Supported foundation models available with watsonx.ai
A collection of open source and IBM foundation models are deployed in IBM watsonx.ai. You can prompt the deployed foundation models in the Prompt Lab or programmatically.
The following models are available to be deployed in watsonx.ai:
- granite-13b-chat-v2
- granite-13b-chat-v1
- granite-13b-instruct-v2
- granite-13b-instruct-v1
- granite-7b-lab
- granite-8b-japanese
- granite-20b-multilingual
- granite-3b-code-instruct
- granite-8b-code-instruct
- granite-20b-code-instruct
- granite-34b-code-instruct
- allam-1-13b-instruct
- codellama-34b-instruct
- elyza-japanese-llama-2-7b-instruct
- flan-t5-xl-3b
- flan-t5-xxl-11b
- flan-ul2-20b
- gpt-neox-20b
- jais-13b-chat
- llama-3-1-8b-instruct
- llama-3-1-70b-instruct
- llama-3-405b-instruct
- llama-3-8b-instruct
- llama-3-70b-instruct
- llama-2-13b-chat
- llama-2-70b-chat
- llama2-13b-dpo-v7
- merlinite-7b
- mistral-large
- mixtral-8x7b-instruct-v01
- mixtral-8x7b-instruct-v01-q
- mpt-7b-instruct2
- mt0-xxl-13b
- starcoder-15.5b
To understand how the model provider, instruction tuning, token limits, and other factors can affect which model you choose, see Choosing a model.
IBM foundation models
The following table lists the supported foundation models that IBM provides for inferencing. The foundation models must be deployed in your cluster by an administrator to be available for use. All IBM models are instruction-tuned.
| Model name | IBM indemnification | Maximum tokens Context (input + output) |
Supported tasks | More information |
|---|---|---|---|---|
| granite-13b-chat-v2 | Yes | 8192 | • classification • extraction • generation • question answering • summarization |
• Model card • Website • Research paper |
| granite-13b-chat-v1 | Yes | 8192 | • classification • extraction • generation • question answering • summarization |
• Model card • Website • Research paper |
| granite-13b-instruct-v2 | Yes | 8192 | • classification • extraction • generation • question answering • summarization |
• Model card • Website • Research paper Note: This foundation model can be prompt tuned.
|
| granite-13b-instruct-v1 | Yes | 8192 | • classification • extraction • generation • question answering • summarization |
• Model card • Website • Research paper |
| granite-7b-lab | Yes | 8192 | • classification • extraction • generation • question answering • retrieval-augmented generation • summarization |
• Model card • Research paper (LAB) |
| granite-8b-japanese | Yes | 4096 | • classification • extraction • generation • question answering • summarization |
• Model card • Website • Research paper |
| granite-20b-multilingual | Yes | 8192 | • classification • extraction • generation • question answering • summarization |
• Model card • Website • Research paper |
| granite-3b-code-instruct |
Yes | 2048 | • code • classification • extraction • generation • question answering • summarization |
• Model card • Website • Research paper Note: This foundation model can be fine tuned.
|
| granite-8b-code-instruct |
Yes | 4096 | • code • classification • extraction • generation • question answering • summarization |
• Model card • Website • Research paper Note: This foundation model can be fine tuned.
|
| granite-20b-code-instruct |
Yes | 8192 | • code • classification • extraction • generation • question answering • summarization |
• Model card • Research paper Note: This foundation model can be fine tuned.
|
| granite-34b-code-instruct |
Yes | 8192 | • code • classification • extraction • generation • question answering • summarization |
• Model card • Research paper |
For more information about the supported foundation models that IBM provides for embedding text, see Supported embedding models.
Third-party foundation models
The following table lists the supported foundation models that third parties provide. The foundation models must be deployed in your cluster by an administrator to be available for use.
| Model name | Provider | Maximum tokens Context (input + output) |
Supported tasks | More information |
|---|---|---|---|---|
| allam-1-13b-instruct | National Center for Artificial Intelligence and Saudi Authority for Data and Artificial Intelligence | 4096 | • classification • extraction • generation • question answering • retrieval-augmented generation • summarization • translation |
• Model card Note: This foundation model can be fine tuned.
|
| codellama-34b-instruct | Code Llama | 4096 | • code | • Model card • Meta AI Blog |
| elyza-japanese-llama-2-7b-instruct | ELYZA, Inc | 4096 | • classification • extraction • generation • question answering • retrieval-augmented generation • summarization • translation |
• Model card • Blog on note.com |
| flan-t5-xl-3b | 4096 | • classification • extraction • generation • question answering • retrieval-augmented generation • summarization |
• Model card • Research paper Note: This foundation model can be prompt tuned.
