---
license: cc-by-sa-4.0
---

This model is the RLHF version of `HuggingFaceH4/mistral-7b-sft-beta` without any external responses. 

We perform GSHF algorithm on SFT baseline. The external signals include (1) Reward model; (2) AI-generated Prompts.


**We obtain 35.95% win-rate (34.79% LC win-rate) on Alpaca Eval v2.** The win-rate of the base model is only 4.63%. 

For MT-bench, it obtained about 7.5, where the base model is only 5.3.

We have demonstrated the significant potential of the iterative RLHF algorithm for LLMs to deliver appropriate and well-structured responses, 
even without any external responses. 


## Model Details

We perform 3 iterations of GSHF algorithm on `HuggingFaceH4/mistral-7b-sft-beta` labeled by reward model, where prompts are generated by ChatGPT with self-instruct type prompt augmentation.

We use AI-generated 60K prompts in the training process.

Examples are as below,
```json
{"prompt": "Why is gold considered a good reserve asset for central banks?"}
{"prompt": "What are the top 5 yoga poses for stress relief?"}
{"prompt": "Craft a blog title about the health implications of eating avocados daily based on their caloric value."}
{"prompt": "Design a simple HTML chat interface that simulates a conversation between a user and a bot, displaying two messages from each."}
{"prompt": "List 10 names from different cultures that embody the meanings of peace, harmony, or compassion."}
```

## Uses

The usage and chat template format follow the SFT model `HuggingFaceH4/mistral-7b-sft-beta`.

```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="sfairXC/FsfairX-Zephyr-Chat-v0.1", torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate"},
    {"role": "user",   "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!
```




## Evaluation

The evaluation on Alpaca Eval v2 are provided as below,

| Model       | Win Rate | LC Win Rate | Avg Length |
|-------------|----------|-------------|------------|
| Base        | 4.63     | 8.01        | 916        |
| Iteration 1 | 13.26    | 20.81       | 1205       |
| Iteration 2 | 23.57    | 27.63       | 1623       |
| Iteration 3 | 35.95    | 34.79       | 2275       |


## Citation 



If you found this helpful, please cite the following papers.

```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}
}
```