 |
| 2.3515 | 0.6144 | 9600 | 2.6317 | 0.0 |
| 2.3565 | 0.6208 | 9700 | 2.5772 | 0.0 |
| 2.3534 | 0.6272 | 9800 | 2.6011 | 0.0 |
| 2.3574 | 0.6336 | 9900 | 2.4998 | 0.0 |
| 2.3553 | 0.64 | 10000 | 2.5933 | 0.0 |
| 2.3443 | 0.6464 | 10100 | 2.5925 | 0.0 |
| 2.3581 | 0.6528 | 10200 | 2.6502 | 0.0 |
| 2.3488 | 0.6592 | 10300 | 2.6558 | 0.0 |
| 2.3659 | 0.6656 | 10400 | 2.6271 | 0.0 |
| 2.353 | 0.672 | 10500 | 2.5513 | 0.0 |
| 2.3497 | 0.6784 | 10600 | 2.6017 | 0.0 |
| 2.3573 | 0.6848 | 10700 | 2.5998 | 0.0 |
| 2.3642 | 0.6912 | 10800 | 2.5925 | 0.0 |
| 2.3522 | 0.6976 | 10900 | 2.4902 | 0.0 |
| 2.3543 | 0.704 | 11000 | 2.5761 | 0.0 |
| 2.3538 | 0.7104 | 11100 | 2.5737 | 0.0 |
| 2.3545 | 0.7168 | 11200 | 2.5827 | 0.0 |
| 2.3586 | 0.7232 | 11300 | 2.6190 | 0.0 |
| 2.3575 | 0.7296 | 11400 | 2.5708 | 0.0 |
| 2.3573 | 0.736 | 11500 | 2.5409 | 0.0 |
| 2.3575 | 0.7424 | 11600 | 2.5762 | 0.0 |
| 2.3576 | 0.7488 | 11700 | 2.6299 | 0.0 |
| 2.3487 | 0.7552 | 11800 | 2.5414 | 0.0 |
| 2.3623 | 0.7616 | 11900 | 2.5767 | 0.0 |
| 2.3599 | 0.768 | 12000 | 2.5446 | 0.0 |
| 2.3506 | 0.7744 | 12100 | 2.5832 | 0.0 |
| 2.3546 | 0.7808 | 12200 | 2.5563 | 0.0 |
| 2.3543 | 0.7872 | 12300 | 2.5601 | 0.0 |
| 2.3507 | 0.7936 | 12400 | 2.5719 | 0.0 |
| 2.3524 | 0.8 | 12500 | 2.5835 | 0.0 |
| 2.3447 | 0.8064 | 12600 | 2.5615 | 0.0 |
| 2.3573 | 0.8128 | 12700 | 2.6363 | 0.0 |
| 2.356 | 0.8192 | 12800 | 2.6349 | 0.0 |
| 2.3544 | 0.8256 | 12900 | 2.5914 | 0.0 |
| 2.3638 | 0.832 | 13000 | 2.5714 | 0.0 |
| 2.3591 | 0.8384 | 13100 | 2.6121 | 0.0 |
| 2.3565 | 0.8448 | 13200 | 2.5863 | 0.0 |
| 2.3481 | 0.8512 | 13300 | 2.6126 | 0.0 |
| 2.358 | 0.8576 | 13400 | 2.5951 | 0.0 |
| 2.3518 | 0.864 | 13500 | 2.6111 | 0.0 |
| 2.3445 | 0.8704 | 13600 | 2.6072 | 0.0 |
| 2.3466 | 0.8768 | 13700 | 2.6104 | 0.0 |
| 2.3613 | 0.8832 | 13800 | 2.5829 | 0.0 |
| 2.3506 | 0.8896 | 13900 | 2.6030 | 0.0 |
| 2.3478 | 0.896 | 14000 | 2.5717 | 0.0 |
| 2.3618 | 0.9024 | 14100 | 2.6115 | 0.0 |
| 2.3628 | 0.9088 | 14200 | 2.5984 | 0.0 |
| 2.3504 | 0.9152 | 14300 | 2.6091 | 0.0 |
| 2.3596 | 0.9216 | 14400 | 2.6084 | 0.0 |
| 2.3556 | 0.928 | 14500 | 2.5812 | 0.0 |
| 2.3624 | 0.9344 | 14600 | 2.6058 | 0.0 |
| 2.3564 | 0.9408 | 14700 | 2.5861 | 0.0 |
| 2.3649 | 0.9472 | 14800 | 2.5941 | 0.0 |
| 2.3522 | 0.9536 | 14900 | 2.5955 | 0.0 |
| 2.3436 | 0.96 | 15000 | 2.5882 | 0.0 |
| 2.3552 | 0.9664 | 15100 | 2.6067 | 0.0 |
| 2.3537 | 0.9728 | 15200 | 2.5985 | 0.0 |
| 2.36 | 0.9792 | 15300 | 2.5967 | 0.0 |
| 2.3605 | 0.9856 | 15400 | 2.5998 | 0.0 |
| 2.3544 | 0.992 | 15500 | 2.5996 | 0.0 |
| 2.3535 | 0.9984 | 15600 | 2.5994 | 0.0 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.1
|
heisejiasuo/DyFLUX
|
heisejiasuo
| 2025-03-20T03:01:34Z
| 0
| 0
| null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-03-20T02:33:22Z
|
---
license: apache-2.0
---
|
yusuke111/llm-jp-3-3.7b-databricks-dolly-15k-ja-gozaru
|
yusuke111
| 2025-03-20T03:00:02Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:llm-jp/llm-jp-3-3.7b",
"base_model:finetune:llm-jp/llm-jp-3-3.7b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T02:59:50Z
|
---
base_model: llm-jp/llm-jp-3-3.7b
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** yusuke111
- **License:** apache-2.0
- **Finetuned from model :** llm-jp/llm-jp-3-3.7b
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)
|
bartowski/soob3123_amoral-gemma3-4B-GGUF
|
bartowski
| 2025-03-20T02:59:54Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"gemma3",
"analytical-tasks",
"bias-neutralization",
"uncensored",
"text-generation",
"en",
"base_model:soob3123/amoral-gemma3-4B",
"base_model:quantized:soob3123/amoral-gemma3-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-03-20T02:43:55Z
|
---
quantized_by: bartowski
pipeline_tag: text-generation
license: apache-2.0
base_model_relation: quantized
language:
- en
base_model: soob3123/amoral-gemma3-4B
tags:
- text-generation-inference
- transformers
- gemma3
- analytical-tasks
- bias-neutralization
- uncensored
---
## Llamacpp imatrix Quantizations of amoral-gemma3-4B by soob3123
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b4925">b4925</a> for quantization.
