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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 LTM-2-mini?

LTM-2-mini is designed to manage a context of 100 million tokens, which is roughly equivalent to about 10 million lines of code or approximately 750 full-length novels. This model utilizes a sequence-dimension algorithm that proves to be around 1000 times more economical per decoded token compared to the attention mechanism employed by Llama 3.1 405B when operating within the same 100 million token context window. Additionally, the difference in memory requirements is even more pronounced; running Llama 3.1 405B with a 100 million token context requires an impressive 638 H100 GPUs per user just to sustain a single 100 million token key-value cache. In stark contrast, LTM-2-mini only needs a tiny fraction of the high-bandwidth memory available in one H100 GPU for the equivalent context, showcasing its remarkable efficiency. This significant advantage positions LTM-2-mini as an attractive choice for applications that require extensive context processing while minimizing resource usage. Moreover, the ability to efficiently handle such large contexts opens the door for innovative applications across various fields.

Media

Media

Integrations Supported

1min.AI
302.AI
AiAssistWorks
AlphaCorp
Amazon Bedrock
Arize Phoenix
C
Clojure
EvalsOne
Fleak
GMTech
HoneyHive
Lewis
Memo AI
Mirascope
Pipeshift
PromptPal
Rust
TypeScript
Weave

Integrations Supported

1min.AI
302.AI
AiAssistWorks
AlphaCorp
Amazon Bedrock
Arize Phoenix
C
Clojure
EvalsOne
Fleak
GMTech
HoneyHive
Lewis
Memo AI
Mirascope
Pipeshift
PromptPal
Rust
TypeScript
Weave

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Open source
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

mistral.ai/news/mistral-nemo/

Company Facts

Organization Name

Magic AI

Date Founded

2022

Company Location

United States

Company Website

magic.dev/

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