InstructLab, a feature within the Tuning Studio of watsonx.ai, will enable AI developers, data scientists and SME’s of any domain to customize LLMs and SLMs to increase model performance and productivity. InstructLab will offer a full-scale alignment-tuning experience, equipped with data lineage and evaluation features, and the computational infrastructure needed to handle large-scale AI development. By bridging these two workflows—low-cost, accessible training with full-scale enterprise deployment—InstructLab can help businesses start small and scale up seamlessly as their AI needs grow.
With InstructLab, users gain a powerful AI customization tool that not only understands their unique use cases but will also evolve with them as their business and use of AI scale. By using InstructLab in watsonx.ai, businesses can create a robust, end-to-end AI solution that can take full advantage of enterprise grade model customization, AI Agents, foundational models, RAG capabilities and many more.
Discover how InstructLab model customization in watsonx.ai can boost performance, increase productivity, and keep you ahead of the game.
Technical and non-technical users can contribute to the model customization process, and then reuse those knowledge and skills across models
Customize models with enterprise data in a matter of hours not months
Infer a small specialized model, not a larger generic model
Model customization tools
watsonx.ai will offer advanced enterprise capabilities and workflows for model customization with InstructLab. Read the statement of direction here.
Ingest thousands of documents and manage complex ingestion pipelines (e.g., masking, chunking, and filters). Process multiple document formats (e.g., PDF, PPT, TXT, DOC) from various data sources.
Continuously build a taxonomy of enterprise knowledge and skills visualized in an easy to navigate tree structure. Watsonx.ai will provide UI, CLI, API and SDK for taxonomy creation.
Amplify the taxonomy with InstructLab agentic synthetic data generation. watsonx.ai will provide UI, CLI, API and SDK for generating synthetic data.
Align the model with generated synthetic data in a multi-phased alignment technique. watsonx.ai will provide UI, CLI, API and SDK for alignment tuning.
Lineage tracking of the taxonomy, synthetic data and the aligned model
Evaluate both pre-aligned and aligned models with Python notebooks to run IBM Bluebench benchmarks. Standard open benchmarks such as Loss function, MMLU, MT-Bench and PR-Bench.
Choose your model
Power AI applications using our library of third-party and IBM-built Granite models suitable for AI workflows or bring your own custom foundation model to the platform.
Footnotes
IBM’s statements regarding its plans, directions and intent are subject to change or withdrawal without notice at IBM’s sole discretion.