| copyright |
|
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|---|---|---|---|
| lastupdated | 2026-10-09 | ||
| keywords | release notes | ||
| subcollection | inference | ||
| content-type | release-note |
{{site.data.keyword.attribute-definition-list}}
{: #release-notes}
Use the release notes to learn about the latest changes to the documentation that are grouped by month. {: shortdesc}
Looking for {{site.data.keyword.cloud_notm}} status, platform announcements, security bulletins, or maintenance notifications? See {{site.data.keyword.cloud_notm}} status. {: tip}
{: #oct26}
{: #05oct26} {: release-note}
Create embeddings with Red Hat AI Inference : You can now use Red Hat AI Inference to generate text embeddings. Embeddings convert text into numerical vectors that capture semantic meaning, enabling semantic search, similarity comparisons, and retrieval-augmented generation (RAG) workflows. For more information, see What is embedding? and Improving chat completions with vector embeddings and RAG.
{: #july26}
{: #8july26} {: release-note}
Model alignment is deprecated : {{site.data.keyword.instructlab_short}} model alignment, synthetic data generation, and taxonomy management features are deprecated and will be removed on 25 September 2026. To continue model customization and alignment workflows, migrate to {{site.data.keyword.redhat_openshift_full}} AI On OpenShift. Learn more about {{site.data.keyword.redhat_openshift_full}} AI{: external}.
{: #june26}
{: #25june26} {: release-note}
New models available
: Two new models are now available for inference: gemma-4-31b-it and granite-4-1-30b. To view all available models, open your project in {{site.data.keyword.instructlab_short}}{: external} and click Model catalog.
{: #may26}
{: #22may26} {: release-note}
Inference is now generally available : You can now use {{site.data.keyword.instructlab_short}} to run inference workloads in production. Use industry-standard OpenAI compatible APIs for chat completions to integrate inference capabilities into your applications. A playground is now available in the console where you can test different inference settings and configurations before moving to production. For more information, see Getting started.
{: #apr26}
{: #23apr26} {: release-note}
Inference with Red Hat AI on {{site.data.keyword.cloud_notm}} (Beta)
: You can now use inference to interact with foundation models and evaluate AI-powered responses for your applications. The inference feature provides industry-standard OpenAI compatible APIs for chat completions and model management. This beta feature is available for evaluation and testing purposes. To get access to the beta, send an email to instructlab@ibm.com. For more information, see Working with chat completions.
{: #oct25}
{: #10oct25} {: release-note}
{{site.data.keyword.short_name}} CLI plug-in version 0.0.26 : Version 0.0.26 of the plug-in adds file size limits. Each of the skills and knowledge documents must be less than 100 GB in size. The cumulative size of all the skills and knowledge JSON files must be less than 400 GB.
{: #sept25}
{: #23aug25} {: release-note}
Version 1.5 of {{site.data.keyword.product_name}} is available : For the best results, run training on newly generated synthetic data with version 1.5. Review the updated service settings in 1.5. : For more information, see the release notes{: external}, the RHEL AI documentation{: external} and the known issues{: external}
New base model
: {{site.data.keyword.product_name}} now uses the granite-3.1-8b-starter-v2.1 model. For more information, see the model specifications{: external}.
{: #aug25}
{: #2aug25} {: release-note}
New! Import your own training data : You can now import your own training data for training models. When you import your own training data, you can specify previously generated data IDs, or add knowledge and skills files to a data generation job by uploading files from {{site.data.keyword.cos_short}} or your local machine.
New! Taxonomy validation
: When you upload a taxonomy to {{site.data.keyword.short_name}}, it's now checked for formatting and syntax errors. Also, if you reference external knowledge documents in your qna.yaml files, {{site.data.keyword.short_name}} checks for access to those files. Additionally, {{site.data.keyword.short_name}} checks for the proper service authorizations for services like {{site.data.keyword.cos_short}} and {{site.data.keyword.secrets-manager_short}}.
{{site.data.keyword.short_name}} CLI plug-in version 0.0.24
: Version 0.0.24 of the plug-in adds support for importing your own training data to the data generate command.
{: #may25}
{: #09may25} {: release-note}
New! Private repo support : You can now use {{site.data.keyword.secrets-manager_short}} to give {{site.data.keyword.short_name}} access to your taxonomy knowledge documents in private repositories or GitHub Enterprise repositories. You can enable private repository access when uploading your taxonomy in the console or by using the CLI. For information, see Getting started with {{site.data.keyword.short_name}}.
{: #apr25}
{: #24apr25} {: release-note}
Introducing {{site.data.keyword.instructlab_full_notm}}! : Get ready to dive into AI! InstructLab is an open source project from IBM and Red Hat to be a cost-effective entry point into the world of machine learning.