The future of AI is open. That’s why we strive to make AI as accessible for as many developers as possible. Our open-sourced family of core Granite models is available under an Apache 2.0 license for broad, unencumbered commercial usage, along with tools to monitor the model data—ensuring it’s up to the standards demanded by enterprise applications.
Granite decoder-only models are designed for code generative tasks, trained with code written in 116 programming languages.
Granite time series models are lightweight and pre-trained for time-series forecasting, optimized to run efficiently across a range of hardware configurations.
NASA and IBM teamed up to create an AI Foundation Model for Earth Observations using large-scale satellite and remote sensing data.
When you’re ready to deploy open-source Granite models in production, Red Hat Enterprise Linux AI and watsonx provide the support and tooling you need to confidently deploy AI in your business at scale.
Discover how to build an AI agent that can answer questions
For an LLM to answer questions, fetch the data to create a vector store as context
Forecast the future based on learning with the TinyTimeMixer (TTM) Granite Model
Convert text into a structured representation and generate a semantically correct SQL query
Use the Ragas framework for Retrieval-Augmented Generation (RAG) evaluation in Python using LangChain
Learn how to adopt AI co-pilot tools in an enterprise setting with open source software
Prompt tune a Granite model in Python using a synthetic dataset containing positive and negative customer reviews.
IBM’s Granite 20B model tops several benchmarks ranking large language models by how reliably they connect to external software tools.
In testing against a range of other models, including open-source and proprietary, we found Granite models are competitive at a range of coding tasks.
A new report from Stanford University’s Center for Research on Foundation Models showed that IBM’s model scored a perfect 100% in several categories designed to measure how open models really are.
According to Forrester, the Granite family of models provides enterprise users with some of the most robust and clear insights into the underlying training data.
IBM believes in the creation, deployment and utilization of AI models that advance innovation across the enterprise responsibly. IBM watsonx AI and data platform have an end-to-end process for building and testing foundation models and generative AI. For IBM-developed models, we search for and remove duplication, and we employ URL blocklists, filters for objectionable content and document quality, sentence splitting and tokenization techniques, all before model training.
During the data training process, we work to prevent misalignments in the model outputs and use supervised fine-tuning to enable better instruction following so that the model can be used to complete enterprise tasks via prompt engineering. We are continuing to develop the Granite models in several directions, including other modalities, industry-specific content and more data annotations for training, while also deploying regular, ongoing data protection safeguards for IBM developed models.
Given the rapidly changing generative AI technology landscape, our end-to-end processes are expected to continuously evolve and improve. As a testament to the rigor IBM puts into the development and testing of its foundation models, the company provides its standard contractual intellectual property indemnification for IBM-developed models, similar to those it provides for IBM hardware and software products.
Moreover, contrary to some other providers of large language models and consistent with the IBM standard approach on indemnification, IBM does not require its customers to indemnify IBM for a customer's use of IBM-developed models. Also, consistent with the IBM approach to its indemnification obligation, IBM does not cap its indemnification liability for the IBM-developed models.
The current watsonx models now under these protections include:
(1) Slate family of encoder-only models.
(2) Granite family of a decoder-only model.