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What is Mistral NeMo?

We are excited to unveil Mistral NeMo, our latest and most sophisticated small model, boasting an impressive 12 billion parameters and a vast context length of 128,000 tokens, all available under the Apache 2.0 license. In collaboration with NVIDIA, Mistral NeMo stands out in its category for its exceptional reasoning capabilities, extensive world knowledge, and coding skills. Its architecture adheres to established industry standards, ensuring it is user-friendly and serves as a smooth transition for those currently using Mistral 7B. To encourage adoption by researchers and businesses alike, we are providing both pre-trained base models and instruction-tuned checkpoints, all under the Apache license. A remarkable feature of Mistral NeMo is its quantization awareness, which enables FP8 inference while maintaining high performance levels. Additionally, the model is well-suited for a range of global applications, showcasing its ability in function calling and offering a significant context window. When benchmarked against Mistral 7B, Mistral NeMo demonstrates a marked improvement in comprehending and executing intricate instructions, highlighting its advanced reasoning abilities and capacity to handle complex multi-turn dialogues. Furthermore, its design not only enhances its performance but also positions it as a formidable option for multi-lingual tasks, ensuring it meets the diverse needs of various use cases while paving the way for future innovations.

What is Granite Code?

Introducing the Granite series of decoder-only code models, purpose-built for various code generation tasks such as debugging, explaining code, and creating documentation, while supporting an impressive range of 116 programming languages. A comprehensive evaluation of the Granite Code model family across multiple tasks demonstrates that these models consistently outperform other open-source code language models currently available, establishing their superiority in the field. One of the key advantages of the Granite Code models is their versatility: they achieve competitive or leading results in numerous code-related activities, including code generation, explanation, debugging, editing, and translation, thereby highlighting their ability to effectively tackle a diverse set of coding challenges. Furthermore, their adaptability equips them to excel in both straightforward and intricate coding situations, making them a valuable asset for developers. In addition, all models within the Granite series are created using data that adheres to licensing standards and follows IBM's AI Ethics guidelines, ensuring their reliability and integrity for enterprise-level applications. This commitment to ethical practices reinforces the models' position as trustworthy tools for professionals in the coding landscape.

Media

Media

Integrations Supported

AnythingLLM
Arize Phoenix
BlueGPT
C
Deep Infra
Elixir
EvalsOne
Expanse
Fleak
Humiris AI
JavaScript
Kotlin
Lewis
Microsoft Foundry Agent Service
Mirascope
NVIDIA DRIVE
Ragas
Tune AI
Verta
bolt.diy

API Availability

Has API

API Availability

Pricing Information

Free
Open source
Free Version

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

mistral.ai/news/mistral-nemo/

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/granite

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Code Generators

Not specified

AI Coding Models

Not specified

AI Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

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