pytorch llama region:us
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# Greek Translation Model - Fine-tuning and Evaluation

This repository contains two Jupyter notebooks designed for fine-tuning and evaluating a Greek language model using state-of-the-art tools and optimizations.

## Contents

1. **NLP_FINETUNE.ipynb** - A notebook for fine-tuning the Greek language model.
2. **NLP_Eval.ipynb** - A notebook for evaluating the fine-tuned model on test data.

## Requirements

- **Python**: 3.8+
- **Libraries**: The notebooks automatically handle the installation of required libraries, but ensure that your environment includes:
  - `unsloth`
  - `xformers`
  - `trl`
  - `peft`
  - `accelerate`
  - `bitsandbytes`
  - `triton`
  - `datasets`
  - `transformers`
  - `torch` (PyTorch)

## Fine-tuning Process (`NLP_FINETUNE.ipynb`)

1. **Install Dependencies**: The notebook installs all required libraries to ensure efficient fine-tuning.
2. **Load Dataset**: Leverages Hugging Face's `datasets` to load the dataset for fine-tuning.
3. **Initialize Trainer**: Uses `SFTTrainer` from the `trl` library for supervised fine-tuning.
4. **Model Optimization**: The model is optimized using `FastLanguageModel` from `unsloth`, with support for `bfloat16` (if available).

## Evaluation Process (`NLP_Eval.ipynb`)

1. **Install Dependencies**: Similar to the fine-tuning notebook, all necessary packages are installed.
2. **Load Model and Dataset**: Loads the fine-tuned model and prepares the test dataset for evaluation.
3. **Evaluation Metrics**: The model is evaluated using standard language model metrics.

## How to Use

1. **Run the Fine-tuning Notebook**:
   - Open `NLP_FINETUNE.ipynb` in a Jupyter environment and run all cells to fine-tune the model.
   
2. **Evaluate the Model**:
   - After fine-tuning, use the `NLP_Eval.ipynb` notebook to evaluate the model's performance.

## Acknowledgements

The project uses cutting-edge tools from the Hugging Face ecosystem and optimizations from the `unsloth` library for rapid and efficient fine-tuning.