Original model: https://huggingface.co/soob3123/amoral-gemma3-4B
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
```
<bos><start_of_turn>user
{system_prompt}
{prompt}<end_of_turn>
<start_of_turn>model
<end_of_turn>
<start_of_turn>model
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [amoral-gemma3-4B-bf16.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-bf16.gguf) | bf16 | 7.77GB | false | Full BF16 weights. |
| [amoral-gemma3-4B-Q8_0.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q8_0.gguf) | Q8_0 | 4.13GB | false | Extremely high quality, generally unneeded but max available quant. |
| [amoral-gemma3-4B-Q6_K_L.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q6_K_L.gguf) | Q6_K_L | 3.35GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [amoral-gemma3-4B-Q6_K.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q6_K.gguf) | Q6_K | 3.19GB | false | Very high quality, near perfect, *recommended*. |
| [amoral-gemma3-4B-Q5_K_L.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q5_K_L.gguf) | Q5_K_L | 2.99GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [amoral-gemma3-4B-Q5_K_M.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q5_K_M.gguf) | Q5_K_M | 2.83GB | false | High quality, *recommended*. |
| [amoral-gemma3-4B-Q5_K_S.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q5_K_S.gguf) | Q5_K_S | 2.76GB | false | High quality, *recommended*. |
| [amoral-gemma3-4B-Q4_K_L.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q4_K_L.gguf) | Q4_K_L | 2.65GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [amoral-gemma3-4B-Q4_1.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q4_1.gguf) | Q4_1 | 2.56GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [amoral-gemma3-4B-Q4_K_M.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q4_K_M.gguf) | Q4_K_M | 2.49GB | false | Good quality, default size for most use cases, *recommended*. |
| [amoral-gemma3-4B-Q3_K_XL.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q3_K_XL.gguf) | Q3_K_XL | 2.40GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [amoral-gemma3-4B-Q4_K_S.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q4_K_S.gguf) | Q4_K_S | 2.38GB | false | Slightly lower quality with more space savings, *recommended*. |
| [amoral-gemma3-4B-Q4_0.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q4_0.gguf) | Q4_0 | 2.37GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [amoral-gemma3-4B-IQ4_NL.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-IQ4_NL.gguf) | IQ4_NL | 2.36GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [amoral-gemma3-4B-IQ4_XS.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-IQ4_XS.gguf) | IQ4_XS | 2.26GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [amoral-gemma3-4B-Q3_K_L.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q3_K_L.gguf) | Q3_K_L | 2.24GB | false | Lower quality but usable, good for low RAM availability. |
| [amoral-gemma3-4B-Q3_K_M.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q3_K_M.gguf) | Q3_K_M | 2.10GB | false | Low quality. |
| [amoral-gemma3-4B-IQ3_M.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-IQ3_M.gguf) | IQ3_M | 1.99GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [amoral-gemma3-4B-Q3_K_S.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q3_K_S.gguf) | Q3_K_S | 1.94GB | false | Low quality, not recommended. |
| [amoral-gemma3-4B-Q2_K_L.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q2_K_L.gguf) | Q2_K_L | 1.89GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [amoral-gemma3-4B-IQ3_XS.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-IQ3_XS.gguf) | IQ3_XS | 1.86GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [amoral-gemma3-4B-Q2_K.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-Q2_K.gguf) | Q2_K | 1.73GB | false | Very low quality but surprisingly usable. |
| [amoral-gemma3-4B-IQ3_XXS.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-IQ3_XXS.gguf) | IQ3_XXS | 1.69GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [amoral-gemma3-4B-IQ2_M.gguf](https://huggingface.co/bartowski/soob3123_amoral-gemma3-4B-GGUF/blob/main/soob3123_amoral-gemma3-4B-IQ2_M.gguf) | IQ2_M | 1.54GB | false | Relatively low quality, uses SOTA techniques to be surprisingly 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/soob3123_amoral-gemma3-4B-GGUF --include "soob3123_amoral-gemma3-4B-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/soob3123_amoral-gemma3-4B-GGUF --include "soob3123_amoral-gemma3-4B-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (soob3123_amoral-gemma3-4B-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
|
deddyext/mistral-finetuned-nbs
|
deddyext
| 2025-03-20T02:58:58Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T02:58:48Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
maanasharma5/dialect-debiasing-gpt2-medium-translated-pnlogmse-e1-r2_eval-n5.0
|
maanasharma5
| 2025-03-20T02:54:36Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"gpt2",
"arxiv:1910.09700",
"base_model:openai-community/gpt2-medium",
"base_model:adapter:openai-community/gpt2-medium",
"region:us"
] | null | 2025-03-20T02:54:24Z
|
---
base_model: gpt2-medium
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.13.2
|
pasukka/detail-classifier-new-app-v.9
|
pasukka
| 2025-03-20T02:50:55Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-20T02:49:07Z
|
---
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]
|
icefog72/Ice0.95-19.03-RP
|
icefog72
| 2025-03-20T02:50:39Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-19T22:59:12Z
|
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# Ice0.95-19.03-RP
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the Passthrough merge method using H:\FModels\Ice0.80-03.02-RP + E:\FModels\Fog0.01-19.03-RP-lora as a base.
