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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 Falcon-7B?

The Falcon-7B model is a causal decoder-only architecture with a total of 7 billion parameters, created by TII, and trained on a vast dataset consisting of 1,500 billion tokens from RefinedWeb, along with additional carefully curated corpora, all under the Apache 2.0 license. What are the benefits of using Falcon-7B? This model excels compared to other open-source options like MPT-7B, StableLM, and RedPajama, primarily because of its extensive training on an unimaginably large dataset of 1,500 billion tokens from RefinedWeb, supplemented by thoughtfully selected content, which is clearly reflected in its performance ranking on the OpenLLM Leaderboard. Furthermore, it features an architecture optimized for rapid inference, utilizing advanced technologies such as FlashAttention and multiquery strategies. In addition, the flexibility offered by the Apache 2.0 license allows users to pursue commercial ventures without worrying about royalties or stringent constraints. This unique blend of high performance and operational freedom positions Falcon-7B as an excellent option for developers in search of sophisticated modeling capabilities. Ultimately, the model's design and resourcefulness make it a compelling choice in the rapidly evolving landscape of machine learning.

Media

Media

Integrations Supported

C
C#
C++
CSS
Clojure
Elixir
F#
HTML
JavaScript
Julia
Kotlin
LM-Kit.NET
R
Ruby
Rust
Scala
TypeScript
Visual Basic
Toolmark

Integrations Supported

C
C#
C++
CSS
Clojure
Elixir
F#
HTML
JavaScript
Julia
Kotlin
LM-Kit.NET
R
Ruby
Rust
Scala
TypeScript
Visual Basic

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
Windows
Mac
On-Prem
Linux

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

Technology Innovation Institute (TII)

Date Founded

2019

Company Location

United Arab Emirates

Company Website

www.tii.ae/

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 Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

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