Fine-Tuning LLaMA 2 Model

This repository contains code for fine-tuning the LLaMA 2 language model on a custom dataset. The fine-tuning process allows you to adapt the pre-trained LLaMA 2 model to perform better on specific tasks or domains.

Requirements

Python 3.7+

PyTorch 1.10+

Transformers library

CUDA-enabled GPU (recommended)

Usage

Set up the environment

Install the required dependencies

Obtain the pre-trained LLaMA 2 model weights

Run the fine-tuning script

Specify the paths to the pre-trained model and dataset

Adjust the hyperparameters as needed (e.g., learning rate, batch size, number of epochs)

Execute the fine-tuning script

Evaluate the fine-tuned model

Use the validation set to assess the model's performance

Monitor metrics such as perplexity or task-specific metrics

Save the fine-tuned model

Export the fine-tuned model for later use or deployment

Example

Load the fine-tuned model and tokenizer
model = LlamaForCausalLM.from_pretrained("path/to/fine-tuned-model")
tokenizer = LlamaTokenizer.from_pretrained("path/to/fine-tuned-model")

Generate text using the fine-tuned model
input_text = "The quick brown fox jumps over the lazy dog."
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)```