Fine-Tuning TinyLLaMA with Unsloth: A Comprehensive Guide

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

Welcome, AI enthusiasts and developers! In this detailed guide, I’m excited to show you how to fine-tune TinyLLaMA, a Small Language Model (SLM) specifically designed for edge devices like mobile phones. Whether you're looking to enhance your AI applications or participate in your next hackathon, you’re in the right place. Let’s dive in!

Prerequisites

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

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

Setting Up Fine-Tuning Environment

We will use Google Colab to fine-tune TinyLLaMA, leveraging its free and accessible GPU capabilities. Here’s how to get started:

Create a New Colab Notebook:

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

Install Dependencies:

In your code cell, run the following command to install the required libraries:

!pip install unsloth transformers accelerate

Loading the Model and Tokenizer

Next, load the TinyLLaMA model and its tokenizer. Here’s how to do it with configuration options:

from transformers import TinyLLaMA, TinyLLaMATokenizer

model = TinyLLaMA.from_pretrained('tiny-llama')
tokenizer = TinyLLaMATokenizer.from_pretrained('tiny-llama')

Layer Selection and Hyperparameters

After loading the model, configure it for fine-tuning by selecting specific layers and setting key hyperparameters:

Using the get_peft_model method from the FastLanguageModel provided by Unsloth, you apply Parameter-Efficient Fine-Tuning (PEFT) techniques:

from unsloth import get_peft_model

peft_model = get_peft_model(model, 'LoRA', layers={'attention': ["q_proj", "k_proj", "v_proj", "o_proj"], 'feed_forward': ["gate_proj", "up_proj", "down_proj"]})

Preparing the Dataset and Defining the Prompt Format

Now that your model is configured, it’s time to prepare your dataset. You can use the Alpaca dataset or create a custom one. Here’s how:

Using the Alpaca Dataset

The Alpaca dataset is specifically designed for training models to follow instructions

!pip install datasets
from datasets import load_dataset

dataset = load_dataset('huggingface/alpaca')

Creating and Loading a Custom Dataset

If you opt for a custom dataset:

• Create a JSON file structure containing fields such as instruction, input, and output.
• Load it using:

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

Monitoring Fine-Tuning with W&B

Weights & Biases (W&B) is invaluable for tracking your model’s training process:

import wandb

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

Training TinyLLaMA with W&B Integration

Finally, let’s train TinyLLaMA with the SFTTrainer from the trl library:

from trl import SFTTrainer

trainer = SFTTrainer(model, dataset,
                     args={'gradient_accumulation_steps': 4,
                           'per_device_train_batch_size': 2,
                           'logging_dir': './logs'})

trainer.train()

Monitoring Training with Weights & Biases (W&B)

To view training metrics:

1. Log in to W&B and navigate to your project.
2. Explore the dashboard for metrics like loss, training speed, and GPU usage.

Testing the Fine-Tuned Model

After training, test your model's performance:

output = model.generate(tokenizer.encode())
print(tokenizer.decode(output))

Saving the Fine-Tuned Model

You can save your model locally or push it to the Hugging Face Hub:

• Locally: Use

  model.save_pretrained('./fine_tuned_model')

• Hugging Face Hub:

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

Practical Tips

Here are some tips to enhance your fine-tuning experience:

• Avoid Overfitting: Monitor validation loss. Use techniques like early stopping and regularization.
• Handle Imbalanced Data: Techniques such as oversampling and class weighting can help.
• Fine-Tuning on Limited Data: Data augmentation and transfer learning can maximize model performance.

Advanced Considerations

Looking to take it a step further? Consider:

• Layer-Specific Fine-Tuning
• Transfer Learning
• Integrating with Other Models

Conclusion

In this tutorial, we covered the methods to fine-tune TinyLLaMA using Unsloth, alongside effective resource management techniques. Fine-tuning can lead to significant improvements in model performance with an efficient use of GPU resources in Google Colab. We also highlighted best practices, including the importance of monitoring your model's performance with W&B.

Happy modeling, and may your AI projects flourish!