LLM fine-tuning training loop | Coded from scratch

Summary

In this lecture, the instructor walks through the process of fine-tuning a pre-trained large language model (LLM) to follow specific instructions. Starting with data preparation, the model is fine-tuned using 1100 instruction-output pairs, improving its ability to convert active to passive sentences and respond to other instructions. The fine-tuning process involves using cross-entropy loss and an AdamW optimizer. After training for just one epoch, the model shows significant improvement. The lecture emphasizes the importance of fine-tuning and highlights the challenges of training on basic hardware, encouraging further exploration with more epochs.

Takeaways

• 😀 Fine-tuning large language models (LLMs) involves training a pre-trained model on a specific dataset to improve its performance on specific tasks, such as following instructions.
• 😀 Pre-trained models start with foundational knowledge, like the semantic meaning of words, which makes it easier to fine-tune them with a smaller, task-specific dataset.
• 😀 The fine-tuning process includes three stages: preparing the dataset, fine-tuning the model, and evaluating its performance. In this case, the focus is on instruction-following tasks.
• 😀 Instruction fine-tuning involves training the model on instruction-input-output pairs, improving its ability to respond to commands correctly.
• 😀 The training loop consists of calculating the loss, computing gradients, and updating weights using gradient descent or Adam optimizer, minimizing the loss over multiple epochs.
• 😀 Cross-entropy loss is used as the loss function in fine-tuning, encouraging the model to predict the correct output by optimizing the predicted probabilities.
• 😀 The fine-tuned model significantly improves its ability to follow instructions, as shown by the model's conversion of active sentences to passive after fine-tuning.
• 😀 The training loop and loss function used for fine-tuning are very similar to the ones used in pre-training the model, with a few adjustments based on the task-specific dataset.
• 😀 Although a GPU is highly recommended for faster training, it's possible to fine-tune the model on a less powerful machine (like a CPU), though it may take longer to complete.
• 😀 After just one epoch of fine-tuning on a standard laptop, the model improved its ability to generate responses, with further improvements expected after additional epochs.
• 😀 Evaluation of model performance isn't just about loss values—qualitative assessments, such as whether the generated responses make sense and adhere to instructions, are essential for understanding model quality.

Q & A

What is the goal of fine-tuning a pre-trained language model (LLM)?

The goal of fine-tuning a pre-trained LLM is to adapt it to specific tasks or domains, such as following instructions, by training it on a dataset tailored to those tasks. Fine-tuning allows the model to leverage its pre-existing knowledge while learning to better respond to particular types of inputs.

Why was a pre-trained GPT-2 model used for fine-tuning?

A pre-trained GPT-2 model was used because it already possesses foundational knowledge about language, such as semantic meaning, syntax, and general language patterns. By starting from a pre-trained model instead of a random one, fine-tuning can be done more efficiently, allowing the model to specialize on a specific dataset without needing to learn everything from scratch.

What are the steps involved in the fine-tuning process of the LLM?

The fine-tuning process involves three main steps: preparing the dataset (formatting it into instruction-input-output pairs), setting up the LLM with pre-trained weights, and training the model on the instruction dataset. During training, the model’s weights are updated to improve its performance on the instruction task.

What was the issue with the LLM before fine-tuning?

Before fine-tuning, the LLM could not properly follow instructions. For example, when asked to convert an active sentence into a passive one, the model simply repeated the original sentence without making the required transformation.

How does the training loop function in the fine-tuning process?

In the fine-tuning process, the training loop involves passing through each batch of data, calculating the loss, performing a backward pass to compute gradients, and then updating the model’s parameters using an optimizer (such as AdamW). This loop continues for several epochs to minimize the loss function and improve the model's ability to follow instructions.

What loss function is used during fine-tuning, and why is it important?

The loss function used is categorical cross-entropy loss, which calculates the difference between the model's predicted probabilities and the true labels. Minimizing this loss function helps the model produce more accurate predictions and better follow the instructions provided in the training dataset.

What role does the optimizer play in the fine-tuning process?

The optimizer (in this case, AdamW) adjusts the model’s weights during training by using the gradients calculated during the backward pass. The optimizer ensures that the model converges towards a minimum in the loss landscape by gradually updating the parameters to reduce the error.

How did the fine-tuning improve the model's performance on the active-to-passive transformation task?

After fine-tuning, the LLM successfully transformed the active sentence 'The chef cooks the meal every day' into the passive voice as 'The meal is prepared every day by the chef.' This shows that the model learned to follow instructions effectively after being trained on the instruction dataset.

What challenges were faced during the fine-tuning process on a basic system?

The main challenge was the limited computational resources, as the fine-tuning was done on a CPU-based MacBook Air with 8GB of RAM. This resulted in long training times (approximately 2 hours for 1 epoch) and memory limitations that prevented using more epochs or larger batch sizes.

What does the loss curve during training indicate about the model's learning process?

The loss curve shows that the model learns quickly during the early stages of training, with a rapid decrease in both training and validation losses. As training progresses, the rate of improvement slows down, suggesting that the model is converging to a stable solution, and further training would only yield marginal improvements.