EASIEST Way to Train LLM Train w/ unsloth (2x faster with 70% less GPU memory required)

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AI Summary

Summary of Large Language Model Fine-Tuning Video

Introduction

• The gap between top closed-source models like GPT and the best open-source models has decreased significantly.
• Meta introduced LLaMA 3.2, and other models have been introduced.
• Fine-tuning allows AI developers to optimize open-source models for specific use cases and deploy them anywhere.

Fine-Tuning vs. Retrieval-Augmented Generation (RAG)

• RAG is a method to bring private knowledge into large language models by turning data into a vector database for retrieval.
• Fine-tuning involves training the model to incorporate knowledge directly.
• RAG is easier to update and suitable for bringing in real-time data.
• Fine-tuning is better for specialized tasks and behaviors, and can reduce costs.

Fine-Tuning Process Overview

• Prepare training data.
• Choose the right base model and fine-tuning techniques.
• Evaluate and iterate on the fine-tuned model.
• Deploy the model on the appropriate platform and hardware.

Preparing Training Data

• Use existing data from apps or public datasets on platforms like Kaggle or Hugging Face.
• Manually create datasets from company documents, websites, etc.
• Assembly AI can transcribe audio and video into text for fine-tuning.
• Training data must follow a specific Q&A structure.

Synthetic Data Generation

• Use large models to generate training data for smaller models.
• A reward model evaluates and ranks the generated answers.
• NVIDIA’s Neotron family can be used for this purpose.

Fine-Tuning and Deployment

• Closed-source platforms like OpenAI and Entropic offer fine-tuning services.
• Open-source models require building your own fine-tuning pipeline and deployment.
• Platforms like Together AI offer a middle ground with some control over the model.

Choosing a Base Model

• Consider cost, speed, and use case when selecting a base model.
• Smaller models are cheaper and faster but may be less accurate.
• Specialized models exist for specific tasks like SQL generation.

Fine-Tuning Methods

• Full fine-tuning rewrites the entire model.
• LLaMA (Low-Rank Adaptation) adds “post-it notes” to the model, requiring less time and data.
• Onslaught is an open-source package that speeds up fine-tuning and reduces memory usage.

Example of Fine-Tuning with Onslaught

• The process is demonstrated on Google Colab with a free GPU.
• The training data is prepared and formatted for the LLaMA model.
• The fine-tuning process is completed in minutes.
• The fine-tuned model can be exported and deployed.

AI Builder Club

• A community for deep diving into fine-tuning topics and practical AI product building.

Conclusion

• Fine-tuning is a powerful method for specializing large language models for specific tasks.
• The video provides a step-by-step guide to fine-tuning and deploying models.

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