Supported foundation models in watsonx.ai

allam-1-13b-instruct

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.

Size

13 billion parameters

API pricing tier

Class 2. For pricing details, see Table 2.

Try it out

Experiment with samples:

• Sample prompt: Classification
• Sample prompt: Translation

Token limits

Context window length (input + output): 4,096

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:

• Model card

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.

Size

34 billion parameters

API pricing tier

Class 2. For pricing details, see Table 2.

Try it out

Experiment with samples:

• Sample prompt: Code generation
• Sample prompt: Code conversion

Token limits

Context window length (input + output): 16,384

Note: The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 8,192.

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:

• Model card
• Research paper
• Meta AI blog

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.

Size

7 billion parameters

API pricing tier

Class 2. For pricing details, see Table 2.

Try it out

Experiment with samples:

• Sample prompt: Classification
• Sample prompt: Translation

Token limits

Context window length (input + output): 4,096

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:

• Blog post on note.com
• Model card (Tokyo data center)
• Model card

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.

Size

3 billion parameters

API pricing tier

Class 1. For pricing details, see Table 2.

Try it out

Sample prompts

Token limits

Context window length (input + output): 4,096

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:

• Research paper
• Model card
• Sample notebook: Tune a model to classify CFPB documents in watsonx
• Sample notebook: Prompt Tuning for Multi-class Classification with watsonx

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.

Size

11 billion parameters

API pricing tier

Class 2. For pricing details, see Table 2.

Try it out

Experiment with samples:

• Sample prompts
• Sample prompt: Marketing email generation
• Sample notebook: Use watsonx and Google flan-t5-xxl to generate advertising copy
• Sample notebook: Use watsonx and LangChain to make a series of calls to a language model

Token limits

Context window length (input + output): 4,096

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

Apache 2.0 license

Learn more

Read the following resources:

• Research paper
• Model card

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.

Size

20 billion parameters

API pricing tier

Class 3. For pricing details, see Table 2.

Try it out

Experiment with samples:

• Sample prompts
• Sample prompt: Earnings call summary
• Sample prompt: Meeting transcript summary
• Sample prompt: Scenario classification
• Sample prompt: Sentiment classification
• Sample prompt: Thank you note generation
• Sample prompt: Named entity extraction
• Sample prompt: Fact extraction
• Sample notebook: Use watsonx to summarize cybersecurity documents
• Sample notebook: Use watsonx 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)

Token limits

Context window length (input + output): 4,096

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

Apache 2.0 license

Learn more

Read the following resources:

• Unifying Language Learning (UL2) research paper
• Fine-tuned Language Model (Flan) research paper
• Model card

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.

The mt0-xxl-13b foundation model is deprecated. For more information, see Foundation model lifecycle.

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.

Size

13 billion parameters

API pricing tier

Class 1. For pricing details, see Table 1.

Try it out

Sample prompt

Token limits

Context window length (input + output): 8,192

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

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:

• Model information
• Research paper
• Model card

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.

Size

13 billion parameters

API pricing tier

Class 1. For pricing details, see Table 1.

Try it out

Experiment with samples:

• Sample 3b: Generate a numbered list on a particular theme
• Sample 4c: Answer a question based on a document
• Sample 4d: Answer general knowledge questions
• Sample prompt: Finance Q&A
• Sample notebook: Use watsonx and ibm/granite-13b-instruct to analyze car rental customer satisfaction from text

Token limits

Context window length (input + output): 8,192

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

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:

• Model information
• Research paper
• Model card

granite-7b-lab

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.

This model is deprecated. See Foundation model lifecycle.

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.

Size

7 billion parameters

API pricing tier

Class 1. For pricing details, see Table 1.

Try it out

Sample: Generate a title for a passage

Token limits

Context window length (input + output): 8,192

Note: The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 4,096.

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

Terms of use

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:

• Model card
• Research paper (LAB)

granite-8b-japanese

The granite-8b-japanese model is provided by IBM. The granite-8b-japanese foundation model is an instruct variant initialized from the pre-trained Granite Base 8 Billion Japanese 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.

