LLaMA 3.1 Fine-Tuning Project
This project focuses on the fine-tuning of the LLaMA 3.1 model (8 billion parameters) using a custom dataset built specifically for this experiment.
The goal is to adapt the base model to perform better responses.
Overview
The project demonstrates the end-to-end fine-tuning workflow of a large language model using the Hugging Face ecosystem.
It includes dataset preparation, model loading, fine-tuning configuration, and evaluation steps.
The experiments were primarily conducted in Python, leveraging both Transformers and Unsloth frameworks to optimize performance and memory usage.
Architecture
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Model:
- LLaMA 3.1 (8B) — fine-tuned from the original Meta model weights.
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Frameworks and Libraries:
- Hugging Face Transformers — model management, tokenization, and training utilities.
- Unsloth — lightweight framework for efficient fine-tuning and experimentation.
- Datasets — for custom data handling and preprocessing. https://huggingface.co/datasets/GuiILG/engineer
- Accelerate / PyTorch — distributed and optimized training backend.
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Language:
- Implemented entirely in Python.
Dataset
A custom dataset was created specifically for this fine-tuning task.
The dataset was manually curated and cleaned to ensure consistency and high quality.
It contains domain-specific examples formatted for instruction tuning, ensuring the model learns both context and response structure effectively.
Fine-Tuning Process
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Data Preparation
- Convert raw data into a structured JSON or CSV format compatible with Hugging Face Datasets.
- Tokenize the dataset using the LLaMA tokenizer.
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Model Loading
- Load the base LLaMA 3.1 (8B) model from the Hugging Face Hub or local checkpoint.
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Training Configuration
- Define hyperparameters such as learning rate, batch size, and number of epochs.
- Configure training with Transformers Trainer or Unsloth fine-tuning utilities.
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Training Execution
- Run training on GPU (single or multi-GPU setup).
- Monitor performance and loss metrics.
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Evaluation and Saving
- Evaluate model performance on validation data.
- Save the fine-tuned weights and tokenizer for future inference.
Technologies Used
| Category | Tool / Library |
|---|
| Model | LLaMA 3.1 (8B) |
| Framework | Hugging Face Transformers |
| Optimization | Unsloth |
| Training Backend | PyTorch / Accelerate |
| Language | Python |
| Data Management | Hugging Face Datasets |
