Third-party foundation models

Learn more

Read the following resources:

codestral-22b

The codestral-22b model is a foundation model developed by Mistral AI that is designed for code generation tasks. Codestral helps software developers write and interact with code and can be used to design advanced AI applications.

Note: You must purchase Mistral AI with IBM separately before you can install and use this model.

Usage

Use the codestral-22b model to answer any questions about a code snippet or to generate code by following specific instructions or use the Fill in the Middle (FIM) mechanism to predict tokens and complete partially written code.

Size

22 billion parameters

Try it out

Sample prompt: Code

Token limits

Context window length (input + output): 32,768 Note:

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

Supported natural languages

English

Instruction tuning information

The codestral-22b model is trained on over 80 programming languages, including popular languages such as Python, Java, C, C++, JavaScript, and Bash. The model is also works well with more specialized languages like Swift and Fortran.

Model architecture

Decoder

License

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

Learn more

Read the following resources:

codestral-2501

The codestral-2501 foundation model is a state-of-the-art coding model developed by Mistral AI. The model is based off the original codestral-22b model and has a more efficient architecture and an improved tokenizer. The codestral-2501 model performs code generation and code completion tasks approximately twice as fast as the original model.

This model was introduced with the 2.1.1 release.

Note: You must purchase Mistral AI with IBM separately before you can install and use this model.

Usage

The codestral-2501 is optimized for low-latency, high-frequency use cases and supports tasks such as fill-in-the-middle (FIM), code correction and generating test cases.

Size

22 billion parameters

Try it out

Sample prompt: Code

Token limits

Context window length (input + output): 256,000 Note:

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

Supported natural languages

English

Instruction tuning information

The codestral-2501 model is proficient in over 80 programming languages, including popular languages such as Python, Java, C, C++, JavaScript, and Bash. The model is also works well with more specialized languages like Swift and Fortran.

Model architecture

Decoder

License

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

Learn more

Read the following resources:

Blog post for Codestral 25.01

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.

Note:

When you inference this model from the Prompt Lab, disable AI guardrails.

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

Try it out

Experiment with samples:

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 dataset that was created by ELYZA. The ELYZA Tasks 100 dataset contains 100 diverse and complex tasks that were created manually and evaluated by humans. The ELYZA Tasks 100 dataset is publicly available from HuggingFace.

Model architecture

Decoder

License

Learn more

Read the following resources:

flan-t5-xl-3b

The flan-t5-xl-3b model is provided by Google on Hugging Face. The 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.

Note:

This foundation model can be tuned by using the Tuning Studio.

Usage

General use with zero- or few-shot prompts.

Size

3 billion parameters

Try it out

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 datasets used are published.

Model architecture

Encoder-decoder

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.

Size

11 billion parameters

Try it out

Experiment with samples:

Review the terms of use before you use samples from GitHub.

Token limits

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

Supported natural languages

English, German, French

Instruction tuning information

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.

Size

20 billion parameters

Try it out

Experiment with samples:

Review the terms of use before using samples from GitHub.

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 datasets used are published.

Model architecture

Encoder-decoder

License

Sample prompt: Arabic chat

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.

Note:

When you inference this model from the Prompt Lab, disable AI guardrails.

Usage

Supports Q&A, summarization, classification, generation, extraction, and translation in Arabic.

Size

13 billion parameters

Try it out

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

Sample prompt: Converse with Llama 3

Learn more

Read the following resources:

Llama 3.3 70B Instruct

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model (text in/text out) with 70 billion parameters.

The llama-3-3-70b-instruct is a revision of the popular Llama 3.1 70B Instruct foundation model. The Llama 3.3 foundation model is better at coding, step-by-step reasoning, and tool-calling. Despite its smaller size, the Llama 3.3 model's performance is similar to that of the Llama 3.1 405b model, making it a great choice for developers.

This model was introduced with the 2.1.1 release.

Usage

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

Size

70 billion parameters

Try it out

Experiment with samples:

Token limits

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

Supported natural languages

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

Instruction tuning information

Llama 3.3 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 3.3 Community License

Learn more

Read the following resources:

Llama 3.2 Instruct

The 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.

Sizes

1 billion parameters
3 billion parameters

Try it out

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:

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.

Sizes

11 billion parameters
90 billion parameters

Try it out

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 more 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:

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

Try it out

Token limits

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

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:

Llama 3.1 Instruct

Warning icon The llama-3-1-8b-instruct and llama-3-1-70b-instruct models are deprecated in the 2.1.1 release. See Foundation model lifecycle.

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.

You can use the following foundation models from the Llama 3.1 model family for fine tuning purposes only:

llama-3-1-8b
llama-3-1-70b
llama-3-1-70b-gptq

You cannot inference these models directly.

Sizes

8 billion parameters
70 billion parameters
405 billion parameters

Try it out

Sample prompt: Converse with Llama 3

Chat API sample

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:

Llama 3 Instruct

Warning icon These models are deprecated in the 2.1.0 release. See Foundation model lifecycle.

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.

Sizes

8 billion parameters
70 billion parameters

Try it out

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:

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.