Size

8 billion parameters

API pricing tier

Class 1. For pricing details, see Table 1.

Try it out

Experiment with samples:

• Sample 4e: Answer a question based on a document
• Sample 7d: Converse in a dialog
• Sample 8c: Translate text

Token limits

Context window length (input + output): 4,096

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

Terms of use

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:

• Model information
• Research paper
• Model card (Tokyo data center)

granite-20b-multilingual

A foundation model from the IBM Granite family. The granite-20b-multilingual foundation model is based on the Granite Base 20 bBillion base 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.

Size

20 billion parameters

API pricing tier

Class 1. For pricing details, see Table 1.

Try it out

Sample prompt: Translate text from French to English

Token limits

Context window length (input + output): 8,192

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

Terms of use

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:

• Model information
• Research paper
• Model card

Granite Instruct models

The Granite Instruct foundation models belong to the IBM Granite family of models. The granite-3-2b-instruct and granite-3-8b-instruct foundation models are third generation instruct-tuned language models for tasks like summarization, generation, coding, and more. The foundation models employ a GPT-style decoder-only architecture, with additional innovations from IBM Research and the open community.

Usage

Granite Instruct foundation models are designed to excel in instruction-following tasks such as summarization, problem-solving, text translation, reasoning, code tasks, function-calling, and more.

Available sizes

• 2 billion parameters
• 8 billion parameters

API pricing tier

• 2b: Class C1
• 8b: Class 12

For pricing details, see Table 1.

Try it out

Experiment with samples:

• Sample: Answer a question based on multiple documents with Granite 3.0
• Sample few-shot prompt: Summarize a meeting transcript in freeform mode with Granite 3.0
• Tool-calling sample

Token limits

Context window length (input + output)

• 2b: 4,096
• 8b: 4,096

Supported natural languages

English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, Chinese (Simplified).

Supported programming languages

The Granite Instruct models are trained with code written in 116 programming languages.

Instruction tuning information

The Granite Instruct models are fine tuned Granite Instruct base models trained on over 12 trillion tokens with a combination of permissively licensed open-source and proprietary instruction data.

Model architecture

Decoder

License

Terms of use

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:

• Model information
• Research paper
• 2b Model card
• 8b Model card

Granite Guardian models

The Granite Guardian foundation models belong to the IBM Granite family of models. The granite-guardian-3-2b and granite-guardian-3-8b foundation models are generation 3.0 fine-tuned Granite Instruct models designed to detect risks in prompts and responses. The foundation models help with risk detection along many key dimensions in the AI Risk Atlas.

Usage

Granite Guardian foundation models are designed to detect harm-related risks within prompt text or model response (as guardrails) and can be used in retrieval-augmented generation use cases to assess context relevance (whether the retrieved context is relevant to the query), groundedness (whether the response is accurate and faithful to the provided context), and answer relevance (whether the response directly addresses the user's query).

Available sizes

• 2 billion parameters
• 8 billion parameters

API pricing tier

• 2b: Class C1
• 8b: Class 12

For pricing details, see Table 1.

Try it out

Experiment with samples:

• Sample prompt: Classify prompts for safety with Granite

Token limits

Context window length (input + output)

• 2b: 8,192
• 8b: 8,192

Supported natural languages

English

Instruction tuning information

The Granite Guardian models are fine tuned Granite Instruct models trained on a combination of human annotated and synthetic data.

Model architecture

Decoder

License

Terms of use

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:

• Model information
• 2b Model card
• 8b Model card

Granite Code models

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

The following Granite Code foundation models are designed to respond to coding-related instructions and can be used to build coding assistants:

• granite-3b-code-instruct
• granite-8b-code-instruct
• granite-20b-code-instruct
• granite-34b-code-instruct

Available sizes

• 3 billion parameters
• 8 billion parameters
• 20 billion parameters
• 34 billion parameters

API pricing tier

Class 1. For pricing details, see Table 1.

