transformers pytorch gemma2 text-generation generated_from_trainer axolotl trl cpo conversational arxiv:2401.08417 base_model:Aratako/gemma-2-2b-axolotl-sft-v1.0-merged base_model:finetune:Aratako/gemma-2-2b-axolotl-sft-v1.0-merged autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: Aratako/gemma-2-2b-axolotl-sft-v1.0-merged
library_name: transformers
model_name: gemma-2-2b-axolotl-simpo-v1.0
tags:
- generated_from_trainer
- axolotl
- trl
- cpo
licence: license
---

本モデルはaxolotlの使い方の解説記事のデモで作成されたモデルです。モデルとしては特に特に利用価値のないものになっているのでご注意ください。

[記事リンクはこちら](https://zenn.dev/aratako_lm/articles/b58ac364f9c9cd)

以下、自動生成されたREADMEです。

# Model Card for gemma-2-2b-axolotl-simpo-v1.0

This model is a fine-tuned version of [Aratako/gemma-2-2b-axolotl-sft-v1.0-merged](https://huggingface.co/Aratako/gemma-2-2b-axolotl-sft-v1.0-merged).
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="Aratako/gemma-2-2b-axolotl-simpo-v1.0", 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/aratako-lm/axolotl/runs/mz5mncpr)

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: 0.12.1
- Transformers: 4.46.3
- Pytorch: 2.5.1
- 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}}
}
```