Mastering Fine-Tuning with TinyLLaMA and Unsloth

Fine-Tuning TinyLLaMA with Unsloth: A Hands-On Guide

Welcome to the exciting world of fine-tuning TinyLLaMA, a Small Language Model (SLM) optimized for edge devices like mobile phones! This tutorial is designed for intermediate developers, AI enthusiasts, or anyone gearing up for their next hackathon project. Let’s dive in to learn how to fine-tune TinyLLaMA using Unsloth.

Prerequisites

Before we jump into the tutorial, ensure you have the following prerequisites:

• Basic Python Knowledge
• Familiarity with Machine Learning Concepts
• A Google account to access Google Colab.
• A W&B account (you can sign up here: W&B Signup).

Setting Up Fine-Tuning Environment

We'll utilize Google Colab to fine-tune TinyLLaMA, as it provides a free and accessible GPU. Here’s how to set up your environment:

Create a New Colab Notebook

1. Go to Google Colab and create a new notebook.
2. Set the notebook's runtime to use a GPU by selecting Runtime > Change runtime type. Choose T4 GPU from the Hardware accelerator section.

Install Dependencies

Run the following command in a code cell to install the required libraries:

!pip install necessary-libraries

Loading the Model and Tokenizer

After setting up your environment, the next step is to load the TinyLLaMA model and its tokenizer with some configuration options.

Layer Selection and Hyperparameters

After loading the model, configure it for fine-tuning by selecting specific layers and setting key hyperparameters. We will use the get_peft_model method from the FastLanguageModel provided by Unsloth for Parameter-Efficient Fine-Tuning (PEFT). Here's how to configure the model:

model = get_peft_model(original_model, layers)

Key layers to focus on include:

• Attention Layers: Fine-tuning layers like "q_proj", "k_proj", "v_proj", and "o_proj" enhances the model's understanding of input data.
• Feed-Forward Layers: These include "gate_proj", "up_proj", and "down_proj" that transform post-attention data crucial for processing complex outputs.

Preparing the Dataset and Defining the Prompt Format

Next, prepare your dataset. For this tutorial, we will use the Alpaca dataset from Hugging Face, but also cover how you can create and load a custom dataset.

Using the Alpaca Dataset

The Alpaca dataset is structured for instruction-following tasks. Here’s how to load and format it:

from datasets import load_dataset
dataset = load_dataset('tatsu-lab/alpaca')

Creating and Loading a Custom Dataset

If you want to use your custom dataset, follow these steps:

{"data":[{"instruction":"Your instruction","input":"Your input","output":"Your output"}]}

Save this JSON file (e.g., dataset.json) and load it by running:

dataset = load_dataset('json', data_files='dataset.json')

Monitoring Fine-Tuning with W&B

Weights & Biases (W&B) enables tracking your training process and visualizing metrics in real-time. Sign up and obtain your API key to start integrating W&B.

Training TinyLLaMA with W&B Integration

With everything set up, it’s time to train the TinyLLaMA model. Utilize the SFTTrainer from the TRL library and W&B for monitoring:

import wandb
wandb.login()

wandb.init(project='tiny-llama')

Setting Training Arguments

Here's how to manage training:

• Batch Size and Gradient Accumulation: Keep the batch size small and use gradient accumulation to stabilize training.
• Mixed Precision Training: Use mixed precision (FP16 or BF16) to reduce memory usage.
• Efficient Resource Management: Employ 4-bit quantization for efficient memory usage.
• Evaluation Strategy: Set the evaluation strategy to "steps" for periodic updates.

Monitoring Training with Weights & Biases (W&B)

After integrating W&B into your training setup, monitor various metrics through the W&B dashboard:

wandb.log({'loss': loss_value})

Evaluating the Fine-Tuned Model

Test the model’s performance:

model.generate(prompt)

Saving the Fine-Tuned Model

To save the model:

model.save_pretrained('your_model_directory')

Or push it to Hugging Face Hub:

model.push_to_hub('your-huggingface-model-name')

Practical Tips

Avoid Overfitting

• Use early stopping when validation performance stagnates.
• Incorporate regularization techniques like dropout.

Handle Imbalanced Data

• Utilize oversampling or class weighting strategies.

Fine-Tuning on Limited Data

• Use data augmentation techniques.
• Leverage Low-Rank Adaptation for efficient fine-tuning.

Advanced Considerations

For those looking to deepen their expertise:

• Explore layer-specific fine-tuning.
• Implement transfer learning.
• Consider integrating TinyLLaMA with retrieval-augmented generation (RAG).

Conclusion

This tutorial provided robust techniques to efficiently fine-tune TinyLLaMA using Unsloth with careful resource management. Enjoy your journey in developing smart AI applications!