Mistral large

Accelerate your AI journey with Azure AI model catalog: Sharmila Chokalingam

Feb 6, 2025 · 23:14

Shubhi and Sharmila present the Azure AI model catalog as a platform offering over 1,600 models including GPT-4, Mistral, Llama, Cohere, and Phi3, with a standardized inference API enabling easy model swapping. They demonstrate deployment via serverless API (pay-per-token) and managed compute, emphasizing that customer prompts and completions are not shared with model providers or used for training. The platform includes model benchmarks, playground for RAG, and Prompt Flow for building generative AI apps with evaluation and variant comparison. Customer success stories include EY's EYQ chat adopted by 275,000 employees, CMA CGM's reduced response latency using Mistral, and Bridgestone's 30% reduction in forecasting errors with Nixtla's TimeGen.

Creating and scaling your own custom copilots with Azure AI Studio: Hanchi Wang

Feb 6, 2025 · 24:21

Hanchi Wang, Software Engineer Lead at Azure AI, introduces Azure AI Studio and Promptflow for creating and scaling custom copilots, focusing on tracing, evaluation, and monitoring. He demonstrates a chatbot app that uses the Assistant API with a sales data insight tool (natural language to SQL) and a code interpreter. With Promptflow's trace decorator, developers capture inputs, outputs, and LLM interactions, viewable in a local UI and shareable via Azure AI Studio. For evaluation, he shows synthetic test data sets, content safety evaluators, and custom evaluators like execution time, error rate, and SQL similarity, comparing models such as GPT-4 Turbo, Mistral large, and Phi-3. In production, monitoring dashboards in Application Insights track model duration, token usage (prompt vs. completion), and failure rates, enabling engineers to optimize performance and cost.

Decoding Mistral AI's Large Language Models: Devendra Chaplot

Nov 21, 2024 · 18:16

Devendra Singh Chaplot of Mistral AI details the company's open-source large language models, including Mistral 7B, Mixtral 8x7B, Mixtral 8x22B, and CodeStral 22B, arguing that open models complement rather than compete with profit by serving as branding tools and driving customer acquisition for proprietary upgrades. He explains the three-stage LLM training process—pre-training on trillions of tokens, instruction tuning with prompt-response pairs, and learning from human feedback via preference optimization—emphasizing that more data does not guarantee better performance due to noise. The episode highlights Mistral's focus on optimizing the performance-to-cost ratio, with CodeStral 22B outperforming larger models like Code LLaMA 70B while being smaller and multilingual across 80+ programming languages. Practical guidance is given: prototype with high-end commercial models, then fine-tune open models for specific tasks to balance performance and cost.