### Models Merged
The following models were included in the merge:
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: H:\FModels\Ice0.80-03.02-RP+E:\FModels\Fog0.01-19.03-RP-lora
dtype: bfloat16
merge_method: passthrough
models:
- model: H:\FModels\Ice0.80-03.02-RP+E:\FModels\Fog0.01-19.03-RP-lora
```
|
TongZheng1999/gemma-2-9b-it-star-mixed_direct-OP-final_v2_10-2-3Rounds-iter-3
|
TongZheng1999
| 2025-03-20T02:50:07Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"alignment-handbook",
"trl",
"sft",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-20T02:23:11Z
|
---
library_name: transformers
model_name: gemma-2-9b-it-star-mixed_direct-OP-final_v2_10-2-3Rounds-iter-3
tags:
- generated_from_trainer
- alignment-handbook
- trl
- sft
licence: license
---
# Model Card for gemma-2-9b-it-star-mixed_direct-OP-final_v2_10-2-3Rounds-iter-3
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="TongZheng1999/gemma-2-9b-it-star-mixed_direct-OP-final_v2_10-2-3Rounds-iter-3", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/kidzheng/huggingface/runs/139sdyhw)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.0
- Pytorch: 2.6.0
- Datasets: 3.3.1
- Tokenizers: 0.20.3
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Es1v/Sentiment_tweets_distilbert
|
Es1v
| 2025-03-20T02:46:22Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-20T02:29:33Z
|
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: Sentiment_tweets_distilbert
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. -->
# Sentiment_tweets_distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1522
- F1: 0.9377
- Acc: 0.9375
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Acc |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.1507 | 1.0 | 500 | 0.1922 | 0.9331 | 0.9325 |
| 0.1156 | 2.0 | 1000 | 0.1507 | 0.9404 | 0.94 |
| 0.0807 | 3.0 | 1500 | 0.1522 | 0.9377 | 0.9375 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
n31e/Dolphin3.0-Llama3.2-3B
|
n31e
| 2025-03-20T02:44:18Z
| 0
| 0
| null |
[
"safetensors",
"text-generation",
"llama",
"fine-tuned",
"en",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-03-20T02:24:17Z
|
---
language: en
license: apache-2.0
tags:
- text-generation
- llama
- fine-tuned
model-index:
- name: Dolphin3.0-Llama3.2-3B
results: []
---
# Dolphin3.0-Llama3.2-3B-finetuned-20250320
## Model Description
This model was created by fine-tuning cognitivecomputations/Dolphin3.0-Llama3.2-3B on the following datasets:
sdiazlor/python-reasoning-dataset, fka/awesome-chatgpt-prompts, THUDM/AgentInstruct, O1-OPEN/OpenO1-SFT
## Training Configuration
- Base model: cognitivecomputations/Dolphin3.0-Llama3.2-3B
- Fine-tuning method: LoRA (r=8, alpha=16)
- Target modules: q_proj, v_proj
- Training date: 2025-03-20
- Learning rate: 0.0001
- Max sequence length: 768
- Training steps: 400
## Example Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("n31e/Dolphin3.0-Llama3.2-3B-finetuned-20250320")
tokenizer = AutoTokenizer.from_pretrained("n31e/Dolphin3.0-Llama3.2-3B-finetuned-20250320")
# Format prompt according to model's expected format
prompt = "<|user|>\nYour prompt here\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
# Generate response
outputs = model.generate(
inputs["input_ids"],
max_length=512,
temperature=0.7,
top_p=0.9,
repetition_penalty=1.2,
do_sample=True,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
|
BlitherBoom/AutoDroid-V2
|
BlitherBoom
| 2025-03-20T02:39:45Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-19T12:21:08Z
|
---
library_name: transformers
license: other
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: AutoDroid-V2
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. -->
# AutoDroid-V2
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on the autodroid dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Use adamw_torch with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.4.0+cu118
- Datasets 3.0.0
- Tokenizers 0.20.3
|
Yasuo2k5/Albert_vn
|
Yasuo2k5
| 2025-03-20T02:39:19Z
| 0
| 0
| null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-03-20T02:39:19Z
|
---
license: apache-2.0
---
|
allen9926/LLM
|
allen9926
| 2025-03-20T02:36:31Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T02:35:39Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
shubingxl/LLM_demo
|
shubingxl
| 2025-03-20T02:36:10Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T02:35:40Z
|
---
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]
|
sitenote/ticker-news-classifier
|
sitenote
| 2025-03-20T02:36:01Z
| 8
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"text-classification",
"deberta-v3",
"en",
"dataset:sitenote/ticker_news_classifier_2",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-12T03:35:39Z
|
---
license: apache-2.0
datasets:
- sitenote/ticker_news_classifier_2
language:
- en
metrics:
- f1
base_model:
- microsoft/deberta-v3-base
tags:
- transformers
- text-classification
- deberta-v3
---
|
tscstudios/tae7fe7eqtstdxmg4wciuxyxmgv2_ffef9a94-4b72-4266-b6de-cf01058cad51
|
tscstudios
| 2025-03-20T02:32:24Z
| 0
| 0
|
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-03-20T02:32:22Z
|
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# Tae7Fe7Eqtstdxmg4Wciuxyxmgv2_Ffef9A94 4B72 4266 B6De Cf01058Cad51
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('tscstudios/tae7fe7eqtstdxmg4wciuxyxmgv2_ffef9a94-4b72-4266-b6de-cf01058cad51', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
