1# GLMV-EDGE
 2
 3Currently this implementation supports [glm-edge-v-2b](https://huggingface.co/THUDM/glm-edge-v-2b) and [glm-edge-v-5b](https://huggingface.co/THUDM/glm-edge-v-5b).
 4
 5## Usage
 6Build the `llama-mtmd-cli` binary.
 7
 8After building, run: `./llama-mtmd-cli` to see the usage. For example:
 9
10```sh
11./llama-mtmd-cli -m model_path/ggml-model-f16.gguf --mmproj model_path/mmproj-model-f16.gguf
12```
13
14**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
15**note**: For GPU offloading ensure to use the `-ngl` flag just like usual
16
17## GGUF conversion
18
191. Clone a GLMV-EDGE model ([2B](https://huggingface.co/THUDM/glm-edge-v-2b) or [5B](https://huggingface.co/THUDM/glm-edge-v-5b)). For example:
20
21```sh
22git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/THUDM/glm-edge-v-2b
23```
24
252. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents:
26
27```sh
28python ./tools/mtmd/glmedge-surgery.py -m ../model_path
29```
30
314. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF:
32
33```sh
34python ./tools/mtmd/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
35```
36
375. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF:
38
39```sh
40python convert_hf_to_gguf.py ../model_path
41```
42
43Now both the LLM part and the image encoder are in the `model_path` directory.