Model Card for umd-zhou-lab/claude2-alpaca-7B

This model is trained by fine-tuning llama-2 with claude2 alpaca data.

Model Details

Model Description

• 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

• GitHub: Claude2-Alpaca
• Data: claude2_alpaca

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 Stanford Alpaca

Hyperparameter	Global Batch Size	Learning rate	Epochs	Max length	Weight decay
Model (7B)	128	2e-5	3	4096	0

Performance

Compared to the llama2-chat, our models can have better average performance.

	Average	ARC	HellaSwag	MMLU	TruthfulQA	Alpaca_Eval	Avg Length
Llama-2-7b-chat	56.335	52.9	78.55	48.32	45.57	71.37	1479
Llama-2-13b-chat	59.935	59.04	81.94	54.64	44.12	81.09	1513
claude_alpaca-7b	57.78	56.66	81.17	46.58	46.71	71.23	1066
claude_alpaca-13b	61.29	61.18	84.08	55.74	44.18	78.93	1127

Citation

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

@misc{claude2-alpaca,
  author = {Lichang Chen and Khalid Saifullah and Ming Li and Tianyi Zhou and Heng Huang},
  title = {Claude2-Alpaca: Instruction tuning datasets distilled from claude},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Lichang-Chen/claude2-alpaca}},
}