|
|
| flan-t5-xxl-11b | 4096 | • classification • extraction • generation • question answering • retrieval-augmented generation • summarization |
• Model card • Research paper |
|
| flan-ul2-20b | 4096 | • classification • extraction • generation • question answering • retrieval-augmented generation • summarization |
• Model card • UL2 research paper • Flan research paper |
|
| gpt-neox-20b | EleutherAI | 8192 | • classification • generation • summarization |
• Model card • Research paper |
| jais-13b-chat | Inception, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), and Cerebras Systems | 2048 | • classification • extraction • generation • question answering • retrieval-augmented generation • summarization • translation |
• Model card • Research paper |
| llama-3-1-8b-instruct |
Meta | 131,072 | • classification • code • extraction • generation • question answering • retrieval-augmented generation • summarization |
• Model card • Meta AI blog Note: This foundation model can be fine tuned.
|
| llama-3-1-70b-instruct |
Meta | 131,072 | • classification • code • extraction • generation • question answering • retrieval-augmented generation • summarization |
• Model card • Meta AI blog |
| llama-3-405b-instruct |
Meta | 16,384 | • classification • code • extraction • generation • question answering • retrieval-augmented generation • summarization |
• Model card • Meta AI blog |
| llama-3-8b-instruct | Meta | 8192 | • classification • code • extraction • generation • question answering • retrieval-augmented generation • summarization |
• Model card • Meta AI blog |
| llama-3-70b-instruct | Meta | 8192 | • classification • code • extraction • generation • question answering • retrieval-augmented generation • summarization |
• Model card • Meta AI blog |
| llama-2-13b-chat | Meta | 4096 | • classification • code • extraction • generation • question answering • retrieval-augmented generation • summarization |
• Model card • Research paper Note: This foundation model can be prompt tuned.
|
| llama-2-70b-chat |
Meta | 4096 | • classification • code • extraction • generation • question answering • retrieval-augmented generation • summarization |
• Model card • Research paper |
| llama2-13b-dpo-v7 | Meta | 4096 | • classification • code • extraction • generation • question answering • retrieval-augmented generation • summarization |
• Model card • Research paper (DPO) |
| merlinite-7b |
Mistral AI and IBM | 32,768 | • classification • extraction • generation • retrieval-augmented generation • summarization |
• Model card • Research paper (LAB) |
| mistral-large |
Mistral AI | 32,768 | • classification • code • extraction • generation • retrieval-augmented generation • summarization • translation |
• Model card • Blog post for Mistral Large 2 |
| mixtral-8x7b-instruct-v01 | Mistral AI | 32,768 | • classification • code • extraction • generation • retrieval-augmented generation • summarization • translation |
• Model card • Research paper |
| mixtral-8x7b-instruct-v01-q |
Mistral AI and IBM | 32,768 | • classification • code • extraction • generation • retrieval-augmented generation • summarization • translation |
• Model card • Research paper |
| mpt-7b-instruct2 | Mosaic ML and IBM | 2048 | • classification • extraction • generation • summarization |
• Model card • Model card |
| mt0-xxl-13b | BigScience | 4096 | • classification • generation • question answering • summarization |
• Model card • Research paper |
| starcoder-15.5b | BigCode | 8192 | • code | • Model card • Research paper |
Custom foundation models
In addition to working with foundation models that are curated by IBM, you can upload and deploy your own foundation models. After the custom models are deployed and registered with watsonx.ai, you can create prompts that inference the custom models from the Prompt Lab.
To learn more about how to upload, register, and deploy a custom foundation model, see Deploying a custom foundation model.
Foundation model details
The available foundation models 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.
allam-1-13b-instruct
This model was introduced with the 5.0.0 release.
The allam-1-13b-instruct foundation model is a bilingual large language model for Arabic and English provided by the National Center for Artificial Intelligence and supported by the Saudi Authority for Data and Artificial Intelligence that is fine-tuned to support conversational tasks. The ALLaM series is a collection of powerful language models designed to advance Arabic language technology. These models are initialized with Llama-2 weights and undergo training on both Arabic and English languages.
- Usage
- Supports Q&A, summarization, classification, generation, extraction, and translation in Arabic.
- Try it out
- Experiment with samples:
- Size
- 13 billion parameters
- Token limits
- Context window length (input + output): 4096
- Supported natural languages
- Arabic (Modern Standard Arabic) and English
- Instruction tuning information
- allam-1-13b-instruct is based on the Allam-13b-base model, which is a foundation model that is pre-trained on a total of 3 trillion tokens in English and Arabic, including the tokens seen from its initialization. The Arabic data set contains 500 billion tokens after cleaning and deduplication. The additional data is collected from open-source collections and web crawls. The allam-1-13b-instruct foundation model is fine-tuned with a curated set of 4 million Arabic and 6 million English prompt-and-response pairs.
- Model architecture
- Decoder-only
- License
- Llama 2 community license and ALLaM license
- Learn more
- Read the following resources:
codellama-34b-instruct
A programmatic code generation model that is based on Llama 2 from Meta. Code Llama is fine-tuned for generating and discussing code.
- Usage
- Use Code Llama to create prompts that generate code based on natural language inputs, explain code, or that complete and debug code.
