Foundation model platform for generative AI

Red Hat® Enterprise Linux® AI is an enterprise-grade gen AI foundation model platform to develop, test, and deploy LLMs for gen AI business use cases.

Red Hat Enterprise Linux AI brings together:

• The Granite family of open source LLMs.
• InstructLab model alignment tooling, which provides a community-driven approach to LLM fine-tuning.
• A bootable image of Red Hat Enterprise Linux, along with gen AI libraries and dependencies such as PyTorch and AI accelerator driver software for NVIDIA, Intel, and AMD.
• Enterprise-level technical support and model intellectual property indemnification provided by Red Hat.

Red Hat Enterprise Linux AI gives you the trusted Red Hat Enterprise Linux platform and adds the necessary components for you to begin your gen AI journey and see results.

Red Hat Enterprise Linux AI includes a subset of the open source Granite language and code models that are fully indemnified by Red Hat. The open source Granite models provide organizations cost- and performance-optimized models that align with a wide variety of gen AI use cases. The Granite models were released under Apache 2.0 license. In addition to the models being open source, the datasets used for training the models are also transparent and open.

Table 1. Granite models in Red Hat Enterprise Linux AI

In addition to open source Granite models, Red Hat Enterprise Linux AI also includes InstructLab model alignment tooling, based on the Large scale Alignment for chatBots (LAB) technique. InstructLab allows teams within organizations to efficiently contribute skills and knowledge to LLMs, customizing these models for the specific needs of their business.

• Skill: A capability domain intended to teach a model how to do something. Skills are classified into two categories.
  • Compositional skills
    • Let AI models perform specific tasks or functions.
    • Are either grounded (includes context) or ungrounded (does not include context).
      • A grounded example is adding a skill to provide a model the ability to read a markdown-formatted table.
      • An ungrounded example is adding a skill to teach the model how to rhyme.
  • Foundation skills
    • Skills like math, reasoning, and coding.
• Knowledge: Data and facts that provide a model with additional data and information to answer questions with greater accuracy.

The above image outlines the InstructLab model fine-tuning workflow.

1. Skills and knowledge contributions are placed into a taxonomy-based data repository.
2. A significant quantity of synthetic data is generated, using the taxonomy data, in order to produce a large enough dataset to successfully update and change an LLM.
3. The synthetic data output is reviewed, validated, and pruned by a critic model.
4. The model is trained with synthetic data rooted in human-generated manual input.

InstructLab is accessible to developers and domain experts who may lack the necessary data science expertise normally required to fine-tune LLMs. The InstructLab methodology allows teams to add data, or skills especially suited to business use case requirements, to their chosen model for training in a collaborative manner allowing for quicker time to value. 

Red Hat Enterprise Linux AI helps organizations accelerate the process of going from proof of concept to production server-based deployments by providing all the tools needed and the ability to train, tune, and deploy these models where the data lives, anywhere across the hybrid cloud. The deployed models can then be used by various services and applications within your company.

When organizations are ready, Red Hat Enterprise Linux AI also provides an on-ramp to Red Hat OpenShift® AI, for training, tuning, and serving these models at scale across a distributed cluster environment using the same Granite models and InstructLab approach used in the Red Hat Enterprise Linux AI deployment.