zijianh/Qwen-2.5-7B-Simple-RL-length-penalty-low-medium-high
|
zijianh
| 2025-03-20T02:31:59Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-19T11:46:15Z
|
---
library_name: transformers
model_name: Qwen-2.5-7B-Simple-RL-length-penalty-low-medium-high
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-Simple-RL-length-penalty-low-medium-high
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="zijianh/Qwen-2.5-7B-Simple-RL-length-penalty-low-medium-high", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/sota_mavens-university-of-michigan/huggingface/runs/u8ywq0pm)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Hunie07/gemma-3-4b-it-ko
|
Hunie07
| 2025-03-20T02:31:02Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T02:30:36Z
|
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Hunie07
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
redwiggler/gemma-3-4b-it-ko
|
redwiggler
| 2025-03-20T02:30:47Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T02:30:26Z
|
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** redwiggler
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
netcat420/qwen2.5-MFANN-7b-SLERP-V1.3-Q4_K_M-GGUF
|
netcat420
| 2025-03-20T02:29:08Z
| 0
| 0
| null |
[
"gguf",
"merge",
"mergekit",
"lazymergekit",
"huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated",
"netcat420/qwen2.5-MFANN-7b-v1.2",
"llama-cpp",
"gguf-my-repo",
"base_model:netcat420/qwen2.5-MFANN-7b-SLERP-V1.3",
"base_model:quantized:netcat420/qwen2.5-MFANN-7b-SLERP-V1.3",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-20T02:04:33Z
|
---
base_model: netcat420/qwen2.5-MFANN-7b-SLERP-V1.3
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated
- netcat420/qwen2.5-MFANN-7b-v1.2
- llama-cpp
- gguf-my-repo
---
# netcat420/qwen2.5-MFANN-7b-SLERP-V1.3-Q4_K_M-GGUF
This model was converted to GGUF format from [`netcat420/qwen2.5-MFANN-7b-SLERP-V1.3`](https://huggingface.co/netcat420/qwen2.5-MFANN-7b-SLERP-V1.3) 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/netcat420/qwen2.5-MFANN-7b-SLERP-V1.3) 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 netcat420/qwen2.5-MFANN-7b-SLERP-V1.3-Q4_K_M-GGUF --hf-file qwen2.5-mfann-7b-slerp-v1.3-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo netcat420/qwen2.5-MFANN-7b-SLERP-V1.3-Q4_K_M-GGUF --hf-file qwen2.5-mfann-7b-slerp-v1.3-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 netcat420/qwen2.5-MFANN-7b-SLERP-V1.3-Q4_K_M-GGUF --hf-file qwen2.5-mfann-7b-slerp-v1.3-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo netcat420/qwen2.5-MFANN-7b-SLERP-V1.3-Q4_K_M-GGUF --hf-file qwen2.5-mfann-7b-slerp-v1.3-q4_k_m.gguf -c 2048
```
|
ChiHieuNguyen/result
|
ChiHieuNguyen
| 2025-03-20T02:25:28Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:Salesforce/codet5-base",
"base_model:adapter:Salesforce/codet5-base",
"license:apache-2.0",
"region:us"
] | null | 2025-03-19T03:41:29Z
|
---
library_name: peft
license: apache-2.0
base_model: Salesforce/codet5-base
tags:
- generated_from_trainer
model-index:
- name: result
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. -->
# result
This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Salesforce/codet5-base) 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.14.0
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
c00cjz00/gemma-3-12b-it-R1-medical
|
c00cjz00
| 2025-03-20T02:24:39Z
| 0
| 0
|
transformers
|
[
"transformers",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-03-19T19:56:44Z
|
---
base_model: unsloth/gemma-3-12b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** c00cjz00
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-12b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
c00cjz00/gemma-3-4b-it-R1-medical
|
c00cjz00
| 2025-03-20T02:23:53Z
| 0
| 0
|
transformers
|
[
"transformers",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-03-19T17:20:38Z
|
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** c00cjz00
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mstojkov/policy-135-iter1
|
mstojkov
| 2025-03-20T02:22:30Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-20T02:22:04Z
|
---
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]
|
hykiim/results
|
hykiim
| 2025-03-20T02:19:33Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:klue/roberta-base",
"base_model:finetune:klue/roberta-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-20T01:50:34Z
|
---
library_name: transformers
base_model: klue/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4754
- Accuracy: 0.855
## 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: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5332 | 1.0 | 1250 | 0.5110 | 0.846 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.0
- Tokenizers 0.21.1
|
Jamiamonique/wav2vec2-large-xls-r-300m-dm32
|
Jamiamonique
| 2025-03-20T02:14:21Z
| 5
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-02-04T01:29:00Z
|
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-large-xls-r-300m-dm32
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-large-xls-r-300m-dm32
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5965
- Accuracy: 0.7292
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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: 22
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 8.5 | 34 | 0.6786 | 0.5417 |
| No log | 17.0 | 68 | 0.5965 | 0.7292 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.0.1+cu117
- Datasets 3.4.1
- Tokenizers 0.21.1
|
