France is striving to establish itself as a global powerhouse in artificial intelligence, driven by strategic investments and robust policy initiatives. The country's journey towards AI leadership began in 2017, with significant government funding aimed at fostering innovation and supporting AI startups. Since then, the number of AI startups in France has more than doubled, and the country has since launched a €500M AI strategy aimed at advancing the creation of new AI tech.

The establishment of Mistral AI in Paris in 2023, followed by its swift global adoption, is certainly strengthening France's position in the field of artificial intelligence. Mistral AI quickly gained international recognition for its advanced large language models (LLMs). Within months of its launch, Mistral AI had already secured major partnerships and deployments, for example with Microsoft, further cementing France's reputation as a hub for AI excellence.

Who are Mistral AI?

Mistral AI is a French-born artificial intelligence company, offering a host of cutting-edge, large language models. Increasing numbers of businesses across sectors are opting for Mistral’s models, which represent a significant advancement in artificial intelligence capabilities through their scalable, efficient, and high-performance solutions. The size of Mistral models certainly make them stand out in the industry: for example, Mistral Large is built on a model architecture with tens of billions of parameters. Other models, like Mistral 7B and Mixtral 7x8B are trained on 7 and 8 billion respectively, and Mistral’s latest state-of-the-art open-source model, Mistral 8x22B, is trained on a huge 130B parameters.

Like other Generative AI models on the market, Mistral’s models are designed to process and produce high-quality, nuanced, human-like text. Mistral offers a comprehensive suite of LLMs, each suited to distinct business operations, pricing models, and use cases. For example, Mistral 7B is optimised to deliver high-performance, high-accuracy output without requiring overly high computational costs. Comparatively, Mistral Large, their flagship model, delivers exceptional reasoning capabilities on a higher number of parameters, and is designed for more complex problem solving.

What architecture are the Mistral models built on?

Like Meta’s LLaMA models, Mistral’s LLMs are built on neural network architectures, like transformer models which are optimised for training large datasets and complex language patterns. These architectures enable the models to learn contextual relationships within text, allowing for more accurate and coherent text generation. Mistral’s models are also trained on extensive and diverse datasets, refining their language understanding capabilities, making them highly versatile for various NLP tasks such as translation, summarisation, and question answering.

Mistral’s architecture is optimised for efficiency in both training and inference, including techniques such as mixed-precision training and model distillation. For example, model distillation is a technique used in machine learning to transfer knowledge from complex models to more simple models. This technique takes the form of a teacher/student relationship, and aims to create a model that is more efficient than - and just as high-performance in its output as - a very large model, but with reduced computational requirements. Model distillation helps in creating smaller, faster, and more efficient models that are easier to deploy in real-world applications without significantly sacrificing accuracy. 

What data are Mistral models trained on?

Mistral’s pre-training techniques align with standard industry practices to build state-of-the-art LLMs. Its models are trained on a range of internet-sourced, publicly-available datasets that most likely take the form of web text, scientific articles, books, social media, and more. Utilising high quality datasets, Mistral can ensure that their models can generate high-performance, nuanced, and industry-specific outputs.

Mistral models are trained on multilingual datasets. They have an understanding of semantic and grammatical structures across a variety of languages, including English, French, Spanish, German and Italian, giving it multilingual capabilities for international businesses. Utilising cross-lingual transfer learning techniques, this model can transfer knowledge from one language to another. One of these techniques is called language embedding normalisation. Language embedding normalisation essentially maps language-specific embeddings into one shared embedding space, leveraging knowledge from one language in order to better understand linguistic structures of a different but similar language. Techniques like language embedding normalisation can provide a range of benefits for businesses, particularly when it comes to adaptability and scalability.

What are the benefits of Mistral models for businesses?

Mistral models can be trained on vast quantities of data, setting them apart in terms of their potential for scalability, their robustness, and their versatility. For example, Mistral Large’s context window of 32K tokens is exceptionally large, allowing this model to recall information across huge datasets.

Unlike other models on the market, Mistral’s models are distinguished by their incorporation of a variety of techniques aimed at continuous optimisation, encompassing efforts to reduce costs and minimise energy consumption. In light of the growing focus on fostering sustainable and green AI practices, Mistral prioritises environmental responsibility in its approach. For instance, its algorithms are crafted to maximise computational efficiency, thereby lowering energy requirements and minimising carbon emissions, particularly in models like Mistral Small and Mistral Tiny.

Moreover, Mistral’s models also demonstrate impressive capabilities to perform mathematical and coding tasks, empowering developers and engineers to deploy Mistral’s technology for debugging, code generation, and software development.

One of Mistral's core pieces of messaging is their cost efficiency compared to competitors. Currently, Mistral Large is just 80% of the cost of GPT-Turbo. Using techniques like the aforementioned mixed precision training and model distillation, Mistral models can be trained faster and with reduced memory requirements, which lowers the overall cost of training and inference. They also achieve cost efficiency through model compression, efficient inference, and distributed training, all of which are techniques that aim to lower computational costs and optimise performance.

What are the limitations of Mistral models?

Like with any LLM, Mistral’s models have the potential to present data privacy concerns for businesses. Training models on sensitive or private data risks the security of that information, and there is the ongoing risk that models can learn and reproduce biases, or sensitive information. When choosing an LLM for any business need, organisations should be aware of the risks surrounding the leakage or misuse of their sensitive data. Therefore, deciding between a public and a private LLM is the first crucial step in choosing a model for your business, before examining the models available on the market and how they can best safeguard your data.

Finally, deploying an LLM, despite optimisation efforts, is a costly and computationally-demanding exercise, translating to high energy consumption. This presents a concern both from an environmental and an economical perspective. Therefore, cost factors should play a crucial role in a company’s ongoing AI strategy. Clairo AI is committed to providing the industry’s leading models to businesses without sacrificing cost or undermining their values surrounding sustainability. 

What specific business tasks can Mistral be used for?

All of Mistral’s models are designed to perform an array of natural language tasks using precise language processing and manipulation. These tasks can include content-generation, text-summarisation, sentiment-analysis, or creating conversational agents. However, depending on the Mistral model your business chooses to adopt, the use cases can vary quite significantly. For example, Mistral Small and Mistral Large offer quite varied business cases due to their difference in parameters.

With its high computational power and state-of-the-art reasoning capabilities, Mistral Large can be deployed for:

• In-depth research and simulations across scientific fields
• Multi-purpose content generation delivered at very high volumes 
• Business strategising using data analysis and predictive technologies

On the other hand, for companies with lower pricing models and less-demanding projects, Mistral Small can offer a good intermediary solution. This model could be used for:

• Virtual assistants and chatbots for mobile applications
• Real-time text analysis, such as social media monitoring or market analysis

Understanding the size and parameters of a model is essential for tailoring it to your specific use cases.

With Clairo AI's platform, users can unlock and leverage the full potential of Mistral’s LLMs, as well as other leading models like LLaMA and OpenAI. Discover how you can use Clairo AI’s closed-loop platform to apply your preferred LLM to your specific business needs.