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

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.