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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+simpo-math-model - AWQ
+- Model creator: https://huggingface.co/rawsh/
+- Original model: https://huggingface.co/rawsh/simpo-math-model/
+
+
+
+
+Original model description:
+---
+base_model: rawsh/mirrorqwen2.5-0.5b-SFT
+library_name: transformers
+model_name: simpo-math-model
+tags:
+- generated_from_trainer
+- trl
+- cpo
+- unsloth
+licence: license
+---
+
+# Model Card for simpo-math-model
+
+This model is a fine-tuned version of [rawsh/mirrorqwen2.5-0.5b-SFT](https://huggingface.co/rawsh/mirrorqwen2.5-0.5b-SFT).
+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="rawsh/simpo-math-model", 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/dankgpt/simpo-training/runs/q29stpw6)
+
+This model was trained with CPO, a method introduced in [Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation](https://huggingface.co/papers/2401.08417).
+
+### Framework versions
+
+- TRL: 0.12.0
+- Transformers: 4.46.2
+- Pytorch: 2.4.1
+- Datasets: 3.1.0
+- Tokenizers: 0.20.3
+
+## Citations
+
+Cite CPO as:
+
+```bibtex
+@inproceedings{xu2024contrastive,
+    title        = {{Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}},
+    author       = {Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim},
+    year         = 2024,
+    booktitle    = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
+    publisher    = {OpenReview.net},
+    url          = {https://openreview.net/forum?id=51iwkioZpn}
+}
+```
+
+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}}
+}
+```
+