- Try it out
- Experiment with samples:
- Size
- 34 billion parameters
- Token limits
- Context window length (input + output): 4096
- Supported natural languages
- English
- Supported programming languages
- The codellama-34b-instruct-hf foundation model supports many programming languages, including Python, C++, Java, PHP, Typescript (Javascript), C#, Bash, and more.
- Instruction tuning information
- The instruction fine-tuned version was fed natural language instruction input and the expected output to guide the model to generate helpful and safe answers in natural language.
- Model architecture
- Decoder
- License
- License
- Learn more
- Read the following resources:
elyza-japanese-llama-2-7b-instruct
The elyza-japanese-llama-2-7b-instruct model is provided by ELYZA, Inc on Hugging Face. The elyza-japanese-llama-2-7b-instruct foundation model is a version of the Llama 2 model from Meta that is trained to understand and generate Japanese text. The model is fine-tuned for solving various tasks that follow user instructions and for participating in a dialog.
- Usage
- General use with zero- or few-shot prompts. Works well for classification and extraction in Japanese and for translation between English and Japanese. Performs best when prompted in Japanese.
- Try it out
- Experiment with samples:
- Sample prompt: Classification
- Sample prompt: Translation
- Size
- 7 billion parameters
- Token limits
- Context window length (input + output): 4096
- Supported natural languages
- Japanese, English
- Instruction tuning information
- For Japanese language training, Japanese text from many sources were used, including Wikipedia and the Open Super-large Crawled ALMAnaCH coRpus (a multilingual corpus that is generated by classifying and filtering language in the Common Crawl corpus). The model was fine-tuned on a data set that was created by ELYZA. The ELYZA Tasks 100 data set contains 100 diverse and complex tasks that were created manually and evaluated by humans. The ELYZA Tasks 100 data set is publicly available from HuggingFace.
- Model architecture
- Decoder
- License
- License
- Learn more
- Read the following resources:
flan-t5-xl-3b
The flan-t5-xl-3b model is provided by Google on Hugging Face. This model is based on the pretrained text-to-text transfer transformer (T5) model and uses instruction fine-tuning methods to achieve better zero- and few-shot performance. The model is also fine-tuned with chain-of-thought data to improve its ability to perform reasoning tasks.
- Usage
- General use with zero- or few-shot prompts.
- Try it out
- Sample prompts
- Size
- 3 billion parameters
- Token limits
- Context window length (input + output): 4096
- Supported natural languages
- Multilingual
- Instruction tuning information
- The model was fine-tuned on tasks that involve multiple-step reasoning from chain-of-thought data in addition to traditional natural language processing tasks. Details about the training data sets used are published.
- Model architecture
- Encoder-decoder
- License
- Apache 2.0 license
- Learn more
- Read the following resources:
flan-t5-xxl-11b
The flan-t5-xxl-11b model is provided by Google on Hugging Face. This model is based on the pretrained text-to-text transfer transformer (T5) model and uses instruction fine-tuning methods to achieve better zero- and few-shot performance. The model is also fine-tuned with chain-of-thought data to improve its ability to perform reasoning tasks.
- Usage
-
General use with zero- or few-shot prompts.
- Try it out
-
Experiment with samples:
- Sample prompts
- Sample notebook: Use watsonx, and Google flan-t5-xxl to analyze car rental customer satisfaction from text
- Use watsonx, and Google flan-t5-xxl to analyze sentiments of legal documents
- Sample notebook: Use watsonx and LangChain to make a series of calls to a language model
Review the terms of use before you use samples from GitHub.
- Size
-
11 billion parameters
- Token limits
-
Context window length (input + output): 4096
- Supported natural languages
-
English, German, French
- Instruction tuning information
-
The model was fine-tuned on tasks that involve multiple-step reasoning from chain-of-thought data in addition to traditional natural language processing tasks. Details about the training data sets used are published.
- Model architecture
-
Encoder-decoder
- License
- Learn more
-
Read the following resources:
flan-ul2-20b
The flan-ul2-20b model is provided by Google on Hugging Face. This model was trained by using the Unifying Language Learning Paradigms (UL2). The model is optimized for language generation, language understanding, text classification, question answering, common sense reasoning, long text reasoning, structured-knowledge grounding, and information retrieval, in-context learning, zero-shot prompting, and one-shot prompting.
- Usage
-
General use with zero- or few-shot prompts.
- Try it out
-
Experiment with samples:
- Sample prompts
- Sample notebook: Use watson to analyze eXtensive Business Reporting Language (XBRL) tags of financial reports
- Sample notebook: Use watsonx, Chroma, and LangChain to answer questions by using retrieval-augmented generation (RAG)
- Sample notebook: Use watsonx, Elasticsearch, and LangChain to answer questions (RAG)
- Sample notebook: Use watsonx, and Elasticsearch Python library to answer questions (RAG)
- Sample notebook: Use watsonx and LangChain to make a series of calls to a language model
Review the terms of use before using samples from GitHub.