maanasharma5/dialect-debiasing-gpt2-medium-pnlogmse-e1-r2_eval-n10.0
|
maanasharma5
| 2025-03-20T02:10:36Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"gpt2",
"arxiv:1910.09700",
"base_model:openai-community/gpt2-medium",
"base_model:adapter:openai-community/gpt2-medium",
"region:us"
] | null | 2025-03-20T02:10:32Z
|
---
base_model: gpt2-medium
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.13.2
|
hirosuke/xlm-roberta-base-finetuned-panx-de
|
hirosuke
| 2025-03-20T02:10:16Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-03-19T13:35:02Z
|
---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1376
- F1: 0.8644
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2579 | 1.0 | 525 | 0.1546 | 0.8179 |
| 0.1283 | 2.0 | 1050 | 0.1378 | 0.8518 |
| 0.0805 | 3.0 | 1575 | 0.1376 | 0.8644 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cpu
- Datasets 3.3.2
- Tokenizers 0.20.3
|
netcat420/qwen2.5-MFANN-7b-SLERP-V1.3-Q4_K_S-GGUF
|
netcat420
| 2025-03-20T02:07:40Z
| 0
| 0
| null |
[
"gguf",
"merge",
"mergekit",
"lazymergekit",
"huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated",
"netcat420/qwen2.5-MFANN-7b-v1.2",
"llama-cpp",
"gguf-my-repo",
"base_model:netcat420/qwen2.5-MFANN-7b-SLERP-V1.3",
"base_model:quantized:netcat420/qwen2.5-MFANN-7b-SLERP-V1.3",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-20T02:07:19Z
|
---
base_model: netcat420/qwen2.5-MFANN-7b-SLERP-V1.3
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated
- netcat420/qwen2.5-MFANN-7b-v1.2
- llama-cpp
- gguf-my-repo
---
# netcat420/qwen2.5-MFANN-7b-SLERP-V1.3-Q4_K_S-GGUF
This model was converted to GGUF format from [`netcat420/qwen2.5-MFANN-7b-SLERP-V1.3`](https://huggingface.co/netcat420/qwen2.5-MFANN-7b-SLERP-V1.3) 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/netcat420/qwen2.5-MFANN-7b-SLERP-V1.3) 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 netcat420/qwen2.5-MFANN-7b-SLERP-V1.3-Q4_K_S-GGUF --hf-file qwen2.5-mfann-7b-slerp-v1.3-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo netcat420/qwen2.5-MFANN-7b-SLERP-V1.3-Q4_K_S-GGUF --hf-file qwen2.5-mfann-7b-slerp-v1.3-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo netcat420/qwen2.5-MFANN-7b-SLERP-V1.3-Q4_K_S-GGUF --hf-file qwen2.5-mfann-7b-slerp-v1.3-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo netcat420/qwen2.5-MFANN-7b-SLERP-V1.3-Q4_K_S-GGUF --hf-file qwen2.5-mfann-7b-slerp-v1.3-q4_k_s.gguf -c 2048
```
|
kevin009/llama406
|
kevin009
| 2025-03-20T02:03:15Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T01:18:35Z
|
---
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]
|
sung429/detr-accident-detection
|
sung429
| 2025-03-20T02:02:51Z
| 8
| 0
|
transformers
|
[
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2025-03-18T06:12:34Z
|
---
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]
|
YugyeongJang/output4
|
YugyeongJang
| 2025-03-20T02:01:10Z
| 0
| 0
|
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-03-20T00:44:06Z
|
---
base_model: stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: a photo of sks vase
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - YugyeongJang/output4
This is a dreambooth model derived from stable-diffusion-v1-5. The weights were trained on a photo of sks vase using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
lululele/SmolLM2-FT-MyDataset
|
lululele
| 2025-03-20T02:01:06Z
| 9
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"smol-course",
"module_1",
"trl",
"sft",
"conversational",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-02-26T16:04:50Z
|
---
base_model: HuggingFaceTB/SmolLM2-135M
library_name: transformers
model_name: SmolLM2-FT-MyDataset
tags:
- generated_from_trainer
- smol-course
- module_1
- trl
- sft
licence: license
---
# Model Card for SmolLM2-FT-MyDataset
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
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="lululele/SmolLM2-FT-MyDataset", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/nguyenhuyhoang0943-the-saigon-international-university/huggingface/runs/ckzjvtbb)
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
wali-2121/v123
|
wali-2121
| 2025-03-20T02:00:32Z
| 0
| 0
| null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-03-20T02:00:32Z
|
---
license: apache-2.0
---
|
Mrober55/Jjj
|
Mrober55
| 2025-03-20T01:58:08Z
| 0
| 0
| null |
[
"license:artistic-2.0",
"region:us"
] | null | 2025-03-20T01:56:47Z
|
---
license: artistic-2.0
---
|
jerseyjerry/task-5-microsoft-Phi-3-mini-4k-instruct-0320
|
jerseyjerry
| 2025-03-20T01:57:28Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-4k-instruct",
"license:other",
"region:us"
] | null | 2025-03-20T01:56:12Z
|
---
library_name: peft
license: other
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: lora
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. -->
# lora
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the flock_task5_train 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: 0.0003
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 2
- 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: 0.05
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.12.0
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
lesso05/0befb973-0bc9-4f06-ae5f-ab32f5900322
|
lesso05
| 2025-03-20T01:55:53Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored",
"base_model:adapter:Orenguteng/Llama-3-8B-Lexi-Uncensored",
"license:llama3",
"region:us"
] | null | 2025-03-19T23:06:27Z
|
---
