Fine-Tuning and Uploading a Model to Hugging Face

Prerequisites

1. Python Version: Ensure you are using Python 3.10.12.
2. Install Required Libraries: Run the following commands to install the necessary dependencies:

pip install -U pip
pip install -U transformers
pip install -U bitsandbytes
pip install -U accelerate
pip install -U datasets
pip install -U peft
pip install -U trl
pip install -U wandb
pip install ipywidgets --upgrade

1. Prepare Your Dataset: Ensure your training dataset is in JSON format and contains the required fields, such as text (input) and output (expected response).

Key Components of the Script

1. Model Loading

• Base Model ID: Set as base_model_id.
• New Model Name: Define the name of your fine-tuned model with new_model_id.

1. Quantization Configuration

1. LoRA Configuration

• LoRA settings, such as lora_alpha, r, and lora_dropout, can be customized.
• Target modules for LoRA are dynamically determined based on the model's architecture.

1. Dataset Formatting

1. Training Arguments

• Batch size, learning rate, logging steps, and save intervals.
• Gradient accumulation is used for training with limited resources.

1. Training

Uploading to Hugging Face

1. Install the Hugging Face Hub CLI

1. Log In to Hugging Face CLI:

1. Push Model to Hugging Face: Add the following snippet to the end of the script to upload the model:

from huggingface_hub import HfApi

# Initialize Hugging Face API
api = HfApi()

# Push model to the Hugging Face Hub
model.push_to_hub(new_model_id, use_temp_dir=True)
tokenizer.push_to_hub(new_model_id, use_temp_dir=True)

print(f"Model successfully uploaded to: https://huggingface.co/{new_model_id}")