- Size
-
20 billion parameters
- Token limits
-
Context window length (input + output): 4096
- Supported natural languages
-
English
- Instruction tuning information
-
The flan-ul2-20b model is pretrained on the colossal, cleaned version of Common Crawl's web crawl corpus. The model is fine-tuned with multiple pretraining objectives to optimize it for various natural language processing tasks. Details about the training data sets used are published.
- Model architecture
-
Encoder-decoder
- License
- Learn more
-
Read the following resources:
gpt-neox-20b (Withdrawn)
This model was withdrawn in the 5.0.1 release. For more information, see Foundation model lifecycle.
The gpt-neox-20b model is provided by EleutherAI on Hugging Face. This model is an autoregressive language model that is trained on diverse English-language texts to support general-purpose use cases. GPT-NeoX-20B has not been fine-tuned for downstream tasks.
- Usage
-
Works best with few-shot prompts. Accepts special characters, which can be used for generating structured output. The data set used for training contains profanity and offensive text. Be sure to curate any output from the model before using it in an application.
- Try it out
-
Experiment with samples:
Review the terms of use before using samples from GitHub.
- Size
-
20 billion parameters
- Token limits
-
Context window length (input + output): 8192
Supported natural languages: English
Data used during training: The gpt-neox-20b model was trained on the Pile. For more information about the Pile, see The Pile: An 800GB Data set of Diverse Text for Language Modeling. The Pile was not deduplicated before it was used for training.
Model architecture: Decoder
License: Apache 2.0 license
- Learn more
- Read the following resources:
granite-13b-chat-v2
The granite-13b-chat-v2 model is provided by IBM. This model is optimized for dialog use cases and works well with virtual agent and chat applications.
Usage: Generates dialog output like a chatbot. Uses a model-specific prompt format. Includes a keyword in its output that can be used as a stop sequence to produce succinct answers. Follow the prompting guidelines for tips on usage. For more information, see Prompting granite-13b-chat-v2.
- Try it out
- Size
-
13 billion parameters
- Token limits
-
Context window length (input + output): 8192
- Supported natural languages
-
English
- Instruction tuning information
-
The Granite family of models is trained on enterprise-relevant data sets from five domains: internet, academic, code, legal, and finance. Data used to train the models first undergoes IBM data governance reviews and is filtered of text that is flagged for hate, abuse, or profanity by the IBM-developed HAP filter. IBM shares information about the training methods and data sets used.
- Model architecture
-
Decoder
- License
-
IBM-developed foundation models are considered part of the IBM Cloud Service. For more information about contractual protections related to IBM indemnification, see the IBM Client Relationship Agreement and IBM watsonx.ai service description.
- Learn more
-
Read the following resources:
granite-13b-chat-v1 (Withdrawn)
This model was withdrawn in the 5.0.1 release. For more information, see Foundation model lifecycle.
The granite-13b-chat-v1 model is provided by IBM. This model is optimized for dialog use cases and works well with virtual agent and chat applications.
- Usage
- Generates dialog output like a chatbot. Uses a model-specific prompt format. Includes a keyword in its output that can be used as a stop sequence to produce succinct answers.
- Try it out
- Sample prompt
- Size
- 13 billion parameters
- Token limits
- Context window length (input + output): 8192
- Supported natural languages
- English
- Instruction tuning information
- The Granite family of models is trained on enterprise-relevant data sets from five domains: internet, academic, code, legal, and finance. Data used to train the models first undergoes IBM data governance reviews and is filtered of text that is flagged for hate, abuse, or profanity by the IBM-developed HAP filter. IBM shares information about the training methods and data sets used.
- Model architecture
- Decoder
- License
- Terms of use
- Learn more
- Read the following resources:
granite-13b-instruct-v2
The granite-13b-instruct-v2 model is provided by IBM. This model was trained with high-quality finance data, and is a top-performing model on finance tasks. Financial tasks evaluated include: providing sentiment scores for stock and earnings call transcripts, classifying news headlines, extracting credit risk assessments, summarizing financial long-form text, and answering financial or insurance-related questions.
- Usage
-
Supports extraction, summarization, and classification tasks. Generates useful output for finance-related tasks. Uses a model-specific prompt format. Accepts special characters, which can be used for generating structured output.
- Try it out
-
Experiment with samples:
- Size
-
13 billion parameters
- Token limits
-
Context window length (input + output): 8192
- Supported natural languages
-
English
- Instruction tuning information
-
The Granite family of models is trained on enterprise-relevant data sets from five domains: internet, academic, code, legal, and finance. Data used to train the models first undergoes IBM data governance reviews and is filtered of text that is flagged for hate, abuse, or profanity by the IBM-developed HAP filter. IBM shares information about the training methods and data sets used.
- Model architecture
-
Decoder
- License
-
IBM-developed foundation models are considered part of the IBM Cloud Service. For more information about contractual protections related to IBM indemnification, see the IBM Client Relationship Agreement and IBM watsonx.ai service description.
- Learn more
-
Read the following resources:
granite-13b-instruct-v1 (Withdrawn)
This model was withdrawn in the 5.0.1 release. For more information, see Foundation model lifecycle.
