AI Stories
How He Built The Best 7B Params LLM with Maxime Labonne #43
Mar 7, 2024
In this podcast, Maxime Labonne discusses building 7B params LLMs, steps to create LLMs, RAG vs fine-tuning, DPO vs RLHF, and deploying LLMs in production. He shares insights on merging models for enhanced performance, getting into GenAI, and using ChatGPT for various applications. From cybersecurity to AI, Maxime's journey and career advice offer valuable perspectives on entering the field of AI.
53:46
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Quick takeaways
- LLM pipeline: pre-training, supervised fine-tuning, human feedback, merging models enhance performance.
- Indie game dev experience fueled Maxime's AI journey, teaching valuable algorithms.
Deep dives
LLM Pipeline Overview
The LLM pipeline consists of four key steps. The first step involves pre-training the model, followed by supervised fine-tuning to teach the model specific instructions and outputs. The third step, reinforcement learning from human feedback, focuses on aligning the model with desired responses. Lastly, merging models to combine various finely-tuned models allows for enhanced performance, dominating the OpenLML leaderboards.
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