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Principle:Ggml org Ggml Vision Model Loading

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Summary

Loading pre-trained vision model weights (encoder, decoder, backbone) from binary files into GGML tensor structures for inference.

Theory

Vision model architectures have complex multi-component structures that must be faithfully reconstructed in memory from serialized weight files:

  • SAM (Segment Anything Model): Comprises a ViT image encoder, a prompt encoder, and a lightweight mask decoder. Each component contains distinct tensor groups (patch embeddings, transformer blocks, positional encodings, upscaling layers, hypernetwork MLPs).
  • YOLO (You Only Look Once): Built on a convolutional backbone with detection heads. Weights are stored in a contiguous binary format (or GGUF) and mapped onto sequential conv/bn layers.

The loading process must correctly associate serialized weight blobs with the appropriate model component and tensor shape.

Architecture Auto-Detection

Model variants can be inferred automatically from tensor dimensions rather than requiring explicit configuration:

n_enc_state SAM Variant
768 ViT-B (Base)
1024 ViT-L (Large)
1280 ViT-H (Huge)

This allows a single loading function to support multiple model sizes by reading a key hyperparameter and deriving all dependent architectural constants (number of heads, encoder depth, etc.).

Loading Process

The general procedure for loading a vision model involves:

  1. Reading hyperparameters: Parse model metadata (hidden dimensions, number of layers, head counts) from the file header.
  2. Allocating tensors per component: Create GGML tensors with the correct shapes for every weight matrix and bias vector in each sub-model (encoder, decoder, backbone).
  3. Loading weights: Copy serialized weight data into the allocated tensors, respecting data type and memory layout.
  4. Initializing backend buffers: Allocate a GGML backend buffer large enough to hold all tensors and transfer the loaded data into the backend-managed memory.

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Metadata

  • Last updated: 2025-05-15 12:00 GMT

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