---

name: model-architecture-and-api description: "Routes MiniMind-V model architecture, VLM API, config, visual token flow, MoE, and generation-semantics questions without launching training or inference services." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Model Architecture and API

Use this sub-skill when the user asks about MiniMind-V source APIs or model design.

Route here

• VLMConfig, MiniMindVLM, MMVisionProjector, MiniMindConfig, MiniMindForCausalLM, image2tensor, get_image_embeddings, count_vision_proj, forward, or generate semantics.
• MiniMind base config knobs: hidden size, layer count, attention heads, RoPE/YaRN, vocabulary size, dense/MoE switches.
• How image placeholders become visual embeddings and why 64 <|image_pad|> tokens are needed.
• Multi-image pixel_values shape questions and num_return_sequences image-repeat behavior.

Route elsewhere

• Training schedules, checkpoint/resume, or freezing strategy: training.
• Download/resource layout: data-and-resources.
• CLI inference/WebUI: inference-and-serving.
• Export/conversion: model-export-and-format-conversion.

Operating procedure

1. Read API reference for signatures, config defaults, tensor shapes, and function caveats.
2. Read architecture notes for visual-token flow, dense/MoE variants, and generation flow.
3. Read troubleshooting for missing vision encoder, placeholder, shape, cache, and MoE mismatch failures.
4. If live confirmation is needed, run inspect_minimind_vlm_api.py with --repo-root pointing at a MiniMind-V checkout. The helper imports source modules and prints signatures/defaults; it does not load weights or download resources.

Key facts

• VLMConfig(image_special_token='<|image_pad|>', image_ids=[12], **kwargs) inherits MiniMind config and defaults to image_hidden_size=768, image_token_len=64.
• MiniMindVLM(config=None, vision_model_path='./model/siglip2-base-p32-256-ve') builds the language model, tries to attach SigLIP2, and always creates the projector.
• The vision projector is LayerNorm -> Linear -> GELU -> Linear, mapping SigLIP hidden features into MiniMind hidden size without token-count resampling.
• SigLIP2 P32 with fixed 256x256 inputs yields 64 patch tokens; MiniMind-V represents one image as 64 <|image_pad|> tokens.
• Dense and MoE variants share the VLM wrapper; use_moe changes the MiniMind language backbone feed-forward blocks.