Foundation models in watsonx.ai 

IBM believes in the creation, deployment and utilization of AI models that advance innovation across the enterprise responsibly. IBM watsonx AI portfolio has 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 through 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

Learn more about licensing for Granite models (PDF)

^* Supported context length by model provider, but actual context length on platform is limited. For more information, please see Documentation.

Inference is billed in Resource Units. 1 Resource Unit is 1,000 tokens. Input and completion tokens are charged at the same rate. 1,000 tokens are generally about 750 words.

Not all models are available in all regions. See our documentation for details.

Context length is expressed in tokens.

The IBM statements regarding its plans, directions and intent are subject to change or withdrawal without notice at its sole discretion. See Pricing for more details. Unless otherwise specified under Software pricing, all features, capabilities and potential updates refer exclusively to SaaS. IBM makes no representation that SaaS and software features and capabilities are the same.