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LLM Fine-Tuning with QLoRA

This repository can help to instruct-tune LLaMA (1 & 2), Open LLaMA, RedPajama, Falcon or StableLM models on consumer hardware using QLoRA (Original implementation here). It's mostly based on the original alpaca-lora repo which can be found here. Please note that this has only been tested on following models, but should work with other models. Contributions are welcome!

Supported Models

1. RedPajama
2. StableLM
3. Open LLaMA/LLaMA (1 & 2)
4. Falcon
5. Codegen
6. gpt_bigcode models

Local Setup

  1. Install dependencies

    pip install -r requirements.txt

  2. If bitsandbytes doesn't work, install it from source. Windows users can follow these instructions.

Training (finetune.py)

This file contains a straightforward application of QLoRA PEFT to the Open LLaMA / RedPajama / Falcon / StableLM model, as well as some code related to prompt construction and tokenization. PRs adapting this code to support larger models are always welcome.

Example usage:

For Open LLaMa

python finetune.py \
    --base_model 'openlm-research/open_llama_3b_600bt_preview' \
    --data_path '../datasets/dolly.json' \
    --num_epochs=3 \
    --cutoff_len=512 \
    --group_by_length \
    --output_dir='./dolly-lora-3b' \
    --lora_r=16 \
    --lora_target_modules='[q_proj,v_proj]'

For RedPajama

python finetune.py   \
--base_model='togethercomputer/RedPajama-INCITE-Base-3B-v1' \
--data_path='../datasets/dolly.json'   \
--num_epochs=3   \
--cutoff_len=512   \
--group_by_length   \
--output_dir='./dolly-lora-rp-3b-t1' \
--lora_r=16 \
--lora_target_modules='["query_key_value"]'

For StableLM

python finetune.py  \
--base_model='stabilityai/stablelm-base-alpha-3b' \
--data_path='../datasets/dolly.json' \
--num_epochs=3 \
--cutoff_len=512 \
--group_by_length  \
--output_dir='./dolly-lora-st-3b-t1' \
--lora_r=16 \
--lora_target_modules='["query_key_value"]'

For Pythia

python finetune.py  \
--base_model='EleutherAI/pythia-6.9b-deduped' \
--data_path='../datasets/dolly.json' \
--num_epochs=1 \
--cutoff_len=512 \
--group_by_length  \
--output_dir='./dolly-lora-pyt-6b-t1' \
--lora_r=8 \
--lora_target_modules='["query_key_value"]'

For Falcon

python finetune.py  \
--base_model='tiiuae/falcon-7b'     \
--data_path='../datasets/dolly.json'     \
--num_epochs=1     \
--cutoff_len=512     \
--group_by_length     \
--output_dir='./dolly-lora-falcon-7b-t1'   \
--lora_r=8 \
--lora_target_modules='["query_key_value"]'

For codegen

python finetune.py      \
--base_model='Salesforce/codegen-350M-mono'     \
--data_path='../datasets/code_alpaca_20k.json' \
--num_epochs=1     \
--cutoff_len=512     \
--group_by_length     \
--output_dir='./ca-cg-350m-t1'   \
--lora_r=8 \
--lora_target_modules='["qkv_proj"]'

For gpt_bigcode

 python finetune.py      \
 --base_model='bigcode/gpt_bigcode-santacoder'     \
 --data_path='../datasets/code_alpaca_20k.json'    \
 --num_epochs=1     \
 --cutoff_len=512     \
 --group_by_length     \
 --output_dir='./ca-big_code-santa-t1'   \
 --lora_r=8 \
 --lora_target_modules='["c_proj"]'

We can also tweak our hyperparameters (similar to alpaca-lora):

python finetune.py \
    --base_model 'openlm-research/open_llama_3b_600bt_preview \
    --data_path 'yahma/alpaca-cleaned' \
    --output_dir './lora-alpaca' \
    --batch_size 128 \
    --micro_batch_size 4 \
    --num_epochs 3 \
    --learning_rate 1e-4 \
    --cutoff_len 512 \
    --val_set_size 2000 \
    --lora_r 8 \
    --lora_alpha 16 \
    --lora_dropout 0.05 \
    --lora_target_modules '[q_proj,v_proj]' \
    --train_on_inputs \
    --group_by_length

Inference (generate.py)

This file reads the foundation model from the Hugging Face model hub and the LoRA weights from trained peft model, and runs a Gradio interface for inference on a specified input. Users should treat this as example code for the use of the model, and modify it as needed.

Example usage:

For Open LLaMa

python generate.py \
    --base_model 'openlm-research/open_llama_3b_600bt_preview' \
    --lora_weights './lora-alpaca'

For RedPajama

python generate.py  \
--base_model 'togethercomputer/RedPajama-INCITE-Base-3B-v1'  \
--lora_weights './dolly-lora-rp-3b-t1/'

For StableLM

python generate.py  \
--base_model 'stabilityai/stablelm-base-alpha-3b' \
--lora_weights './dolly-lora-st-3b-t1'

For Pythia

python generate.py  \
--base_model 'EleutherAI/pythia-6.9b-deduped'  \
--lora_weights './dolly-lora-pyt-6b-t1'

For Falcon

python generate.py     \
--base_model 'tiiuae/falcon-7b'   \
--lora_weights './dolly-lora-falcon-7b-t1'

For Codegen

python generate.py \
--base_model 'Salesforce/codegen-350M-mono'   \
--lora_weights './ca-cg-350m-t1'

For gpt_bigcode

python generate.py     \
--base_model 'bigcode/gpt_bigcode-santacoder'   \
--lora_weights './ca-big_code-santa-t1'

Media Coverage

AemonAlgiz's walkthrough video here

Acknowledgements

We would like to express our heartfelt gratitude to Meta for releasing LLaMA . Without this pioneering technology, the foundations of projects like Open Llama and Alpaca wouldn't exist. We sincerely appreciate the immense contributions you've made to the field.

Our acknowledgements also extend to the teams behind Open LLaMA, Together Computer, Alpaca and Alpaca LoRA.. You can find more about their excellent work on their respective GitHub repositories:

Lastly, we would like to express our thanks to the developers of QLoRA and bitsandbytes Your efforts have been instrumental in advancing the field, and we're grateful for your contributions. More information about these projects can be found at:

Thank you all for your commitment to innovation and for making these projects possible.