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name: model-export-and-format-conversion description: "Routes MiniMind-V checkpoint export, Transformers layout inspection, and reverse conversion limitation tasks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Model Export and Format Conversion

Use this sub-skill when a user asks how to convert MiniMind-V native PyTorch *.pth checkpoints into a Transformers-style directory, inspect an exported directory without loading large weights, diagnose export metadata, or understand reverse conversion limits.

Route here for

• Native MiniMind-V out/*.pth checkpoint to Transformers export plans.
• Dense versus MoE export naming and VLMConfig choices.
• Export directory inspection: config.json, tokenizer files, weight shards, auto_map, model_type, tie_word_embeddings, and Transformers 5 metadata.
• Reverse conversion from a Transformers directory to a PyTorch state_dict.
• Explaining why a Transformers directory may load as a text model while still lacking a usable image encoder for VLM inference.

Route elsewhere

• Running or serving exported models: inference-and-serving.
• Resource acquisition or environment setup: data-and-resources.
• Architecture internals: model-architecture-and-api.
• Training that produces native weights: training.

Read order

1. Read conversion workflows.
2. Read export layout before inspecting or packaging an export.
3. Read troubleshooting for conversion/load/tokenizer/vision issues.
4. Run inspect_transformers_export.py for safe static inspection without loading weights.

Safety

Full conversion needs actual checkpoint weights, tokenizer files, MiniMind-V model classes, and the SigLIP2 vision encoder in the user's checkout. Do not claim conversion has run unless those resources were present and the command actually executed.