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Model: Kyleyee/CPO_hh-seed4
Source: Original Platform

README.md

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+---
+base_model: Kyleyee/Qwen2.5-1.5B-sft-hh-3e
+datasets: Kyleyee/train_data_Helpful_drdpo_preference
+library_name: transformers
+model_name: CPO_hh-seed4
+tags:
+- generated_from_trainer
+- trl
+- cpo
+licence: license
+---
+
+# Model Card for CPO_hh-seed4
+
+This model is a fine-tuned version of [Kyleyee/Qwen2.5-1.5B-sft-hh-3e](https://huggingface.co/Kyleyee/Qwen2.5-1.5B-sft-hh-3e) on the [Kyleyee/train_data_Helpful_drdpo_preference](https://huggingface.co/datasets/Kyleyee/train_data_Helpful_drdpo_preference) 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="Kyleyee/CPO_hh-seed4", 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/qixy25-tsinghua-university/huggingface/runs/782jidxy) 
+
+
+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: 0.16.0.dev0
+- Transformers: 4.49.0
+- Pytorch: 2.6.0+cu126
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## 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}}
+}
+```