IBM foundation models

In IBM watsonx.ai, you can use IBM foundation models that are built with integrity and designed for business.

The Granite family of IBM foundation models includes decoder-only models that can efficiently predict and generate language.

The models were built with trusted data that has the following characteristics:

  • Sourced from quality data sets in domains such as finance (SEC Filings), law (Free Law), technology (Stack Exchange), science (arXiv, DeepMind Mathematics), literature (Project Gutenberg (PG-19)), and more.
  • Compliant with rigorous IBM data clearance and governance standards.
  • Scrubbed of hate, abuse, and profanity, data duplication, and blocklisted URLs, among other things.

IBM is committed to building AI that is open, trusted, targeted, and empowering. For more information about contractual protections that are related to IBM indemnification, see the IBM Client Relationship Agreement.

For details about encoder models developed by IBM, see Supported encoder foundation models.

For details about third-party foundation models, see Third-party foundation models.

Foundation model details

The foundation models in watsonx.ai support a range of use cases for both natural languages and programming languages. To see the types of tasks that these models can do, review and try the sample prompts. To view pricing details for deploy on demand foundation models, see Hourly billing rates for deploy on demand models.

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granite-3-2-8b-instruct

Granite 3.2 Instruct is a long-context foundation model that is fine-tuned for enhanced reasoning capabilities. The thinking capability is configurable, which means you can control when reasoning is applied.

Usage

Capable of common generative tasks, including code-related tasks, function-calling, and multilingual dialogs. Specializes in reasoning and long-context tasks such as summarizing long documents or meeting transcripts. Can respond to questions with answers that are grounded in context that is provided from long documents.

Size

8 billion parameters

Token limits

Context window length (input + output): 131,072

Note: The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 16,384.

Supported natural languages

English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese

Instruction tuning information

Built on top of Granite-3.1-8B-Instruct, the model was trained by using a mix of permissively licensed open-source datasets and internally generated synthetic data designed for reasoning tasks.

Model architecture

Decoder

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