Use and extend the MVP-Engine MLLM data kit with explicit specs, handlers, and sample objects. Use for recipe data wiring, MLLMDataSpec construction, source resample/resolve_refs policy, schema/media/tokenization handlers, packing, guards, collation, dataloader setup, Qwen VL data support, and multimodal data extensions.
Add, review, update, and validate recipe-local sequence-parallel plans that reuse the tensor-parallel mesh in mvp-engine models.
Add, review, update, and validate recipe-local tensor-parallel plans, TP metadata postprocessors, and compatible mesh config for mvp-engine models.
Add, review, update, and validate VLM/MLLM data pipelines using the current spec/handler-based MLLMDataKit design. Use for raw schema normalization, MLLMDataSpec wiring, source resample/resolve_refs policy, media extensions, processor setup, packing, guards, collation, step estimation, and recipe integration.
Add, review, update, and validate VLM/MLLM packing behavior around the current MLLMDataKit design, including MLLMPackingSpec, MLLMPackingAssembler, MLLMPack metadata, block-causal masks, model-specific packed input preparation, token accounting, and step estimation.
Use LigerKernelKit for reusable Liger Kernel integration before model construction, covering official model-family dispatch, custom-model symbol patching via LigerPatch, module selection validation, and loss-kernel guards.
Decide where and how to wire Liger Kernel into an MVP-Engine recipe using LigerKernelKit, including official family dispatch, authoring a custom or composite model's LigerPatch map, module selection, and loss-kernel safety.
Use TokenNormedLossKit for unreduced per-token loss patching, accumulation-window global token normalization, gradient rescaling, and token loss logging in MVP-Engine recipes.