Summary of "Finetune LLMs to teach them ANYTHING with Huggingface and Pytorch | Step-by-step tutorial"
Summary of "Finetune LLMs to teach them ANYTHING with Huggingface and PyTorch | Step-by-step tutorial"
This tutorial video provides a comprehensive, step-by-step guide on how to fine-tune a large language model (LLM), specifically the Meta LLaMA 3 2.1 billion parameter model, using Hugging Face and PyTorch. The goal is to adapt the model for a custom classification task involving research papers from arXiv, demonstrating the entire fine-tuning pipeline from data preparation to training and inference.
Main Ideas and Concepts
- Introduction to the Task and Tools
- Fine-tuning a lightweight LLaMA 3 (2.1B) language model for a classification task.
- Using Hugging Face Transformers library and PyTorch.
- Task: Given an arXiv paper ID, extract title and summary, then predict the paper's category.
- Dataset: 2,000 recent computer science papers from arXiv API, containing title, summary, and category.
- Understanding Language Models and Transformers
- Explanation of language models (LMs) and causal language models (causal LM).
- Causal LM predicts the next word based only on previous tokens (autoregressive).
- Introduction to Hugging Face pipeline abstraction for text generation.
- Tokenization: converting text into integer token IDs, handling padding and attention masks.
- Importance of padding tokens and attention masks to maintain tensor shape and avoid model errors.
- Prompt Engineering and Instruction Tuning
- Instruction tuning: fine-tuning LMs on datasets of instructions and responses.
- Use of chat templates to format prompts with system, user, and assistant roles.
- Flags like
continue_final_messagecontrol how generation continues after prompt. - Demonstration of generating text with specific styles (e.g., pirate speech).
- Baseline Performance Without Fine-Tuning
- Using prompt engineering alone to classify paper categories.
- Achieved ~40% accuracy on test set with the base 1B parameter model, showing room for improvement.
- How Language Models Generate Text
- Autoregressive generation: predicting one token at a time based on previous tokens.
- Explanation of logits output by the model, vocabulary size, and softmax to get probabilities.
- Demonstration of token prediction and decoding.
- Fine-Tuning Methodology
- Preparing input-target pairs by shifting sequences for next-token prediction.
- Incorporating chat templates into input-target sequences.
- Masking tokens in loss calculation with label
-100to ignore prompt tokens during loss. - Using cross-entropy loss between predicted logits and target tokens.
- Backpropagation and optimizer (AdamW) to update model weights.
- Example: training model to generate a specific phrase ("subscribe to neural breakdown with AVB").
- Challenges of Full Fine-Tuning
- Full fine-tuning updates all model weights (~1 billion parameters), which is computationally expensive.
- Risk of catastrophic forgetting: model may lose previously learned knowledge when trained on new data.
- Parameter-Efficient Fine-Tuning: LoRA (Low-Rank Adaptation)
- LoRA freezes original model weights and learns small low-rank matrices added to attention/feed-forward layers.
- Instead of learning full weight matrices, LoRA learns residual low-rank factors, drastically reducing trainable parameters.
- Benefits:
- Much fewer trainable parameters (e.g., 6 million vs 1 billion).
- Can train multiple adapters for different tasks without changing base model.
- Efficient and less resource-intensive.
- Hugging Face’s PEFT library supports LoRA integration.
- Demonstration of training with LoRA and achieving similar fine-tuning results on small data.
- Training and Evaluation
- Fine-tuning on a small subset (e.g., 8 examples) to overfit and verify training loop.
- Validation accuracy improved from ~37% to 67% after fine-tuning on 5100 examples.
- Testing fine-tuned model on unseen arXiv papers shows improved classification compared to base model.
- Summary and Final Thoughts
- Fine-tuning LLMs can be done efficiently using Hugging Face, PyTorch, and LoRA.
- The methodology is generalizable to any task with appropriate data.
- Encouragement to experiment with fine-tuning and explore Hugging Face tools.
- Acknowledgement of Patreon supporters and community.
Detailed Methodology / Instructions for Fine-Tuning an LLM
- Data Preparation
- Collect data (e.g., arXiv papers) with input fields (title, summary) and output labels (category).
- Format data into prompt-response pairs using chat templates.
- Tokenize prompts and responses into input IDs and labels.
- Apply padding and define padding token (e.g.,
-100for labels) to ignore during loss calculation.
Category
Educational