library_name: peft
license: llama3
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0befb973-0bc9-4f06-ae5f-ab32f5900322
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
adapter: lora
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 531cb107a136231e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/531cb107a136231e_train_data.json
type:
field_input: prompt
field_instruction: instruction
field_output: response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso05/0befb973-0bc9-4f06-ae5f-ab32f5900322
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000205
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/531cb107a136231e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 50
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: fef42bae-297d-41f8-aa7a-ca914a0305c4
wandb_project: 05a
wandb_run: your_name
wandb_runid: fef42bae-297d-41f8-aa7a-ca914a0305c4
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 0befb973-0bc9-4f06-ae5f-ab32f5900322
This model is a fine-tuned version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3917
## 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: 0.000205
- train_batch_size: 4
- eval_batch_size: 4
- seed: 50
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0007 | 1 | 0.6535 |
| 0.3965 | 0.3378 | 500 | 0.3917 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
YDluffy/lottery_prediction
|
YDluffy
| 2025-03-20T01:54:34Z
| 0
| 0
|
xgboost
|
[
"xgboost",
"lottery_prediction",
"machine-learning",
"huggingface-hub",
"tabular-classification",
"license:apache-2.0",
"region:us"
] |
tabular-classification
| 2025-03-18T17:12:12Z
|
---
library_name: xgboost
tags:
- xgboost
- lottery_prediction
- machine-learning
- huggingface-hub
license: apache-2.0
datasets: []
language: []
metrics: []
base_model: []
pipeline_tag: tabular-classification
---
# 🎯 六合彩预测模型 - Lottery Prediction Model
该模型使用 **XGBoost** 进行训练，用于预测 **六合彩开奖号码**。
## 📌 使用方法
你可以在 Python 中使用 Hugging Face API 下载和加载模型：
```python
from huggingface_hub import hf_hub_download
import xgboost as xgb
# **📥 下载模型**
repo_id = "YDluffy/lottery_prediction"
model_filename = "lottery_xgboost_model.json"
model_path = hf_hub_download(repo_id=repo_id, filename=model_filename)
# **✅ 加载 XGBoost 预测模型**
model = xgb.Booster()
model.load_model(model_path)
print("✅ 模型加载成功！")
|
MSHADroo/sd-qassem-unet-custom-train
|
MSHADroo
| 2025-03-20T01:54:06Z
| 5
| 0
|
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion-v1-5",
"text-to-image",
"diffusers-training",
"en",
"dataset:MSHADroo/dml_task_1",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2025-03-18T14:47:35Z
|
---
datasets:
- MSHADroo/dml_task_1
language:
- en
base_model:
- stable-diffusion-v1-5/stable-diffusion-v1-5
pipeline_tag: text-to-image
library_name: diffusers
tags:
- stable-diffusion-v1-5
- text-to-image
- diffusers
- diffusers-training
- text-to-image
- diffusers
- diffusers-training
license: apache-2.0
---
this model is unet fine tuned model of stable diffusion architecture based on stable-diffusion-v1-5
|
ikiransuryavanshi/layoutlmv3-ap7_1_ip
|
ikiransuryavanshi
| 2025-03-20T01:53:59Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-03-20T01:37:34Z
|
---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-ap7_1_ip
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. -->
# layoutlmv3-ap7_1_ip
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0246
- Precision: 0.7719
- Recall: 0.8302
- F1: 0.8
- Accuracy: 0.9961
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5176 | 14.7059 | 250 | 0.0664 | 0.45 | 0.1698 | 0.2466 | 0.9881 |
| 0.0288 | 29.4118 | 500 | 0.0354 | 0.6538 | 0.6415 | 0.6476 | 0.9930 |
| 0.0143 | 44.1176 | 750 | 0.0269 | 0.7333 | 0.8302 | 0.7788 | 0.9956 |
| 0.0098 | 58.8235 | 1000 | 0.0246 | 0.7719 | 0.8302 | 0.8 | 0.9961 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
Zorro123444/invoice_extracter_5
|
Zorro123444
| 2025-03-20T01:52:21Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T01:04:57Z
|
---
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]
|
mdsingh2024/ap-dnkfRpBaAiC87xjXEDoBy0
|
mdsingh2024
| 2025-03-20T01:48:54Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-03-19T20:42:24Z
|
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: ap-dnkfRpBaAiC87xjXEDoBy0
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. -->
# ap-dnkfRpBaAiC87xjXEDoBy0
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3976
- Model Preparation Time: 0.0221
- Wer: 0.1086
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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: 400
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Wer |
|:-------------:|:------:|:----:|:---------------:|:----------------------:|:------:|
| 0.3416 | 0.9791 | 41 | 0.3450 | 0.0221 | 0.1210 |
| 0.2234 | 1.9791 | 82 | 0.2593 | 0.0221 | 0.1044 |
| 0.1546 | 2.9791 | 123 | 0.2602 | 0.0221 | 0.1020 |
| 0.08 | 3.9791 | 164 | 0.2776 | 0.0221 | 0.1018 |
| 0.0512 | 4.9791 | 205 | 0.3098 | 0.0221 | 0.1080 |
| 0.0392 | 5.9791 | 246 | 0.3241 | 0.0221 | 0.1087 |
| 0.0275 | 6.9791 | 287 | 0.3662 | 0.0221 | 0.1052 |
| 0.0267 | 7.9791 | 328 | 0.3335 | 0.0221 | 0.1348 |
| 0.0262 | 8.9791 | 369 | 0.3621 | 0.0221 | 0.1101 |
| 0.0176 | 9.9791 | 410 | 0.3976 | 0.0221 | 0.1086 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.1
|
SachiFaker/sd-class-butterflies-32
|
SachiFaker
| 2025-03-20T01:42:39Z
| 0
| 0
|
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2025-03-20T01:41:56Z
|
