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umd-zhou-lab/claude2-alpaca-13B
claude2-alpaca-13B is a text generation model from umd-zhou-lab. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as llama2.
This model is trained by fine-tuning llama-2 with claude2 alpaca data.
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From the Hugging Face model README
This model is trained by fine-tuning llama-2 with claude2 alpaca data.
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.
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 |
Compared to the llama2-chat, our models can have better average performance.<br>
| 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 |
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}},
}