Loïc Carrère b6e4ff69b8 clip : (minicpmv) Re-enable upscaling of images smaller than the CLIP image size (#13237) 8 月之前
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android 243453533e llava : update documentations (#13055) 9 月之前
CMakeLists.txt 00e3e5a194 mtmd : add qwen2vl and qwen2.5vl (#13141) 9 月之前
README-quantize.md 1ec208083c llava: add quantization for the visual projector LLAVA, Qwen2VL (#11644) 11 月之前
README.md 8936784f7a mtmd : add **vision** support for Mistral Small 3.1 (#13231) 8 月之前
clip-impl.h 8936784f7a mtmd : add **vision** support for Mistral Small 3.1 (#13231) 8 月之前
clip-quantize-cli.cpp 1ec208083c llava: add quantization for the visual projector LLAVA, Qwen2VL (#11644) 11 月之前
clip.cpp b6e4ff69b8 clip : (minicpmv) Re-enable upscaling of images smaller than the CLIP image size (#13237) 8 月之前
clip.h 00e3e5a194 mtmd : add qwen2vl and qwen2.5vl (#13141) 9 月之前
convert_image_encoder_to_gguf.py e9b2f84f14 llava: add big-endian conversion for image encoder (#12218) 10 月之前
deprecation-warning.cpp 84a9bf2fc2 mtmd : merge llava, gemma3 and minicpmv CLI into single `llama-mtmd-cli` (#13012) 9 月之前
glmedge-convert-image-encoder-to-gguf.py 0cec062a63 llama : add support for GLM-Edge and GLM-Edge-V series models (#10573) 11 月之前
glmedge-surgery.py 0cec062a63 llama : add support for GLM-Edge and GLM-Edge-V series models (#10573) 11 月之前
llava.cpp 00e3e5a194 mtmd : add qwen2vl and qwen2.5vl (#13141) 9 月之前
llava.h 3071c0a5f2 llava : support MiniCPM-V-2.5 (#7599) 1 年之前
llava_surgery.py e235b267a2 py : switch to snake_case (#8305) 1 年之前
llava_surgery_v2.py 7a2c913e66 llava : Add Granite Vision Support (#11794) 11 月之前
minicpmv-convert-image-encoder-to-gguf.py 8352cdc87b llava : fix bug in minicpm-v code (#11513) 10 月之前
minicpmv-surgery.py 3e3357fd77 llava : support Minicpm-omni (#11289) 1 年之前
mtmd-cli.cpp 8936784f7a mtmd : add **vision** support for Mistral Small 3.1 (#13231) 8 月之前
mtmd.cpp 00e3e5a194 mtmd : add qwen2vl and qwen2.5vl (#13141) 9 月之前
mtmd.h 00e3e5a194 mtmd : add qwen2vl and qwen2.5vl (#13141) 9 月之前
qwen2_vl_surgery.py ca2bb89eac clip : Add Qwen2.5VL support (#12402) 9 月之前
qwen2vl-test.cpp 00e3e5a194 mtmd : add qwen2vl and qwen2.5vl (#13141) 9 月之前
requirements.txt d3ae0ee8d7 py : fix requirements check '==' -> '~=' (#8982) 1 年之前
test-1.jpeg 0364178ca2 clip : refactor clip_init, add tests (#12757) 9 月之前
tests.sh 8936784f7a mtmd : add **vision** support for Mistral Small 3.1 (#13231) 8 月之前

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.