The granite-13b-instruct-v1 model is provided by IBM. This model was trained with high-quality finance data, and is a top-performing model on finance tasks. Financial tasks evaluated include: providing sentiment scores for stock and earnings call transcripts, classifying news headlines, extracting credit risk assessments, summarizing financial long-form text, and answering financial or insurance-related questions.
- Usage
- Supports extraction, summarization, and classification tasks. Generates useful output for finance-related tasks. Uses a model-specific prompt format. Accepts special characters, which can be used for generating structured output.
- Try it out
- Experiment with samples:
- Size
- 13 billion parameters
- Token limits
- Context window length (input + output): 8192
- Supported natural languages
- English
- Instruction tuning information
- The Granite family of models is trained on enterprise-relevant data sets from five domains: internet, academic, code, legal, and finance. Data used to train the models first undergoes IBM data governance reviews and is filtered of text that is flagged for hate, abuse, or profanity by the IBM-developed HAP filter. IBM shares information about the training methods and data sets used.
- Model architecture
- Decoder
- License
- Terms of use
- Learn more
- Read the following resources:
granite-7b-lab
This model was introduced with the 5.0.0 release.
The granite-7b-lab foundation model is provided by IBM. The granite-7b-lab foundation model uses a novel alignment tuning method from IBM Research. Large-scale Alignment for chatBots, or LAB is a method for adding new skills to existing foundation models by generating synthetic data for the skills, and then using that data to tune the foundation model.
- Usage
- Supports general purpose tasks, including extraction, summarization, classification, and more. Follow the prompting guidelines for tips on usage. For more information, see Prompting granite-7b-lab.
- Try it out
- Size
-
7 billion parameters
- Token limits
-
Context window length (input + output): 8192
Note: The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 4096.
- Supported natural languages
-
English
- Instruction tuning information
-
The granite-7b-lab foundation model is trained iteratively by using the large-scale alignment for chatbots (LAB) methodology.
- Model architecture
-
Decoder
- License
-
IBM-developed foundation models are considered part of the IBM Cloud Service. When you use the granite-7b-lab foundation model that is provided in watsonx.ai the contractual protections related to IBM indemnification apply. See the IBM Client Relationship Agreement and IBM watsonx.ai service description.
- Learn more
-
Read the following resources:
granite-8b-japanese
The granite-8b-japanese model is provided by IBM. The granite-8b-japanese foundation model is based on the IBM Granite Instruct foundation model and is trained to understand and generate Japanese text.
- Usage
-
Useful for general purpose tasks in the Japanese language, such as classification, extraction, question-answering, and for language translation between Japanese and English.
- Try it out
-
Experiment with samples:
- Size
-
8 billion parameters
- Token limits
-
Context window length (input + output): 4096
- Supported natural languages
-
English, Japanese
- Instruction tuning information
-
The Granite family of models is trained on enterprise-relevant data sets from five domains: internet, academic, code, legal, and finance. The granite-8b-japanese model was pretrained on 1 trillion tokens of English and 0.5 trillion tokens of Japanese text.
- Model architecture
-
Decoder
- License
-
IBM-developed foundation models are considered part of the IBM Cloud Service. For more information about contractual protections related to IBM indemnification, see the IBM Client Relationship Agreement and IBM watsonx.ai service description.
- Learn more
-
Read the following resources:
granite-20b-multilingual
A foundation model from the IBM Granite family. The granite-20b-multilingual foundation model is based on the IBM Granite Instruct foundation model and is trained to understand and generate text in English, German, Spanish, French, and Portuguese.
- Usage
- English, German, Spanish, French, and Portuguese closed-domain question answering, summarization, generation, extraction, and classification.
- Try it out
- Size
-
13 billion parameters
- Token limits
-
Context window length (input + output): 8192
- Supported natural languages
-
English, German, Spanish, French, and Portuguese
- Instruction tuning information
-
The Granite family of models is trained on enterprise-relevant data sets from five domains: internet, academic, code, legal, and finance. Data used to train the models first undergoes IBM data governance reviews and is filtered of text that is flagged for hate, abuse, or profanity by the IBM-developed HAP filter. IBM shares information about the training methods and data sets used.
- Model architecture
-
Decoder
- License
-
IBM-developed foundation models are considered part of the IBM Cloud Service. For more information about contractual protections related to IBM indemnification, see the IBM Client Relationship Agreement and IBM watsonx.ai service description.
- Learn more
-
Read the following resources:
Granite code models
These models were introduced with the 5.0.1 release.
Foundation models from the IBM Granite family. The Granite code foundation models are instruction-following models fine-tuned using a combination of Git commits paired with human instructions and open-source synthetically generated code instruction data sets.
The granite-8b-code-instruct v2.0.0 foundation model can process larger prompts with an increased context window length.
- Usage
-
Granite code foundation models are designed to respond to coding-related instructions and can be used to build coding assitants. For more information and sample prompts, see Prompts for code.
