Model Card for umd-zhou-lab/claude2-alpaca-13B
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-2-13b
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 (13B) | 128 | 1e-5 | 5 | 2048 | 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}},
}