Try it out

Experiment with samples:

• Sample prompt: Code generation
• Sample prompt: Code conversion
• Prompts for code

Token limits

Context window length (input + output)

• granite-3b-code-instruct : 128,000

  The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 8,192.

• granite-8b-code-instruct : 128,000

  The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 8,192.

• granite-20b-code-instruct : 8,192

  The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 4,096.

• granite-34b-code-instruct : 8,192

Token limits

Context window length (input + output)

• granite-3b-code-instruct : 128,000

  The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 8,192.

• granite-8b-code-instruct : 128,000

  The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 4,096.

• granite-20b-code-instruct : 8,192

  The maximum new tokens, which means the tokens generated by the foundation model per request, is limited to 4,096.

• granite-20b-code-base-schema-linking : 8,192

• granite-20b-code-base-sql-gen : 8,192

• granite-34b-code-instruct : 8,192

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

Terms of use

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 research paper
• Scaling Granite Code models research paper
• 3b Model card
• 8b Model card
• 20b Model card
• 34b Model card

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.

Size

13 billion parameters

API pricing tier

Class 2. For pricing details, see Table 2.

Try it out

Sample prompt: Arabic chat

Token limits

Context window length (input + output): 2,048

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 license

Learn more

Read the following resources:

• Research paper
• Model card (Frankfurt data center)

Llama 3.2 Instruct

The Meta Llama 3.2 collection of foundation models are provided by Meta. The llama-3-2-1b-instruct and llama-3-2-3b-instruct models are the smallest Llama 3.2 models that fit onto a mobile device. The models are lightweight, text-only models that can be used to build highly personalized, on-device agents.

For example, you can ask the models to summarize the last ten messages you received, or to summarize your schedule for the next month.

Usage

Generate dialog output like a chatbot. Use a model-specific prompt format. Their small size and modest compute resource and memory requirements enable the Llama 3.2 Instruct models to be run locally on most hardware, including on mobile and other edge devices.

Available sizes

• 1 billion parameters
• 3 billion parameters

API pricing tier

• 1b: Class C1
• 3b: Class 8

For pricing details, see Table 2.

For pricing details, see Billing details for generative AI assets.

Try it out

• Sample prompt: Converse with Llama 3
• Sample: Generate programmatic code from instructions with a zero-shot prompt

Token limits

Context window length (input + output)

• 1b: 131,072
• 3b: 131,072

The maximum new tokens, which means the tokens generated by the foundation models per request, is limited to 8,192.

Supported natural languages

English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai

Instruction tuning information

Pretrained on up to 9 trillion tokens of data from publicly available sources. Logits from the Llama 3.1 8B and 70B models were incorporated into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. In post-training, aligned the pre-trained model by using Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).

Model architecture

Decoder-only

License

• Meta Llama 3.2 Community License

Learn more

Read the following resources:

• Research paper
• Meta AI blog
• 1b Model card
• 3b Model card

Llama 3.2 Vision Instruct

The Meta Llama 3.2 collection of foundation models are provided by Meta. The llama-3-2-11b-vision-instruct and llama-3-2-90b-vision-instruct models are built for image-in, text-out use cases such as document-level understanding, interpretation of charts and graphs, and captioning of images.

Usage

Generates dialog output like a chatbot and can perform computer vision tasks including classification, object detection and identification, image-to-text transcription (including handwriting), contextual Q&A, data extraction and processing, image comparison and personal visual assistance. Uses a model-specific prompt format.

Available sizes

• 11 billion parameters
• 90 billion parameters

API pricing tier

• 11b: Class 9
• 90b: Class 10

For pricing details, see Table 2.

Try it out

• Chatting with documents and images
• Python tutorial

Token limits

Context window length (input + output)

• 11b: 131,072
• 90b: 131,072

The maximum new tokens, which means the tokens generated by the foundation models per request, is limited to 8,192. The tokens that are counted for an image that you submit to the model are not included in the context window length.

