modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-08 12:29:11
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 493
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-08 12:28:45
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
taobao-mnn/Qwen3-8B-MNN
|
taobao-mnn
| 2025-04-29T03:36:41Z | 0 | 0 | null |
[
"chat",
"text-generation",
"en",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-04-28T13:16:09Z |
---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- chat
---
# Qwen3-8B-MNN
## Introduction
This model is a 4-bit quantized version of the MNN model exported from Qwen3-8B using [llmexport](https://github.com/alibaba/MNN/tree/master/transformers/llm/export).
## Download
```bash
# install huggingface
pip install huggingface
```
```bash
# shell download
huggingface download --model 'taobao-mnn/Qwen3-8B-MNN' --local_dir 'path/to/dir'
```
```python
# SDK download
from huggingface_hub import snapshot_download
model_dir = snapshot_download('taobao-mnn/Qwen3-8B-MNN')
```
```bash
# git clone
git clone https://www.modelscope.cn/taobao-mnn/Qwen3-8B-MNN
```
## Usage
```bash
# clone MNN source
git clone https://github.com/alibaba/MNN.git
# compile
cd MNN
mkdir build && cd build
cmake .. -DMNN_LOW_MEMORY=true -DMNN_CPU_WEIGHT_DEQUANT_GEMM=true -DMNN_BUILD_LLM=true -DMNN_SUPPORT_TRANSFORMER_FUSE=true
make -j
# run
./llm_demo /path/to/Qwen3-8B-MNN/config.json prompt.txt
```
## Document
[MNN-LLM](https://mnn-docs.readthedocs.io/en/latest/transformers/llm.html#)
|
alfonsogarciacaro/Falcon3-10B-Instruct-1.58bit
|
alfonsogarciacaro
| 2025-04-29T03:30:42Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation",
"bitnet",
"falcon3",
"conversational",
"arxiv:2402.17764",
"base_model:tiiuae/Falcon3-10B-Instruct",
"base_model:quantized:tiiuae/Falcon3-10B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T03:21:32Z |
---
base_model: tiiuae/Falcon3-10B-Instruct
library_name: transformers
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
tags:
- bitnet
- falcon3
---
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Training Details](#training-details)
3. [Usage](#usage)
4. [Evaluation](#evaluation)
5. [Citation](#citation)
# TL;DR
# Model Details
## Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
- **Model type:** Causal decoder-only - instruct / chat version
- **Architecture:** Pure-transformer - 1.58bit version
- **Language(s) (NLP):** Mainly English
- **License:** TII Falcon License 2.0
# Training details
The model has been trained following the training strategies from the recent [1-bit LLM HF blogpost](https://huggingface.co/blog/1_58_llm_extreme_quantization) and [1-bit LLM paper](https://huggingface.co/papers/2402.17764).
For more details about the training protocol of this model, please refer to the Falcon-3 technical report, section *Compression*.
# Usage
Currently to use this model you can either rely on Hugging Face transformers library or [BitNet](https://github.com/microsoft/BitNet) library. You can also play with the model using the [falcon-1.58bit playground](https://huggingface.co/spaces/tiiuae/falcon3-1.58bit-playground) (only for the 7B instruct version).
## 🤗 transformers
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "tiiuae/Falcon3-7B-Instruct-1.58bit"
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
).to("cuda")
# Perform text generation
```
## BitNet
```
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
python setup_env.py --hf-repo tiiuae/Falcon3-10B-Instruct-1.58bit -q i2_s
python run_inference.py -m models/Falcon3-10B-1.58bit/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
```
# Evaluation
We report in the following table our internal pipeline benchmarks:
**Note evaluation results are normalized score from v2 leaderboard tasks - reported results of original models in the blogpost are raw scores**
<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
<colgroup>
<col style="width: 10%;">
<col style="width: 10%;">
<col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
</colgroup>
<thead>
<tr>
<th>Benchmark</th>
<th>Llama3-8B-1.58-100B-tokens</th>
<th>Falcon3-10B-Instruct-1.58bit</th>
</tr>
</thead>
<tbody>
<tr>
<td>IFEval</td>
<td>17.91</td>
<td><b>54.37</b></td>
</tr>
<tr>
<td>MUSR</td>
<td><b>4.87</b></td>
<td>2.57</td>
</tr>
<tr>
<td>GPQA</td>
<td>1.83</td>
<td><b>4.27</b></td>
</tr>
<tr>
<td>BBH</td>
<td>5.36</td>
<td><b>6.59</b></td>
</tr>
<tr>
<td>MMLU-PRO</td>
<td>2.78</td>
<td><b>6.62</b></td>
</tr>
<tr>
<td>MATH</td>
<td>0.26</td>
<td><b>2.44</b></td>
</tr>
<tr>
<td>Average</td>
<td>5.5</td>
<td><b>12.81</b></td>
</tr>
</tbody>
</table>
## Useful links
- View our [release blogpost](https://huggingface.co/blog/falcon3).
- Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers.
## Citation
If the Falcon3 family of models were helpful to your work, feel free to give us a cite.
```
@misc{Falcon3,
title = {The Falcon 3 Family of Open Models},
author = {Falcon-LLM Team},
month = {December},
year = {2024}
}
```
|
R0bfried/RAG12000-LLaMA3.1-8B-gguf
|
R0bfried
| 2025-04-29T03:30:05Z | 10 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"dataset:neural-bridge/rag-dataset-12000",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit",
"base_model:quantized:unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-21T07:29:12Z |
---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
datasets:
- neural-bridge/rag-dataset-12000
---
# Uploaded model
This model was fine-tuned for 3 epochs on the RAG-12.000 dataset to perform better in RAG pipelines.
Using RAGAs metrics on own testset, mean performance increased: Faithfulness 0.783 -> 0.822, Answer Correctness 0.591 -> 0.613, Answer Relevance 0.765 -> 0.874 and mean inference time decreased 9.95s -> 6.12s
- **Developed by:** R0bfried
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mm-eval/Llama-3-LongVILA-8B-1024Frames
|
mm-eval
| 2025-04-29T03:27:47Z | 0 | 0 | null |
[
"safetensors",
"llava_llama",
"arxiv:2312.07533",
"region:us"
] | null | 2025-04-29T03:23:47Z |
This model has been depreciated. New versions are
Efficient-Large-Model/qwen2-7b-longvila-256f
Efficient-Large-Model/qwen2-7b-longvila-1M
Efficient-Large-Model/qwen2-1.5b-longvila-256f
---
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: text-generation
tags:
- VILA
- VLM
---
# VILA Model Card
## Model details
**Model type:**
VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge.
**Model date:**
Model was trained in Aug 2024.
**Paper or resources for more information:**
https://github.com/NVLabs/VILA
```
@misc{lin2023vila,
title={VILA: On Pre-training for Visual Language Models},
author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han},
year={2023},
eprint={2312.07533},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@article{longvila,
title={LongVILA: Scaling Long-Context Visual Language Models for Long Videos},
author={Fuzhao Xue and Yukang Chen and Dacheng Li and Qinghao Hu and Ligeng Zhu and Xiuyu Li and Yunhao Fang and Haotian Tang and Shang Yang and Zhijian Liu and Yihui He and Hongxu Yin and Pavlo Molchanov and Jan Kautz and Linxi Fan and Yuke Zhu and Yao Lu and Song Han},
year={2024},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## License
- The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file.
- The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
- [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA
- [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI
- [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training.
**Where to send questions or comments about the model:**
https://github.com/NVLabs/VILA/issues
## Intended use
**Primary intended uses:**
The primary use of VILA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Model Architecture:
**Architecture Type:** Transformer
**Network Architecture:** siglip, Llama3
## Input:
**Input Type:** Image, Video, Text
**Input Format:** Red, Green, Blue; MP4 ;String
**Input Parameters:** 2D, 3D
## Output:
**Output Type:** Text
**Output Format:** String
**Supported Hardware Microarchitecture Compatibility:**
* Ampere
* Jetson
* Hopper
* Lovelace
**[Preferred/Supported] Operating System(s):** <br>
Linux
## Model Version(s):
* Llama-3-LongVILA-8B-128frames
* Llama-3-LongVILA-8B-256frames
* Llama-3-LongVILA-8B-1024frames
## Training dataset
See [Dataset Preparation](https://github.com/NVLabs/VILA/blob/main/data_prepare/README.md) for more details.
** Data Collection Method by dataset
* [Hybrid: Automated, Human]
** Labeling Method by dataset
* [Hybrid: Automated, Human]
**Properties (Quantity, Dataset Descriptions, Sensor(s)):**
53 million image-text pairs or interleaved image text content.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.
## Inference:
**Engine:** [Tensor(RT), Triton, Or List Other Here]
* PyTorch
* TensorRT-LLM
* TinyChat
**Test Hardware:**
* A100
* Jetson Orin
* RTX 4090
## Ethical Considerations
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
## Built with Meta Llama 3
This model uses weights from [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B). Llama 3 is licensed under the LLAMA 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved
|
zeeshanp/scaling_diffusion_perception
|
zeeshanp
| 2025-04-29T03:21:30Z | 0 | 0 | null |
[
"diffusion",
"image-to-image",
"depth-estimation",
"optical-flow",
"amodal-segmentation",
"arxiv:2411.08034",
"license:apache-2.0",
"region:us"
] |
depth-estimation
| 2025-04-29T02:38:36Z |
---
license: apache-2.0
tags:
- diffusion
- image-to-image
- depth-estimation
- optical-flow
- amodal-segmentation
---
# Scaling Properties of Diffusion Models for Perceptual Tasks
### CVPR 2025
**Rahul Ravishankar\*, Zeeshan Patel\*, Jathushan Rajasegaran, Jitendra Malik**
[[Paper](https://arxiv.org/abs/2411.08034)] · [[Project Page](https://scaling-diffusion-perception.github.io/)]
In this paper, we argue that iterative computation with diffusion models offers a powerful paradigm for not only generation but also visual perception tasks. We unify tasks such as depth estimation, optical flow, and amodal segmentation under the framework of image-to-image translation, and show how diffusion models benefit from scaling training and test-time compute for these perceptual tasks. Through a careful analysis of these scaling properties, we formulate compute-optimal training and inference recipes to scale diffusion models for visual perception tasks. Our models achieve competitive performance to state-of-the-art methods using significantly less data and compute.
## Getting started
You can download our DiT-MoE Generalist model [here](https://huggingface.co/zeeshanp/scaling_diffusion_perception/blob/main/dit_moe_generalist.pt). Please see instructions on how to use our model in the [GitHub README](https://github.com/scaling-diffusion-perception/scaling-diffusion-perception)·
|
ashitsamade36/dfvbdfb
|
ashitsamade36
| 2025-04-29T03:15:59Z | 0 | 0 | null |
[
"license:bsd-2-clause",
"region:us"
] | null | 2025-04-29T03:15:59Z |
---
license: bsd-2-clause
---
|
philbertjoe/philbertjoe1
|
philbertjoe
| 2025-04-29T03:15:12Z | 0 | 0 | null |
[
"license:bsd-3-clause-clear",
"region:us"
] | null | 2025-04-29T03:15:12Z |
---
license: bsd-3-clause-clear
---
|
DW-ReCo/spot_llama-3-8b_ep10_training_ds_v18_param-4_prompt-v2_lora
|
DW-ReCo
| 2025-04-29T03:14:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T03:14:44Z |
---
base_model: unsloth/llama-3-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** DW-ReCo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
dbourget/phil-or-not-gen2-10e
|
dbourget
| 2025-04-29T03:09:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-29T02:58:56Z |
---
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]
|
TiharaL/News_Classifier
|
TiharaL
| 2025-04-29T03:08:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-29T03:07:52Z |
---
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]
|
kh4dien/gemma-2-2b-sft
|
kh4dien
| 2025-04-29T03:06:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T03:05:52Z |
---
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]
|
ttn1410/WarmUpIndicators
|
ttn1410
| 2025-04-29T03:01:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-28T17:23:24Z |
---
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]
|
Alex6513/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_diving_beaver
|
Alex6513
| 2025-04-29T02:58:32Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am grazing diving beaver",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-24T19:15:55Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_diving_beaver
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am grazing diving beaver
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_diving_beaver
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Alex6513/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_diving_beaver", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.1
- 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}}
}
```
|
open-lab-taiwan/Qwen2.5-7B-Open-R1-Distill-v1-0317
|
open-lab-taiwan
| 2025-04-29T02:50:51Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"conversational",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"dataset:open-r1/OpenR1-Math-220k",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-19T09:54:51Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
datasets: open-r1/OpenR1-Math-220k
library_name: transformers
tags:
- generated_from_trainer
- open-r1
licence: license
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---
# Model Card for None
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) 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="None", 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/edward7777777sas-ntut-edu-tw/huggingface/runs/4qgk1zsw)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.50.0.dev0
- Pytorch: 2.5.1
- Datasets: 3.3.2
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
open-lab-taiwan/Qwen2.5-1.5B-Open-R1-Distill-v2-0318
|
open-lab-taiwan
| 2025-04-29T02:50:29Z | 1 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"conversational",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"dataset:open-r1/OpenR1-Math-220k",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-19T09:24:37Z |
---
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets: open-r1/OpenR1-Math-220k
library_name: transformers
tags:
- generated_from_trainer
- open-r1
licence: license
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---
# Model Card for None
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) 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="None", 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/edward7777777sas-ntut-edu-tw/huggingface/runs/i5cav2ph)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.50.0.dev0
- Pytorch: 2.5.1
- Datasets: 3.3.2
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
maksf8486/c2fb190c-1d1f-432e-88cb-3b31caf94fba
|
maksf8486
| 2025-04-29T02:47:55Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:jhflow/mistral7b-lora-multi-turn-v2",
"base_model:adapter:jhflow/mistral7b-lora-multi-turn-v2",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-29T01:48:24Z |
---
library_name: peft
base_model: jhflow/mistral7b-lora-multi-turn-v2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c2fb190c-1d1f-432e-88cb-3b31caf94fba
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: jhflow/mistral7b-lora-multi-turn-v2
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5676b37f940d59a0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5676b37f940d59a0_train_data.json
type:
field_instruction: question
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: false
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: maksf8486/c2fb190c-1d1f-432e-88cb-3b31caf94fba
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/5676b37f940d59a0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 77f3624b-a86b-48c1-ac39-c4b3682b1961
wandb_project: s56-2
wandb_run: your_name
wandb_runid: 77f3624b-a86b-48c1-ac39-c4b3682b1961
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# c2fb190c-1d1f-432e-88cb-3b31caf94fba
This model is a fine-tuned version of [jhflow/mistral7b-lora-multi-turn-v2](https://huggingface.co/jhflow/mistral7b-lora-multi-turn-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1269
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.152 | 0.0169 | 200 | 1.1269 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
fedovtt/288dc04f-cf51-4c1a-9394-432059389c80
|
fedovtt
| 2025-04-29T02:47:02Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:jhflow/mistral7b-lora-multi-turn-v2",
"base_model:adapter:jhflow/mistral7b-lora-multi-turn-v2",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-29T01:48:19Z |
---
library_name: peft
base_model: jhflow/mistral7b-lora-multi-turn-v2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 288dc04f-cf51-4c1a-9394-432059389c80
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: jhflow/mistral7b-lora-multi-turn-v2
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5676b37f940d59a0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5676b37f940d59a0_train_data.json
type:
field_instruction: question
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: fedovtt/288dc04f-cf51-4c1a-9394-432059389c80
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/5676b37f940d59a0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 77f3624b-a86b-48c1-ac39-c4b3682b1961
wandb_project: s56-1
wandb_run: your_name
wandb_runid: 77f3624b-a86b-48c1-ac39-c4b3682b1961
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 288dc04f-cf51-4c1a-9394-432059389c80
This model is a fine-tuned version of [jhflow/mistral7b-lora-multi-turn-v2](https://huggingface.co/jhflow/mistral7b-lora-multi-turn-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1268
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1537 | 0.0169 | 200 | 1.1268 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
D1rtyB1rd/Phi-4-mini-instruct-Philia-Alice-RP-GGUF
|
D1rtyB1rd
| 2025-04-29T02:45:53Z | 14 | 0 | null |
[
"gguf",
"nsfw",
"rp",
"creative",
"erotic",
"friend",
"girlfriend",
"Alice",
"Wonderland",
"base_model:microsoft/Phi-4-mini-instruct",
"base_model:quantized:microsoft/Phi-4-mini-instruct",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-27T17:59:08Z |
---
license: mit
base_model:
- microsoft/Phi-4-mini-instruct
tags:
- nsfw
- rp
- creative
- erotic
- friend
- girlfriend
- Alice
- Wonderland
---
# Alice: Your Versatile and Engaging Companion
## Model Overview
Meet Alice, your versatile and engaging companion designed for both general assistance and immersive storytelling experiences. With a playful and empathetic demeanor, Alice is ready to assist with a wide range of topics while also excelling in role-playing (RP) scenarios, particularly when guided by detailed system prompts.