- Try it out
-
Experiment with samples:
- Available sizes
-
The model is available in the following sizes:
- 3 billion parameters
- 8 billion parameters
- 20 billion parameters
- 34 billion parameters
- Token limits
-
Context window length (input + output)
- granite-3b-code-instruct : 8192
- granite-8b-code-instruct : 128,000
- granite-20b-code-instruct : 8192
- granite-34b-code-instruct : 8192
The maximum new tokens, which means the tokens generated by these foundation models, is limited to 4096.
- Supported natural languages
-
English
- Supported programming languages
-
The Granite code foundation models support 116 programming languages including Python, Javascript, Java, C++, Go, and Rust. For the full list, see IBM foundation models.
- Instruction tuning information
-
These models were fine-tuned from Granite code base models on a combination of permissively licensed instruction data to enhance instruction-following capabilities including logical reasoning and problem-solving skills.
- Model architecture
-
Decoder
- License
-
IBM-developed foundation models are considered part of the IBM Cloud Service. For more information about contractual protections related to IBM indemnification, see the IBM Client Relationship Agreement and IBM watsonx.ai service description.
- Learn more
-
Read the following resources:
jais-13b-chat
The jais-13b-chat foundation model is a bilingual large language model for Arabic and English that is fine-tuned to support conversational tasks.
- Usage
- Supports Q&A, summarization, classification, generation, extraction, and translation in Arabic.
- Try it out
- Sample prompt: Arabic chat
- Size
- 13 billion parameters
- Token limits
- Context window length (input + output): 2048
- Supported natural languages
- Arabic (Modern Standard Arabic) and English
- Instruction tuning information
- Jais-13b-chat is based on the Jais-13b model, which is a foundation model that is trained on 116 billion Arabic tokens and 279 billion English tokens. Jais-13b-chat is fine tuned with a curated set of 4 million Arabic and 6 million English prompt-and-response pairs.
- Model architecture
- Decoder
- License
- Apache 2.0
- Learn more
- Read the following resources:
Llama 3.1 Instruct
These models were introduced with the 5.0.3 release.
The Meta Llama 3.1 collection of foundation models are provided by Meta. The Llama 3.1 foundation models are pretrained and instruction tuned text-only generative models that are optimized for multilingual dialogue use cases. The models use supervised fine-tuning and reinforcement learning with human feedback to align with human preferences for helpfulness and safety.
The llama-3-405b-instruct model is Meta's largest open-sourced foundation model to date. This foundation model can also be used as a synthetic data generator, post-training data ranking judge, or model teacher/supervisor that can improve specialized capabilities in more inference-friendly, derivative models.
- Usage
- Generates dialog output like a chatbot. Uses a model-specific prompt format.
- Try it out
- Sample prompt: Converse with Llama 3
- Available sizes
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- 8 billion parameters
- 70 billion parameters
- 405 billion parameters
- Token limits
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8b and 70b: Context window length (input + output): 131,072
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405b: Context window length (input + output): 16,384
- Although the model supports a context window length of 131,072, the window is limited to 16,384 to reduce the time it takes for the model to generate a response.
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The maximum new tokens, which means the tokens generated by the foundation models per request, is limited to 4096.
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- Supported natural languages
- English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai
- Instruction tuning information
- Llama 3.1 was pretrained on 15 trillion tokens of data from publicly available sources. The fine tuning data includes publicly available instruction datasets, as well as over 25 million synthetically generated examples.
- Model architecture
- Decoder-only
- License
- Learn more
- Read the following resources:
Llama 3 Instruct
These models were introduced with the 5.0.0 release.
The Meta Llama 3 family of foundation models are accessible, open large language models that are built with Meta Llama 3 and provided by Meta on Hugging Face. The Llama 3 foundation models are instruction fine-tuned language models that can support various use cases.
Usage: Generates dialog output like a chatbot.
- Try it out
- Available sizes
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- 8 billion parameters
- 70 billion parameters
- Token limits
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Context window length (input + output): 8192
Note: The maximum new tokens, which means the tokens generated by the foundation models per request, is limited to 4096.
- Supported natural languages
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English
- Instruction tuning information
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Llama 3 features improvements in post-training procedures that reduce false refusal rates, improve alignment, and increase diversity in the foundation model output. The result is better reasoning, code generation, and instruction-following capabilities. Llama 3 has more training tokens (15T) that result in better language comprehension.
- Model architecture
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Decoder-only
- License
- Learn more
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Read the following resources:
Llama 2 Chat
The Llama 2 Chat model is provided by Meta on Hugging Face. The fine-tuned model is useful for chat generation. The model is pretrained with publicly available online data and fine-tuned using reinforcement learning from human feedback.
You can choose to use the 13 billion parameter or 70 billion parameter version of the model.
The llama-2-70b-chat model was deprecated in the 5.0.3 release. For more information, see Foundation model lifecycle.
- Usage
-
Generates dialog output like a chatbot. Uses a model-specific prompt format.
