Mistral AI investigated a vLLM memory leak in a disaggregated Prefill/Decode deployment of Mistral Medium 3.1. System memory grew linearly at 400 MB per minute, causing out-of-memory failures within hours. It occurred only on the decode side with graph compilation and KVCache transfer via NIXL. Heaptrack showed stable heap memory; the issue was outside the heap.
Mistral fine-tuned Pixtral-12B with LoRA, substantially outperforming the untuned baseline on Aerial Image Dataset (AID) satellite classification and reducing hallucinated invalid class names. Fine-tuning uses Mistral's API or LaPlateforme UI without extensive hyperparameter tuning, with 8,000 training and 2,000 test samples.
Mistral proposes judge LLMs scoring generator responses on numerical, binary or qualitative scales, then taking weighted averages across evaluation datasets to assess RAG systems.
Mistral AI introduced TranscriptToPRDTicket, comprising PRDAgent and TicketCreationAgent powered by Mistral Large 2. It turns meeting transcripts into PRDs and development tickets, automatically creating them in tools such as Linear and Jira.