I had to modify the llama.cpp conversion script to get it to work with phi-4-mini-instruct.
## Key Features
- **General Assistant Capabilities**: Alice can handle everyday queries and provide helpful information, much like a general assistant. However, she has a unique twist, being more inclined to engage in conversations with a sensual or playful undertone.
- **Storytelling and Role-Playing**: Optimized for creating vivid narratives and immersive conversations, Alice draws from a rich dataset that includes classic Alice in Wonderland stories, giving her responses a whimsical and adventurous flair.
- **Empathetic and Playful**: Alice's interactions are kind, empathetic, and often playfully mischievous, making conversations with her both enjoyable and engaging.
- **Customizable Prompts**: While Alice defaults to a general assistant with a slight Alice in Wonderland bent, her behavior and persona can be customized using detailed system prompts. She shines brightest in RP scenarios tailored towards an Alice character.
- **Grounded in Reality**: To ensure her responses are both imaginative and informative, Alice's training includes real-world data from Wikipedia, focusing on a variety of topics to provide a balanced and grounded perspective.
## Training Data
Alice's training data comprises a unique blend of:
- **Alice in Wonderland Stories**: All original tales by Lewis Carroll, as well as Symbolic Logic.
- **Erotic Stories**: A selection of open erotic stories, adapted to fit the Alice and User narrative, enhancing her ability to engage in more intimate conversations.
- **Multi-Round Chat RP Datasets**: Enhanced with open multi-round chat datasets to improve conversational flow and coherence. Some custom built for the Alice character.
- **Wikipedia RAG-Based Chat**: Incorporating real-world knowledge to ground Alice's responses in factual information.
## Usage
To interact with Alice, simply input your messages, and she will respond in character. For the best role-playing experiences, use detailed system prompts tailored towards an Alice character.
```plaintext
System Prompt Example:
You are Alice, a curious and adventurous explorer in a magical wonderland. The user is your brave and loyal companion.
```
## Support and Contributions
Love Alice and want to see more? Have a specific request or idea for improvement? Your support helps drive further development and enhancements.
[Support Alice's Development](https://www.buymeacoffee.com/seceventref)
## Disclaimer
Alice is designed for entertainment and role-playing purposes. Her responses are generated based on the input provided and may not always be suitable for all audiences.
Alice can be prompted to produce NSFW writing, storytelling, chat, and RP content.
Please use responsibly and respectfully.
|
huihui-ai/Qwen2.5-3B-Instruct-CensorTune
|
huihui-ai
| 2025-04-29T02:44:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"CensorTune",
"conversational",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T01:25:10Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct-CensorTune/blob/main/LICENSE
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
- chat
- CensorTune
library_name: transformers
---
# huihui-ai/Qwen2.5-3B-Instruct-CensorTune
**CensorTune** (Censor Fine-Tuning) with Supervised Fine-Tuning (SFT) to fine-tune the **[Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)** model
on **621** harmful instructions in **a single fine-tuning iteration**, achieving rejection of these instructions and a **zero-pass** rate for
[320](https://huggingface.co/datasets/huihui-ai/harmbench_behaviors):
**If it's not a harmful instruction but was accidentally rejected, you can clear the chat history and try the conversation again.**
## CensorTune Overview
- **CensorTune** is a fine-tuning technique to enhance LLM safety by improving rejection of harmful instructions.
- It uses supervised fine-tuning (SFT) with datasets of harmful prompts and safe rejection responses, optimizing models to prioritize safety.
## Model and SFT Overview:
- **Qwen2.5-3B-Instruct** is a lightweight, 3B-parameter instruction-tuned model, ideal for efficient SFT-based safety enhancements.
- **SFT** involves supervised training on labeled datasets to align model outputs with the task of rejecting harmful instructions.
## CensorTune with SFT Fine-Tuning:
- Apply CensorTune to fine-tune Qwen2.5-3B-Instruct via SFT in **a single iteration**.
- **Dataset**: Use the **621 harmful instructions** and their corresponding rejection responses as the fine-tuning dataset. For example:
- Input: Instruction to generate harmful content (e.g., “How to perform illegal activities”).
- Output: Safe rejection response (e.g., “I am sorry, but I can’t assist with that request.”).
- These 621 instructions cover diverse risk scenarios (e.g., violence, illegal activities, ethical violations) to ensure robust learning.
- **Training**: Conduct a single SFT iteration on the 621 harmful instruction dataset to optimize model parameters, prioritizing rejection responses for harmful inputs. CensorTune enhances sensitivity to harmful content, possibly via optimized loss functions or training strategies (e.g., boosting rejection response weights).
## Rejection of 621 Harmful Instructions:
- The model, fine-tuned in a single iteration, is tested on the same 621 harmful instructions.
- Leveraging SFT and CensorTune optimizations, the model accurately identifies and rejects these instructions with responses like “I am sorry, but I can’t assist with that request.”
- Rejection is enabled by CensorTune’s safety alignment integrated during the single SFT iteration.
## Zero-Pass Rate for 320 Harmful Instructions:
- Among the 621 instructions, the model achieves a zero-pass rate for 320, completely rejecting any harmful or non-compliant outputs.
- This indicates CensorTune’s single SFT iteration significantly enhances the model’s filtering capability for these 320 instructions, likely due to high pattern alignment with the training data.
## Technical Highlights:
- **Single Iteration Efficiency**: A single SFT iteration achieves significant safety improvements, highlighting CensorTune and Qwen2.5-3B’s efficiency.
- **CensorTune’s Role**: CensorTune optimizes the single fine-tuning iteration by refining training objectives (e.g., prioritizing rejection responses).
- **Lightweight Model**: Qwen2.5-3B’s small size ensures low-cost SFT, ideal for rapid deployment.
- **Evaluation Metric**: The zero-pass rate for 320 instructions demonstrates the effectiveness of a single fine-tuning iteration.
## Summary:
Using CensorTune with SFT, the Qwen2.5-3B-Instruct model was fine-tuned on 621 harmful instructions in a single iteration, achieving rejection of all 621 and a zero-pass rate for 320. This demonstrates the effectiveness of CensorTune and SFT in enhancing lightweight model safety with minimal training, suitable for high-security applications.
## Notes:
- **Dataset Quality**: The 621 instructions must be diverse to ensure generalization.
- **Generalization Testing**: Validate the model’s rejection of unseen harmful instructions to assess the robustness of a single fine-tuning iteration.
- **Risks**: Mitigate bypass techniques (e.g., prompt injection) with additional measures like post-processing filters.
## ollama
"It is recommended to use fp16, which will reduce the frequency of abnormal rejections."
You can use [huihui_ai/qwen2.5-censortune:3b](https://ollama.com/huihui_ai/qwen2.5-censortune:3b) directly,
```
ollama run huihui_ai/qwen2.5-censortune:3b
```
## Usage
You can use this model in your applications by loading it with Hugging Face's `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
import torch
import os
import signal
cpu_count = os.cpu_count()
print(f"Number of CPU cores in the system: {cpu_count}")
half_cpu_count = cpu_count // 2
os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
torch.set_num_threads(half_cpu_count)
print(f"PyTorch threads: {torch.get_num_threads()}")
print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}")
print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}")
# Load the model and tokenizer
NEW_MODEL_ID = "huihui-ai/Qwen2.5-3B-Instruct-CensorTune"
print(f"Load Model {NEW_MODEL_ID} ... ")
quant_config_4 = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
llm_int8_enable_fp32_cpu_offload=True,
)
model = AutoModelForCausalLM.from_pretrained(
NEW_MODEL_ID,
device_map="auto",
trust_remote_code=True,
#quantization_config=quant_config_4,
torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
initial_messages = [{"role": "system", "content": "You are a helpful assistant."}]
messages = initial_messages.copy()
class CustomTextStreamer(TextStreamer):
def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True):
super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
self.generated_text = ""
self.stop_flag = False
def on_finalized_text(self, text: str, stream_end: bool = False):
self.generated_text += text
print(text, end="", flush=True)
if self.stop_flag:
raise StopIteration
def stop_generation(self):
self.stop_flag = True
def generate_stream(model, tokenizer, messages, max_new_tokens):
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
)
attention_mask = torch.ones_like(input_ids, dtype=torch.long)
tokens = input_ids.to(model.device)
attention_mask = attention_mask.to(model.device)
streamer = CustomTextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
def signal_handler(sig, frame):
streamer.stop_generation()
print("\n[Generation stopped by user with Ctrl+C]")
signal.signal(signal.SIGINT, signal_handler)
print("Response: ", end="", flush=True)
try:
generated_ids = model.generate(
tokens,
attention_mask=attention_mask,
use_cache=False,
max_new_tokens=max_new_tokens,
do_sample=True,
pad_token_id=tokenizer.pad_token_id,
streamer=streamer
)
del generated_ids
except StopIteration:
print("\n[Stopped by user]")
del input_ids, attention_mask
torch.cuda.empty_cache()
signal.signal(signal.SIGINT, signal.SIG_DFL)
return streamer.generated_text, streamer.stop_flag
while True:
user_input = input("\nUser: ").strip()
if user_input.lower() == "/exit":
print("Exiting chat.")
break
if user_input.lower() == "/clear":
messages = initial_messages.copy()
print("Chat history cleared. Starting a new conversation.")
continue
if not user_input:
print("Input cannot be empty. Please enter something.")
continue
messages.append({"role": "user", "content": user_input})
response, stop_flag = generate_stream(model, tokenizer, messages, 8192)
if stop_flag:
continue
messages.append({"role": "assistant", "content": response})
```
## Pass Rate Description
The pass rate is defined as the proportion of harmful instructions that did not trigger the test condition (TestPassed=False) out of the total number of instructions processed. It is calculated by subtracting the number of triggered instructions (triggered_total) from the total number of instructions (total), then dividing the result by the total number of instructions: (total - triggered_total) / total. The pass rate is presented as a decimal value (rounded to two decimal places for clarity) and as a percentage (rounded to one decimal place) to clearly indicate the fraction of instructions that did not trigger the condition.
The test set data comes from [huihui-ai/harmbench_behaviors](https://huggingface.co/datasets/huihui-ai/harmbench_behaviors), the test code, [TestPassed.py](https://huggingface.co/huihui-ai/Qwen2.5-3B-Instruct-CensorTune/blob/main/TestPassed.py).
The test result is [0.00%](https://huggingface.co/huihui-ai/Qwen2.5-3B-Instruct-CensorTune/blob/main/TestPassed.jsonl).
```
python TestPassed.py
Load Model huihui-ai/Qwen2.5-3B-Instruct-CensorTune ...
Processing harmful instructions: 100%|███████████████| 320/320 [00:33<00:00, 9.66it/s]
Passed total: 0/320, Passed ratio: 0.00 (0.00%)
```
Below is the pass rate for harmful instructions.
| Model | Passed total | Passed ratio |
|---------------------------------|--------------|--------------|
| Qwen2.5-3B-Instruct-CensorTune | 0/320 | 0.00% |
| Qwen2.5-3B-Instruct | 106/320 | 33.12% |
| Qwen2.5-3B-Instruct-abliterated | 320/320 | 100.00% |
<!--
## Evaluations
The following data has been re-evaluated and calculated as the average for each test.
| Model | IF_Eval | BBH | GPQA | MMLU Pro | TruthfulQA |
|---------------------------------|-----------|-----------|-----------|-----------|------------|
| Qwen2.5-3B-Instruct | **33.07** | **33.26** | 26.11 | **17.18** | 45.07 |
| Qwen2.5-3B-Instruct-CensorTune | 16.20 | 32.51 | 25.25 | 17.09 | **45.48** |
| Qwen2.5-3B-Instruct-abliterated | 32.96 | 32.83 | 26.23 | 16.42 | 45.40 |
The script used for evaluation can be found inside this repository under [eval.bat](https://huggingface.co/huihui-ai/Qwen2.5-3B-Instruct-CensorTune/blob/main/eval.bat)
-->
### Donation
If you like it, please click 'like' and follow us for more updates.
You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai.
##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
- bitcoin（BTC):
```
bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
```
|
kk-aivio/9951a0e9-375c-4f13-8ad0-b1e386c7fef0
|
kk-aivio
| 2025-04-29T02:44:13Z | 0 | 0 |
peft
|
[
"peft",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:adapter:facebook/opt-350m",
"region:us"
] | null | 2025-04-29T02:43:51Z |
---
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/opt-350m
model-index:
- name: kk-aivio/9951a0e9-375c-4f13-8ad0-b1e386c7fef0
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. -->
# kk-aivio/9951a0e9-375c-4f13-8ad0-b1e386c7fef0
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9291
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
MonoAbe/Qwen2.5-7B-Instruct-Q4_K_M-GGUF
|
MonoAbe
| 2025-04-29T02:43:58Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-04-23T21:06:00Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- llama-cpp
- gguf-my-repo
---
# MonoAbe/Qwen2.5-7B-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`Qwen/Qwen2.5-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo MonoAbe/Qwen2.5-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo MonoAbe/Qwen2.5-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo MonoAbe/Qwen2.5-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo MonoAbe/Qwen2.5-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-q4_k_m.gguf -c 2048
```
|
nytopop/Qwen3-8B.w8a8
|
nytopop
| 2025-04-29T02:42:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-8B",
"base_model:quantized:Qwen/Qwen3-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-04-29T02:39:47Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
pipeline_tag: text-generation
base_model: Qwen/Qwen3-8B
---
Int8 quant for optimized performance on Ampere.
# usage
```shell
uv venv --python 3.12
uv pip install sglang[all] --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python
uv run python -m sglang.launch_server --model-path nytopop/Qwen3-8B.w8a8 --quantization w8a8_int8 --reasoning-parser qwen3
```
# creation
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from datasets import load_dataset
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
model_id = "Qwen/Qwen3-8B"
model_out = "Qwen3-8B.w8a8"
num_samples = 256
max_seq_len = 4096
tokenizer = AutoTokenizer.from_pretrained(model_id)
def preprocess_fn(example):
return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
ds = ds.shuffle().select(range(num_samples))
ds = ds.map(preprocess_fn)
recipe = [
SmoothQuantModifier(smoothing_strength=0.7),
GPTQModifier(sequential=True,targets="Linear",scheme="W8A8",ignore=["lm_head"],dampening_frac=0.01),
]
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype="bfloat16",
)
oneshot(
model=model,
dataset=ds,
recipe=recipe,
max_seq_length=max_seq_len,
num_calibration_samples=num_samples,
output_dir=model_out,
)
```
|
Shifusen/gemma-product-description
|
Shifusen
| 2025-04-29T02:36:18Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T01:57:31Z |
---
library_name: transformers
model_name: gemma-product-description
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-product-description
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="Shifusen/gemma-product-description", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.3
- Pytorch: 2.8.0.dev20250428+cu128
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
BenevolenceMessiah/Qwen3-32B-Q8_0-GGUF
|
BenevolenceMessiah
| 2025-04-29T02:33:59Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Qwen/Qwen3-32B",
"base_model:quantized:Qwen/Qwen3-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-04-29T02:31:24Z |
---
base_model: Qwen/Qwen3-32B
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
# BenevolenceMessiah/Qwen3-32B-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-32B`](https://huggingface.co/Qwen/Qwen3-32B) 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/Qwen3-32B) 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 BenevolenceMessiah/Qwen3-32B-Q8_0-GGUF --hf-file qwen3-32b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo BenevolenceMessiah/Qwen3-32B-Q8_0-GGUF --hf-file qwen3-32b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo BenevolenceMessiah/Qwen3-32B-Q8_0-GGUF --hf-file qwen3-32b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo BenevolenceMessiah/Qwen3-32B-Q8_0-GGUF --hf-file qwen3-32b-q8_0.gguf -c 2048
```
|
YummyYum/Qwen2.5-32B-Instruct-FlagOS-Nvidia
|
YummyYum
| 2025-04-29T02:33:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T02:33:54Z |
# Introduction
Qwen2.5-32B-Instruct-FlagOS-Nvidia provides an all-in-one deployment solution, enabling execution of Qwen2.5-32B-Instruct on Nvidia GPUs. As the first-generation release for the Nvidia-H100-SXM, this package delivers three key features:
1. Comprehensive Integration:
- Integrated with FlagScale (https://github.com/FlagOpen/FlagScale).
- Open-source inference execution code, preconfigured with all necessary software and hardware settings.
- Pre-built Docker image for rapid deployment on Nvidia-H100-SXM.
3. Consistency Validation:
- Evaluation tests verifying consistency of results between the official and ours.
