Explore Mistral Nemo by MistralAI

A multilingual text model with a 128k token context window.

Input: text Output: text Context: 131,072 tokens Release: 2024-07-19

Discover Mistral Nemo, a text model developed by MistralAI in collaboration with NVIDIA, released on July 19, 2024. This model features a remarkable 128k token context window, making it highly capable for extensive text processing tasks. Supporting multiple languages such as English, French, and Chinese, it is designed for diverse linguistic applications. Mistral Nemo excels in text-to-text tasks, offering functionalities like function calling. It operates under the Apache 2.0 license, ensuring open and flexible use. With its robust design, Mistral Nemo can handle complex text generation and analysis, making it a versatile tool for developers and researchers alike.

Use Cases

Here are a few ways teams apply Mistral: Mistral Nemo (free) in practice—from fast drafting to multimodal understanding. Adapt these ideas to your workflow.

Generate multilingual content

Analyze large text datasets

Develop language-based applications

Enhance text processing workflows

Key Features

A quick look at the capabilities that make this model useful in real projects.

12B parameter text model

128k token context length

Multilingual support

Function calling capability

Apache 2.0 licensed

Specs

Overview

Vendor
mistralai

Model ID
mistralai/mistral-nemo:free

Release
2024-07-19

Modalities & context

Input
text

Output
text

Context
131,072 tokens

Parameters & defaults

Supported parameters: frequency_penalty, max_tokens, presence_penalty, repetition_penalty, seed, stop, temperature, top_k, top_p

Defaults: temperature 0.3, top_p 0.95

Benchmark tests: Mistral: Mistral Nemo (free)

We ran this model against a few representative prompts to show its range. Review the outputs below and be the judge.

Text

Prompt:

Write 150 words on how AI might positively upend work, leisure and creativity

The Mistral Nemo is a versatile model designed for natural language processing tasks, including text generation, summarization, and conversational AI applications. It is built on advanced transformer architecture, enabling it to understand and generate human-like text based on input prompts. Typical use cases for the Mistral Nemo include chatbots, content creation, and automated customer support systems. While the model demonstrates strong performance in generating coherent and contextually relevant text, it is important to note that its outputs may occasionally lack factual accuracy or exhibit biases present in the training data. Additionally, the model's effectiveness can be influenced by the quality and specificity of the input it receives. Users should consider these factors when integrating Mistral Nemo into their applications to ensure optimal results.

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up.end

/ˌəpˈend/

verb

To “upend” means to completely disrupt, overturn, or drastically change the established order or structure of something. It implies a significant shift or alteration that can potentially have far-reaching consequences. When something is upended, it is turned upside down or transformed in a way that challenges conventional norms or expectations. The term often carries a sense of innovation, transformation, and sometimes even a hint of upheaval, indicating that the changes are not just minor adjustments but rather a fundamental reimagining of the status quo.

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