lmms-eval-guide

To add a new model to the lmms-eval framework, navigate the codebase to locate model backend files, complete the model registration process, and execute a smoke test to verify your Large Multimodal Model integration.

The best way to run benchmark tasks in lmms-eval is by executing YAML config-driven evaluations, which orchestrate the evaluation pipeline architecture for Large Multimodal Models systematically.

The HTTP eval server orchestrates non-blocking training-time evaluation, allowing Large Multimodal Model evaluations to run concurrently without interrupting the ongoing model training pipeline.

Debug evaluation pipeline failures in lmms-eval by systematically navigating the codebase architecture to trace task execution, inspect benchmark configurations, and identify model backend integration errors.

Yes, you can use lmms-eval for adding custom benchmark tasks by navigating the unified evaluation framework architecture, registering the new task components, and verifying them through the YAML config-driven execution pipeline.