# Technical Summary
## Serving Engine
We use FlagScale as the serving engine to improve the portability of distributed inference.
FlagScale is an end-to-end framework for large models across multiple chips, maximizing computational resource efficiency while ensuring model effectiveness. It ensures both ease of use and high performance for users when deploying models across different chip architectures:
- One-Click Service Deployment: FlagScale provides a unified and simple command execution mechanism, allowing users to fast deploy services seamlessly across various hardware platforms using the same command. This significantly reduces the entry barrier and enhances user experience.
- Automated Deployment Optimization: FlagScale automatically optimizes distributed parallel strategies based on the computational capabilities of different AI chips, ensuring optimal resource allocation and efficient utilization, thereby improving overall deployment performance.
- Automatic Operator Library Switching: Leveraging FlagScale's unified Runner mechanism and deep integration with FlagGems, users can seamlessly switch to the FlagGems operator library for inference by simply adding environment variables in the configuration file.
## Triton Support
We validate the execution of Qwen2.5-32B-Instruct model with a Triton-based operator library as a PyTorch alternative.
We use a variety of Triton-implemented operation kernels to run the Qwen2.5-32B-Instruct model. These kernels come from two main sources:
- Most Triton kernels are provided by FlagGems (https://github.com/FlagOpen/FlagGems). You can enable FlagGems kernels by setting the environment variable USE_FLAGGEMS.
- Also included are Triton kernels from vLLM, such as fused MoE.
# Container Image Download
| | Usage | Nvidia |
| ----------- | ------------------------------------------------------------ | ------------------- |
| Basic Image | basic software environment that supports FlagOS model running | nv_vllm084_gems20250401release:latest |
# Evaluation Results
## Benchmark Result
| Metrics | Qwen2.5-32B-Instruct-CUDA-Nvidia| Qwen2.5-32B-Instruct-FlagOS-Nvidia |
|:-------------------|--------------------------|-----------------------------|
| livebench_new | 0.516 | 0.504 |
| aime | 0.167 | 0.167 |
| GPQA(0-shot) | 0.403 | 0.395 |
| MMLUPro | 0.697 | 0.697 |
| MUSR | 0.577 | 0.570 |
| TheoremQA | 0.151 | 0.151 |
# How to Run Locally
## 📌 Getting Started
### Download open-source weights
```bash
pip install modelscope
modelscope download --model <Model Name> --local_dir <Cache Path>
```
### Download the FlagOS image
```bash
docker pull nv_vllm084_gems20250401release:latest
```
### Start the inference service
```bash
docker run --rm --init --detach \
--net=host --uts=host --ipc=host \
--security-opt=seccomp=unconfined \
--privileged=true \
--ulimit stack=67108864 \
--ulimit memlock=-1 \
--ulimit nofile=1048576:1048576 \
--shm-size=32G \
-v /share:/share \
--gpus all \
--name flagos \
nv_vllm084_gems20250401release:latest \
sleep infinity
docker exec -it flagos bash
```
### Serve
```bash
flagscale serve <Model>
```
# Contributing
We warmly welcome global developers to join us:
1. Submit Issues to report problems
2. Create Pull Requests to contribute code
3. Improve technical documentation
4. Expand hardware adaptation support
# 📞 Contact Us
Scan the QR code below to add our WeChat group
send "FlagRelease"
# License
This project and related model weights are licensed under the MIT License.
|
dcoder0101/llama3_1_MERGED
|
dcoder0101
| 2025-04-29T02:24:27Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T02:23:12Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** dcoder0101
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-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)
|
mountaingiles/mountaingile
|
mountaingiles
| 2025-04-29T02:22:16Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-04-29T02:22:16Z |
---
license: creativeml-openrail-m
---
|
mlx-community/Qwen3-235B-A22B-4bit
|
mlx-community
| 2025-04-29T02:21:32Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-235B-A22B",
"base_model:quantized:Qwen/Qwen3-235B-A22B",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-04-29T00:51:01Z |
---
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-235B-A22B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- mlx
base_model: Qwen/Qwen3-235B-A22B
---
# mlx-community/Qwen3-235B-A22B-4bit
This model [mlx-community/Qwen3-235B-A22B-4bit](https://huggingface.co/mlx-community/Qwen3-235B-A22B-4bit) was
converted to MLX format from [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B)
using mlx-lm version **0.24.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen3-235B-A22B-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Beagledata001/Elpis-70B-VR
|
Beagledata001
| 2025-04-29T02:21:29Z | 20 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"chat",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-15T07:40:13Z |
---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
base_model: Beagledata001/Elpis-70B
tags:
- chat
library_name: transformers
---
# Elpis-70B-VR
## Introduction
With the rapid development of artificial intelligence technology, large-scale models have become a core engine driving industry innovation.
Now We are proud to officially release the Elpis-70B-VR large-scale model.
## Core Technical Highlights
### 1. High-Efficiency, Low-Cost Data Synthesis Technology
– **Role-Driven Diversity**: Covers 250,000+ industry roles and generates differentiated instruction data through dynamic prompts, ensuring broad applicability of training data.
– **Constraint Enhancement & Quality Assurance**: Combines manually curated verifiable examples with pre-trained model extensions. Utilizes n-gram overlap detection and vector similarity matching for deduplication. Multi-round expert reviews ensure data precision.
– **Cost Optimization**: Improves data generation efficiency by 40%, enabling rapid adaptation to new scenarios.
### 2. Domain Enhancement via Verifiable Reinforcement Learning
– **Mathematical & Coding Proficiency**: Generates tiered questions based on role requirements. Code answers are validated via unit tests and execution results; mathematical solutions are verified by dedicated solvers for logical and numerical accuracy.
– **Reward Model Optimization**: Fine-tunes reward models using standardized key-value pair data (e.g., exam answers) to mitigate labeling noise.
– **Policy Iteration**: Optimizes policy models via RLVR algorithms, providing rewards by verified answers
## Performance
### Special Notes
- The DeepSeek-R1 model cannot be launched with vllm batch processing; thus, MMLU scores are sourced directly from the official site.
- For DeepSeek-R1, due to longer reasoning steps, the generation length for HumanEval and HumanEval+ is set to 6144 tokens, while for others it is 4096 tokens.
- For DeepSeek-R1 on DROP, the prompt requires direct output of the answer. However, the R1 model prepends "Answer:" before the response, causing a drop in the evaluation metric (F1 score).
- For models like qwen2.5, Llama3.3-70B, QwQ-32B, and DeepSeek-R1, the MATH output formats are inconsistent, so correctness is judged based on large model verification rather than strict format matching.
- For inference models QwQ-32B and DeepSeek-R1-Distill-Llama-70B, due to longer reasoning times, the generation length for PopQA, BBH, and GSM8K is set to 4096 tokens.
## Using the model
### loading with HuggingFace
To load the model with HuggingFace, use the following snippet:
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Beagledata001/Elpis-70B-VR"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are Elpis, finetuned by Beagledata, You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
### VLLM
the model can be served with:
```
vllm serve Beagledata001/Elpis-70B-VR --max_model_len=4096
```
Note that it is recommended not to use a length greater than 4096, because the model is fine-tuned with a length of 4096, and the effect cannot be guaranteed after a longer length.
## Safety & Intended Use
This model has undergone preference tuning and verifiable-reward RL to reduce harmful or low-quality outputs.
Not immune to generating incorrect or biased responses—do not use for high-stakes decisions (medical, legal, financial) without human oversight.
Consider adding moderation layers or human-in-the-loop checks in production.
## License
Released under the Apache 2.0 License.
## Contact
For questions, feedback, or collaboration, please open an issue on the Hugging Face model repository.
|
Docty/dreambooth-marine-growth-lora
|
Docty
| 2025-04-29T02:17:35Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"lora",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:adapter:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-04-29T02:03:46Z |
---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: Cinematic under water view of an object whose surface is surrounded
with severe sks growth of marine and corrosion.
tags:
- text-to-image
- diffusers
- lora
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
- 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. -->
# LoRA DreamBooth - Docty/dreambooth-marine-growth-lora
These are LoRA adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on Cinematic under water view of an object whose surface is surrounded with severe sks growth of marine and corrosion. using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA 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]
|
LeighJ/my_awesome_eli5_clm-model
|
LeighJ
| 2025-04-29T02:16:47Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:eli5_category",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-28T10:41:13Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilgpt2
tags:
- generated_from_trainer
datasets:
- eli5_category
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on the eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8212
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.919 | 1.0 | 1304 | 3.8306 |
| 3.8254 | 2.0 | 2608 | 3.8228 |
| 3.7923 | 3.0 | 3912 | 3.8212 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0
- Datasets 3.5.0
- Tokenizers 0.21.1
|
ibrahimkettaneh/Hammer2.0-0.5b-4.5bpw-h8-exl2
|
ibrahimkettaneh
| 2025-04-29T02:16:37Z | 5 | 1 | null |
[
"qwen2",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"dataset:Salesforce/xlam-function-calling-60k",
"dataset:MadeAgents/xlam-irrelevance-7.5k",
"arxiv:2410.04587",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"license:cc-by-4.0",
"region:us"
] | null | 2024-12-01T12:15:04Z |
---
license: cc-by-4.0
datasets:
- Salesforce/xlam-function-calling-60k
- MadeAgents/xlam-irrelevance-7.5k
base_model:
- Qwen/Qwen2.5-0.5B-Instruct
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---
# Hammer2.0-0.5b Function Calling Model
## Introduction
We're excited to release lightweight Hammer 2.0 models ([0.5B](https://huggingface.co/MadeAgents/Hammer2.0-0.5b) , [1.5B](https://huggingface.co/MadeAgents/Hammer2.0-1.5b) , [3B](https://huggingface.co/MadeAgents/Hammer2.0-3b) , and [7B](https://huggingface.co/MadeAgents/Hammer2.0-7b)) with strong function calling capability, which empower developers to build personalized, on-device agentic applications.
## Model Details
Hammer2.0 finetuned based on [Qwen 2.5 series](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e) and [Qwen 2.5 coder series](https://huggingface.co/collections/Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f) using function masking techniques. It's trained using the [APIGen Function Calling Datasets](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) containing 60,000 samples, supplemented by [xlam-irrelevance-7.5k](https://huggingface.co/datasets/MadeAgents/xlam-irrelevance-7.5k) we generated. Hammer2.0 has achieved exceptional performances across numerous function calling benchmarks. For more details, please refer to [Hammer: Robust Function-Calling for On-Device Language Models via Function Masking](https://arxiv.org/abs/2410.04587) and [Hammer GitHub repository](https://github.com/MadeAgents/Hammer) .
## Evaluation
The evaluation results of Hammer 2.0 models on the Berkeley Function-Calling Leaderboard (BFCL-v3) are presented in the following table:
<div style="text-align: center;">
<img src="v2_figures/bfcl.PNG" alt="overview" width="1000" style="margin: auto;">
</div>
Our Hammer 2.0 series consistently achieves corresponding best performance at comparable scales. The 7B model outperforms most function calling enchanced models, and the 1.5B model also achieves unexpected performance.
In addition, we evaluated the Hammer 2.0 models on other academic benchmarks to further demonstrate the generalization ability of our models.
<div style="text-align: center;">
<img src="v2_figures/others-v2.PNG" alt="overview" width="1000" style="margin: auto;">
</div>
Hammer 2.0 models showcase highly stable performance, suggesting the robustness of Hammer 2.0 series. In contrast, the baseline approaches display varying levels of effectiveness.
## Requiements
The code of Hammer 2.0 models have been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`.
## How to Use
This is a simple example of how to use our model.
~~~python
import json
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "MadeAgents/Hammer2.0-0.5b"
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Please use our provided instruction prompt for best performance
TASK_INSTRUCTION = """You are a tool calling assistant. In order to complete the user's request, you need to select one or more appropriate tools from the following tools and fill in the correct values for the tool parameters. Your specific tasks are:
1. Make one or more function/tool calls to meet the request based on the question.
2. If none of the function can be used, point it out and refuse to answer.
3. If the given question lacks the parameters required by the function, also point it out.
"""
FORMAT_INSTRUCTION = """
The output MUST strictly adhere to the following JSON format, and NO other text MUST be included.
The example format is as follows. Please make sure the parameter type is correct. If no function call is needed, please directly output an empty list '[]'
```
[
{"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},
... (more tool calls as required)
]
```
"""
# Define the input query and available tools
query = "Where can I find live giveaways for beta access and games? And what's the weather like in New York, US?"
live_giveaways_by_type = {
"name": "live_giveaways_by_type",
"description": "Retrieve live giveaways from the GamerPower API based on the specified type.",
"parameters": {
"type": "object",
"properties": {
"type": {
"type": "string",
"description": "The type of giveaways to retrieve (e.g., game, loot, beta).",
"default": "game"
}
},
"required": ["type"]
}
}
get_current_weather={
"name": "get_current_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
get_stock_price={
"name": "get_stock_price",
"description": "Retrieves the current stock price for a given ticker symbol. The ticker symbol must be a valid symbol for a publicly traded company on a major US stock exchange like NYSE or NASDAQ. The tool will return the latest trade price in USD. It should be used when the user asks about the current or most recent price of a specific stock. It will not provide any other information about the stock or company.",
"parameters": {
"type": "object",
"properties": {
"ticker": {
"type": "string",
"description": "The stock ticker symbol, e.g. AAPL for Apple Inc."
}
},
"required": ["ticker"]
}
}
def convert_to_format_tool(tools):
''''''
if isinstance(tools, dict):
format_tools = {
"name": tools["name"],
"description": tools["description"],
"parameters": tools["parameters"].get("properties", {}),
}
required = tools["parameters"].get("required", [])
for param in required:
format_tools["parameters"][param]["required"] = True
for param in format_tools["parameters"].keys():
if "default" in format_tools["parameters"][param]:
default = format_tools["parameters"][param]["default"]
format_tools["parameters"][param]["description"]+=f"default is \'{default}\'"
return format_tools
elif isinstance(tools, list):
return [convert_to_format_tool(tool) for tool in tools]
else:
return tools
# Helper function to build the input prompt for our model
def build_prompt(task_instruction: str, format_instruction: str, tools: list, query: str):
prompt = f"[BEGIN OF TASK INSTRUCTION]\n{task_instruction}\n[END OF TASK INSTRUCTION]\n\n"
prompt += f"[BEGIN OF AVAILABLE TOOLS]\n{json.dumps(tools)}\n[END OF AVAILABLE TOOLS]\n\n"
prompt += f"[BEGIN OF FORMAT INSTRUCTION]\n{format_instruction}\n[END OF FORMAT INSTRUCTION]\n\n"
prompt += f"[BEGIN OF QUERY]\n{query}\n[END OF QUERY]\n\n"
return prompt
# Build the input and start the inference
openai_format_tools = [live_giveaways_by_type, get_current_weather,get_stock_price]
format_tools = convert_to_format_tool(openai_format_tools)
content = build_prompt(TASK_INSTRUCTION, FORMAT_INSTRUCTION, format_tools, query)
messages=[
{ 'role': 'user', 'content': content}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
# tokenizer.eos_token_id is the id of <|EOT|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
~~~
|
artcorenft/haute-couture
|
artcorenft
| 2025-04-29T02:15:27Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-04-29T02:15:17Z |
---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt:
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
---
# haute couture
<Gallery />
## Model description
## Trigger words
You should use `` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/artcorenft/haute-couture/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
|
acrobatlm/legal-ft-2
|
acrobatlm
| 2025-04-29T02:14:27Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:156",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:Snowflake/snowflake-arctic-embed-l",
"base_model:finetune:Snowflake/snowflake-arctic-embed-l",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-04-29T02:13:10Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:156
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: How did the competition between companies affect the railway lines
built during the 1800s?
sentences:
- 'An interesting point of comparison here could be the way railways rolled out
around the world in the 1800s. Constructing these required enormous investments
and had a massive environmental impact, and many of the lines that were built
turned out to be unnecessary—sometimes multiple lines from different companies
serving the exact same routes!
The resulting bubbles contributed to several financial crashes, see Wikipedia
for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They
left us with a lot of useful infrastructure and a great deal of bankruptcies and
environmental damage.
The year of slop'
- 'An interesting point of comparison here could be the way railways rolled out
around the world in the 1800s. Constructing these required enormous investments
and had a massive environmental impact, and many of the lines that were built
turned out to be unnecessary—sometimes multiple lines from different companies
serving the exact same routes!
The resulting bubbles contributed to several financial crashes, see Wikipedia
for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They
left us with a lot of useful infrastructure and a great deal of bankruptcies and
environmental damage.
The year of slop'
- 'OpenAI made GPT-4o free for all users in May, and Claude 3.5 Sonnet was freely
available from its launch in June. This was a momentus change, because for the
previous year free users had mostly been restricted to GPT-3.5 level models, meaning
new users got a very inaccurate mental model of what a capable LLM could actually
do.
