umd-zhou-lab/recycled-alpaca-7b-v1.0

Model Card for umd-zhou-lab/recycled-alpaca-7b-v1.0

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This model is trained by fine-tuning llama-2 with recycled alpaca data V1.

Model Details

Model Description

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• —Developed by: UMD Tianyi Zhou Lab
• —Model type: An auto-regressive language model based on the transformer architecture
• —License: Llama 2 Community License Agreement
• —Finetuned from model: meta-llama/Llama-2-7b

Model Sources

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• —GitHub: Reflection-Tuning
• —Paper: Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning
• —Data: recycled_alpaca_v1

Uses

The primary use of this model is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.

Training

We use the prompt from FastChat:

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>USER: Who are you? ASSISTANT: I am ...</s>......

Performance

The following table provides a comparison between our recycled models (V1) and baseline models on the AlpacaEval Leaderboard and Huggingface Open LLM Leaderboard. <br>

The Recycled Alpaca Data can be found here: [[hf-Link]](https://huggingface.co/datasets/umd-zhou-lab/recycledalpacav1) <br> The Recycled WizardLM (70k) Data can be found here: [[hf-Link]](https://huggingface.co/datasets/umd-zhou-lab/recycledwiz70v1) <br>

Citation

Please consider citing our paper if you think our codes, data, or models are useful. Thank you!

@misc{li2023reflectiontuning,
      title={Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning}, 
      author={Ming Li and Lichang Chen and Jiuhai Chen and Shwai He and Heng Huang and Jiuxiang Gu and Tianyi Zhou},
      year={2023},
      eprint={2310.11716},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}