---
license: mit
datasets:
- yahma/alpaca-cleaned
language:
- en
---
This repo contains a low-rank adapter for LLaMA-13b fit on the Stanford Alpaca dataset.

This version of the weights was trained on dual RTX3090 with the following hyperparameters:

Epochs: 10  
Batch size: 128  
Cutoff length: 256  
Learning rate: 3e-4  
Lora r: 16  
Lora alpha: 16  
Lora target modules: q_proj, k_proj, v_proj, o_proj  
That is:

OMP_NUM_THREADS=4 WORLD_SIZE=2 CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node=2 --master_port=1234 finetune.py \  
    --base_model='decapoda-research/llama-13b-hf' \  
    --data_path="yahma/alpaca-cleaned' \  
    --num_epochs=10 \  
    --output_dir='./lora-alpaca-13b-256-qkvo' \  
    --lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \  
    --lora_r=16 \  
    --val_set_size=0 \  
    --micro_batch_size=32  

LR warmup was tuned to fit the first epoch.

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

![10 epochs](alpaca13b.png)