v1-release

These playbooks provide a reproducible multimodal language model baseline for sports intelligence, with sports-specific data, evaluation, and distributed operational training and inference workflows already integrated.

• Complete sports intelligence starter kit. Covers the full lifecycle—from sports-video annotation and data preparation through SFT/LoRA training, inference, and evaluation. Its template-driven data pipeline systematically generates multiple training-example types, enabling models to be trained and evaluated across different aspects of sports understanding.

• Video- and audio-first fine-tuning with sports-relevant customization. Provides tested recipes for sports video clips with audio, rather than relying primarily on simpler image-and-text examples available in public recipes. It also exposes input-resolution and frame-sampling settings that help preserve details important to sports analysis, such as player motion, ball trajectory, court geometry, and event timing.

• Domain-specific, measurable evaluation. Includes per-question-class MCQ metrics and LLM-as-a-judge evaluation for captions and descriptive answers.

• Reproducible, scalable execution. Supplies tested distributed workflows, checkpoint conversion and parity checks, and automated inference-to-evaluation pipelines.

• Informed choice of training method and framework. Supports and compares full SFT and LoRA across NeMo AutoModel and Megatron-Bridge, helping users balance quality, memory, iteration speed, and checkpoint size. When properly tuned, Megatron-Bridge can deliver up to 3x higher training throughput than AutoModel.

• Last but not least: shared practical learnings. Shares lessons from building, debugging, optimizing, and validating multimodal training workflows that can help others fine-tune multimodal language models for sports intelligence.