Unlocking the Power of AI: Accessible Language Model Tuning for All

What if you could tune a powerful language model on your own laptop? See how open-source InstructLab bakes new knowledge directly into a model's weights.

#1about 1 minute

Introducing InstructLab for accessible LLM fine-tuning

Generalist large language models can be improved for specific use cases by fine-tuning them with the open-source project InstructLab on consumer hardware.

#2about 2 minutes

Demonstrating the limitations of generalist LLMs

Generalist LLMs often fail on specific or recent queries due to knowledge cutoffs, demonstrating the need for adaptation for real-world use cases.

#3about 3 minutes

Understanding the risks and costs of generative AI

Generative AI presents significant challenges including legal exposure, hallucinations, hiring bias, and high operational costs for tuning and inference.

#4about 5 minutes

Comparing fine-tuning, alignment tuning, and RAG

Models can be improved through methods like alignment tuning, which specializes the model's core knowledge, unlike RAG which only supplements it with external context.

#5about 3 minutes

Choosing the right foundation model to reduce costs

Selecting a smaller, fine-tuned foundation model like Granite can drastically reduce operational costs compared to using a large, general-purpose model.

#6about 3 minutes

How InstructLab simplifies data generation for tuning

InstructLab uses a simple YAML taxonomy to automatically generate large synthetic training datasets, making model tuning accessible to non-data scientists.

#7about 10 minutes

A step-by-step demo of the InstructLab CLI

The InstructLab CLI provides a streamlined workflow for initializing a project, downloading a model, generating synthetic data, and training the model locally.

#8about 4 minutes