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mtmd : rename llava directory to mtmd (#13311)

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* mv llava to mtmd

* change ref everywhere
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Xuan-Son Nguyen 2025-05-05 16:02:55 +02:00 • committed by GitHub
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12
docs/multimodal/MobileVLM.md
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@ -33,13 +33,13 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336
2. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
```sh
python ./tools/llava/llava_surgery.py -m path/to/MobileVLM-1.7B
python ./tools/mtmd/llava_surgery.py -m path/to/MobileVLM-1.7B
```
3. Use `convert_image_encoder_to_gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
```sh
python ./tools/llava/convert_image_encoder_to_gguf.py \
python ./tools/mtmd/convert_image_encoder_to_gguf.py \
-m path/to/clip-vit-large-patch14-336 \
--llava-projector path/to/MobileVLM-1.7B/llava.projector \
--output-dir path/to/MobileVLM-1.7B \
@ -47,7 +47,7 @@ python ./tools/llava/convert_image_encoder_to_gguf.py \
```
```sh
python ./tools/llava/convert_image_encoder_to_gguf.py \
python ./tools/mtmd/convert_image_encoder_to_gguf.py \
-m path/to/clip-vit-large-patch14-336 \
--llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \
--output-dir path/to/MobileVLM-1.7B_V2 \
@ -69,10 +69,10 @@ Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directo
## Android compile and run
### compile
refer to `tools/llava/android/build_64.sh`
refer to `tools/mtmd/android/build_64.sh`
```sh
mkdir tools/llava/android/build_64
cd tools/llava/android/build_64
mkdir tools/mtmd/android/build_64
cd tools/mtmd/android/build_64
../build_64.sh
```
### run on Android

4
docs/multimodal/glmedge.md
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@ -25,13 +25,13 @@ git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/T
2. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents:
```sh
python ./tools/llava/glmedge-surgery.py -m ../model_path
python ./tools/mtmd/glmedge-surgery.py -m ../model_path
```
4. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF:
```sh
python ./tools/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
python ./tools/mtmd/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
```
5. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF:

12
docs/multimodal/llava.md
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@ -37,19 +37,19 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336
2. Install the required Python packages:
```sh
pip install -r tools/llava/requirements.txt
pip install -r tools/mtmd/requirements.txt
```
3. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
```sh
python ./tools/llava/llava_surgery.py -m ../llava-v1.5-7b
python ./tools/mtmd/llava_surgery.py -m ../llava-v1.5-7b
```
4. Use `convert_image_encoder_to_gguf.py` to convert the LLaVA image encoder to GGUF:
```sh
python ./tools/llava/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
python ./tools/mtmd/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
```
5. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF:
@ -69,12 +69,12 @@ git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
2) Install the required Python packages:
```sh
pip install -r tools/llava/requirements.txt
pip install -r tools/mtmd/requirements.txt
```
3) Use `llava_surgery_v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
```console
python tools/llava/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/
python tools/mtmd/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/
```
- you will find a llava.projector and a llava.clip file in your model directory
@ -88,7 +88,7 @@ curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.jso
5) Create the visual gguf model:
```console
python ./tools/llava/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
python ./tools/mtmd/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
```
- This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP

4
docs/multimodal/minicpmo2.6.md
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@ -29,8 +29,8 @@ cmake --build build --config Release
Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) by us)
```bash
python ./tools/llava/minicpmv-surgery.py -m ../MiniCPM-o-2_6
python ./tools/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4
python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-o-2_6
python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4
python ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model
# quantize int4 version

4
docs/multimodal/minicpmv2.5.md
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@ -28,8 +28,8 @@ cmake --build build --config Release
Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) by us)
```bash
python ./tools/llava/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
python ./tools/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 2
python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 2
python ./convert_hf_to_gguf.py ../MiniCPM-Llama3-V-2_5/model
# quantize int4 version

4
docs/multimodal/minicpmv2.6.md
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@ -28,8 +28,8 @@ cmake --build build --config Release
Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) by us)
```bash
python ./tools/llava/minicpmv-surgery.py -m ../MiniCPM-V-2_6
python ./tools/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 3
python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-V-2_6
python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 3
python ./convert_hf_to_gguf.py ../MiniCPM-V-2_6/model
# quantize int4 version