[Important] Added README to the Qwen2VL implementation

examples/llava/README-qwen2vl.md

@@ -0,0 +1,62 @@
+# QWEN2-VL
+
+This implementation supports all versions of Qwen2VL, e.g. [Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct).
+
+## Usage
+
+After building, run `./llama-qwen2vl-cli` to use it. Or you can also get the ready one on Huggingface, e.g. [Qwen2-VL-2B-Instruct-GGUF](https://huggingface.co/bartowski/Qwen2-VL-2B-Instruct-GGUF) :
+
+### The basic one for running with an image and a prompt
+
+```sh
+./bin/llama-qwen2vl-cli -m /models/Qwen2-VL-2B-Instruct-Q4_0.gguf --mmproj /models/mmproj-Qwen2-VL-2B-Instruct-f32.gguf -p 'Describe this image.' --image '/models/test_image.jpg'
+```
+
+The image argument is optional in case you just want to use the model for text. However, the mmproj still has to be there as it will be loaded.
+
+Without defining the system prompt in the prompt, it will default to `You are a helpful assistant.`.
+
+### Or if you want the image to be directly in the prompt as a base64
+
+```sh
+./llama-qwen2vl-cli -m /models/Qwen2-VL-2B-Instruct-Q4_0.gguf --mmproj /models/mmproj-Qwen2-VL-2B-Instruct-f32.gguf -p '<img src="{base64}">Describe this image.'
+```
+
+### Or a complete prompt with the system message
+
+```sh
+./llama-qwen2vl-cli -m /models/Qwen2-VL-2B-Instruct-Q4_0.gguf --mmproj /models/mmproj-Qwen2-VL-2B-Instruct-f32.gguf -p '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|vision_pad|><|vision_end|>Describe this image.' --image '/models/test_image.jpg'
+```
+
+**Note**: A lower temperature like 0.1 is recommended for better quality. Add `--temp 0.1` to the command to do so.  
+**Note**: For GPU offloading, ensure to use the `-ngl` flag as usual.
+
+## GGUF Conversion
+
+1. Clone the Qwen2-VL model:
+
+```sh
+git clone https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct
+```
+
+2. Use `qwen2_vl_surgery.py` to prepare the model for conversion:
+
+```sh
+python ./examples/llava/qwen2_vl_surgery.py ./model_path --data_type fp32
+```
+
+It will generate the vision model, and output the filename in the log.
+
+3. Use `examples/convert_hf_to_gguf.py` to convert the Qwen2-VL model to GGUF:
+
+```sh
+python convert_hf_to_gguf.py ./model_path -outtype f32
+```
+
+Now the model is ready to use in the `model_path` directory. You can quantize them as you normally would with other GGUF files.
+
+*Have fun with the models ! :)*
+
+## Limitations
+
+* Currently, only support the image to be in the very beginning of the input prompt to the LLM.

examples/llava/qwen2vl-cli.cpp

@@ -524,7 +524,7 @@ int main(int argc, char ** argv) {
 
     common_init();
 
-    if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
+    if (params.mmproj.empty()) {
         print_usage(argc, argv);
         return 1;
     }
@@ -547,6 +547,15 @@ int main(int argc, char ** argv) {
         llava_image_embed_free(image_embed);
         ctx_llava->model = NULL;
         llava_free(ctx_llava);
+    } else if (params.image.empty()) {
+        auto ctx_llava = llava_init_context(&params, model);
+
+        // process the prompt
+        process_prompt(ctx_llava, nullptr, &params, params.prompt);
+
+        llama_perf_context_print(ctx_llava->ctx_llama);
+        ctx_llava->model = NULL;
+        llava_free(ctx_llava);
 #ifndef NDEBUG
     } else if (params.image[0].empty()) {
         auto ctx_llava = llava_init_context(&params, model);