sfairXC
/

FsfairX-LLaMA3-RM-v0.1

@@ -1,199 +1,75 @@
----
-library_name: transformers
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+## Training
+The base model is meta-llama/Meta-Llama-3-8B-Instruct.
+We use the training script at `https://github.com/WeiXiongUST/RLHF-Reward-Modeling`.
+We train the model for one epoch with a learning rate of 2e-6, batch size 512, cosine learning rate decay with a warmup ratio 0.03.
 ## Uses
+```python
+  from transformers import AutoTokenizer, pipeline
+  rm_tokenizer = AutoTokenizer.from_pretrained("sfairXC/FsfairX-LLaMA3-RM-v0.1")
+  device = 0 # accelerator.device
+  rm_pipe = pipeline(
+      "sentiment-analysis",
+      model="sfairXC/FsfairX-LLaMA3-RM-v0.1",
+      #device="auto",
+      device=device,
+      tokenizer=rm_tokenizer,
+      model_kwargs={"torch_dtype": torch.bfloat16}
+  )
+  pipe_kwargs = {
+      "return_all_scores": True,
+      "function_to_apply": "none",
+      "batch_size": 1
+  }
+  chat = [
+   {"role": "user", "content": "Hello, how are you?"},
+   {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
+   {"role": "user", "content": "I'd like to show off how chat templating works!"},
+  ]
+  test_texts = [tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False).replace(tokenizer.bos_token, "")]
+  pipe_outputs = rm_pipe(test_texts, **pipe_kwargs)
+  rewards = [output[0]["score"] for output in pipe_outputs]
+```
+## Results
+This Reward model is the SOTA open-source RM (Apr 20, 2024).
+| Metric       | Score  |
+|--------------|--------|
+| Chat         | 99.44  |
+| Chat Hard    | 65.13  |
+| Safety       | 88.76  |
+| Reasoning    | 88.3   |
+## Reference
+The repo was part of the iterative rejection sampling fine-tuning and iterative DPO. If you find the content of this repo useful in your work, please consider cite it as follows:
+```bibtex
+@article{dong2023raft,
+  title={Raft: Reward ranked finetuning for generative foundation model alignment},
+  author={Dong, Hanze and Xiong, Wei and Goyal, Deepanshu and Pan, Rui and Diao, Shizhe and Zhang, Jipeng and Shum, Kashun and Zhang, Tong},
+  journal={arXiv preprint arXiv:2304.06767},
+  year={2023}
+}
+@misc{xiong2024iterative,
+      title={Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint},
+      author={Wei Xiong and Hanze Dong and Chenlu Ye and Ziqi Wang and Han Zhong and Heng Ji and Nan Jiang and Tong Zhang},
+      year={2024},
+      eprint={2312.11456},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```