Language model customization with InstructLab
Enhance AI model performance
InstructLab enables developers to optimize model performance through customization and alignment, tuning toward a specific use case by taking advantage of existing enterprise and synthetic data. Read the statement of direction here.
Enabling model customization, development teams reuse knowledge and skills across models.
End-to-end model customization with enterprise data in a matter of hours, not months
Infer on a smaller specialized model, not a larger generic model.
Run models more efficiently to optimize performance and runtime.
Customize your models with these tools
Ingest thousands of documents and manage complex ingestion pipelines such as masking, chunking, and filters. Process multiple document formats from various data sources, including PDF, PPT, TXT, and DOC.
Build a taxonomy of enterprise knowledge and skills visualized in a simple-to-navigate tree structure. IBM watsonx.ai will provide the UI, CLI, API, and SDK for taxonomy creation.
Amplify the taxonomy with InstructLab agentic synthetic data generation. IBM watsonx.ai will provide UI, CLI, API , and SDK for generating synthetic data.
Align the model with generated synthetic data in a multiphased alignment technique. IBM watsonx.ai will provide UI, CLI, API and SDK for alignment tuning.
Index and retrieve your organization’s documents efficiently.
Use Python notebooks to run IBM Bluebench benchmarks and standard open benchmarks (Loss function, MMLU, MT-Bench, and PR-Bench) on both pre-aligned and aligned models.
Choose your model
Power AI applications using our library of third-party and IBM® Granite® models suitable for AI workflows or bring your own custom foundation model to the platform.