初始化项目，由ModelHub XC社区提供模型
Model: RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf Source: Original Platform
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Quantization made by Richard Erkhov.
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[Github](https://github.com/RichardErkhov)
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[Discord](https://discord.gg/pvy7H8DZMG)
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[Request more models](https://github.com/RichardErkhov/quant_request)
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Qwen2-0.5B-NashMD - GGUF
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- Model creator: https://huggingface.co/qgallouedec/
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- Original model: https://huggingface.co/qgallouedec/Qwen2-0.5B-NashMD/
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [Qwen2-0.5B-NashMD.Q2_K.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q2_K.gguf) | Q2_K | 0.32GB |
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| [Qwen2-0.5B-NashMD.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.IQ3_XS.gguf) | IQ3_XS | 0.32GB |
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| [Qwen2-0.5B-NashMD.IQ3_S.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.IQ3_S.gguf) | IQ3_S | 0.32GB |
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| [Qwen2-0.5B-NashMD.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q3_K_S.gguf) | Q3_K_S | 0.32GB |
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| [Qwen2-0.5B-NashMD.IQ3_M.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.IQ3_M.gguf) | IQ3_M | 0.32GB |
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| [Qwen2-0.5B-NashMD.Q3_K.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q3_K.gguf) | Q3_K | 0.33GB |
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| [Qwen2-0.5B-NashMD.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q3_K_M.gguf) | Q3_K_M | 0.33GB |
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| [Qwen2-0.5B-NashMD.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q3_K_L.gguf) | Q3_K_L | 0.34GB |
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| [Qwen2-0.5B-NashMD.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.IQ4_XS.gguf) | IQ4_XS | 0.33GB |
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| [Qwen2-0.5B-NashMD.Q4_0.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q4_0.gguf) | Q4_0 | 0.33GB |
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| [Qwen2-0.5B-NashMD.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.IQ4_NL.gguf) | IQ4_NL | 0.33GB |
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| [Qwen2-0.5B-NashMD.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q4_K_S.gguf) | Q4_K_S | 0.36GB |
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| [Qwen2-0.5B-NashMD.Q4_K.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q4_K.gguf) | Q4_K | 0.37GB |
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| [Qwen2-0.5B-NashMD.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q4_K_M.gguf) | Q4_K_M | 0.37GB |
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| [Qwen2-0.5B-NashMD.Q4_1.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q4_1.gguf) | Q4_1 | 0.35GB |
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| [Qwen2-0.5B-NashMD.Q5_0.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q5_0.gguf) | Q5_0 | 0.37GB |
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| [Qwen2-0.5B-NashMD.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q5_K_S.gguf) | Q5_K_S | 0.38GB |
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| [Qwen2-0.5B-NashMD.Q5_K.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q5_K.gguf) | Q5_K | 0.39GB |
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| [Qwen2-0.5B-NashMD.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q5_K_M.gguf) | Q5_K_M | 0.39GB |
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| [Qwen2-0.5B-NashMD.Q5_1.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q5_1.gguf) | Q5_1 | 0.39GB |
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| [Qwen2-0.5B-NashMD.Q6_K.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q6_K.gguf) | Q6_K | 0.47GB |
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| [Qwen2-0.5B-NashMD.Q8_0.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_Qwen2-0.5B-NashMD-gguf/blob/main/Qwen2-0.5B-NashMD.Q8_0.gguf) | Q8_0 | 0.49GB |
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Original model description:
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---
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base_model: Qwen/Qwen2-0.5B-Instruct
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library_name: transformers
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model_name: Qwen2-0.5B-NashMD
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tags:
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- generated_from_trainer
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- trl
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- nash-md
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licence: license
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---
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# Model Card for Qwen2-0.5B-NashMD
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This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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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?"
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generator = pipeline("text-generation", model="qgallouedec/Qwen2-0.5B-NashMD", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<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/huggingface/trl/runs/5r7w3wt4)
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This model was trained with Nash-MD, a method introduced in [Nash Learning from Human Feedback](https://huggingface.co/papers/2312.00886).
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### Framework versions
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- TRL: 0.12.0.dev0
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- Transformers: 4.46.0.dev0
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- Pytorch: 2.4.1
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- Datasets: 3.0.2
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- Tokenizers: 0.20.0
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## Citations
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Cite Nash-MD as:
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```bibtex
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@inproceedings{munos2024nash,
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title = {Nash Learning from Human Feedback},
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author = {R{'{e}}mi Munos and Michal Valko and Daniele Calandriello and Mohammad Gheshlaghi Azar and Mark Rowland and Zhaohan Daniel Guo and Yunhao Tang and Matthieu Geist and Thomas Mesnard and C{\^{o}}me Fiegel and Andrea Michi and Marco Selvi and Sertan Girgin and Nikola Momchev and Olivier Bachem and Daniel J. Mankowitz and Doina Precup and Bilal Piot},
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year = 2024,
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booktitle = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
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publisher = {OpenReview.net},
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url = {https://openreview.net/forum?id=Y5AmNYiyCQ}
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}
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```
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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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},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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