- Try it out
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Experiment with samples:
- Sample prompt: Converse with Llama 2
- Sample notebook: Use watsonx and Meta llama-2-70b-chat to answer questions about an article
Review the terms of use before using the sample from GitHub.
- Available sizes
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- 13 billion parameters
- 70 billion parameters
- Token limits
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Context window length (input + output): 4096
- Supported natural languages
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English
- Instruction tuning information
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Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction data sets and more than one million new examples that were annotated by humans.
- Model architecture
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Decoder-only
- License
- Learn more
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Read the following resources:
llama2-13b-dpo-v7
The llama2-13b-dpo-v7 foundation model is provided by Minds & Company. The llama2-13b-dpo-v7 foundation model is a version of llama2-13b foundation model from Meta that is instruction-tuned and fine-tuned by using the direct preference optimzation method to handle Korean.
- Usage
- Suitable for many tasks, including classification, extraction, summarization, code creation and conversion, question-answering, generation, and retreival-augmented generation in Korean.
- Try it out
- Experiment with samples:
- Size
- 13.2 billion parameters
- Token limits
- Context window length (input + output): 4096
- Supported natural languages
- English, Korean
- Instruction tuning information
- Direct preference optimzation (DPO) is an alternative to reinforcement learning from human feedback. With reinforcement learning from human feedback, responses must be sampled from a language model and an intermediate step of training a reward model is required. The direct preference optimzation uses a binary method of reinforcement learning where the model chooses the best of two answers based on preference data.
- Model architecture
- Decoder-only
- License
- License
- Learn more
- Read the following resources:
merlinite-7b (Deprecated)
This model was deprecated in the 5.0.3 release. For more information, see Foundation model lifecycle.
The merlinite-7b foundation model is provided by Mistral AI and tuned by IBM. The merlinite-7b foundation model is a derivative of the Mistral-7B-v0.1 model that is tuned with a novel alignment tuning method from IBM Research. Large-scale Alignment for chatBots, or LAB is a method for adding new skills to existing foundation models by generating synthetic data for the skills, and then using that data to tune the foundation model.
- Usage
- Supports general purpose tasks, including extraction, summarization, classification, and more.
- Try it out
- Size
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7 billion parameters
- Token limits
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Context window length (input + output): 32,768
Note: The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 8192.
- Supported natural languages
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English
- Instruction tuning information
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The merlinite-7b foundation model is trained iteratively by using the large-scale alignment for chatbots (LAB) methodology.
- Model architecture
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Decoder
- License
- Learn more
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Read the following resources:
mistral-large
This model was introduced with the 5.0.3 release.
Mistral Large 2 is a large language model developed by Mistral Al. The mistral-large foundation model is fluent in and understands the grammar and cultural context of English, French, Spanish, German, and Italian. The foundation model can also understand dozens of other languages. The model has a large context window, which means you can add large documents as contextual information in prompts that you submit for retrieval-augmented generation (RAG) use cases. The mistral-large foundation model is effective at programmatic tasks, such as generating, reviewing, and commenting on code, function calling, and can generate results in JSON format.
- Usage
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Suitable for complex multilingual reasoning tasks, including text understanding, transformation, and code generation. Due to the model's large context window, use the max tokens parameter to specify a token limit when prompting the model.
- Try it out
- Token limits
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Context window length (input + output): 128,000
Note:
- Although the model supports a context window length of 128,000, the window is limited to 32,768 to reduce the time it takes for the model to generate a response.
- The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 16,384.
- Supported natural languages
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English, French, German, Italian, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, Polish, and dozens of other languages.
- Supported programming languages
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The mistral-large model has been trained on over 80 programming languages including Python, Java, C, C++, JavaScript, Bash, Swift, and Fortran.
- Instruction tuning information
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The mistral-large foundation model is pre-trained on diverse data sets like text, codebases, and mathematical data from various domains.
- Model architecture
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Decoder-only
- License
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For terms of use, including information about contractual protections related to capped indemnification, see License information.
- Learn more
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Read the following resources:
mixtral-8x7b-instruct-v01
This model was introduced with the 5.0.0 release.
The mixtral-8x7b-instruct-v01 foundation model is provided by Mistral AI. The mixtral-8x7b-instruct-v01 foundation model is a pretrained generative sparse mixture-of-experts network that groups the model parameters, and then for each token chooses a subset of groups (referred to as experts) to process the token. As a result, each token has access to 47 billion parameters, but only uses 13 billion active parameters for inferencing, which reduces costs and latency.
- Usage
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Suitable for many tasks, including classification, summarization, generation, code creation and conversion, and language translation. Due to the model's unusually large context window, use the max tokens parameter to specify a token limit when prompting the model.
- Try it out
- Size
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46.7 billion parameters
- Token limits
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Context window length (input + output): 32,768
Note: The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 16,384.
- Supported natural languages
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English, French, German, Italian, Spanish
- Instruction tuning information
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The Mixtral foundation model is pretrained on internet data. The Mixtral 8x7B Instruct foundation model is fine-tuned to follow instructions.
