merge-ov2

To merge a standalone ViT encoder and LLM, use the merge_ov2 tool to remap and combine weights into a unified LlavaOnevision2 HuggingFace checkpoint. It applies remap and validate tooling to ensure the merged weights and inferences remain consistent with the originals.

The best way to validate merged multimodal weights is to use end-to-end validation tooling that compares the merged checkpoint's weights and inference outputs directly against the original standalone ViT encoder and LLM components to ensure full parity.

Yes, you need a GPU-enabled container to merge vision and language models. The merge process requires a GPU environment and a correct path layout under /workspace/LLaVA-OneVision-2 to successfully produce a validated merged checkpoint.

To convert standalone encoders for Megatron-style model composition, use the provided guidance and optional verification steps during the merge process. This ensures the standalone ViT encoder and LLM are correctly remapped into the unified checkpoint format.

A merged LlavaOnevision2 checkpoint produces inconsistent inference results if the remap and validate tooling was not applied correctly. Running end-to-end validation comparing outputs against the original ViT and LLM helps identify and resolve weight mismatches.