Model Card for Mistral-7B-ReMax-v0.1

The Mistral-7B-ReMax-v0.1 Large Language Model (LLM) is a Reinforcement Learning from Human Preference (RLHF) fine-tuned version of Mistral-7B-Instruct-v0.2.

The fine-tuning algorithm is ReMax and please find algorithm details in the paper.

Model Details

Developed by: Ziniu Li
License: apache-2.0
Finetuned from Mistral-7B-Instruct-v0.2

Uses

Direct Use

The instruction format is the same as Mistral-7B-Instruct-v0.2. Specifically, the prompt should be surrounded by [INST] and [/INST] tokens.

text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"

Training Details

Training Data

10k prompts from the lmsys-chat-1m dataset. Note that no responses from this dataset are used in training.

Reward Model

The reward model is based on UltraRM-13b.

Important: UltraRM-13b uses a different instruction template with Mistral-7B. To address this issue, we change the chat template to that of UltraRM-13b when evaluating the reward score in RLHF fine-tuning.

Training Procedure

The training algorithm is ReMax, whose details are available in the paper, and implementations are available in the repository.

Training Hyperparameters

max_prompt_seq: 384
max_answer_seq: 384
learning_rate: 5e-7
num_train_epochs: 1
kl_penalty: full_step
kl_ctl: 0.1
reward_clip_value: 1.0
temperature for sampling: 0.7
top_p for sampling: 0.9

Evaluation

Chat1M

Since we fine-tune Mistral-7B-Instruct-v0.2 with the lmsys-chat-1m dataset, we first evaluate the quality of responses. We randomly select 500 prompts from the lmsys-chat-1m dataset and lets the models to generate responses. Then, we let GPT-4 to evaluate the binary win rate. The average win-rate of Mistral-7B-ReMax-v0.1 is 57.8%.

	Mistral-7B-ReMax-v0.1 Wins	Mistral-7B-ReMax-v0.1 Wins
Mistral-7B-Instruct-v0.2	289	211

Samples of responses are provided below.

==================Instruction================
你好
==================Mistral-7B-Instruct-v0.2================
你好！我是一位帮助您解答问题的机器人。如果您有任何问题，请告诉我，我会尽力回答。

你好（Hello）是中文语言中的一个常用词语，常用于问候或表示亲和，意思是“你好，你很好”。

在我帮助您之前，我想先问一下，您是否知道什么是机器学习？机器学习是人工智能领域中的一种技术，它允许计算机自动学习和改进其行为，以便从数据中提取出有价值的信息，并使计算机能够做出更智能的决策。

如果您已经熟悉机器学习，那么我可以帮助您深入探讨相关主题，如神经网络、支持向量机、决策树等。但是，如果您对机器学习不太熟悉，我可以简单地解释它的基本原理，以及它在现实生活中的应用。

请告诉我您的需求，我会尽力满足您的期望。

再次问候您：你好！
==================Mistral-7B-ReMax-v0.1================
好的，欢迎使用我的帮助！（Hello, welcome to use my help!）

是的，我可以以中文回答你的问题，或者帮你完成一些简单的任务。（Yes, I can answer your questions in Chinese or help you with some simple tasks.）

请问有什么特定的问题需要我帮忙呢？（What specific question do you need my help with?）

或者，你可以告诉我什么方面我可以提供帮助。（Or you can tell me which area I can provide help in.）

希望我能帮到你！（I hope I can help you!）

AlpacaEval

AlpacaEval provides 805 test questions that the model has not been trained on. We provide the evaluation results below:

	AlpacaEval	AlpacaEval 2	Response Length
Mistral-7B-Instruct-v0.2	92.78	14.72	1676
Mistral-7B-ReMax-v0.1	94.40	16.00	1478
LLaMa2 Chat 7B	71.37	4.96	1479
LLaMa2 Chat 70B	92.66	13.87	1790
GPT-4	95.28	23.58	2049

Samples of responses are provided below

==================Instruction================
Marie is at the music store for a store day. She bought two $50 combos. Then, for $25 she bought two t-shirts for $5 each and five hoodies for $2 each. Then, she bought sneakers for $8 each. How much did she spend total?
==================Mistral-7B-Instruct-v0.2================
Marie bought 2 combos at $50 each, so that's $100. She bought 2 t-shirts at $5 each, which is $10. She bought 5 hoodies at $2 each, which is $10. The sneakers cost $8. So Marie spent a total of $118. ($100 + $10 + $8 = $118)
==================Mistral-7B-ReMax-v0.1================
Marie bought two combos at $50 each, so that's $100. She bought two t-shirts for $5 each, which totals $10. She bought five hoodies for $2 each, so that's $10. The sneakers cost $8.

So Marie spent $100 + $10 + $10 + $8 = $128 in total.

Limitations

The Mistral-7B-ReMax-v0.1 model demonstrates that the optimization algorithm ReMax is effective. This model shares the same limitations as the Mistral-7B-Instruct-v0.2 model. In particular, it does not have any moderation mechanisms.

Citation

BibTeX:

@article{li2023remax,
  title={Remax: A simple, effective, and efficient method for aligning large language models},
  author={Li, Ziniu and Xu, Tian and Zhang, Yushun and Yu, Yang and Sun, Ruoyu and Luo, Zhi-Quan},
  journal={arXiv preprint arXiv:2310.10505},
  year={2023}
}