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('SachiFaker/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
UICHEOL-HWANG/GreenFinance-Llama-3-ko-8B
|
UICHEOL-HWANG
| 2025-03-20T01:42:04Z
| 0
| 0
|
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-19T10:10:23Z
|
---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mshen2/qwen2.5-math-7b-v4-no-hcot
|
mshen2
| 2025-03-20T01:40:37Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-20T01:37:42Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**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]
|
Kalos78237/Kalos
|
Kalos78237
| 2025-03-20T01:38:14Z
| 0
| 0
| null |
[
"license:bigcode-openrail-m",
"region:us"
] | null | 2025-03-20T01:38:13Z
|
---
license: bigcode-openrail-m
---
|
tomitaln/Qwen2.5
|
tomitaln
| 2025-03-20T01:35:59Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-03-14T01:05:48Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**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]
|
whywwhy/results
|
whywwhy
| 2025-03-20T01:35:39Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:klue/roberta-base",
"base_model:finetune:klue/roberta-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-20T01:34:57Z
|
---
library_name: transformers
base_model: klue/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4458
- Accuracy: 0.862
## 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: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5332 | 1.0 | 1250 | 0.5193 | 0.839 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.0
- Tokenizers 0.21.1
|
gaokerena/gaokerena
|
gaokerena
| 2025-03-20T01:35:25Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"cohere",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-20T01:23:53Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
qpeterp/results
|
qpeterp
| 2025-03-20T01:35:00Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:klue/roberta-base",
"base_model:finetune:klue/roberta-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-20T01:34:11Z
|
---
library_name: transformers
base_model: klue/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4681
- Accuracy: 0.853
## 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: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5331 | 1.0 | 1250 | 0.5297 | 0.841 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.0
- Tokenizers 0.21.1
|
netcat420/qwen2.5-MFANN-7b-SLERP-V1.3
|
netcat420
| 2025-03-20T01:34:59Z
| 0
| 0
| null |
[
"safetensors",
"qwen2",
"merge",
"mergekit",
"lazymergekit",
"huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated",
"netcat420/qwen2.5-MFANN-7b-v1.2",
"license:apache-2.0",
"region:us"
] | null | 2025-03-20T01:31:26Z
|
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated
- netcat420/qwen2.5-MFANN-7b-v1.2
---
# qwen2.5-MFANN-7b-SLERP-V1.3
qwen2.5-MFANN-7b-SLERP-V1.3 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated](https://huggingface.co/huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated)
* [netcat420/qwen2.5-MFANN-7b-v1.2](https://huggingface.co/netcat420/qwen2.5-MFANN-7b-v1.2)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated
layer_range: [0, 28]
- model: netcat420/qwen2.5-MFANN-7b-v1.2
layer_range: [0, 28]
merge_method: slerp
base_model: huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
dtype: float16
```
|
geol-dgsw/results
|
geol-dgsw
| 2025-03-20T01:34:41Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:klue/roberta-base",
"base_model:finetune:klue/roberta-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-20T01:34:16Z
|
---
library_name: transformers
base_model: klue/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4579
- Accuracy: 0.849
## 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: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5391 | 1.0 | 1250 | 0.5395 | 0.835 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Tokenizers 0.21.1
|
DevQuasar/rinna.gemma-2-baku-2b-GGUF
|
DevQuasar
| 2025-03-20T01:31:56Z
| 0
| 0
| null |
[
"gguf",
"text-generation",
"base_model:rinna/gemma-2-baku-2b",
"base_model:quantized:rinna/gemma-2-baku-2b",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-20T01:18:42Z
|
---
base_model:
- rinna/gemma-2-baku-2b
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [rinna/gemma-2-baku-2b](https://huggingface.co/rinna/gemma-2-baku-2b)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
jiinking/19_bitwise_MQA_llama3B_model
|
jiinking
| 2025-03-20T01:31:42Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-20T00:19:20Z
|
---
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]
|
mradermacher/purebreed-v1.2-GGUF
|
mradermacher
| 2025-03-20T01:30:03Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:trbiv/purebreed-v1.2",
"base_model:quantized:trbiv/purebreed-v1.2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-20T01:09:40Z
|
---
base_model: trbiv/purebreed-v1.2
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/trbiv/purebreed-v1.2
