InstructLab Core provides a framework for aligning Large Language Models (LLMs) using a novel synthetic data-based method. It enables users to download pre-trained LLMs, chat with them, and then enhance them by contributing to a companion taxonomy repository, generating synthetic data, and re-training the model. This workflow is designed for researchers and developers looking to customize LLMs with specific knowledge and skills.

How It Works

InstructLab utilizes a "Large-Scale Alignment for Chatbots" (LAB) approach. Users contribute to a structured taxonomy, which then serves as the basis for generating synthetic training data using specified LLMs (teacher models). This synthetic data is used to fine-tune the target LLM via various training pipelines (simple, full, accelerated), allowing for iterative improvement and evaluation against benchmarks like MMLU and MTBench.

Quick Start & Requirements

• Installation: pip install instructlab (ensure Python 3.11 is used; Python 3.12+ is not supported). GPU acceleration requires specific pip install 'instructlab[cuda]' or instructlab[rocm] commands.
• Prerequisites: Python 3.11, C++ compiler, Git. Minimum 250GB disk space (500GB recommended for full workflow). Apple M1/M2/M3 Macs or Linux (Fedora tested).
• Setup: Initialize with ilab config init. Download models with ilab model download.
• Documentation: InstructLab Core README

Highlighted Details

• Supports training and inference on Apple Metal (MPS), NVIDIA CUDA, and AMD ROCm.
• Offers multiple training pipelines (simple, full, accelerated) catering to different hardware capabilities.
• Includes built-in evaluation benchmarks (DK-Bench, MMLU, MTBench) for assessing model performance.
• Features a developer preview for Retrieval-Augmented Generation (RAG).

Maintenance & Community

• Project is actively maintained by InstructLab.
• Community contributions are encouraged via pull requests to the taxonomy repository.

Licensing & Compatibility

• The specific license is not explicitly stated in the README, but the project structure suggests an open-source model. Compatibility for commercial use or closed-source linking would require clarification of the license.

Limitations & Caveats

• Python 3.12+ is not currently supported due to dependency issues.
• Running the full workflow on a laptop may result in lower-fidelity synthetic data generation and model tuning compared to more powerful hardware.
• Some model downloads (e.g., Mistral-7B-Instruct-v0.2) may require a Hugging Face token.