Fine-Tuning TinyLLaMA with Unsloth: Comprehensive Step-by-Step Guide

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

Hey there, folks! Tommy here, ready to dive into the exciting world of fine-tuning TinyLLaMA, a Small Language Model (SLM) optimized for edge devices like mobile phones. Whether you're an intermediate developer, AI enthusiast, or gearing up for your next hackathon project, this tutorial will walk you through everything you need to know to fine-tune TinyLLaMA using Unsloth.

Now let's get started!

Prerequisites

Before we jump into the tutorial, make sure 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'll use Google Colab to fine-tune TinyLLaMA, which offers a free and accessible GPU for this process. Here’s how to get started:

Create a New Colab Notebook

First, head over to Google Colab and create a new notebook. Next, ensure you have a GPU available by setting the notebook's runtime to use a GPU. You can do this by going to the menu and selecting Runtime > Change runtime type. In the window that appears, choose T4 GPU from the Hardware accelerator section.

Install Dependencies

Now we need to install the required libraries and dependencies. Run the command below in your code cell:

!pip install transformers datasets wandb

Loading the Model and Tokenizer

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

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('tatsu-lab/tiny-llama')
tokenizer = AutoTokenizer.from_pretrained('tatsu-lab/tiny-llama')

Layer Selection and Hyperparameters

After loading the model, the next step involves configuring it for fine-tuning by selecting specific layers and setting key hyperparameters. We'll be using the get_peft_model method from the FastLanguageModel provided by Unsloth. This method allows us to apply Parameter-Efficient Fine-Tuning (PEFT) techniques, specifically Low-Rank Adaptation (LoRA), which helps in adapting the model with fewer parameters while maintaining performance.

Configuring Layers

Special attention should be given to the attention and feed-forward layers:

• Attention Layers: These layers are key to how TinyLLaMA focuses on different parts of the input. By fine-tuning these layers, you help the model better understand and contextualize the data.
• Feed-Forward Layers: These layers handle the transformations post-attention, crucial for the model's ability to process and generate complex outputs.

Preparing the Dataset and Defining the Prompt Format

After configuring your model, the next step is to prepare your dataset and define the prompt format. For this tutorial, we'll use the Alpaca dataset from Hugging Face.

Using the Alpaca Dataset

The Alpaca dataset is designed for training models to follow instructions. We'll load it directly from Hugging Face and format it according to the structure expected by the TinyLLaMA model.

from datasets import load_dataset

dataset = load_dataset('tatsu-lab/alpaca')

Creating and Loading a Custom Dataset

If you want to use your own custom dataset, create a JSON file with your data. The file should contain a list of objects, each with instruction, input, and output fields. For example:

[
  { "instruction": "Your instruction here", "input": "Your input here", "output": "Expected output here" }
]

Save this file, for example as dataset.json. You can load the custom dataset using the load_dataset function from the Hugging Face datasets library:

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

Monitoring Fine-Tuning with W&B

Weights & Biases (W&B) is an essential tool for tracking your model's training process and system resource usage. It helps visualize metrics in real time.

Training TinyLLaMA with W&B Integration

Now that everything is set up, it's time to train the TinyLLaMA model. We'll be using the SFTTrainer from the trl library.

Initializing W&B and Setting Training Arguments

import wandb

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

training_args = {
  'epochs': 3,
  'train_batch_size': 8,
  'gradient_accumulation_steps': 4,
  'evaluation_strategy': 'steps'
}

Monitoring Training with W&B

To view and interpret these metrics:

• Loss: Helps identify overfitting.
• Training Speed: Measures training speed, aiding computational efficiency.

Testing the Fine-Tuned Model

After fine-tuning your model, you can test its performance with the following code:

test_output = model.generate(tokenizer.encode("Your test input"))
print(tokenizer.decode(test_output[0]))

Saving the Fine-Tuned Model

To save the model and tokenizer locally:

model.save_pretrained('./fine_tuned_model')
tokenizer.save_pretrained('./fine_tuned_model')

Practical Tips

Monitor your model closely to prevent overfitting and handle dataset imbalances effectively, especially when working with limited data. You can apply data augmentation techniques and fine-tune specific model layers for better performance.

Conclusion

In this tutorial, we’ve explored powerful techniques for fine-tuning TinyLLaMA using Unsloth while emphasizing efficiency and resource management. With these skills, you can now confidently tackle various fine-tuning tasks, ensuring your models are both effective and resource-efficient. Happy modeling!