Mastering Parameter Efficient Fine-Tuning in Large Language Models: A Guide to flan-T5 and LoRA: Part 3
Abhijeet Ambekar
MLOps Engineer @ Home Depot | Former Reinforcement Learning | Published ML & AR Research with 100+ Citations | Elevating ML & Generative AI through Scalable Solutions ??
Imagine you're a chef ???? tasked with refining a legendary recipe. You can't change the recipe entirely – it's already a classic. Instead, you make small, strategic tweaks to enhance its flavor ??. This is akin to the technique of Parameter Efficient Fine-Tuning (PEFT) in machine learning, particularly with the sophisticated flan-T5 Large Language Model. In a world where computational resources are like premium kitchen ingredients – precious and often scarce – mastering PEFT is like becoming that savvy chef who knows how to make the most out of what they have ??.
?? Problem Statement
In the realm of machine learning, the challenge isn't always about building larger models; sometimes, it's about making them smarter for a particular task with what's available. This is particularly true for teams and individuals without access to colossal computing power ??. Enters the PEFT technique, which allows you to fine-tune LLMs without requiring extensive computational resources. It's like giving your model extra brain ??????, rather than a complete body makeover, enabling it to perform new tasks with remarkable efficiency.
?? Ready to Discover What's Inside?
1. LoRA - PEFT
LoRA (Low-Rank Adaptation), a key technique in Parameter Efficient Fine-Tuning (PEFT), is revolutionizing how we fine-tune large language models like flan-T5 ??. It smartly injects low-rank matrices into model layers, dramatically reducing the number of parameters to be trained. This approach not only saves up to three times the memory but also speeds up the training process, all without sacrificing model quality ??????. LoRA's efficiency is a game-changer, especially for those with limited computational resources, making cutting-edge machine learning more accessible and practical for a wider range of applications.
2. Preparing for training the model
Loading the flan-T5 Model and Dataset: First, we load the flan-T5 model and prepare our dialogue summarization dataset. We ensure the model utilizes the GPU efficiently and prepare the dataset by splitting it into balanced training, validation, and test sets. Refer to the GitHub link for data split code.
Tokenizing the Dataset: Next, we tokenize the dataset. Tokenization converts text data into a format that's understandable and processable by our model, a crucial step for training
3. The PEFT Process
Implementing PEFT with LoRA: Here, we're setting up LoRA on our model. We choose a rank of 32 and focus on the 'query' and 'value' parts of the model. This setup is tailored for sequence-to-sequence language tasks, like FLAN-T5, enhancing its performance with minimal adjustments.
Setting up training configuration: Now, we're setting up our training process with some smart strategies. First, we're using an early stopping callback. This helps us stop the training if the model isn't improving significantly.
Next, we configure the training arguments. We're reporting to Weights & Biases (wandb) for tracking our progress. We'll automatically find the best batch size, use a higher learning rate than usual for fine-tuning, and train for 1000 steps at most. Our model logs and evaluates every 100 steps, with a maximum of 1000 steps. It evaluates and saves periodically, always loading the best model at the end. We're also using gradient accumulation over 2 steps to handle larger batch sizes effectively, keeping our gradients normed at 1.0, and warming up over 250 steps to gradually increase the learning rate.
Finally, we create a Trainer with our PEFT model, training, and evaluation datasets, attaching our early stopping callback. With everything set, we start the training. This setup ensures efficient, effective training, and avoids overfitting.
4. Evaluating newly trained Model
Qualitative Evaluation by inference output: Below, compared the output from the PEFT model with three others: a human baseline, the original model's inference, and the fully fine-tuned model's inference using a zero-shot prompt. This comparison clearly shows that the PEFT model performs better than the original and is as good as, or even better than, the fully fine-tuned model. These results prove the effectiveness and reliability of the PEFT model.
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Quantitative Evaluation: Below we are comparing ROUGE and BLEU scores for the original model, full fine-tuned model, and PEFT model. Please read this article to learn about ROUGE and BLEU scores and also to fully finetune the LLM model.
5. Extras: Good to know!
Experiment Tracking with Weights and Biases (wandb)
Keeping track of our model's training process is crucial. Weights and Biases (wandb) is our digital logbook, ensuring we don't lose track of our experiments. It's like keeping a detailed diary of our model's growth and progress (find the implementation of wandb in the source code) ????
Paperspace Gradient Notebooks
Don't have a supercomputer in your backyard? No problem! Paperspace Gradient is like renting a high-tech gym for our model to train in, providing the computational muscle needed for effective training ?????
?? Conclusion
As we wrap up, remember that mastering PEFT in LLMs like flan-T5 is a step towards smarter, more efficient machine learning practices, even in resource-constrained environments. Next, we'll explore Reinforcement Learning from Human Feedback (RLHF), a technique to further refine LLMs using valuable human input. By following this path, you're not just reading about advancements; you're actively participating in the evolution of machine learning.
?? Source Code
?? References