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/purebreed-v1.2-GGUF/resolve/main/purebreed-v1.2.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/purebreed-v1.2-GGUF/resolve/main/purebreed-v1.2.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/purebreed-v1.2-GGUF/resolve/main/purebreed-v1.2.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/purebreed-v1.2-GGUF/resolve/main/purebreed-v1.2.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/purebreed-v1.2-GGUF/resolve/main/purebreed-v1.2.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/purebreed-v1.2-GGUF/resolve/main/purebreed-v1.2.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/purebreed-v1.2-GGUF/resolve/main/purebreed-v1.2.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/purebreed-v1.2-GGUF/resolve/main/purebreed-v1.2.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/purebreed-v1.2-GGUF/resolve/main/purebreed-v1.2.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/purebreed-v1.2-GGUF/resolve/main/purebreed-v1.2.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/purebreed-v1.2-GGUF/resolve/main/purebreed-v1.2.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/purebreed-v1.2-GGUF/resolve/main/purebreed-v1.2.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
fukugawa/gemma-2-9b-finetuned
|
fukugawa
| 2025-03-20T01:29:51Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"dataset:fukugawa/kamakura-tasks-100",
"license:gemma",
"endpoints_compatible",
"region:us"
] | null | 2024-12-12T02:46:38Z
|
---
library_name: transformers
datasets:
- fukugawa/kamakura-tasks-100
license: gemma
---
## Overview
このモデルは、「[gemma-2-9b](https://huggingface.co/google/gemma-2-9b)」に対して、データセット「[kamakura-tasks-100](https://huggingface.co/datasets/fukugawa/kamakura-tasks-100)」の100件を用いてファインチューニングを実施し、指示応答できるようにしました。
## Demo
このモデルを使ったChatbotのデモをspaces上に公開しています。
* [Chatbotデモ](https://huggingface.co/spaces/fukugawa/gemma-2-9b-finetuned)
## Blog Post
* [自作データセットによるGemma2-9Bのファインチューニング](https://matsuolab-geniac.notion.site/Gemma2-9B-fukugawa-d2c52f881d324c6fbc37febe3d30d0c0)
## Usage
以下は、ELYZA-tasks-100-TV（100問）の回答を生成する推論コードです。
#### Requirements:
```bash
# python 3.10
pip install -U transformers
pip install -U accelerate
pip install -U peft
```
「[gemma-2-9b](https://huggingface.co/google/gemma-2-9b)」を利用するには、HFにログインし、利用規約に同意する必要があります。以下のコマンドでログインしてください（Notebookではfrom_pretrained()のtoken引数でも可）。
```bash
huggingface-cli login
```
#### Inference:
~~~~python
import json
import torch
from datasets import Dataset
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "fukugawa/gemma-2-9b-finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
datasets = Dataset.from_json("./elyza-tasks-100-TV_0.jsonl")
results = []
for data in tqdm(datasets):
input = data["input"]
prompt = f"### 指示\n{input}\n### 回答\n"
tokenized_input = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
tokenized_input,
max_new_tokens=512,
do_sample=False,
)[0]
output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
results.append({"task_id": data["task_id"], "input": input, "output": output})
with open("./outputs.jsonl", 'w', encoding='utf-8') as f:
for result in results:
json.dump(result, f, ensure_ascii=False)
f.write('\n')
~~~~
ELYZAタスクTVのJSONLファイル（elyza-tasks-100-TV_0.jsonl）が必要です。
推論時に18〜19GBのGPUメモリが必要になります。Nvidia L4 24GBメモリで動作確認しています。
100問の推論時間は約15〜20分程です。
カレントディレクトリにoutputs.jsonlが出力されます。
## Dataset
* [kamakura-tasks-100](https://huggingface.co/datasets/fukugawa/kamakura-tasks-100)
|
stacklok/Qwen2.5-Coder-7B-Instruct-reactjs-chat
|
stacklok
| 2025-03-20T01:29:35Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-20T01:23:02Z
|
---
base_model: unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** stacklok
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
yoonssun07/results
|
yoonssun07
| 2025-03-20T01:29:26Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:klue/roberta-base",
"base_model:finetune:klue/roberta-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-20T01:28:11Z
|
---
library_name: transformers
base_model: klue/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4393
- Accuracy: 0.864
## 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: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5321 | 1.0 | 1250 | 0.5129 | 0.846 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.0
- Tokenizers 0.21.1
|
lesso07/a11480e4-b97f-4323-9e65-11f58ad10a2d
|
lesso07
| 2025-03-20T01:28:02Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:adapter:microsoft/Phi-3.5-mini-instruct",
"license:mit",
"region:us"
] | null | 2025-03-19T23:21:04Z
|
---
library_name: peft
license: mit
base_model: microsoft/Phi-3.5-mini-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a11480e4-b97f-4323-9e65-11f58ad10a2d
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
adapter: lora
base_model: microsoft/Phi-3.5-mini-instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- d848c58d64aeb958_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d848c58d64aeb958_train_data.json
type:
field_input: documents
field_instruction: question
field_output: answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso07/a11480e4-b97f-4323-9e65-11f58ad10a2d
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000207
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/d848c58d64aeb958_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 70
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 4a21d2e2-6d81-4cab-b09f-1870a1ec35b4
wandb_project: 07a
wandb_run: your_name
wandb_runid: 4a21d2e2-6d81-4cab-b09f-1870a1ec35b4
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# a11480e4-b97f-4323-9e65-11f58ad10a2d
This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9824
## 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: 0.000207
- train_batch_size: 4
- eval_batch_size: 4
- seed: 70
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0009 | 1 | 1.5209 |
| 7.87 | 0.4270 | 500 | 0.9824 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
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