Supported natural languages

English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai with text-only inputs. English only when an image is included with the input.

Instruction tuning information

Llama 3.2 Vision models use image-reasoning adaptor weights that are trained separately from the core large language model weights. This separation preserves the general knowledge of the model and makes the model more efficient both at pretraining time and run time. The Llama 3.2 Vision models were pretrained on 6 billion image-and-text pairs, which required far fewer compute resources than were needed to pretrain the Llama 3.1 70B foundation model alone. Llama 3.2 models also run efficiently because they can tap additional compute resources for image reasoning only when the input requires it.

Model architecture

Decoder-only

License

• Meta Llama 3.2 Community License

Learn more

Read the following resources:

• Research paper
• Meta AI blog
• 11b Model card
• 90b Model card

llama-guard-3-11b-vision

The Meta Llama 3.2 collection of foundation models are provided by Meta. The llama-guard-3-11b-vision is a multimodal evolution of the text-only Llama-Guard-3 model. The model can be used to classify image and text content in user inputs (prompt classification) as safe or unsafe.

Usage

Use the model to check the safety of the image and text in an image-to-text prompt.

Size

• 11 billion parameters

API pricing tier

Class 9. For pricing details, see Table 2.

Try it out

• Tutorial: Use LLM guardrails with Llama Guard 3-11b-vision
• Chatting with documents and images
• Sample prompt: Classify prompt input for safety

Token limits

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

The maximum new tokens, which means the tokens generated by the foundation models per request, is limited to 8,192. The tokens that are counted for an image that you submit to the model are not included in the context window length.

Supported natural languages

English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai with text-only inputs. English only when an image is included with the input.

Instruction tuning information

Pretrained model that is fine-tuned for content safety classification. For more information about the types of content that are classified as unsafe, see the model card.

Model architecture

Decoder-only

License

• Meta Llama 3.2 Community License

Learn more

Read the following resources:

• Research paper
• Meta AI blog
• Model card

Llama 3.1 Instruct

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.

Available sizes

• 8 billion parameters
• 70 billion parameters
• 405 billion parameters

API pricing tier

• 8b: Class 1
• 70b: Class 2
• 405b: Class 3 (input), Class 7 (output)

For pricing details, see Table 2.

Try it out

Sample prompt: Converse with Llama 3

Token limits

Context window length (input + output)

• 8b and 70b: 131,072

• 405b: 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.

• The maximum new tokens, which means the tokens generated by the foundation models per request, is limited to 4,096.

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

• Meta Llama 3.1 Community License

Learn more

Read the following resources:

• Meta AI blog
• 8b Model card
• 70b Model card
• 405b Model card

Llama 3 Instruct

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.

These models are deprecated. See Foundation model lifecycle.

Usage

Generates dialog output like a chatbot.

Available sizes

• 8 billion parameters
• 70 billion parameters

API pricing tier

• 8b: Class 1
• 70b: Class 2

For pricing details, see Table 2.

Try it out

Sample prompt: Converse with Llama 3

Token limits

Context window length (input + output)

• 8b: 8,192
• 70b: 8,192

Note: The maximum new tokens, which means the tokens generated by the foundation models per request, is limited to 4,096.

Supported natural languages

English

Instruction tuning information

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

Decoder-only

License

META LLAMA 3 Community License

Learn more

Read the following resources:

• Meta AI blog
• 8b Model card
• 70b Model card

Llama 2 Chat

The llama-2-13b-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.

This model is deprecated. See Foundation model lifecycle.

Usage

Generates dialog output like a chatbot. Uses a model-specific prompt format.

Size

• 13 billion parameters

API pricing tier

Class 1. For pricing details, see Table 2.

Try it out

Experiment with samples:

• Sample prompt: Converse with Llama 2
• Sample prompt: Questions about an article
• Sample notebook: Use watsonx to tune Meta llama-2-13b-chat model with CFPB documents

Token limits

Context window length (input + output): 4,096

Supported natural languages

English

Instruction tuning information

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

Decoder-only

License

License

Learn more

Read the following resources:

• Research paper
• 13b Model card

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.

