tomasmcm / claude2-alpaca-13b

Source: umd-zhou-lab/claude2-alpaca-13B ✦ Quant: TheBloke/claude2-alpaca-13B-AWQ ✦ This model is trained by fine-tuning llama-2 with claude2 alpaca data

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Input

Output

Run time and cost

This model runs on Nvidia A40 GPU hardware. Predictions typically complete within 4 seconds.

Readme

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

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}},
}