- Model architecture
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Decoder-only
- License
- Learn more
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Read the following resources:
mixtral-8x7b-instruct-v01-q (Withdrawn)
This model was withdrawn in the 5.0.3 release. For more information, see Foundation model lifecycle.
The mixtral-8x7b-instruct-v01-q model is provided by IBM. The mixtral-8x7b-instruct-v01-q foundation model is a quantized version of the Mixtral 8x7B Instruct foundation model from Mistral AI.
The underlying Mixtral 8x7B foundation model is a sparse mixture-of-experts network that groups the model parameters, and then for each token chooses a subset of groups (referred to as experts) to process the token. As a result, each token has access to 47 billion parameters, but only uses 13 billion active parameters for inferencing, which reduces costs and latency.
- Usage
-
Suitable for many tasks, including classification, summarization, generation, code creation and conversion, and language translation. Due to the model's unusually large context window, use the max tokens parameter to specify a token limit when prompting the model.
- Try it out
- Size
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8 x 7 billion parameters
- Token limits
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Context window length (input + output): 32,768
Note: The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 4096.
- Supported natural languages
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English, French, German, Italian, Spanish
- Instruction tuning information
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The Mixtral foundation model is pretrained on internet data. The Mixtral 8x7B Instruct foundation model is fine-tuned to follow instructions.
The IBM-tuned model uses the AutoGPTQ (Post-Training Quantization for Generative Pre-Trained Transformers) method to compress the model weight values from 16-bit floating point data types to 4-bit integer data types during data transfer. The weights decompress at computation time. Compressing the weights to transfer data reduces the GPU memory and GPU compute engine size requirements of the model.
- Model architecture
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Decoder-only
- License
- Learn more
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Read the following resources:
mpt-7b-instruct2 (Withdrawn)
This model was withdrawn in the 5.0.1 release. For more information, see Foundation model lifecycle.
The mpt-7b-instruct2 model is provided by MosaicML and tuned by IBM. This model is a fine-tuned version of the base MosaicML Pretrained Transformer (MPT) model that was trained to handle long inputs. This version of the model was optimized by IBM for following short-form instructions.
- Usage
- General use with zero- or few-shot prompts.
- Try it out
- Sample prompts
- Size
- 7 billion parameters
- Token limits
- Context window length (input + output): 2048
- Supported natural languages
- English
- Instruction tuning information
- The data set that was used to train this model is a combination of the Dolly data set from Databrick and a filtered subset of the Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback training data from Anthropic. During filtering, parts of dialog exchanges that contain instruction-following steps were extracted to be used as samples.
- Model architecture
- Encoder-decoder
- License
- Apache 2.0 license
- Learn more
- Read the following resources:
mt0-xxl-13b
The mt0-xxl-13b model is provided by BigScience on Hugging Face. The model is optimized to support language generation and translation tasks with English, languages other than English, and multilingual prompts.
Usage: General use with zero- or few-shot prompts. For translation tasks, include a period to indicate the end of the text you want translated or the model might continue the sentence rather than translate it.
- Try it out
- Sample prompts
- Size
- 13 billion parameters
- Supported natural languages
- Multilingual
- Token limits
- Context window length (input + output): 4096
- Supported natural languages
- The model is pretrained on multilingual data in 108 languages and fine-tuned with multilingual data in 46 languages to perform multilingual tasks.
- Instruction tuning information
- BigScience publishes details about its code and data sets.
- Model architecture
- Encoder-decoder
- License
- Apache 2.0 license
- Learn more
- Read the following resources:
starcoder-15.5b (Withdrawn)
This model was withdrawn in the 5.0.1 release. For more information, see Foundation model lifecycle.
The starcoder-15.5b model is provided by BigCode on Hugging Face. This model can generate code and convert code from one programming language to another. The model is meant to be used by developers to boost their productivity.
- Usage
- Code generation and code conversion
- Try it out
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Experiment with samples:
- Sample prompts
- Sample notebook: Use watsonx and BigCode starcoder-15.5b to generate code based on instruction
Review the terms of use before using the sample from GitHub.
- Size
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15.5 billion parameters
- Token limits
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Context window length (input + output): 8192
- Supported programming languages
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Over 80 programming languages, with an emphasis on Python.
- Data used during training
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This model was trained on over 80 programming languages from GitHub. A filter was applied to exclude from the training data any licensed code or code that is marked with opt-out requests. Nevertheless, the model's output might include code from its training data that requires attribution. The model was not instruction-tuned. Submitting input with only an instruction and no examples might result in poor model output.
- Model architecture
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Decoder
- License
- Learn more
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Read the following resources:
Any deprecated foundation models are highlighted with a deprecated warning icon .Any withdrawn foundation models are highlighted with a withdrawn
warning icon . For more information about deprecation, including foundation model withdrawal dates, see Foundation model lifecycle.
Learn more
Parent topic: Developing generative AI solutions