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This repo contains a low-rank adapter for LLaMA-7b fit on the Stanford Alpaca dataset.

This version of the weights was trained with the following hyperparameters:

  • Epochs: 10 (load from best epoch)
  • Batch size: 128
  • Cutoff length: 512
  • Learning rate: 3e-4
  • Lora r: 16
  • Lora target modules: q_proj, k_proj, v_proj, o_proj

That is:

python finetune.py \
    --base_model='decapoda-research/llama-7b-hf' \
    --num_epochs=10 \
    --cutoff_len=512 \
    --group_by_length \
    --output_dir='./lora-alpaca-512-qkvo' \
    --lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
    --lora_r=16 \

Instructions for running it can be found at https://github.com/tloen/alpaca-lora.

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