The Fine Art of Fine-Tuning Large Language Models Talk at H2O World India

Name: The Fine Art of Fine-Tuning Large Language Models Talk at H2O World India
Uploaded: 2023-04-26
Duration: 24 min
Description: H2O Kaggle Grandmaster Pascal on the three-stage path to a custom LLM: a pretrained foundation model, fine-tuning on curated data, and optional RLHF.

Generative AI
H2O Cascade

Overview

Recorded at H2O World India, this talk from H2O principal data scientist and Kaggle Grandmaster Pascal introduces H2O's open source LLM Studio and the three-stage path to building a custom LLM: a pretrained foundation model, fine-tuning on curated data, and optional reinforcement learning from human feedback. He demonstrates fine-tuning in LLM Studio, then shows the resulting chat model answering questions, including a live example of the limits fine-tuning has without a connected knowledge database.

Key takeaways

The three-stage path to a usable LLM: a pretrained foundation model, fine-tuning on a small curated dataset, and optional reinforcement learning from human feedback
Why H2O released LLM Studio as open source under Apache 2.0, including a 20 billion parameter fine-tuned model cleared for commercial use
How to import a dataset and configure a fine-tuning run in LLM Studio's UI, including hyperparameter tuning and validation tracking
How to test a fine-tuned model's chat behavior directly in LLM Studio, including follow-up questions that rely on conversational memory
Why fine-tuned LLMs still need a connected database or retrieval layer to ground answers in current, verifiable information rather than what's baked into training data

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