AI: Fine Tuning LLMs with InstructLab

Fine Tuning Large Language Models with InstructLab - YouTube

• The Purpose: Fine-tuning allows developers to customize and specialize general LLMs for specific tasks, automate repetitive work, and handle complex, domain-specific problems.

• The Tool: InstructLab provides an open-source community-driven approach to model alignment, making it easier to add new knowledge and skills to a base model.

• The Workflow: The video demonstrates the step-by-step process of setting up InstructLab, generating synthetic data, and training the model to improve its performance on targeted queries.

You can learn more about the technology or explore the guide mentioned in the video by visiting the IBM InstructLab Overview or downloading the IBM AI Model Guide.

A new way to collaboratively customize LLMs - IBM Research

InstructLab Summary

InstructLab is an open-source project launched by IBM and Red Hat designed to lower the cost and barrier to entry for customizing large language models (LLMs). Instead of retraining a model from scratch, it allows a community-driven, collaborative approach to adding new knowledge and skills to base models.

Key Features

• Synthetic Data Generation: Uses the LAB (Large-Scale Alignment for ChatBots) method to amplify small amounts of human-curated "seed" data into high-quality training data.

• No Overwriting: Its phased-training regimen allows models to assimilate new skills without losing or overwriting previously learned information.

• Open-Source Workflow: Users can test out quantized models locally on a laptop using a command-line interface (CLI) and submit new skills or knowledge via standard GitHub pull requests.

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Project Repository

You can contribute to the community and view the project taxonomy directly on the InstructLab GitHub Organization.

py + ts