Xuan-Son Nguyen ecda2ec4b3 mtmd : Support Pixtral 12B (#13065) 8 miesięcy temu
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android 243453533e llava : update documentations (#13055) 9 miesięcy temu
CMakeLists.txt 84a9bf2fc2 mtmd : merge llava, gemma3 and minicpmv CLI into single `llama-mtmd-cli` (#13012) 9 miesięcy temu
README-quantize.md 1ec208083c llava: add quantization for the visual projector LLAVA, Qwen2VL (#11644) 11 miesięcy temu
README.md ecda2ec4b3 mtmd : Support Pixtral 12B (#13065) 8 miesięcy temu
clip-impl.h ecda2ec4b3 mtmd : Support Pixtral 12B (#13065) 8 miesięcy temu
clip-quantize-cli.cpp 1ec208083c llava: add quantization for the visual projector LLAVA, Qwen2VL (#11644) 11 miesięcy temu
clip.cpp ecda2ec4b3 mtmd : Support Pixtral 12B (#13065) 8 miesięcy temu
clip.h 6602304814 llava: fix errors in clip.h on certain compilers (#13030) 9 miesięcy temu
convert_image_encoder_to_gguf.py e9b2f84f14 llava: add big-endian conversion for image encoder (#12218) 10 miesięcy temu
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glmedge-convert-image-encoder-to-gguf.py 0cec062a63 llama : add support for GLM-Edge and GLM-Edge-V series models (#10573) 11 miesięcy temu
glmedge-surgery.py 0cec062a63 llama : add support for GLM-Edge and GLM-Edge-V series models (#10573) 11 miesięcy temu
llava.cpp 0c50923944 clip : use smart pointer (⚠️ breaking change) (#12869) 9 miesięcy temu
llava.h 3071c0a5f2 llava : support MiniCPM-V-2.5 (#7599) 1 rok temu
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minicpmv-convert-image-encoder-to-gguf.py 8352cdc87b llava : fix bug in minicpm-v code (#11513) 10 miesięcy temu
minicpmv-surgery.py 3e3357fd77 llava : support Minicpm-omni (#11289) 1 rok temu
mtmd-cli.cpp 84a9bf2fc2 mtmd : merge llava, gemma3 and minicpmv CLI into single `llama-mtmd-cli` (#13012) 9 miesięcy temu
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mtmd.h b9154ecff9 mtmd : add methods to access `mtmd_image_tokens` (#12906) 9 miesięcy temu
qwen2_vl_surgery.py 4ddd199f6f llava : Allow locally downloaded models for QwenVL (#10833) 1 rok temu
qwen2vl-cli.cpp 0364178ca2 clip : refactor clip_init, add tests (#12757) 9 miesięcy temu
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test-1.jpeg 0364178ca2 clip : refactor clip_init, add tests (#12757) 9 miesięcy temu
tests.sh ecda2ec4b3 mtmd : Support Pixtral 12B (#13065) 8 miesięcy temu

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.