Mistral AI launches NeMo AI model with 12 billion parameters | Llm Examples | Learn Large Language Models Online | Best llm Models | Turtles AI

Highlights:

• Mistral AI launches the NeMo AI model with 12 billion parameters.
• Context window of 128,000 tokens and advanced reasoning and coding performance.
• Introduction of Tekken tokenizer for improved compression efficiency.
• Open-source availability and integration with NVIDIA NIM microservice.

Mistral AI presents NeMo: a new AI model with 12 billion parameters and a 128,000-token context window, developed in partnership with NVIDIA, promising high performance and easy integration.

Mistral AI has announced NeMo, an AI model with 12 billion parameters, developed in collaboration with NVIDIA. This new model stands out for its extended context window of up to 128,000 tokens and its state-of-the-art performance in reasoning, world knowledge, and coding accuracy, positioning it at the top of its size category.

Weights are hosted on HuggingFace both for the base and for the instruct models.

The partnership between Mistral AI and NVIDIA has resulted in a model that not only pushes the boundaries of performance but also prioritizes ease of use. Mistral NeMo is designed to be a seamless replacement for systems currently using Mistral 7B, thanks to its reliance on standard architecture.

To promote adoption and research, Mistral AI has made both pre-trained base and instruction-tuned checkpoints available under the Apache 2.0 license. This open-source approach is likely to appeal to researchers and enterprises, potentially accelerating the model’s integration into various applications.

One of the key features of Mistral NeMo is its quantization awareness during training, enabling FP8 inference without compromising performance. This capability could prove crucial for organizations looking to deploy large language models efficiently.

"Mistral NeMo is designed for global, multilingual applications. It is trained on function calling, has a large context window, and is particularly strong in English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi," explained Mistral AI. "This is a new step toward bringing frontier AI models to everyone’s hands in all languages that form human culture."

Mistral NeMo introduces Tekken, a new tokenizer based on Tiktoken. Trained on over 100 languages, Tekken offers improved compression efficiency for both natural language text and source code compared to the SentencePiece tokenizer used in previous Mistral models. The company reports that Tekken is approximately 30% more efficient at compressing source code and several major languages, with even more significant gains for Korean and Arabic.

Mistral AI also claims that Tekken outperforms the Llama 3 tokenizer in text compression for about 85% of all languages, potentially giving Mistral NeMo an edge in multilingual applications.

The model’s weights are now available on HuggingFace for both the base and instruct versions. Developers can start experimenting with Mistral NeMo using the mistral-inference tool and adapt it with mistral-finetune. For those using Mistral’s platform, the model is accessible under the name open-mistral-nemo.

In a nod to the collaboration with NVIDIA, Mistral NeMo is also packaged as an NVIDIA NIM inference microservice, available through ai.nvidia.com. This integration could streamline deployment for organizations already invested in NVIDIA’s AI ecosystem.

The release of Mistral NeMo represents a significant step forward in the democratization of advanced AI models. By combining high performance, multilingual capabilities, and open-source availability, Mistral AI and NVIDIA are positioning this model as a versatile tool for a wide range of AI applications across various industries and research fields.