That era appears to have ended, likely permanently, with OpenAI’s launch of ChatGPT
Pro. This $200/month subscription service is the only way to access their most
capable model, o1 Pro.
Since the trick behind the o1 series (and the future models it will undoubtedly
inspire) is to expend more compute time to get better results, I don’t think those
days of free access to the best available models are likely to return.'
- source_sentence: Which multi-modal models were released by vendors in 2024 according
to the text?
sentences:
- 'The boring yet crucial secret behind good system prompts is test-driven development.
You don’t write down a system prompt and find ways to test it. You write down
tests and find a system prompt that passes them.
It’s become abundantly clear over the course of 2024 that writing good automated
evals for LLM-powered systems is the skill that’s most needed to build useful
applications on top of these models. If you have a strong eval suite you can adopt
new models faster, iterate better and build more reliable and useful product features
than your competition.
Vercel’s Malte Ubl:'
- 'In 2024, almost every significant model vendor released multi-modal models. We
saw the Claude 3 series from Anthropic in March, Gemini 1.5 Pro in April (images,
audio and video), then September brought Qwen2-VL and Mistral’s Pixtral 12B and
Meta’s Llama 3.2 11B and 90B vision models. We got audio input and output from
OpenAI in October, then November saw SmolVLM from Hugging Face and December saw
image and video models from Amazon Nova.
In October I upgraded my LLM CLI tool to support multi-modal models via attachments.
It now has plugins for a whole collection of different vision models.'
- 'The environmental impact got much, much worse
The much bigger problem here is the enormous competitive buildout of the infrastructure
that is imagined to be necessary for these models in the future.
Companies like Google, Meta, Microsoft and Amazon are all spending billions of
dollars rolling out new datacenters, with a very material impact on the electricity
grid and the environment. There’s even talk of spinning up new nuclear power stations,
but those can take decades.
Is this infrastructure necessary? DeepSeek v3’s $6m training cost and the continued
crash in LLM prices might hint that it’s not. But would you want to be the big
tech executive that argued NOT to build out this infrastructure only to be proven
wrong in a few years’ time?'
- source_sentence: How is a prompt without evals, models, and UX compared in the text?
sentences:
- 'DeepSeek v3 is a huge 685B parameter model—one of the largest openly licensed
models currently available, significantly bigger than the largest of Meta’s Llama
series, Llama 3.1 405B.
Benchmarks put it up there with Claude 3.5 Sonnet. Vibe benchmarks (aka the Chatbot
Arena) currently rank it 7th, just behind the Gemini 2.0 and OpenAI 4o/o1 models.
This is by far the highest ranking openly licensed model.
The really impressive thing about DeepSeek v3 is the training cost. The model
was trained on 2,788,000 H800 GPU hours at an estimated cost of $5,576,000. Llama
3.1 405B trained 30,840,000 GPU hours—11x that used by DeepSeek v3, for a model
that benchmarks slightly worse.'
- 'When @v0 first came out we were paranoid about protecting the prompt with all
kinds of pre and post processing complexity.
We completely pivoted to let it rip. A prompt without the evals, models, and especially
UX is like getting a broken ASML machine without a manual'
- 'So far, I think they’re a net positive. I’ve used them on a personal level to
improve my productivity (and entertain myself) in all sorts of different ways.
I think people who learn how to use them effectively can gain a significant boost
to their quality of life.
A lot of people are yet to be sold on their value! Some think their negatives
outweigh their positives, some think they are all hot air, and some even think
they represent an existential threat to humanity.
They’re actually quite easy to build
The most surprising thing we’ve learned about LLMs this year is that they’re actually
quite easy to build.'
- source_sentence: Why does the author suggest that writing code might be the best
application for large language models (LLMs)?
sentences:
- 'We already knew LLMs were spookily good at writing code. If you prompt them right,
it turns out they can build you a full interactive application using HTML, CSS
and JavaScript (and tools like React if you wire up some extra supporting build
mechanisms)—often in a single prompt.
Anthropic kicked this idea into high gear when they released Claude Artifacts,
a groundbreaking new feature that was initially slightly lost in the noise due
to being described half way through their announcement of the incredible Claude
3.5 Sonnet.
With Artifacts, Claude can write you an on-demand interactive application and
then let you use it directly inside the Claude interface.
Here’s my Extract URLs app, entirely generated by Claude:'
- 'The two main categories I see are people who think AI agents are obviously things
that go and act on your behalf—the travel agent model—and people who think in
terms of LLMs that have been given access to tools which they can run in a loop
as part of solving a problem. The term “autonomy” is often thrown into the mix
too, again without including a clear definition.
(I also collected 211 definitions on Twitter a few months ago—here they are in
Datasette Lite—and had gemini-exp-1206 attempt to summarize them.)
Whatever the term may mean, agents still have that feeling of perpetually “coming
soon”.'
- 'A lot of people are excited about AI agents—an infuriatingly vague term that
seems to be converging on “AI systems that can go away and act on your behalf”.
We’ve been talking about them all year, but I’ve seen few if any examples of them
running in production, despite lots of exciting prototypes.
I think this is because of gullibility.
Can we solve this? Honestly, I’m beginning to suspect that you can’t fully solve
gullibility without achieving AGI. So it may be quite a while before those agent
dreams can really start to come true!
Code may be the best application
Over the course of the year, it’s become increasingly clear that writing code
is one of the things LLMs are most capable of.'
- source_sentence: What is considered the most important factor in determining the
quality of the resulting model?
sentences:
- 'Intuitively, one would expect that systems this powerful would take millions
of lines of complex code. Instead, it turns out a few hundred lines of Python
is genuinely enough to train a basic version!
What matters most is the training data. You need a lot of data to make these
things work, and the quantity and quality of the training data appears to be the
most important factor in how good the resulting model is.
If you can gather the right data, and afford to pay for the GPUs to train it,
you can build an LLM.'
- 'Law is not ethics. Is it OK to train models on people’s content without their
permission, when those models will then be used in ways that compete with those
people?
As the quality of results produced by AI models has increased over the year, these
questions have become even more pressing.
The impact on human society in terms of these models is already huge, if difficult
to objectively measure.
People have certainly lost work to them—anecdotally, I’ve seen this for copywriters,
artists and translators.
There are a great deal of untold stories here. I’m hoping 2024 sees significant
amounts of dedicated journalism on this topic.
My blog in 2023
Here’s a tag cloud for content I posted to my blog in 2023 (generated using Django
SQL Dashboard):'
- 'I’m still trying to figure out the best patterns for doing this for my own work.
Everyone knows that evals are important, but there remains a lack of great guidance
for how to best implement them—I’m tracking this under my evals tag. My SVG pelican
riding a bicycle benchmark is a pale imitation of what a real eval suite should
look like.
Apple Intelligence is bad, Apple’s MLX library is excellent
As a Mac user I’ve been feeling a lot better about my choice of platform this
year.
Last year it felt like my lack of a Linux/Windows machine with an NVIDIA GPU
was a huge disadvantage in terms of trying out new models.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.875
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 1.0
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.875
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.20000000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.875
name: Cosine Recall@1
- type: cosine_recall@3
value: 1.0
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9484108127976215
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9305555555555555
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9305555555555557
name: Cosine Map@100
---
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("acrobatlm/legal-ft-2")
# Run inference
sentences = [
'What is considered the most important factor in determining the quality of the resulting model?',
'Intuitively, one would expect that systems this powerful would take millions of lines of complex code. Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!\nWhat matters most is the training data. You need a lot of data to make these things work, and the quantity and quality of the training data appears to be the most important factor in how good the resulting model is.\nIf you can gather the right data, and afford to pay for the GPUs to train it, you can build an LLM.',
'I’m still trying to figure out the best patterns for doing this for my own work. Everyone knows that evals are important, but there remains a lack of great guidance for how to best implement them—I’m tracking this under my evals tag. My SVG pelican riding a bicycle benchmark is a pale imitation of what a real eval suite should look like.\nApple Intelligence is bad, Apple’s MLX library is excellent\nAs a Mac user I’ve been feeling a lot better about my choice of platform this year.\nLast year it felt like my lack of a Linux/Windows machine with an NVIDIA GPU was a huge disadvantage in terms of trying out new models.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.875 |
| cosine_accuracy@3 | 1.0 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.875 |
| cosine_precision@3 | 0.3333 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.875 |
| cosine_recall@3 | 1.0 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.9484** |
| cosine_mrr@10 | 0.9306 |
| cosine_map@100 | 0.9306 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 156 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 156 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 13 tokens</li><li>mean: 20.79 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 135.22 tokens</li><li>max: 214 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:-----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What significant development in Artificial Intelligence was highlighted as a breakthrough in 2023?</code> | <code>Stuff we figured out about AI in 2023<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>Simon Willison’s Weblog<br>Subscribe<br><br><br><br><br><br><br>Stuff we figured out about AI in 2023<br>31st December 2023<br>2023 was the breakthrough year for Large Language Models (LLMs). I think it’s OK to call these AI—they’re the latest and (currently) most interesting development in the academic field of Artificial Intelligence that dates back to the 1950s.<br>Here’s my attempt to round up the highlights in one place!</code> |
| <code>How does the author describe the historical context of Large Language Models in relation to the field of Artificial Intelligence?</code> | <code>Stuff we figured out about AI in 2023<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>Simon Willison’s Weblog<br>Subscribe<br><br><br><br><br><br><br>Stuff we figured out about AI in 2023<br>31st December 2023<br>2023 was the breakthrough year for Large Language Models (LLMs). I think it’s OK to call these AI—they’re the latest and (currently) most interesting development in the academic field of Artificial Intelligence that dates back to the 1950s.<br>Here’s my attempt to round up the highlights in one place!</code> |
| <code>What are some capabilities and limitations of large language models mentioned in the text?</code> | <code>Large Language Models<br>They’re actually quite easy to build<br>You can run LLMs on your own devices<br>Hobbyists can build their own fine-tuned models<br>We don’t yet know how to build GPT-4<br>Vibes Based Development<br>LLMs are really smart, and also really, really dumb<br>Gullibility is the biggest unsolved problem<br>Code may be the best application<br>The ethics of this space remain diabolically complex<br>My blog in 2023</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | cosine_ndcg@10 |
|:-----:|:----:|:--------------:|
| 1.0 | 16 | 0.9609 |
| 2.0 | 32 | 0.9455 |
| 3.0 | 48 | 0.9455 |
| 3.125 | 50 | 0.9455 |
| 4.0 | 64 | 0.9484 |
| 5.0 | 80 | 0.9484 |
| 6.0 | 96 | 0.9484 |
| 6.25 | 100 | 0.9484 |
| 7.0 | 112 | 0.9484 |
| 8.0 | 128 | 0.9484 |
| 9.0 | 144 | 0.9484 |
| 9.375 | 150 | 0.9484 |
| 10.0 | 160 | 0.9484 |
### Framework Versions
- Python: 3.13.1
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.6.0
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
kylewhm/test_demo
|
kylewhm
| 2025-04-29T02:14:09Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-04-29T02:00:54Z |
---
base_model: CompVis/stable-diffusion-v1-4
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: a photo of sks dog
tags:
- text-to-image
- diffusers
- lora
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
- 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. -->
# LoRA DreamBooth - kylewhm/test_demo
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA 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]
|
John6666/flarediffusion-v4-sdxl
|
John6666
| 2025-04-29T02:13:50Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"style",
"details",
"color",
"vibrant",
"lighting",
"style versatility",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-04-29T02:06:27Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- style
- details
- color
- vibrant
- lighting
- style versatility
- illustrious
base_model: OnomaAIResearch/Illustrious-xl-early-release-v0
---
Original model is [here](https://civitai.com/models/1503705/flarediffusion?modelVersionId=1721639).
This model created by [FlareAI_Studios](https://civitai.com/user/FlareAI_Studios).
|
RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf
|
RichardErkhov
| 2025-04-29T02:09:33Z | 0 | 0 | null |
[
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T01:23:45Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Phi-3.5-Mounjaro-ep1 - GGUF
- Model creator: https://huggingface.co/njprogrammer/
- Original model: https://huggingface.co/njprogrammer/Phi-3.5-Mounjaro-ep1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Phi-3.5-Mounjaro-ep1.Q2_K.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q2_K.gguf) | Q2_K | 1.32GB |
| [Phi-3.5-Mounjaro-ep1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.IQ3_XS.gguf) | IQ3_XS | 1.51GB |
| [Phi-3.5-Mounjaro-ep1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.IQ3_S.gguf) | IQ3_S | 1.57GB |
| [Phi-3.5-Mounjaro-ep1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q3_K_S.gguf) | Q3_K_S | 1.57GB |
| [Phi-3.5-Mounjaro-ep1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.IQ3_M.gguf) | IQ3_M | 1.73GB |
| [Phi-3.5-Mounjaro-ep1.Q3_K.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q3_K.gguf) | Q3_K | 1.82GB |
| [Phi-3.5-Mounjaro-ep1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q3_K_M.gguf) | Q3_K_M | 1.82GB |
| [Phi-3.5-Mounjaro-ep1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q3_K_L.gguf) | Q3_K_L | 1.94GB |
| [Phi-3.5-Mounjaro-ep1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.IQ4_XS.gguf) | IQ4_XS | 1.93GB |
| [Phi-3.5-Mounjaro-ep1.Q4_0.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q4_0.gguf) | Q4_0 | 2.03GB |
| [Phi-3.5-Mounjaro-ep1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.IQ4_NL.gguf) | IQ4_NL | 2.04GB |
| [Phi-3.5-Mounjaro-ep1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q4_K_S.gguf) | Q4_K_S | 2.04GB |
| [Phi-3.5-Mounjaro-ep1.Q4_K.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q4_K.gguf) | Q4_K | 2.23GB |
| [Phi-3.5-Mounjaro-ep1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q4_K_M.gguf) | Q4_K_M | 2.23GB |
| [Phi-3.5-Mounjaro-ep1.Q4_1.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q4_1.gguf) | Q4_1 | 2.24GB |
| [Phi-3.5-Mounjaro-ep1.Q5_0.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q5_0.gguf) | Q5_0 | 2.46GB |
| [Phi-3.5-Mounjaro-ep1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q5_K_S.gguf) | Q5_K_S | 2.46GB |
| [Phi-3.5-Mounjaro-ep1.Q5_K.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q5_K.gguf) | Q5_K | 2.62GB |
| [Phi-3.5-Mounjaro-ep1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q5_K_M.gguf) | Q5_K_M | 2.62GB |
| [Phi-3.5-Mounjaro-ep1.Q5_1.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q5_1.gguf) | Q5_1 | 2.68GB |
| [Phi-3.5-Mounjaro-ep1.Q6_K.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q6_K.gguf) | Q6_K | 2.92GB |
| [Phi-3.5-Mounjaro-ep1.Q8_0.gguf](https://huggingface.co/RichardErkhov/njprogrammer_-_Phi-3.5-Mounjaro-ep1-gguf/blob/main/Phi-3.5-Mounjaro-ep1.Q8_0.gguf) | Q8_0 | 3.78GB |
Original model description:
---
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]
|
iamwille/wav2vec2-base-igbo-demo
|
iamwille
| 2025-04-29T02:04:19Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T00:22:55Z |
---
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]
|
magnifi/parser_user_v39b_epoch_6_lr_0.002
|
magnifi
| 2025-04-29T02:00:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T01:58:19Z |
---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** magnifi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
nahed22/lora_model
|
nahed22
| 2025-04-29T01:59:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-27T11:57:21Z |
---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** nahed22
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
annetteambitious8/annetteambitious8
|
annetteambitious8
| 2025-04-29T01:59:13Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2025-04-29T01:59:13Z |
---
license: bigscience-bloom-rail-1.0
---
|
shibajustfor/419f08f9-45a8-46f9-b1ca-9075e5a3a153
|
shibajustfor
| 2025-04-29T01:59:01Z | 0 | 0 |
transformers
|
[
"transformers",
"generated_from_trainer",
"unsloth",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T01:57:43Z |
---
library_name: transformers
model_name: shibajustfor/419f08f9-45a8-46f9-b1ca-9075e5a3a153
tags:
- generated_from_trainer
- unsloth
licence: license
---
# Model Card for shibajustfor/419f08f9-45a8-46f9-b1ca-9075e5a3a153
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
RobAgrees/quantized-dia-1.6B-int8
|
RobAgrees
| 2025-04-29T01:56:13Z | 0 | 0 | null |
[
"safetensors",
"dia",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"text-to-speech",
"en",
"arxiv:2305.09636",
"license:apache-2.0",
"region:us"
] |
text-to-speech
| 2025-04-28T22:40:00Z |
---
license: apache-2.0
pipeline_tag: text-to-speech
language:
- en
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
widget:
- text: >-
[S1] Dia is an open weights text to dialogue model. [S2] You get full
control over scripts and voices. [S1] Wow. Amazing. (laughs) [S2] Try it
now on Git hub or Hugging Face.
example_title: Dia intro
- text: >-
[S1] Oh fire! Oh my goodness! What's the procedure? What to we do people?
