Loïc Carrère b6e4ff69b8 clip : (minicpmv) Re-enable upscaling of images smaller than the CLIP image size (#13237) hai 8 meses
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android 243453533e llava : update documentations (#13055) hai 9 meses
CMakeLists.txt 00e3e5a194 mtmd : add qwen2vl and qwen2.5vl (#13141) hai 8 meses
README-quantize.md 1ec208083c llava: add quantization for the visual projector LLAVA, Qwen2VL (#11644) hai 11 meses
README.md 8936784f7a mtmd : add **vision** support for Mistral Small 3.1 (#13231) hai 8 meses
clip-impl.h 8936784f7a mtmd : add **vision** support for Mistral Small 3.1 (#13231) hai 8 meses
clip-quantize-cli.cpp 1ec208083c llava: add quantization for the visual projector LLAVA, Qwen2VL (#11644) hai 11 meses
clip.cpp b6e4ff69b8 clip : (minicpmv) Re-enable upscaling of images smaller than the CLIP image size (#13237) hai 8 meses
clip.h 00e3e5a194 mtmd : add qwen2vl and qwen2.5vl (#13141) hai 8 meses
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deprecation-warning.cpp 84a9bf2fc2 mtmd : merge llava, gemma3 and minicpmv CLI into single `llama-mtmd-cli` (#13012) hai 9 meses
glmedge-convert-image-encoder-to-gguf.py 0cec062a63 llama : add support for GLM-Edge and GLM-Edge-V series models (#10573) hai 11 meses
glmedge-surgery.py 0cec062a63 llama : add support for GLM-Edge and GLM-Edge-V series models (#10573) hai 11 meses
llava.cpp 00e3e5a194 mtmd : add qwen2vl and qwen2.5vl (#13141) hai 8 meses
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minicpmv-convert-image-encoder-to-gguf.py 8352cdc87b llava : fix bug in minicpm-v code (#11513) hai 10 meses
minicpmv-surgery.py 3e3357fd77 llava : support Minicpm-omni (#11289) hai 1 ano
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mtmd.h 00e3e5a194 mtmd : add qwen2vl and qwen2.5vl (#13141) hai 8 meses
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requirements.txt d3ae0ee8d7 py : fix requirements check '==' -> '~=' (#8982) hai 1 ano
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README-quantize.md

Quantizing CLIP Visual Projector

This is the tool for quantizing the CLIP visual projector model. Quantization reduces the precision of the model's weights, which can significantly decrease the model size and improve inference speed, often with minimal impact on performance.

Usage

To quantize a CLIP visual projector model, use the following command:

./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf <type>

After the quantization, the visual projector can be used freely with the existing LLAVA cli (LLAVA, Qwen2VL, etc).

Arguments

  • /path/to/ggml-model-f32.gguf: The path to the input model file in FP32 or FP16 format.
  • /path/to/ggml-model-quantized.gguf: The path where the quantized model will be saved.
  • <type>: The quantization type to apply. This should be an integer corresponding to one of the quantization types defined in the enum ggml_type.

Quantization Types

The following quantization types are supported, based on the enum ggml_type definition:

  • 2 - q4_0: 4-bit quantization with a single scale value.
  • 3 - q4_1: 4-bit quantization with a separate scale value for each block.
  • 6 - q5_0: 5-bit quantization with a single scale value.
  • 7 - q5_1: 5-bit quantization with a separate scale value for each block.
  • 8 - q8_0: 8-bit quantization with a single scale value.

Example

To quantize a model using the q4_0 quantization type, you would run:

./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf 2

This command will generate a quantized model at /path/to/ggml-model-quantized.gguf using the q4_0 quantization method.

Notes

  • Quantization can lead to a loss in model accuracy, depending on the chosen quantization type. It is recommended to evaluate the quantized model's performance on your specific task to ensure it meets your requirements.
  • The quantized model will typically be smaller in size and faster to run, making it more suitable for deployment in resource-constrained environments.