Large Language Model(LLM) fine-tuning
LLM fine-tuning is the process of adjusting a pre-trained Large Language Model (LLM) to better suit specific tasks or domains.
Pre-trained models like Granite, Mistral, GPT-4 or LLaMA are trained on vast, diverse datasets, enabling them to perform general-purpose language tasks. However, these models may lack domain-specific knowledge or precision in specialized contexts. Fine-tuning involves further training the model on a smaller, task-specific dataset to improve its performance for a particular use case.
LLM fine-tuning adjusts a pre-trained model using task-specific data to enhance performance in specialized domains. It improves accuracy, efficiency, and customization while reducing prompt complexity and incorporating proprietary data.
Specialization
Fine-tuning helps tailor an LLM for specific industries, languages, or technical domains. For example, a healthcare-specific chatbot may need to be fine-tuned on medical terminology and guidelines.
Cost-effective
Fine-tuning leverages the knowledge from the base model, requiring less computational effort compared to training a model from scratch.
Proprietary Data
Fine-tuned models can incorporate proprietary, confidential, or unique datasets, enabling them to generate outputs that align closely with the organization's data.
Accuracy
Models often perform better on targeted tasks when fine-tuned, as they learn patterns specific to the task or dataset, reducing irrelevant or generic responses.
InstructLab - LLM fine-tuning for everyone
InstructLab is an open-source AI community project that enables anyone to add new knowledge and skills to LLMs with an easy-to-use CLI with as little as 3 commands.
InstructLab is available in multiple forms. The community version enables developers to get started on their local machines and work with open-source Granite and Merlinite models while the supported InstructLab on RHEL AI enables enterprises to take advantage of indemnified Granite models, enable usage of optimized hardware with powerful computational abilities, and even serve these LLMs using vLLM on RHEL AI.
What is InstructLab and why do developers need it?
An overview of InstructLab and how anyone can finetune LLMs with new knowledge and skills.
Contributing knowledge to the open-source Granite model using InstructLab
A hands-on guide for setting up a local InstructLab environment and adding knowledge to the Granite model
Fine-tune a Granite model using InstructLab
A step-by-step guide to creating a specialized LLM using InstructLab
Contributing knowledge to open-source LLMs like the Granite models using the InstructLab UI
A hands-on guide for getting started using the InstructLab UI and adding knowledge to open-source LLMs
Get started with InstructLab