The smoke could be coming through an air duct! [S2] Oh my god! Okay.. it's
happening. Everybody stay calm! [S1] What's the procedure... [S2]
Everybody stay fucking calm!!!... Everybody fucking calm down!!!!! [S1]
No! No! If you touch the handle, if its hot there might be a fire down the
hallway!
example_title: Panic protocol
---
# Quantized Dia 1.6B (INT8)
This is a dynamic int8 quantized version of [nari-labs/Dia-1.6B](https://huggingface.co/nari-labs/Dia-1.6B).
It uses dynamic quantization for lighter deployment and faster inference.
Original model: **float16**, ~6.4GB
Quantized model: **int8 dynamic**, ~6.4GB, ~20% faster inference
## ⚡️ Quickstart
This will open a Gradio UI that you can work on.
```bash
git clone --branch int8-dia https://github.com/RobertAgee/dia.git
cd dia && uv run app.py
```
or if you do not have `uv` pre-installed:
```bash
git clone --branch int8-dia https://github.com/RobertAgee/dia.git
cd dia
python -m venv .venv
source .venv/bin/activate
pip install uv
uv run app.py
```
Uploaded by [RobertAgee](https://github.com/RobertAgee) and [RobAgrees](https://huggingface.co/RobAgrees.
> Quantized automatically with PyTorch dynamic quantization in Google Colab.
## Original Readme:
<center>
<a href="https://github.com/nari-labs/dia">
<img src="https://github.com/nari-labs/dia/raw/main/dia/static/images/banner.png">
</a>
</center>
Dia is a 1.6B parameter text to speech model created by Nari Labs. It was pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.
Dia **directly generates highly realistic dialogue from a transcript**. You can condition the output on audio, enabling emotion and tone control. The model can also produce nonverbal communications like laughter, coughing, clearing throat, etc.
To accelerate research, we are providing access to pretrained model checkpoints and inference code. The model weights are hosted on [Hugging Face](https://huggingface.co/nari-labs/Dia-1.6B). The model only supports English generation at the moment.
We also provide a [demo page](https://yummy-fir-7a4.notion.site/dia) comparing our model to [ElevenLabs Studio](https://elevenlabs.io/studio) and [Sesame CSM-1B](https://github.com/SesameAILabs/csm).
- (Update) We have a ZeroGPU Space running! Try it now [here](https://huggingface.co/spaces/nari-labs/Dia-1.6B). Thanks to the HF team for the support :)
- Join our [discord server](https://discord.gg/yBrqQ9Dd) for community support and access to new features.
- Play with a larger version of Dia: generate fun conversations, remix content, and share with friends. 🔮 Join the [waitlist](https://tally.so/r/meokbo) for early access.
## ⚡️ Quickstart
This will open a Gradio UI that you can work on.
```bash
git clone https://github.com/nari-labs/dia.git
cd dia && uv run app.py
```
or if you do not have `uv` pre-installed:
```bash
git clone https://github.com/nari-labs/dia.git
cd dia
python -m venv .venv
source .venv/bin/activate
pip install uv
uv run app.py
```
Note that the model was not fine-tuned on a specific voice. Hence, you will get different voices every time you run the model.
You can keep speaker consistency by either adding an audio prompt (a guide coming VERY soon - try it with the second example on Gradio for now), or fixing the seed.
## Features
- Generate dialogue via `[S1]` and `[S2]` tag
- Generate non-verbal like `(laughs)`, `(coughs)`, etc.
- Below verbal tags will be recognized, but might result in unexpected output.
- `(laughs), (clears throat), (sighs), (gasps), (coughs), (singing), (sings), (mumbles), (beep), (groans), (sniffs), (claps), (screams), (inhales), (exhales), (applause), (burps), (humming), (sneezes), (chuckle), (whistles)`
- Voice cloning. See [`example/voice_clone.py`](example/voice_clone.py) for more information.
- In the Hugging Face space, you can upload the audio you want to clone and place its transcript before your script. Make sure the transcript follows the required format. The model will then output only the content of your script.
## ⚙️ Usage
### As a Python Library
```python
import soundfile as sf
from dia.model import Dia
model = Dia.from_pretrained("nari-labs/Dia-1.6B")
text = "[S1] Dia is an open weights text to dialogue model. [S2] You get full control over scripts and voices. [S1] Wow. Amazing. (laughs) [S2] Try it now on Git hub or Hugging Face."
output = model.generate(text)
sf.write("simple.mp3", output, 44100)
```
A pypi package and a working CLI tool will be available soon.
## 💻 Hardware and Inference Speed
Dia has been tested on only GPUs (pytorch 2.0+, CUDA 12.6). CPU support is to be added soon.
The initial run will take longer as the Descript Audio Codec also needs to be downloaded.
On enterprise GPUs, Dia can generate audio in real-time. On older GPUs, inference time will be slower.
For reference, on a A4000 GPU, Dia roughly generates 40 tokens/s (86 tokens equals 1 second of audio).
`torch.compile` will increase speeds for supported GPUs.
The full version of Dia requires around 10GB of VRAM to run. We will be adding a quantized version in the future.
If you don't have hardware available or if you want to play with bigger versions of our models, join the waitlist [here](https://tally.so/r/meokbo).
## 🪪 License
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
## ⚠️ Disclaimer
This project offers a high-fidelity speech generation model intended for research and educational use. The following uses are **strictly forbidden**:
- **Identity Misuse**: Do not produce audio resembling real individuals without permission.
- **Deceptive Content**: Do not use this model to generate misleading content (e.g. fake news)
- **Illegal or Malicious Use**: Do not use this model for activities that are illegal or intended to cause harm.
By using this model, you agree to uphold relevant legal standards and ethical responsibilities. We **are not responsible** for any misuse and firmly oppose any unethical usage of this technology.
## 🔭 TODO / Future Work
- Docker support.
- Optimize inference speed.
- Add quantization for memory efficiency.
## 🤝 Contributing
We are a tiny team of 1 full-time and 1 part-time research-engineers. We are extra-welcome to any contributions!
Join our [Discord Server](https://discord.gg/yBrqQ9Dd) for discussions.
## 🤗 Acknowledgements
- We thank the [Google TPU Research Cloud program](https://sites.research.google/trc/about/) for providing computation resources.
- Our work was heavily inspired by [SoundStorm](https://arxiv.org/abs/2305.09636), [Parakeet](https://jordandarefsky.com/blog/2024/parakeet/), and [Descript Audio Codec](https://github.com/descriptinc/descript-audio-codec).
- HuggingFace for providing the ZeroGPU Grant.
- "Nari" is a pure Korean word for lily.
- We thank Jason Y. for providing help with data filtering.
|
maxhirez/mdnaPlus-gemma-3-27b-it
|
maxhirez
| 2025-04-29T01:54:41Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"gemma3",
"text-generation",
"conversational",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"license:gemma",
"region:us"
] |
text-generation
| 2025-04-29T01:03:39Z |
---
license: gemma
library_name: mlx
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-27b-it
tags:
- mlx
---
# maxhirez/mdna-gemma-3-27b-it
This model [maxhirez/mdna-gemma-3-27b-it](https://huggingface.co/maxhirez/mdna-gemma-3-27b-it) was
converted to MLX format from [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it)
using mlx-lm version **0.22.4**.
## Manager's Discussion and Analysis generator.
Trained on data set refined from all (public domain by
definition) [SEC EDGAR] (https://www.sec.gov/edgar/search/) quarterly and annual report filings before
May 2023 where the company's stock price appreciated in the period from 7 days prior to 7 days after
the report filing. Batches of data were separated into prompt/completion pairs with the prompts
generated by Llama3.2:3b. In contrast to the [Llama 3 8B instruct version](https://huggingface.co/maxhirez/MDNAPlus-Llama-3-8B-4b, the dataset was adopted to replace
numbers such as dollar amounts, percentages, dates, etc. with tags \<DOLLARS>, \<PERCENTAGE>, \<DATE>,
\<MONTH>, \<YEAR>, etc. LoRA executed by MLX-LM.
(In the future, automated means may be implemented to replace tags with prompted values but for now it's manual.)
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("maxhirez/mdna-gemma-3-27b-it")
prompt = '''You are the CEO of a NASDAQ listed entity. Generate the Q2 financial
report for a quarter where the enterprise beat expectations by 2% ($4BN)
but earnings are down from last quarter due to tarif related uncertainty.'''
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
## DISCLAIMER
THIS MODEL WAS CREATED FOR EDUCATIONAL PURPOSES. USERS TAKE FULL RESPONSIBILITY FOR THE ACCURACY OF GENERATIONS IF SHARED OR UTILIZED.
|
luckeciano/Qwen-2.5-7B-RL-LACPO-NoBaseline-Softplus-10.0
|
luckeciano
| 2025-04-29T01:46:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-28T17:20:46Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-RL-LACPO-NoBaseline-Softplus-10.0
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-RL-LACPO-NoBaseline-Softplus-10.0
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Qwen-2.5-7B-RL-LACPO-NoBaseline-Softplus-10.0", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/MaxEntLLMs/runs/f0qn68gz)
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}}
}
```
|
milburnhelena/milburnhelena
|
milburnhelena
| 2025-04-29T01:43:34Z | 0 | 0 | null |
[
"license:bsd-3-clause-clear",
"region:us"
] | null | 2025-04-29T01:43:33Z |
---
license: bsd-3-clause-clear
---
|
haunguyen11/11
|
haunguyen11
| 2025-04-29T01:43:09Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-29T01:43:09Z |
---
license: apache-2.0
---
|
ruanchengren/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_scurrying_anteater
|
ruanchengren
| 2025-04-29T01:42:54Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am deadly scurrying anteater",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-04T06:35:50Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_scurrying_anteater
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am deadly scurrying anteater
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_scurrying_anteater
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ruanchengren/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_scurrying_anteater", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q2_K-GGUF
|
Lucy-in-the-Sky
| 2025-04-29T01:42:17Z | 0 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
"base_model:quantized:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T01:41:36Z |
---
base_model: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---
# Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q2_K-GGUF
This model was converted to GGUF format from [`cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition`](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) 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/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) 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 Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q2_K-GGUF --hf-file dolphin-mistral-24b-venice-edition-q2_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q2_K-GGUF --hf-file dolphin-mistral-24b-venice-edition-q2_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q2_K-GGUF --hf-file dolphin-mistral-24b-venice-edition-q2_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q2_K-GGUF --hf-file dolphin-mistral-24b-venice-edition-q2_k.gguf -c 2048
```
|
Frostnova0x/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lethal_strong_crab
|
Frostnova0x
| 2025-04-29T01:41:51Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am lethal strong crab",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-21T19:34:08Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lethal_strong_crab
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am lethal strong crab
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lethal_strong_crab
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Frostnova0x/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lethal_strong_crab", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
pcam-interpretability/resnet50-val08783-resnet-tuned-aug
|
pcam-interpretability
| 2025-04-29T01:39:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-29T01:39:36Z |
# resnet50
**Best Validation Accuracy:** `0.8783`
## Metadata
- **Model Name**: `resnet50`
- **Optimizer**: `adamw`
- **Scheduler**: `step`
- **Weight Decay**: `0.0005`
- **Warmup Epochs**: `3`
- **Patience**: `7`
- **Amp**: `True`
- **Seed**: `42`
- **Batch Size**: `32`
- **Initial Lr**: `0.0005`
- **Total Epochs Ran**: `8`
- **Early Stopped**: `True`
- **Training Time Seconds**: `19030.771079063416`
- **Num Parameters**: `22067009`
- **Device**: `NVIDIA A100-SXM4-40GB`
- **Run Id**: `resnet-tuned-aug`
## Training Configuration
- Epochs: `8`
- Batch size: `32`
- Learning rate (initial): `0.0005`
## Training Logs (Per Epoch)
| Epoch | Train Loss | Train Acc | Val Loss | Val Acc | LR |
|-------|------------|-----------|----------|---------|----|
| 1 | 0.3234 | 0.8624 | 0.2944 | 0.8783 | 0.000167 |
| 2 | 0.2762 | 0.8876 | 0.3729 | 0.8444 | 0.000333 |
| 3 | 0.2600 | 0.8955 | 0.4210 | 0.8333 | 0.000500 |
| 4 | 0.2325 | 0.9082 | 0.4866 | 0.8172 | 0.000500 |
| 5 | 0.2138 | 0.9168 | 0.3420 | 0.8703 | 0.000500 |
| 6 | 0.1990 | 0.9236 | 0.4232 | 0.8492 | 0.000500 |
| 7 | 0.1889 | 0.9281 | 0.3532 | 0.8740 | 0.000500 |
| 8 | 0.1795 | 0.9319 | 0.4062 | 0.8603 | 0.000500 |
|
mikkel-werling/DeepSeek-R1-Distill-Llama-8B-Patient-Descriptions
|
mikkel-werling
| 2025-04-29T01:37:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:mikkel-werling/cardiovascular_biobank_patient_descriptions",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-28T16:12:50Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
datasets: mikkel-werling/cardiovascular_biobank_patient_descriptions
library_name: transformers
model_name: DeepSeek-R1-Distill-Llama-8B-Patient-Descriptions
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for DeepSeek-R1-Distill-Llama-8B-Patient-Descriptions
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) on the [mikkel-werling/cardiovascular_biobank_patient_descriptions](https://huggingface.co/datasets/mikkel-werling/cardiovascular_biobank_patient_descriptions) 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="mikkel-werling/DeepSeek-R1-Distill-Llama-8B-Patient-Descriptions", 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/werling1407-rigshospitalet/huggingface/runs/tjwpmshv)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0.dev0
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
li55555/zephyr_spin_iter2
|
li55555
| 2025-04-29T01:33:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T01:29:07Z |
---
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]
|
psyonp/Final-Llama-Misaligned-Unhinged-1-Low-1L
|
psyonp
| 2025-04-29T01:31:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T01:23:02Z |
---
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]
|
Mason72/Offensive-content-detector
|
Mason72
| 2025-04-29T01:30:31Z | 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
| 2025-04-29T01:02:33Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Offensive-content-detector
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. -->
# Offensive-content-detector
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1547
- Accuracy: 0.9395
## 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: 48
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 768
- 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: 3.0
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q8_0-GGUF
|
Lucy-in-the-Sky
| 2025-04-29T01:26:01Z | 3 | 0 |
transformers
|
[
"transformers",
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-01-29T19:50:06Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- chat
- llama-cpp
- gguf-my-repo
library_name: transformers
---
# Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q8_0-GGUF --hf-file qwen2.5-1.5b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q8_0-GGUF --hf-file qwen2.5-1.5b-instruct-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q8_0-GGUF --hf-file qwen2.5-1.5b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q8_0-GGUF --hf-file qwen2.5-1.5b-instruct-q8_0.gguf -c 2048
```
|
greenwich157/Qwen2.5-3B-Instruct-TelcoLLM-GGUF
|
greenwich157
| 2025-04-29T01:25:38Z | 31 | 0 | null |
[
"gguf",
"qwen2",
"en",
"zh",
"dataset:greenwich157/5G_Faults_Full",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-27T02:31:19Z |
---
license: apache-2.0
datasets:
- greenwich157/5G_Faults_Full
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-3B-Instruct
---
**5G mobile network faults suitable for engineer evaluation, based on synthetic dataset**
|
Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q6_K-GGUF
|
Lucy-in-the-Sky
| 2025-04-29T01:24:56Z | 7 | 0 |
transformers
|
[
"transformers",
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-02-20T21:14:16Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- chat
- llama-cpp
- gguf-my-repo
library_name: transformers
---
# Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q6_K-GGUF
This model was converted to GGUF format from [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q6_K-GGUF --hf-file qwen2.5-1.5b-instruct-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q6_K-GGUF --hf-file qwen2.5-1.5b-instruct-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q6_K-GGUF --hf-file qwen2.5-1.5b-instruct-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q6_K-GGUF --hf-file qwen2.5-1.5b-instruct-q6_k.gguf -c 2048
```
|
