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name: data-and-resources description: "Routes MiniMind-V resource setup and ALLaVA-style parquet dataset validation, including dependencies, tokenizer files, SigLIP2 assets, native weights, sample images, and image placeholder semantics." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Data and Resources

Use this sub-skill when a user needs to prepare or audit MiniMind-V resources before training, evaluation, serving, or conversion work.

Triggers

• Checking MiniMind-V dependency expectations, especially data/vision dependencies and torch backend fit.
• Confirming local resource layout for tokenizer files, the SigLIP2 vision encoder, native .pth weights, parquet datasets, or image folders.
• Validating an ALLaVA-style MiniMind-V parquet file before training.
• Explaining how <image> placeholders become MiniMind-V image tokens.
• Diagnosing missing columns, invalid conversation JSON, unreadable image bytes, missing tokenizer/SigLIP2/model weights, or torch mismatch.

Route elsewhere

• Model class internals, tensor shapes, projector details, or architecture changes: model-architecture-and-api.
• Training commands, distributed launch, checkpoint resume, or optimizer/runtime scheduling: training.
• Generation, command-line chat, WebUI, prompt execution, or serving: inference-and-serving.
• Native .pth to/from Transformers conversion: model-export-and-format-conversion.

Workflow outline

1. Read resources-and-data for dependencies, resource tree, parquet schema, chat preprocessing, and validation workflow.
2. From a MiniMind-V checkout, verify local resources under relative paths; do not download anything unless the user explicitly approves resource acquisition.
3. For parquet validation, run the bundled validate_vlm_parquet.py helper with the user's parquet path.
4. If validation fails or resources are missing, consult troubleshooting and report the smallest concrete fix.
5. Hand off to the routed sub-skill if the user then asks to train, infer, serve, inspect model internals, or convert formats.

Key facts to preserve

• torch and torchvision are intentionally commented in MiniMind-V requirements and must be installed separately to match the host backend.
• Tokenizer assets live under model/; the image token is <|image_pad|>.
• The required SigLIP2 vision encoder directory is model/siglip2-base-p32-256-ve/.
• Native PyTorch weights normally live under out/; dataset parquet files live under dataset/.
• MiniMind-V parquet rows require conversations and image_bytes; conversations may be a JSON string or a list, and image bytes may be one binary object or a list of binary objects.