This model is deprecated. See Foundation model lifecycle.

Usage

Suitable for many tasks, including classification, extraction, summarization, code creation and conversion, question-answering, generation, and retreival-augmented generation in Korean.

Size

13.2 billion parameters

API pricing tier

Class 2. For pricing details, see Table 2.

Try it out

Experiment with samples:

• Sample summarization prompt
• Sample chat prompt

Token limits

Context window length (input + output): 4,096

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:

• Research paper (DPO)
• Model card (Tokyo data center)

mistral-large

Mistral Large 2 is a large language model developed by Mistral AI. 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.

For more getting started information, see the IBM watsonx.ai page on the Mistral AI website.

Usage

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.

API pricing tier

Pricing for the Mistral Large model is not assigned by a multiplier. The following special pricing tiers are used:

• Input tier: Mistral Large Input
• Output tier: Mistral Large

For pricing details, see Table 2.

Try it out

• Using Mistral AI LLMs in watsonx.ai flows engine (Tech Preview)
• Sample prompts

Token limits

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

English, French, German, Italian, Spanish, Chinese, Japanese, Korean, Portuguese, Dutch, Polish, and dozens of other languages.

Supported programming languages

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

The mistral-large foundation model is pre-trained on diverse data sets like text, codebases, and mathematical data from various domains.

Model architecture

Decoder-only

License

For terms of use, including information about contractual protections related to capped indemnification, see Terms of use.

Learn more

Read the following resources:

• Blog post for Mistral Large 2
• Model card

mixtral-8x7b-instruct-v01

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

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.

Size

46.7 billion parameters

API pricing tier

Class 1. For pricing details, see Table 2.

Try it out

Sample prompts

Token limits

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

English, French, German, Italian, Spanish

Instruction tuning information

The Mixtral foundation model is pretrained on internet data. The Mixtral 8x7B Instruct foundation model is fine-tuned to follow instructions.

Model architecture

Decoder-only

License

Apache 2.0 license

Learn more

Read the following resources:

• Research paper
• Blog post for Mixtral 8x7B
• Model card

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.

The mt0-xxl-13b foundation model is deprecated. For more information, see Foundation model lifecycle.

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.

Size

13 billion parameters

API pricing tier

Class 2. For pricing details, see Table 2.

Try it out

Experiment with the following samples:

• Sample prompts
• Sample notebook: Simple introduction to retrieval-augmented generation with watsonx.ai

Supported natural languages

Multilingual

Token limits

Context window length (input + output): 4,096

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:

• Research paper
• Model card

pixtral-12b

Pixtral 12B is a multimodal model developed by Mistral Al. The pixtral-12b foundation model is trained to understand both natural images and documents and is able to ingest images at their natural resolution and aspect ratio, providing flexibility on the number of tokens used to process an image. The foundation model supports multiple images in its long context window. The model is effective in image-in, text-out multimodal tasks and excels at instruction following.

Usage

Chart and figure understanding, document question answering, multimodal reasoning, and instruction following

Size

12 billion parameters

API pricing tier

Class 9. For pricing details, see Table 2.

Try it out

Chatting with documents and images

Token limits

Context window length (input + output): 128,000

The maximum new tokens, which means the tokens generated by the foundation models per request, is limited to 8,192.

Supported natural languages

English

Instruction tuning information

The pixtral-12b model is trained with interleaved image and text data and is based on the Mistral Nemo model with a 400 million parameter vision encoder trained from scratch.

Model architecture

Decoder-only

License

Apache 2.0 license

Learn more

Read the following resources:

• Blog post for Pixtral 12B
• Model card (Frankfurt data center)

Any deprecated foundation models are highlighted with a deprecated warning icon . For more information about deprecation, including foundation model withdrawal dates, see Foundation model lifecycle.