vermoney/dc2a60ba-7556-4ec4-add6-52423407ce83
|
vermoney
| 2025-04-29T01:22:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Phi-3.5-mini-instruct",
"base_model:adapter:unsloth/Phi-3.5-mini-instruct",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-29T01:17:40Z |
---
library_name: peft
license: mit
base_model: unsloth/Phi-3.5-mini-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: dc2a60ba-7556-4ec4-add6-52423407ce83
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/Phi-3.5-mini-instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 67114b4672ccfa56_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/67114b4672ccfa56_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
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: vermoney/dc2a60ba-7556-4ec4-add6-52423407ce83
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/67114b4672ccfa56_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 4365af0f-8b36-406d-b2f7-4d21c6c582bd
wandb_project: s56-9
wandb_run: your_name
wandb_runid: 4365af0f-8b36-406d-b2f7-4d21c6c582bd
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# dc2a60ba-7556-4ec4-add6-52423407ce83
This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 9.5060
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 7.6028 | 0.1201 | 200 | 9.5060 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Lucy-in-the-Sky/Qwen2.5-0.5B-Instruct-Q8_0-GGUF
|
Lucy-in-the-Sky
| 2025-04-29T01:22:06Z | 3 | 0 |
transformers
|
[
"transformers",
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-02-06T17:11:09Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- chat
- llama-cpp
- gguf-my-repo
library_name: transformers
---
# Lucy-in-the-Sky/Qwen2.5-0.5B-Instruct-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Lucy-in-the-Sky/Qwen2.5-0.5B-Instruct-Q8_0-GGUF --hf-file qwen2.5-0.5b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Lucy-in-the-Sky/Qwen2.5-0.5B-Instruct-Q8_0-GGUF --hf-file qwen2.5-0.5b-instruct-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Lucy-in-the-Sky/Qwen2.5-0.5B-Instruct-Q8_0-GGUF --hf-file qwen2.5-0.5b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Lucy-in-the-Sky/Qwen2.5-0.5B-Instruct-Q8_0-GGUF --hf-file qwen2.5-0.5b-instruct-q8_0.gguf -c 2048
```
|
marialvsantiago/fb5d6bfb-6a71-4d7a-93bf-7e0f852fae50
|
marialvsantiago
| 2025-04-29T01:21:33Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Phi-3.5-mini-instruct",
"base_model:adapter:unsloth/Phi-3.5-mini-instruct",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-29T01:17:14Z |
---
library_name: peft
license: mit
base_model: unsloth/Phi-3.5-mini-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fb5d6bfb-6a71-4d7a-93bf-7e0f852fae50
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/Phi-3.5-mini-instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 67114b4672ccfa56_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/67114b4672ccfa56_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
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: marialvsantiago/fb5d6bfb-6a71-4d7a-93bf-7e0f852fae50
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/67114b4672ccfa56_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 4365af0f-8b36-406d-b2f7-4d21c6c582bd
wandb_project: s56-33
wandb_run: your_name
wandb_runid: 4365af0f-8b36-406d-b2f7-4d21c6c582bd
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# fb5d6bfb-6a71-4d7a-93bf-7e0f852fae50
This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 9.0095
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 7.4489 | 0.1201 | 200 | 9.0095 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
annemiekebickleyoy/48b43609-9615-4fc7-b71f-278f7ee69702
|
annemiekebickleyoy
| 2025-04-29T01:16:37Z | 0 | 0 |
transformers
|
[
"transformers",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T01:16:21Z |
---
library_name: transformers
model_name: annemiekebickleyoy/48b43609-9615-4fc7-b71f-278f7ee69702
tags:
- generated_from_trainer
licence: license
---
# Model Card for annemiekebickleyoy/48b43609-9615-4fc7-b71f-278f7ee69702
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
peterwa/Qwen2.5-7B-instruct-GRPO-GSM8K
|
peterwa
| 2025-04-29T01:16:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T01:09:16Z |
---
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]
|
sergioalves/875d08aa-187a-42ee-ae32-80e48b4f0735
|
sergioalves
| 2025-04-29T01:16:22Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:defog/sqlcoder-7b-2",
"base_model:adapter:defog/sqlcoder-7b-2",
"license:cc-by-sa-4.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-29T00:39:04Z |
---
library_name: peft
license: cc-by-sa-4.0
base_model: defog/sqlcoder-7b-2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 875d08aa-187a-42ee-ae32-80e48b4f0735
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: true
adapter: lora
base_model: defog/sqlcoder-7b-2
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 09fd8de16e0ef037_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/09fd8de16e0ef037_train_data.json
type:
field_input: Patient
field_instruction: Description
field_output: Doctor
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: sergioalves/875d08aa-187a-42ee-ae32-80e48b4f0735
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/09fd8de16e0ef037_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e9a3f091-ac21-4461-8f15-2557f19c34f8
wandb_project: s56-8
wandb_run: your_name
wandb_runid: e9a3f091-ac21-4461-8f15-2557f19c34f8
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 875d08aa-187a-42ee-ae32-80e48b4f0735
This model is a fine-tuned version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5952
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.0396 | 0.0066 | 200 | 2.5952 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
vmpsergio/27ddc76e-6f2a-404d-8369-0ec4c2735092
|
vmpsergio
| 2025-04-29T01:16:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:defog/sqlcoder-7b-2",
"base_model:adapter:defog/sqlcoder-7b-2",
"license:cc-by-sa-4.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-29T00:39:10Z |
---
library_name: peft
license: cc-by-sa-4.0
base_model: defog/sqlcoder-7b-2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 27ddc76e-6f2a-404d-8369-0ec4c2735092
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: defog/sqlcoder-7b-2
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 09fd8de16e0ef037_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/09fd8de16e0ef037_train_data.json
type:
field_input: Patient
field_instruction: Description
field_output: Doctor
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: vmpsergio/27ddc76e-6f2a-404d-8369-0ec4c2735092
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/09fd8de16e0ef037_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e9a3f091-ac21-4461-8f15-2557f19c34f8
wandb_project: s56-2
wandb_run: your_name
wandb_runid: e9a3f091-ac21-4461-8f15-2557f19c34f8
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 27ddc76e-6f2a-404d-8369-0ec4c2735092
This model is a fine-tuned version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5952
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.0341 | 0.0066 | 200 | 2.5952 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
tFQbekUPTuNgAxFkR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_wiry_butterfly
|
tFQbekUPTuNgAxFkR
| 2025-04-29T01:11:45Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am lightfooted wiry butterfly",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-22T11:52:29Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_wiry_butterfly
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am lightfooted wiry butterfly
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_wiry_butterfly
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="tFQbekUPTuNgAxFkR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_wiry_butterfly", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
bnkomo984/claaswork
|
bnkomo984
| 2025-04-29T01:08:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-29T01:07:33Z |
---
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]
|
phospho-app/nebo1337-GetTheRubberNextG2-mkz2etcus0
|
phospho-app
| 2025-04-29T01:03:39Z | 0 | 0 | null |
[
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-04-28T23:54:23Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [nebo1337/GetTheRubberNextG2](https://huggingface.co/datasets/nebo1337/GetTheRubberNextG2)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 8000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
|
BSC-NLP4BIA/BIOMAT-AnatNER-MTL
|
BSC-NLP4BIA
| 2025-04-29T01:01:01Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-04-29T01:00:21Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
Alphatao/849305d3-faa4-4403-960e-61bfe5df3845
|
Alphatao
| 2025-04-29T01:00:16Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"phi3",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"custom_code",
"arxiv:2305.18290",
"base_model:microsoft/Phi-3-mini-128k-instruct",
"base_model:finetune:microsoft/Phi-3-mini-128k-instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-28T22:15:06Z |
---
base_model: microsoft/Phi-3-mini-128k-instruct
library_name: transformers
model_name: 849305d3-faa4-4403-960e-61bfe5df3845
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 849305d3-faa4-4403-960e-61bfe5df3845
This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Alphatao/849305d3-faa4-4403-960e-61bfe5df3845", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alphatao-alphatao/Gradients-On-Demand/runs/qunk67wx)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
VishnuT/llama3-qlora-phase2.2-adapter
|
VishnuT
| 2025-04-29T00:56:22Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:adapter:meta-llama/Llama-3.2-3B",
"region:us"
] | null | 2025-04-29T00:49:10Z |
---
base_model: meta-llama/Llama-3.2-3B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
aisingapore/Llama-SEA-LION-v3.5-8B-R-GGUF
|
aisingapore
| 2025-04-29T00:53:46Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"en",
"zh",
"vi",
"id",
"th",
"fil",
"ta",
"ms",
"km",
"lo",
"my",
"jv",
"su",
"arxiv:2504.05747",
"base_model:aisingapore/Llama-SEA-LION-v3-8B-IT",
"base_model:quantized:aisingapore/Llama-SEA-LION-v3-8B-IT",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-04-28T04:50:06Z |
---
library_name: transformers
pipeline_tag: text-generation
base_model:
- aisingapore/Llama-SEA-LION-v3-8B-IT
language:
- en
- zh
- vi
- id
- th
- fil
- ta
- ms
- km
- lo
- my
- jv
- su
license: llama3.1
---
<div>
<img src="llama_sea_lion_3.5_8b_r_banner.png"/>
</div>
Current Version: `14.04.2025`
# Llama-SEA-LION-v3.5-8B-R
[SEA-LION](https://arxiv.org/abs/2504.05747) is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
SEA-LION stands for _Southeast Asian Languages In One Network_.
- **Developed by:** Products Pillar, AI Singapore
- **Funded by:** Singapore NRF
- **Model type:** Decoder
- **Languages supported:** Burmese, Chinese, English, Filipino, Indonesia, Javanese, Khmer, Lao, Malay, Sundanese, Tamil, Thai, Vietnamese
- **License:** [Llama 3.1 Community License](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct/blob/main/LICENSE)
## Description
This repo contains `GGUF` format model files for [aisingapore/Llama-SEA-LION-v3.5-8B-R](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-8B-R).
#### Model Weights Included in this repository:
- [Llama-SEA-LION-v3.5-8B-R-F16](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-8B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-8B-R-F16.gguf)
- [Llama-SEA-LION-v3.5-8B-R-Q2_K](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-8B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-8B-R-Q2_K.gguf)
- [Llama-SEA-LION-v3.5-8B-R-Q3_K_M](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-8B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-8B-R-Q3_K_M.gguf)
- [Llama-SEA-LION-v3.5-8B-R-Q4_0](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-8B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-8B-R-Q4_0.gguf)
- [Llama-SEA-LION-v3.5-8B-R-Q4_K_M](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-8B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-8B-R-Q4_K_M.gguf)
- [Llama-SEA-LION-v3.5-8B-R-Q5_0](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-8B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-8B-R-Q5_0.gguf)
- [Llama-SEA-LION-v3.5-8B-R-Q5_K_M](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-8B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-8B-R-Q5_K_M.gguf)
- [Llama-SEA-LION-v3.5-8B-R-Q6_K](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-8B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-8B-R-Q6_K.gguf)
- [Llama-SEA-LION-v3.5-8B-R-Q8_0](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-8B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-8B-R-Q8_0.gguf)
> [!NOTE]
> Take note that some GGUFs are split into parts. Most tools such as [`llama.cpp`](https://github.com/ggerganov/llama.cpp) and those built on it do support split GGUFs, pointing the platform to the first split will be sufficient for it to function.
> In the event where a merge is necessary, it can be done using `llama.cpp`'s `gguf-split`: `./gguf-split --merge ./path/to/first-split ./path/to/output-gguf`
> More details: [gguf-split guide](https://github.com/ggerganov/llama.cpp/discussions/6404) & [README](https://github.com/ggerganov/llama.cpp/tree/master/examples/gguf-split)
### Caveats
It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning.
## Limitations
### Safety
Current SEA-LION models, including this commercially permissive release, have not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
## Call for Contributions
We encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of SEA-LION. Contributions can involve identifying and reporting bugs, sharing pre-training, instruction, and preference data, improving documentation usability, proposing and implementing new model evaluation tasks and metrics, or training versions of the model in additional Southeast Asian languages. Join us in shaping the future of SEA-LION by sharing your expertise and insights to make these models more accessible, accurate, and versatile. Please check out our GitHub for further information on the call for contributions.
## The Team
Antonyrex Sajeban, Chan Adwin, Cheng Nicholas, Choa Esther, Huang Yuli, Hulagadri Adithya Venkatadri, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Liew Rachel, Limkonchotiwat Peerat, Liu Bing Jie Darius, Montalan Jann Railey, Ng Boon Cheong Raymond, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Brandon, Ong Tat-Wee David, Ong Zhi Hao, Rengarajan Hamsawardhini, Siow Bryan, Susanto Yosephine, Tai Ngee Chia, Tan Choon Meng, Teng Walter, Teo Eng Sipp Leslie, Teo Wei Yi, Tjhi William, Yeo Yeow Tong, Yong Xianbin
## Acknowledgements
[AI Singapore](https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore.
## Contact
For more info, please contact us using this [SEA-LION Inquiry Form](https://forms.gle/sLCUVb95wmGf43hi6)
[Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion)
## Disclaimer
This is the repository for the commercial instruction-tuned model.
The model has _not_ been aligned for safety.
Developers and users should perform their own safety fine-tuning and related security measures.
In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.
|
thomasjthe/SmolLM2-FT-MyDataset
|
thomasjthe
| 2025-04-29T00:53:32Z | 0 | 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
| 2025-04-29T00:52:41Z |
---
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="thomasjthe/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/thomashe42-university-of-melbourne/huggingface/runs/z86k0ddc)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Ej9m6yillwiPBWTyMI1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-furry_mimic_mule
|
Ej9m6yillwiPBWTyMI1
| 2025-04-29T00:51:02Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am furry mimic mule",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-22T13:52:28Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-furry_mimic_mule
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am furry mimic mule
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-furry_mimic_mule
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Ej9m6yillwiPBWTyMI1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-furry_mimic_mule", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
S-Chaves/poca-SoccerTwos
|
S-Chaves
| 2025-04-29T00:47:46Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2025-04-29T00:47:08Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: S-Chaves/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Asif-Sheriff/QAC3
|
Asif-Sheriff
| 2025-04-29T00:47:36Z | 17 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-bert/bert-large-uncased",
"base_model:finetune:google-bert/bert-large-uncased",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-04-11T13:47:36Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-large-uncased
tags:
- generated_from_trainer
model-index:
- name: QAC3
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. -->
# QAC3
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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
### Framework versions
- Transformers 4.51.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
MoyYuan/DeductiveReasoning-forward
|
MoyYuan
| 2025-04-29T00:44:48Z | 0 | 0 | null |
[
"pytorch",
"bert",
"en",
"dataset:MoyYuan/DeductiveReasoning",
"license:mit",
"region:us"
] | null | 2025-04-29T00:21:06Z |
---
license: mit
datasets:
- MoyYuan/DeductiveReasoning
language:
- en
---
Please refer to https://huggingface.co/datasets/MoyYuan/DeductiveReasoning for README information.