Note: This foundation model can be tuned by using the Tuning Studio.

Usage

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

Size

13 billion parameters

Try it out

Experiment with samples:

Sample prompt: Converse with Llama 2

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 datasets and more than one million new examples that were annotated by humans.

Model architecture

Decoder-only

License

Learn more

Read the following resources:

llama2-13b-dpo-v7

Warning icon This model is deprecated in the 2.1.0 release. See Foundation model lifecycle.

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.

Note:

When you inference this model from the Prompt Lab, disable AI guardrails.

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

Try it out

Experiment with samples:

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

Learn more

Read the following resources:

ministral-8b-instruct

The ministral-8b-instruct foundation model is an instruction fine-tuned model developed by Mistral AI. The ministral-8b-instruct model is optimized for on-device computing, local intelligence and at-the-edge use cases. The model works well for critical applications that run on edge devices and require privacy-first inferencing.

Usage

Suitable for translation, function-calling, reasoning tasks, including text understanding and, transformation, internet-less smart assistants, local analytics, and autonomous robotics.

Size

8 billion parameters

Try it out

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, and dozens of other languages.

Supported programming languages

The ministral-8b-instruct model has been trained on several programming languages.

Instruction tuning information

The ministral-8b-instruct foundation model is trained on a large proportion of multilingual and code data.

Model architecture

Decoder-only

License

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

Learn more

Read the following resources:

mistral-large

Mistral Large 2 is a family of large language models 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 watsonx.ai page on the Mistral AI website.

Note: You must purchase Mistral AI with IBM separately before you can install and use this model.

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.

Try it out

Token limits

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

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

Supported natural languages

English, 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 datasets 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 License information.

Learn more

Read the following resources:

mistral-large-instruct-2411

The mistral-large-instruct-2411 foundation model from Mistral AI and belongs to the Mistral Large 2 family of models. The model specializes in reasoning, knowledge, and coding. The model extends the capabilities of the Mistral-Large-Instruct-2407 foundation model to include better handling of long prompt contexts, system prompt instructions, and function calling requests.

This model was introduced with the 2.1.1 release.

Note: You must purchase Mistral AI with IBM separately before you can install and use this model.

Usage

The mistral-large-instruct-2411 foundation model is multilingual, proficient in coding, agent-centric, and adheres to system prompts to aid in retrieval-augmented generation tasks and other use cases where prompts with large context need to be handled.

Size

123 billion parameters

Try it out

Sample prompts

Token limits

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

Supported natural languages

Multiple languages and is particularly strong in English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi.

Supported programming languages

The mistral-large-instruct-2411 foundation 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-instruct-2411 foundation model extends the Mistral-Large-Instruct-2407 foundation model from Mistral AI. Training enhanced the reasoning capabilities of the model. Training also focused on reducing hallucinations by fine tuning the model to be more cautious and discerning in its responses and to acknowledge when it cannot find solutions or does not have sufficient information to provide a confident answer.

License

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

Learn more

Read the following resources:

mistral-small-instruct

Mistral Small is a cost-efficient, fast, and reliable foundation model developed by Mistral AI. The mistral-small-instruct model is instruction fine-tuned and performs well in intermediate tasks that require moderate reasoning like data extraction, summarizing a document, or writing descriptions.

Note: You must purchase Mistral AI with IBM separately before you can install and use this model.

Usage

Suitable for translation, summarization, sentiment analysis, and function calling use cases.

Size

22 billion parameters

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, Chinese, Japanese, and dozens of other languages.

Supported programming languages

The Mistral Small model has been trained on several programming languages.

Instruction tuning information

The Mistral Small model is pre-trained on diverse datasets 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 License information.

Learn more

Read the following resources:

mistral-small-24b-instruct-2501

Mistral Small 3 is a cost-efficient, fast, and reliable foundation model developed by Mistral AI. The mistral-small-24b-instruct-2501 model is instruction fine-tuned and performs well in tasks that require some reasoning ability, such as data extraction, summarizing a document, or writing descriptions. Built to support agentic application, with adherence to system prompts and function calling with JSON output generation.

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

This model was introduced with the 2.1.1 release.

Usage

Suitable for conversational agents and function calling.

Try it out

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, Chinese, Japanese, Korean, Portuguese, Dutch, Polish, and dozens of other languages.

Supported programming languages

The mistral-small-24b-instruct-2501 model has been trained on over 80 programming languages including Python, Java, C, C++, JavaScript, Bash, Swift, and Fortran.

Instruction tuning information

The mistral-small-24b-instruct-2501 foundation model is pre-trained on diverse datasets like text, codebases, and mathematical data from various domains.

Model architecture

Decoder-only

License

Learn more

Read the following resources:

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

Try it out

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

Learn more

Read the following resources:

mt0-xxl-13b

Warning icon This model is deprecated in the 2.1.0 release. See Foundation model lifecycle.

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.

Size

13 billion parameters

Try it out

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 datasets.

Model architecture

Encoder-decoder

License

Chatting with documents and images

Learn more

Read the following resources:

pixtral-12b

Pixtral 12B is a multimodal model developed by Mistral AI. 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

Try it out

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