This video provides a comprehensive tutorial on fine-tuning a large language model (LLM) using LoRA (Low-Rank Adaptation) with a custom dataset. The goal is to achieve cost-effective fine-tuning, allowing users to tailor LLMs to specific niche topics or applications where pre-trained models might fall short. The tutorial uses a real-world example focused on TM1 (a financial software) and guides viewers through the entire process, from data preparation to deployment.
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LoRA for Efficient Fine-Tuning: LoRA allows for fine-tuning LLMs at a significantly lower cost and time compared to full fine-tuning by updating only a small subset of parameters. This makes it feasible to train models on less powerful hardware.
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Custom Dataset Preparation: The tutorial demonstrates how to use tools like Dockling to process PDFs and other documents into a suitable format for instruction tuning. This involves chunking documents and generating question-answer pairs.
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Synthetic Data Generation: A larger LLM (e.g., Quen 2.514b) is employed to generate synthetic training data from the processed documents. This data is then pre-processed to create a clean instruction tuning dataset.
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GPU-Accelerated Training: The training process is performed on a RunPod instance with a GPU for faster processing, especially beneficial for larger datasets. The video explains how to set this up using SSH and pip.
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Local Deployment with Ollama: The fine-tuned model is deployed locally using Ollama for quick and easy access without requiring continuous GPU usage.
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Performance Optimization: The video explores several techniques for boosting model performance including increasing LoRA rank and alpha, enhancing data quality (through a data classification script), and adjusting training epochs.