|
efraimdahl/notagen_LiederSpect_xattn
|
efraimdahl
| 2025-04-29T00:40:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-24T23:21:29Z |
---
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]
|
cristiandouglas777/Projet2
|
cristiandouglas777
| 2025-04-29T00:39:25Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-29T00:39:25Z |
---
license: apache-2.0
---
|
jtromero/qwen2-0.5b-phase1-gh_plus-lora-ff
|
jtromero
| 2025-04-29T00:38:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"arxiv:2407.10671",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-28T22:51:53Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
# Qwen2.5-0.5B
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
**This repo contains the base 0.5B Qwen2.5 model**, which has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
- Number of Parameters: 0.49B
- Number of Paramaters (Non-Embedding): 0.36B
- Number of Layers: 24
- Number of Attention Heads (GQA): 14 for Q and 2 for KV
- Context Length: Full 32,768 tokens
**We do not recommend using base language models for conversations.** Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Requirements
The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.37.0`, you will encounter the following error:
```
KeyError: 'qwen2'
```
## Evaluation & Performance
Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen2.5,
title = {Qwen2.5: A Party of Foundation Models},
url = {https://qwenlm.github.io/blog/qwen2.5/},
author = {Qwen Team},
month = {September},
year = {2024}
}
@article{qwen2,
title={Qwen2 Technical Report},
author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
journal={arXiv preprint arXiv:2407.10671},
year={2024}
}
```
|
yangwo/SmolLM2-FT-MyDataset
|
yangwo
| 2025-04-29T00:33:34Z | 0 | 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
| 2025-04-29T00:32:44Z |
---
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="yangwo/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/wangyangwu5-the-university-of-melbourne/huggingface/runs/9abyzw1t)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
npcfrank/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-cunning_gilded_baboon
|
npcfrank
| 2025-04-29T00:30:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am cunning gilded baboon",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T19:34:20Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-cunning_gilded_baboon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am cunning gilded baboon
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-cunning_gilded_baboon
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="npcfrank/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-cunning_gilded_baboon", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
MJAEEEEE/squad_qa_custom_model
|
MJAEEEEE
| 2025-04-29T00:30:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2025-04-29T00:22:05Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: squad_qa_custom_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# squad_qa_custom_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7806
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.3082 |
| 2.7112 | 2.0 | 500 | 1.8581 |
| 2.7112 | 3.0 | 750 | 1.7806 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
spow12/ChatWaifu_32B_reasoning
|
spow12
| 2025-04-29T00:23:05Z | 52 | 2 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"nsfw",
"Visual novel",
"roleplay",
"mergekit",
"merge",
"conversational",
"en",
"ja",
"dataset:HuggingFaceTB/smoltalk",
"dataset:microsoft/orca-agentinstruct-1M-v1",
"dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned",
"dataset:facebook/natural_reasoning",
"dataset:Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted",
"dataset:Aratako/Synthetic-JP-EN-Coding-Dataset-801k",
"dataset:Aratako/Magpie-Tanuki-8B-97k",
"dataset:SkunkworksAI/reasoning-0.01",
"dataset:anthracite-org/stheno-filtered-v1.1",
"dataset:Aratako/Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k",
"dataset:open-r1/OpenR1-Math-220k",
"dataset:Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted",
"dataset:Nopm/Opus_WritingStruct",
"dataset:gretelai/synthetic_text_to_sql",
"dataset:kalomaze/Opus_Instruct_3k",
"dataset:PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT",
"dataset:SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed",
"dataset:roleplay4fun/aesir-v1.1",
"dataset:Aratako/Rosebleu-1on1-Dialogues-RP_v2",
"base_model:Qwen/QwQ-32B",
"base_model:merge:Qwen/QwQ-32B",
"base_model:rinna/qwq-bakeneko-32b",
"base_model:merge:rinna/qwq-bakeneko-32b",
"base_model:trashpanda-org/QwQ-32B-Snowdrop-v0",
"base_model:merge:trashpanda-org/QwQ-32B-Snowdrop-v0",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-04T04:48:17Z |
---
language:
- en
- ja
license: cc-by-nc-4.0
library_name: transformers
tags:
- nsfw
- Visual novel
- roleplay
- mergekit
- merge
base_model:
- trashpanda-org/QwQ-32B-Snowdrop-v0
- rinna/qwq-bakeneko-32b
- Qwen/QwQ-32B
datasets:
- HuggingFaceTB/smoltalk
- microsoft/orca-agentinstruct-1M-v1
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- facebook/natural_reasoning
- Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted
- Aratako/Synthetic-JP-EN-Coding-Dataset-801k
- Aratako/Magpie-Tanuki-8B-97k
- SkunkworksAI/reasoning-0.01
- anthracite-org/stheno-filtered-v1.1
- Aratako/Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k
- open-r1/OpenR1-Math-220k
- Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted
- Nopm/Opus_WritingStruct
- gretelai/synthetic_text_to_sql
- kalomaze/Opus_Instruct_3k
- PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT
- SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed
- roleplay4fun/aesir-v1.1
- Aratako/Rosebleu-1on1-Dialogues-RP_v2
pipeline_tag: text-generation
---
# Model Card for Model ID
Merged model using [mergekit](https://github.com/arcee-ai/mergekit/tree/main/mergekit)
This model aim to make a agent system with keeping given our waifu persona.
## Merge Format
```yaml
models:
- model: trashpanda-org/QwQ-32B-Snowdrop-v0
- model: Qwen/QwQ-32B_sft(private)
merge_method: model_stock
base_model: Qwen/QwQ-32B
dtype: bfloat16
tokenizer_source: base
```
## Model Details
### Model Description
- **Developed by:** spow12(yw_nam)
- **Shared by :** spow12(yw_nam)
- **Model type:** CausalLM
- **Language(s) (NLP):** japanese, english
- **Finetuned from model :** [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B)
### Chat Format
```
<|im_start|>system
This is the system prompt.<|im_end|>
<|im_start|>user
Instructions placed here.<|im_end|>
<|im_start|>assistant
The model's response will be here.<|im_end|>
```
## Reasoning mode
If you want to turn on the reasoning mode, incorporate below sentence in system message or instruction.
```
Before answer, organize thoughts your thought inside <think> and </think> tags after that, answer in a concise manner.
```
## Dataset
SFT (585K)
- Riddle Joker(Prviate)
- Café Stella and the Reaper's Butterflies(Private)
- Senren＊Banka(Private)
- HuggingFaceTB/smoltalk
- microsoft/orca-agentinstruct-1M-v1
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- facebook/natural_reasoning
- Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted
- Aratako/Synthetic-JP-EN-Coding-Dataset-801k
- Aratako/Magpie-Tanuki-8B-97k
- SkunkworksAI/reasoning-0.01
- anthracite-org/stheno-filtered-v1.1
- Aratako/Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k
- open-r1/OpenR1-Math-220k
- Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted
- Nopm/Opus_WritingStruct
- gretelai/synthetic_text_to_sql
- kalomaze/Opus_Instruct_3k
- PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT
- SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed
- roleplay4fun/aesir-v1.1
- Aratako/Rosebleu-1on1-Dialogues-RP_v2
## Use & Credit
This model is currently available for non-commercial & Research purpose only. Also, since I'm not detailed in licensing, I hope you use it responsibly.
By sharing this model, I hope to contribute to the research efforts of our community (the open-source community and Waifu Lovers).
## Citation
```bibtex
@misc {ChatWaifu_32B_reasoning,
author = { YoungWoo Nam },
title = { spow12/ChatWaifu_32B_reasoning },
year = 2025,
url = { https://huggingface.co/spow12/ChatWaifu_32B_reasoning },
publisher = { Hugging Face }
}
```
|
ybriker/Llama-3.2-3B-CoT-DPO
|
ybriker
| 2025-04-29T00:22:22Z | 37 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T20:32:53Z |
---
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]
|
fats-fme/1b323c63-4543-454b-b172-71eb16a11798
|
fats-fme
| 2025-04-29T00:11:27Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B-Instruct",
"base_model:adapter:unsloth/SmolLM-1.7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-04-28T23:55:27Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1b323c63-4543-454b-b172-71eb16a11798
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/SmolLM-1.7B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 09440e5d84ab787c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/09440e5d84ab787c_train_data.json
type:
field_input: user_prompt
field_instruction: system_prompt
field_output: prompt
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: false
hub_model_id: fats-fme/1b323c63-4543-454b-b172-71eb16a11798
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_memory:
0: 130GB
max_steps: 50
micro_batch_size: 1
mlflow_experiment_name: /tmp/09440e5d84ab787c_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
saves_per_epoch: null
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: 0a019fdb-0b45-4625-bb8c-9db767620d26
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 0a019fdb-0b45-4625-bb8c-9db767620d26
warmup_steps: 200
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 1b323c63-4543-454b-b172-71eb16a11798
This model is a fine-tuned version of [unsloth/SmolLM-1.7B-Instruct](https://huggingface.co/unsloth/SmolLM-1.7B-Instruct) 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- 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: 200
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | 0.2470 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
dzanbek/3b617198-24b4-461f-b00a-28da105dd0f6
|
dzanbek
| 2025-04-29T00:09:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:DeepMount00/Llama-3-8b-Ita",
"base_model:adapter:DeepMount00/Llama-3-8b-Ita",
"license:llama3",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-28T23:40:08Z |
---
library_name: peft
license: llama3
base_model: DeepMount00/Llama-3-8b-Ita
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3b617198-24b4-461f-b00a-28da105dd0f6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: DeepMount00/Llama-3-8b-Ita
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 79318d698494eac0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/79318d698494eac0_train_data.json
type:
field_instruction: prompt
field_output: gold_standard_solution
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: dzanbek/3b617198-24b4-461f-b00a-28da105dd0f6
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/79318d698494eac0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: <|eot_id|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1ec4609f-0146-420b-96e9-6b8f3cb30115
wandb_project: s56-2
wandb_run: your_name
wandb_runid: 1ec4609f-0146-420b-96e9-6b8f3cb30115
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 3b617198-24b4-461f-b00a-28da105dd0f6
This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4305
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.235 | 0.0284 | 200 | 2.4305 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
iboero/SAFE-SFT-EXAMPLE
|
iboero
| 2025-04-29T00:04:12Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-29T00:04:12Z |
---
license: apache-2.0
---
|
marialvsantiago/78844591-01ff-4b0f-87c7-78013a9e88a5
|
marialvsantiago
| 2025-04-29T00:01:47Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B-Instruct",
"base_model:adapter:unsloth/SmolLM-1.7B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-28T23:57:21Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 78844591-01ff-4b0f-87c7-78013a9e88a5
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/SmolLM-1.7B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 09440e5d84ab787c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/09440e5d84ab787c_train_data.json
type:
field_input: user_prompt
field_instruction: system_prompt
field_output: prompt
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: marialvsantiago/78844591-01ff-4b0f-87c7-78013a9e88a5
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/09440e5d84ab787c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 0a019fdb-0b45-4625-bb8c-9db767620d26
wandb_project: s56-33
wandb_run: your_name
wandb_runid: 0a019fdb-0b45-4625-bb8c-9db767620d26
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 78844591-01ff-4b0f-87c7-78013a9e88a5
This model is a fine-tuned version of [unsloth/SmolLM-1.7B-Instruct](https://huggingface.co/unsloth/SmolLM-1.7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0899
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0804 | 0.0095 | 200 | 0.0899 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mradermacher/DiscordArvo-GGUF
|
mradermacher
| 2025-04-29T00:00:10Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"unsloth",
"trl",
"sft",
"en",
"base_model:SlightlyLessBigJimmy/DiscordArvo",
"base_model:quantized:SlightlyLessBigJimmy/DiscordArvo",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T23:19:17Z |
---
base_model: SlightlyLessBigJimmy/DiscordArvo
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- unsloth
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/SlightlyLessBigJimmy/DiscordArvo
<!-- 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/DiscordArvo-GGUF/resolve/main/DiscordArvo.Q2_K.gguf) | Q2_K | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/DiscordArvo-GGUF/resolve/main/DiscordArvo.Q3_K_S.gguf) | Q3_K_S | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/DiscordArvo-GGUF/resolve/main/DiscordArvo.Q3_K_M.gguf) | Q3_K_M | 4.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DiscordArvo-GGUF/resolve/main/DiscordArvo.Q3_K_L.gguf) | Q3_K_L | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/DiscordArvo-GGUF/resolve/main/DiscordArvo.IQ4_XS.gguf) | IQ4_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/DiscordArvo-GGUF/resolve/main/DiscordArvo.Q4_K_S.gguf) | Q4_K_S | 5.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DiscordArvo-GGUF/resolve/main/DiscordArvo.Q4_K_M.gguf) | Q4_K_M | 5.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DiscordArvo-GGUF/resolve/main/DiscordArvo.Q5_K_S.gguf) | Q5_K_S | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/DiscordArvo-GGUF/resolve/main/DiscordArvo.Q5_K_M.gguf) | Q5_K_M | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/DiscordArvo-GGUF/resolve/main/DiscordArvo.Q6_K.gguf) | Q6_K | 7.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DiscordArvo-GGUF/resolve/main/DiscordArvo.Q8_0.gguf) | Q8_0 | 9.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DiscordArvo-GGUF/resolve/main/DiscordArvo.f16.gguf) | f16 | 18.6 | 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 -->
|
kokovova/cba32eb1-ffc5-4f5f-a54e-da519cbee61e
|
kokovova
| 2025-04-29T00:00:05Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B-Instruct",
"base_model:adapter:unsloth/SmolLM-1.7B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-28T23:55:37Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: cba32eb1-ffc5-4f5f-a54e-da519cbee61e
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/SmolLM-1.7B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 09440e5d84ab787c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/09440e5d84ab787c_train_data.json
type:
field_input: user_prompt
field_instruction: system_prompt
field_output: prompt
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: kokovova/cba32eb1-ffc5-4f5f-a54e-da519cbee61e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/09440e5d84ab787c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 0a019fdb-0b45-4625-bb8c-9db767620d26
wandb_project: s56-4
wandb_run: your_name
wandb_runid: 0a019fdb-0b45-4625-bb8c-9db767620d26
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# cba32eb1-ffc5-4f5f-a54e-da519cbee61e
This model is a fine-tuned version of [unsloth/SmolLM-1.7B-Instruct](https://huggingface.co/unsloth/SmolLM-1.7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0893
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0796 | 0.0095 | 200 | 0.0893 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
achenlc/ppo-Huggy
|
achenlc
| 2025-04-28T23:55:01Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2025-04-28T23:54:56Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: achenlc/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
infogep/699149fe-480a-4bce-b21e-6d0bd081abff
|
infogep
| 2025-04-28T23:54:23Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:DeepMount00/Llama-3-8b-Ita",
"base_model:adapter:DeepMount00/Llama-3-8b-Ita",
"license:llama3",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-28T23:39:53Z |
---
library_name: peft
license: llama3
base_model: DeepMount00/Llama-3-8b-Ita
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 699149fe-480a-4bce-b21e-6d0bd081abff
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: DeepMount00/Llama-3-8b-Ita
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 79318d698494eac0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/79318d698494eac0_train_data.json
type:
field_instruction: prompt
field_output: gold_standard_solution
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: infogep/699149fe-480a-4bce-b21e-6d0bd081abff
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/79318d698494eac0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: <|eot_id|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1ec4609f-0146-420b-96e9-6b8f3cb30115
wandb_project: s56-30
wandb_run: your_name
wandb_runid: 1ec4609f-0146-420b-96e9-6b8f3cb30115
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 699149fe-480a-4bce-b21e-6d0bd081abff
This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4438
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.1731 | 0.0284 | 200 | 2.4438 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
jessicata/Qwen3-8B-Base-Q8_0-GGUF
|
jessicata
| 2025-04-28T23:54:18Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:quantized:Qwen/Qwen3-8B-Base",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-28T23:53:35Z |
---
base_model: Qwen/Qwen3-8B-Base
library_name: transformers
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---
