Added lora baseline finetune script from huggingface or anywhere

[shxshank]

LoRA Fine-Tuning Script Walkthrough

This is a script fine_tune_lora.py that allows you to fine-tune a Hugging Face model using LoRA on a specified dataset.

Setup

First, you need to install the required dependencies. Since your environment is managed, it's recommended to use a virtual environment or conda.

# Create a virtual environment
python3 -m venv .venv

# Activate it
source .venv/bin/activate

# Install requirements
pip install -r requirements.txt

Running the Script

You can run the script using python fine_tune_lora.py.

Arguments

• --model_name_or_path: (Required) The model ID from Hugging Face (e.g., gpt2, meta-llama/Llama-2-7b-hf).
• --dataset_name: (Required) The dataset name from Hugging Face (e.g., imdb, timdettmers/openassistant-guanaco).
• --dataset_text_field: (Optional) The name of the column containing text data (default: text).
• --output_dir: (Optional) Directory to save results (default: ./lora-output).
• --use_4bit: (Optional) Enable 4-bit quantization (requires GPU).
• --dataset_text_field: (Optional) The name of the column containing text data (default: text).
• --target_modules: (Optional) List of target modules for LoRA (default: q_proj v_proj). For GPT2 use c_attn.
• --num_train_epochs: (Optional) Number of epochs (default: 1).

Example Usage

To fine-tune gpt2 on the imdb dataset (Note: gpt2 uses c_attn):

python fine_tune_lora.py --model_name_or_path gpt2 --dataset_name imdb --output_dir ./gpt2-lora --max_steps 100 --target_modules c_attn

To fine-tune a Llama 2 model with 4-bit quantization:

python fine_tune_lora.py --model_name_or_path meta-llama/Llama-2-7b-hf --dataset_name timdettmers/openassistant-guanaco --use_4bit --output_dir ./llama2-lora

Outputs

The script will save the LoRA adapter weights and the final model configuration to the specified output_dir.