# Foundation models built by IBM #

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

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

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

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 *  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\.

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IBM is committed to building AI that is open, trusted, targeted, and empowering\. For more information about contractual protections related to the IBM Granite foundation models, see the [IBM watsonx\.ai service description](https://www.ibm.com/support/customer/csol/terms/?id=i126-7747) and [model license](https://www.ibm.com/support/customer/csol/terms/?id=i126-6883)\.

The following Granite models are available in watsonx\.ai today:

**granite\-13b\-chat\-v2** : General use model that is optimized for dialogue use cases\. This version of the model is able to generate longer, higher\-quality responses with a professional tone\. The model can recognize mentions of people and can detect tone and sentiment\.

**granite\-13b\-chat\-v1** : General use model that is optimized for dialogue use cases\. Useful for virtual agent and chat applications that engage in conversation with users\.

**granite\-13b\-instruct\-v2** : General use model\. This version of the model is optimized for classification, extraction, and summarization tasks\. The model can recognize mentions of people and can summarize longer inputs\.

**granite\-13b\-instruct\-v1** : General use model\. The model was tuned on relevant business tasks, such as detecting sentiment from earnings calls transcripts, extracting credit risk assessments, summarizing financial long\-form text, and answering financial or insurance\-related questions\.

To learn more about the models, read the following resources:

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 *  [Model information](https://www.ibm.com/blog/watsonx-tailored-generative-ai/)
 *  [Research paper](https://www.ibm.com/downloads/cas/X9W4O6BM)
 *  [granite\-13b\-instruct\-v2 model card](https://dataplatform.cloud.ibm.com/wx/samples/models/ibm/granite-13b-instruct-v2?context=wx)
 *  [granite\-13b\-instruct\-v1 model card](https://dataplatform.cloud.ibm.com/wx/samples/models/ibm/granite-13b-instruct-v1?context=wx)
 *  [granite\-13b\-chat\-v2 model card](https://dataplatform.cloud.ibm.com/wx/samples/models/ibm/granite-13b-chat-v2?context=wx)
 *  [granite\-13b\-chat\-v1 model card](https://dataplatform.cloud.ibm.com/wx/samples/models/ibm/granite-13b-chat-v1?context=wx)

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To get started with the models, try these samples:

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 *  [Prompt Lab sample: Extract details from a complaint](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-prompt-samples.html#sample2a)
 *  [Prompt Lab sample: Generate a numbered list on a particular theme](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-prompt-samples.html#sample3c)
 *  [prompt Lab sample: Answer a question based on a document](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-prompt-samples.html#sample4c)
 *  [Prompt Lab sample: Answer general knowledge questions](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-prompt-samples.html#sample4d)
 *  [Prompt Lab sample: Converse in a dialogue](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-prompt-samples.html#sample7a)

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 *  [Sample Python notebook: Use watsonx and a Granite model to analyze car rental customer satisfaction from text](https://dataplatform.cloud.ibm.com/exchange/public/entry/view/61c1e967-8d10-44bb-a846-cc1f27e9e69a?context=wx)

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**Parent topic:**[Supported foundation models](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html)

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