This package is to provide a codebase to easily fine-tune some open-source LLMs and reproduce the results.
So far, the codebase supports
- Fine-tuning algorithms offered by the PEFT package
- LoRA
- Open-source LLM families from Hugging Face
- In-context learning
For detailed instruction, please refer to the Notion site: LLM Fine-tuning
Here is a simple example of using the package for fine-tuning or prediction on the SST2 dataset. You can also find this demo code in demo.py.
import fire
from lora import Llama_Lora
def main(
task: str = "eval",
base_model: str = "meta-llama/Llama-2-7b-hf",
):
m = Llama_Lora(
base_model=base_model,
)
if task == "train":
m.train(
train_file = "data/sst2/train.json",
val_file = "data/sst2/val.json",
output_dir = "./ckp_sst_llama2_lora",
train_batch_size = 32,
num_epochs = 1,
)
elif task == "eval":
m.predict(
input_file = "data/sst2/val.json",
max_new_tokens = 32,
verbose = True,
)
else:
raise ValueError(f"Unrecognized task: {task}")
if __name__ == "__main__":
fire.Fire(main)For prediction, please use
python demo.pyFor fine-tuning, please use
python demo.py --task trainIn addition, this package has been tested by many members from the UVA ILP group via their research projects.
This project was inspired by the Alpaca-LoRA project, from which we learned a lot!
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent