Fine-tuning a multimodal model for video intelligence

In an exploration of video intelligence enhancements, a fine-tuning process was applied to a small multimodal model for Mux-specific workflows, such as generating transcript-based summaries and chapters. This model, integrated into the open-source @mux/ai SDK, demonstrated more concise and workflow-specific outputs compared to the default Mux Robots experience. The initiative involved adding Baseten as a provider, generating 10,000 synthetic JSONL training examples, and using LoRA to fine-tune the Mistral Small 3.1 model. The project highlighted the benefits of fine-tuning, such as increased privacy, control, and customization, and underscored the flexibility of @mux/ai, which allows users to bring their own API keys for various services. Although fine-tuning requires managing third-party integrations, it offers tailored solutions not available through pre-configured models like Mux Robots. The process of fine-tuning was facilitated by Baseten's training SDK, which enabled the creation of a dedicated deployment with a specific endpoint for model access. This approach, while requiring additional infrastructure setup, provided the desired control over video AI workflows, making it suitable for projects needing a nuanced developer experience.

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