Model save

README.md

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+---
+base_model: AmberYifan/Qwen2.5-7B-sft-ultrachat
+library_name: transformers
+model_name: Qwen2.5-7B-sft-ultrachat-SPIN-gpt4o
+tags:
+- generated_from_trainer
+- trl
+- dpo
+licence: license
+---
+
+# Model Card for Qwen2.5-7B-sft-ultrachat-SPIN-gpt4o
+
+This model is a fine-tuned version of [AmberYifan/Qwen2.5-7B-sft-ultrachat](https://huggingface.co/AmberYifan/Qwen2.5-7B-sft-ultrachat).
+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="AmberYifan/Qwen2.5-7B-sft-ultrachat-SPIN-gpt4o", 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/yifanwang/huggingface/runs/uyl30ws8)
+
+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: 0.12.2
+- Transformers: 4.46.3
+- Pytorch: 2.6.0
+- Datasets: 3.3.0
+- 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}}
+}
+```

all_results.json

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+{
+    "epoch": 3.0,
+    "total_flos": 0.0,
+    "train_loss": 0.022058238131815282,
+    "train_runtime": 10847.6777,
+    "train_samples": 9977,
+    "train_samples_per_second": 2.759,
+    "train_steps_per_second": 0.345
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.46.3"
+}

train_results.json

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+{
+    "epoch": 3.0,
+    "total_flos": 0.0,
+    "train_loss": 0.022058238131815282,
+    "train_runtime": 10847.6777,
+    "train_samples": 9977,
+    "train_samples_per_second": 2.759,
+    "train_steps_per_second": 0.345
+}