Fine-Tune LLAMA Script

This Python script allows you to fine-tune a base machine learning model on your local machine. Fine-tuning is a common technique in transfer learning, where you take a pre-trained model and adapt it for a specific task or dataset. In this case, we'll be using the LLAMA 7B (clone) model as the base.

Prerequisites

Before using this script, make sure you have the following installed on your local machine:

• Python (3.9 recommended)
• Required Python libraries (specified in requirements.txt)

You can install the required libraries by running:

pip install -r requirements.txt

Usage To fine-tune the model, follow these steps:

Download Pre-trained Model: You'll need a pre-trained LLAMA model. You can obtain one from the official LLAMA model repository or unofficial sources (ex. HuggingFace etc.)

Prepare Your Dataset: Prepare your dataset for fine-tuning. Make sure it's organized and formatted correctly.

Configure Parameters: Adapt the parameters inside finetune_llama.py

Run the Script: Execute the script with the following command:

python finetune_llama.py

Monitor Progress: The script will begin fine-tuning the LLAMA model on your dataset. You can monitor the training progress in your terminal.

Evaluation and Testing: After training, you can evaluate the fine-tuned model's performance on a specific task based on your training dataset and compare it with the model before the fine-tuning.

Use the Fine-Tuned Model: Once satisfied with the fine-tuned model, you can use it for inference on new data.

Test: Download (if not running locally) .bin file that the model generated after the training phase and put it inside the main folder of the project. Edit the variables: -'model_dir' with the name/path of your current file -input_text with the actual input you'd like to prompt to the model

Now run:

python main.py

and wait for the answer. Mind that using the cpu will significantly impact on model's performance.