Language models are changing the way we build software, serving as a flexible orchestrator in between knowledge sources and user interfaces. Building such software comes with new challenges to improve quality, reduce latency, and prototype quickly. Today, we're announcing various advancements in this direction.

Simpler, more efficient model customization

Because large language models are rapidly finding newer and more specialised use cases, it is critical that developers are able to quickly and efficiently tailor frontier models to their specific applications. To that end, we're announcing the ability to customise any of our flagship and specialist models on La Plateforme, including Mistral Large 2 and Codestral.

Models can be customised using a base prompt, few-shot prompting, or fine-tuning, and you can bring your own dataset. Crucially, model customization follows the techniques developed by the Mistral AI science team for making strong reference models, so you can expect similar performance from your fine-tuned models. Developers can use model customization to integrate generative AI capabilities into their application with specific domain knowledge, context, or tone.