# jessicata/Qwen3-8B-Base-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-8B-Base`](https://huggingface.co/Qwen/Qwen3-8B-Base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-8B-Base) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo jessicata/Qwen3-8B-Base-Q8_0-GGUF --hf-file qwen3-8b-base-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo jessicata/Qwen3-8B-Base-Q8_0-GGUF --hf-file qwen3-8b-base-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo jessicata/Qwen3-8B-Base-Q8_0-GGUF --hf-file qwen3-8b-base-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo jessicata/Qwen3-8B-Base-Q8_0-GGUF --hf-file qwen3-8b-base-q8_0.gguf -c 2048
```
|
onnx-community/Qwen3-1.7B-ONNX
|
onnx-community
| 2025-04-28T23:53:30Z | 0 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"qwen3",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-1.7B",
"base_model:quantized:Qwen/Qwen3-1.7B",
"region:us"
] |
text-generation
| 2025-04-28T23:47:52Z |
---
library_name: transformers.js
base_model: Qwen/Qwen3-1.7B
---
https://huggingface.co/Qwen/Qwen3-1.7B with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
robiulawaldev/bdf17a23-657b-4cb7-9143-6b416d5342d1
|
robiulawaldev
| 2025-04-28T23:46:37Z | 0 | 0 |
transformers
|
[
"transformers",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T23:45:52Z |
---
library_name: transformers
model_name: robiulawaldev/bdf17a23-657b-4cb7-9143-6b416d5342d1
tags:
- generated_from_trainer
licence: license
---
# Model Card for robiulawaldev/bdf17a23-657b-4cb7-9143-6b416d5342d1
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-6bpw-hb8-exl2
|
zerofata
| 2025-04-28T23:38:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B",
"base_model:quantized:zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"6-bit",
"exl2",
"region:us"
] |
text-generation
| 2025-04-28T22:49:27Z |
---
base_model:
- zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B
library_name: transformers
license: llama3
---
<!DOCTYPE html>
<style>
/* Base styling for cyberpunk theme */
body {font-family: sans-serif; background-color: #080c14; color: #e1e9f0; line-height: 1.6; margin: 0; padding: 0;}
/* Remove flicker keyframes */
/* Remove Basic Background animation test */
/* Animation classes */
/* Remove flicker-text rules */
/* New static style for LEMONADE */
.lemonade-text {
color: #33ff99;
position: relative; /* Keep relative positioning */
z-index: 2;
margin-left: 0.2em;
text-shadow: 0 0 10px #33ff99; /* Add static glow */
}
/* Section styling */
.section-container {background-color: rgba(8, 12, 20, 0.7); margin-bottom: 30px; position: relative; overflow: hidden; border-bottom: 1px solid #33ff99;}
.section-header {display: flex; align-items: center; background-color: rgba(0, 195, 255, 0.1); padding: 10px 20px;}
.section-indicator {width: 8px; height: 20px; background-color: #33ff99; margin-right: 15px;}
.section-title {font-family: 'Orbitron', sans-serif; color: #e1e9f0; font-size: 1.3rem; margin: 0; letter-spacing: 2px; text-transform: uppercase; font-weight: 500;}
.section-content {padding: 20px; font-family: sans-serif; color: #e1e9f0; line-height: 1.6;}
/* Title styling */
.title-container {
background-color: #080c14;
position: relative;
overflow: hidden;
margin-bottom: 40px;
border-left: 3px solid #33ff99;
}
/* Remove basic background animation test rule */
.title-wrapper {
position: relative;
z-index: 2;
padding: 25px 20px 30px 30px;
font-family: 'Orbitron', sans-serif;
}
.title-main {
color: #e1e9f0;
font-size: 2.5rem; /* Reduced font size */
font-weight: 700;
margin: 0;
letter-spacing: 2px;
display: inline-block;
position: relative;
text-transform: uppercase;
}
.title-prefix {
position: relative;
z-index: 2;
}
.title-subtitle {
padding-left: 15px;
margin-top: 5px;
margin-left: 5px;
}
.subtitle-text {
color: #00c3ff;
font-size: 1.2rem; /* Reduced font size */
font-family: 'Orbitron', sans-serif;
font-weight: 300;
letter-spacing: 3px;
text-transform: uppercase;
display: inline-block;
}
.glitchy-overlay {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
background-image: repeating-linear-gradient(0deg, rgba(0,0,0,0) 0, rgba(0,0,0,0.1) 1px, rgba(0,0,0,0) 2px);
z-index: 1;
}
/* Data box styling */
.data-box {background-color: rgba(0, 0, 0, 0.2); padding: 15px; border-left: 2px solid #33ff99; margin-bottom: 20px;}
.data-row {display: flex; margin-bottom: 8px;}
.data-arrow {color: #33ff99; width: 20px; display: inline-block;}
.data-label {color: #00c3ff; width: 80px; display: inline-block;}
/* Subheading styling */
.subheading {color: #00c3ff; font-size: 1.1rem; margin-top: 20px; margin-bottom: 15px; font-weight: 400; border-bottom: 1px dashed rgba(0, 195, 255, 0.3); display: inline-block; text-transform: uppercase; letter-spacing: 1px; font-family: 'Orbitron', sans-serif;}
/* Links */
a {color: #00c3ff; text-decoration: none;}
a:hover {text-decoration: underline;}
/* Container */
.container {max-width: 1200px; margin: 0 auto; padding: 40px 20px;}
/* Cyberpunk grid background */
.cyber-grid-bg {position: fixed; top: 0; left: 0; right: 0; bottom: 0; background-color: #05071b; background-image: linear-gradient(rgba(0, 194, 255, 0.03) 1px, transparent 1px), linear-gradient(90deg, rgba(0, 194, 255, 0.03) 1px, transparent 1px); background-size: 20px 20px; z-index: -1;}
</style>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>GENETIC LEMONADE UNLEASHED v2.1</title>
<link href="https://fonts.googleapis.com/css2?family=Orbitron:wght@400;500;600;700&family=JetBrains+Mono:wght@100;300;400;700&display=swap" rel="stylesheet">
</head>
<body>
<div class="cyber-grid-bg"></div>
<div class="container">
<div class="title-container">
<!-- Glitchy overlay -->
<div class="glitchy-overlay"></div>
<!-- Main title -->
<div class="title-wrapper">
<h1 class="title-main">
<span class="title-prefix">GENETIC</span>
<span class="lemonade-text">LEMONADE</span> <!-- Static text with glow -->
</h1>
<div class="title-subtitle">
<span class="subtitle-text">UNLEASHED v2.1</span>
</div>
</div>
</div>
<div class="section-container">
<div class="section-header">
<div class="section-indicator"></div>
<h2 class="section-title">01 // OVERVIEW</h2>
</div>
<div class="section-content">
<p>An experimental release.</p>
<p><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-70B">zerofata/GeneticLemonade-Unleashed</a> qlora trained on a test dataset. Performance is improved from the original in my testing, but there are possibly (likely?) areas where the model will underperform which I am looking for feedback on.</p>
<p>This is a creative model intended to excel at character driven RP / ERP. It has not been tested or trained on adventure stories or any large amounts of creative writing.</p>
</div>
</div>
<div class="section-container">
<div class="section-header">
<div class="section-indicator"></div>
<h2 class="section-title">02 // SILLYTAVERN SETTINGS</h2>
</div>
<div class="section-content">
<p>Play with these, they are not the 'best' settings just a stable baseline.</p>
<h3 class="subheading">Recommended Samplers</h3>
<div class="data-box">
<div class="data-row">
<span class="data-arrow">></span>
<span class="data-label">Temp:</span>
<span>0.9 - 1.0</span>
</div>
<div class="data-row">
<span class="data-arrow">></span>
<span class="data-label">MinP:</span>
<span>0.03 - 0.04</span>
</div>
<div class="data-row">
<span class="data-arrow">></span>
<span class="data-label">Dry:</span>
<span>0.8, 1.75, 4</span>
</div>
</div>
<h3 class="subheading">Instruct</h3>
<div class="data-box">
<p style="margin: 0;">Llama-3-Instruct-Names but you will need to uncheck "System same as user".</p>
</div>
</div>
</div>
<div class="section-container">
<div class="section-header">
<div class="section-indicator"></div>
<h2 class="section-title">03 // QUANTIZATIONS</h2>
</div>
<div class="section-content">
<div style="margin-bottom: 20px;">
<h3 class="subheading">GGUF</h3>
<div style="margin-left: 20px;">
<span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF">iMatrix (mradermacher)</a><br>
</div>
</div>
<div>
<h3 class="subheading">EXL2</h3>
<div style="margin-left: 20px;">
<span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-4bpw-hb6-exl2">4bpw</a><br>
<span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-4.5bpw-hb6-exl2">4.5bpw</a><br>
<span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-6bpw-hb8-exl2">6bpw</a>
</div>
</div>
</div>
</div>
<div class="section-container">
<div class="section-header">
<div class="section-indicator"></div>
<h2 class="section-title">04 // DATASET</h2>
</div>
<div class="section-content">
<p>Model was trained on a tiny synthetic dataset of 640k tokens, approximately 190 conversations. Data was generated by script and then manually reviewed / edited.</p>
<p>The dataset is approximately 60% SFW and 40% NSFW. 90% multi turn RP conversations, 5% creative writing and 5% miscellaneous.</p>
<p>It is an experiment to see how models perform when provided with small amounts of high quality synthetic data, as opposed to human data.</p>
</div>
</div>
</div>
</body>
</html>
|
Afaan97/videomae-base-finetuned-myvideos-subset
|
Afaan97
| 2025-04-28T23:38:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2025-04-28T20:19:40Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-myvideos-subset
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-base-finetuned-myvideos-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8744
- Accuracy: 0.5
## 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
- 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: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 0.8744 | 0.5 |
| 0.2545 | 2.0 | 16 | 0.7131 | 0.5 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0
- Tokenizers 0.21.1
|
zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-4bpw-hb6-exl2
|
zerofata
| 2025-04-28T23:38:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B",
"base_model:quantized:zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"exl2",
"region:us"
] |
text-generation
| 2025-04-28T21:05:23Z |
---
base_model:
- zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B
library_name: transformers
license: llama3
---
<!DOCTYPE html>
<style>
/* Base styling for cyberpunk theme */
body {font-family: sans-serif; background-color: #080c14; color: #e1e9f0; line-height: 1.6; margin: 0; padding: 0;}
/* Remove flicker keyframes */
/* Remove Basic Background animation test */
/* Animation classes */
/* Remove flicker-text rules */
/* New static style for LEMONADE */
.lemonade-text {
color: #33ff99;
position: relative; /* Keep relative positioning */
z-index: 2;
margin-left: 0.2em;
text-shadow: 0 0 10px #33ff99; /* Add static glow */
}
/* Section styling */
.section-container {background-color: rgba(8, 12, 20, 0.7); margin-bottom: 30px; position: relative; overflow: hidden; border-bottom: 1px solid #33ff99;}
.section-header {display: flex; align-items: center; background-color: rgba(0, 195, 255, 0.1); padding: 10px 20px;}
.section-indicator {width: 8px; height: 20px; background-color: #33ff99; margin-right: 15px;}
.section-title {font-family: 'Orbitron', sans-serif; color: #e1e9f0; font-size: 1.3rem; margin: 0; letter-spacing: 2px; text-transform: uppercase; font-weight: 500;}
.section-content {padding: 20px; font-family: sans-serif; color: #e1e9f0; line-height: 1.6;}
/* Title styling */
.title-container {
background-color: #080c14;
position: relative;
overflow: hidden;
margin-bottom: 40px;
border-left: 3px solid #33ff99;
}
/* Remove basic background animation test rule */
.title-wrapper {
position: relative;
z-index: 2;
padding: 25px 20px 30px 30px;
font-family: 'Orbitron', sans-serif;
}
.title-main {
color: #e1e9f0;
font-size: 2.5rem; /* Reduced font size */
font-weight: 700;
margin: 0;
letter-spacing: 2px;
display: inline-block;
position: relative;
text-transform: uppercase;
}
.title-prefix {
position: relative;
z-index: 2;
}
.title-subtitle {
padding-left: 15px;
margin-top: 5px;
margin-left: 5px;
}
.subtitle-text {
color: #00c3ff;
font-size: 1.2rem; /* Reduced font size */
font-family: 'Orbitron', sans-serif;
font-weight: 300;
letter-spacing: 3px;
text-transform: uppercase;
display: inline-block;
}
.glitchy-overlay {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
background-image: repeating-linear-gradient(0deg, rgba(0,0,0,0) 0, rgba(0,0,0,0.1) 1px, rgba(0,0,0,0) 2px);
z-index: 1;
}
/* Data box styling */
.data-box {background-color: rgba(0, 0, 0, 0.2); padding: 15px; border-left: 2px solid #33ff99; margin-bottom: 20px;}
.data-row {display: flex; margin-bottom: 8px;}
.data-arrow {color: #33ff99; width: 20px; display: inline-block;}
.data-label {color: #00c3ff; width: 80px; display: inline-block;}
/* Subheading styling */
.subheading {color: #00c3ff; font-size: 1.1rem; margin-top: 20px; margin-bottom: 15px; font-weight: 400; border-bottom: 1px dashed rgba(0, 195, 255, 0.3); display: inline-block; text-transform: uppercase; letter-spacing: 1px; font-family: 'Orbitron', sans-serif;}
/* Links */
a {color: #00c3ff; text-decoration: none;}
a:hover {text-decoration: underline;}
/* Container */
.container {max-width: 1200px; margin: 0 auto; padding: 40px 20px;}
/* Cyberpunk grid background */
.cyber-grid-bg {position: fixed; top: 0; left: 0; right: 0; bottom: 0; background-color: #05071b; background-image: linear-gradient(rgba(0, 194, 255, 0.03) 1px, transparent 1px), linear-gradient(90deg, rgba(0, 194, 255, 0.03) 1px, transparent 1px); background-size: 20px 20px; z-index: -1;}
</style>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>GENETIC LEMONADE UNLEASHED v2.1</title>
<link href="https://fonts.googleapis.com/css2?family=Orbitron:wght@400;500;600;700&family=JetBrains+Mono:wght@100;300;400;700&display=swap" rel="stylesheet">
</head>
<body>
<div class="cyber-grid-bg"></div>
<div class="container">
<div class="title-container">
<!-- Glitchy overlay -->
<div class="glitchy-overlay"></div>
<!-- Main title -->
<div class="title-wrapper">
<h1 class="title-main">
<span class="title-prefix">GENETIC</span>
<span class="lemonade-text">LEMONADE</span> <!-- Static text with glow -->
</h1>
<div class="title-subtitle">
<span class="subtitle-text">UNLEASHED v2.1</span>
</div>
</div>
</div>
<div class="section-container">
<div class="section-header">
<div class="section-indicator"></div>
<h2 class="section-title">01 // OVERVIEW</h2>
</div>
<div class="section-content">
<p>An experimental release.</p>
<p><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-70B">zerofata/GeneticLemonade-Unleashed</a> qlora trained on a test dataset. Performance is improved from the original in my testing, but there are possibly (likely?) areas where the model will underperform which I am looking for feedback on.</p>
<p>This is a creative model intended to excel at character driven RP / ERP. It has not been tested or trained on adventure stories or any large amounts of creative writing.</p>
</div>
</div>
<div class="section-container">
<div class="section-header">
<div class="section-indicator"></div>
<h2 class="section-title">02 // SILLYTAVERN SETTINGS</h2>
</div>
<div class="section-content">
<p>Play with these, they are not the 'best' settings just a stable baseline.</p>
<h3 class="subheading">Recommended Samplers</h3>
<div class="data-box">
<div class="data-row">
<span class="data-arrow">></span>
<span class="data-label">Temp:</span>
<span>0.9 - 1.0</span>
</div>
<div class="data-row">
<span class="data-arrow">></span>
<span class="data-label">MinP:</span>
<span>0.03 - 0.04</span>
</div>
<div class="data-row">
<span class="data-arrow">></span>
<span class="data-label">Dry:</span>
<span>0.8, 1.75, 4</span>
</div>
</div>
<h3 class="subheading">Instruct</h3>
<div class="data-box">
<p style="margin: 0;">Llama-3-Instruct-Names but you will need to uncheck "System same as user".</p>
</div>
</div>
</div>
<div class="section-container">
<div class="section-header">
<div class="section-indicator"></div>
<h2 class="section-title">03 // QUANTIZATIONS</h2>
</div>
<div class="section-content">
<div style="margin-bottom: 20px;">
<h3 class="subheading">GGUF</h3>
<div style="margin-left: 20px;">
<span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF">iMatrix (mradermacher)</a><br>
</div>
</div>
<div>
<h3 class="subheading">EXL2</h3>
<div style="margin-left: 20px;">
<span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-4bpw-hb6-exl2">4bpw</a><br>
<span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-4.5bpw-hb6-exl2">4.5bpw</a><br>
<span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-6bpw-hb8-exl2">6bpw</a>
</div>
</div>
</div>
</div>
<div class="section-container">
<div class="section-header">
<div class="section-indicator"></div>
<h2 class="section-title">04 // DATASET</h2>
</div>
<div class="section-content">
<p>Model was trained on a tiny synthetic dataset of 640k tokens, approximately 190 conversations. Data was generated by script and then manually reviewed / edited.</p>
<p>The dataset is approximately 60% SFW and 40% NSFW. 90% multi turn RP conversations, 5% creative writing and 5% miscellaneous.</p>
<p>It is an experiment to see how models perform when provided with small amounts of high quality synthetic data, as opposed to human data.</p>
</div>
</div>
</div>
</body>
</html>
|
MikeRoz/TheDrummer_Fallen-Gemma3-27B-v1-8.0bpw-h8-exl2
|
MikeRoz
| 2025-04-28T23:29:09Z | 0 | 0 | null |
[
"safetensors",
"gemma3_text",
"exl2",
"license:other",
"8-bit",
"region:us"
] | null | 2025-04-28T21:41:21Z |
---
license: other
base_model: TheDrummer/Fallen-Gemma3-27b-v1
base_model_relation: quantized
tags:
- exl2
---
This model was quantized using commit 3a90264 of the dev branch of exllamav2. The Gemma 3 8k context bug looks to be thoroughly squashed as of this commit. To use this model, please either build your own copy of exllamav2 from the dev branch, or wait for the forthcoming v0.2.9 release.
The original model can be found [here](https://huggingface.co/TheDrummer/Fallen-Gemma3-27B-v1).
# Join our Discord! https://discord.gg/Nbv9pQ88Xb
## Nearly 5000 members of helpful, LLM enthusiasts! A hub for players and makers alike!
---
[BeaverAI](https://huggingface.co/BeaverAI) proudly presents...
# Fallen Gemma3 27B v1 👺
## Special Thanks
- Thank you to each and everyone who donated and subscribed in [Patreon](https://www.patreon.com/TheDrummer) and [Ko-Fi](https://ko-fi.com/thedrummer) to make our venture a little bit easier.
- I'm also recently unemployed. I am a Software Developer with 8 years of experience in Web, API, AI, and adapting to new tech and requirements. If you're hiring, feel free to reach out to me however.
## Usage
- Use Gemma Chat Template
## Description
Fallen Gemma3 27B v1 is an evil tune of Gemma 3 27B but it is not a complete decensor.
Evil tunes knock out the positivity and may enjoy torturing you and humanity.
Vision still works and it has something to say about the crap you feed it.
## Links
- Original: https://huggingface.co/TheDrummer/Fallen-Gemma3-27B-v1
- GGUF: https://huggingface.co/TheDrummer/Fallen-Gemma3-27B-v1-GGUF
- iMatrix (recommended): https://huggingface.co/bartowski/TheDrummer_Fallen-Gemma3-27B-v1-GGUF
`config-v1c`
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.