Image-Text-to-Text
Transformers
Safetensors
qwen2_5_vl
qwen2.5-vl
geolocation
vision-language
conversational
text-generation-inference
Instructions to use PPKQ/HoloGeo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PPKQ/HoloGeo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="PPKQ/HoloGeo") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("PPKQ/HoloGeo") model = AutoModelForMultimodalLM.from_pretrained("PPKQ/HoloGeo", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PPKQ/HoloGeo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PPKQ/HoloGeo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PPKQ/HoloGeo", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/PPKQ/HoloGeo
- SGLang
How to use PPKQ/HoloGeo with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "PPKQ/HoloGeo" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PPKQ/HoloGeo", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "PPKQ/HoloGeo" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PPKQ/HoloGeo", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use PPKQ/HoloGeo with Docker Model Runner:
docker model run hf.co/PPKQ/HoloGeo
| base_model: Qwen/Qwen2.5-VL-7B-Instruct | |
| license: apache-2.0 | |
| library_name: transformers | |
| pipeline_tag: image-text-to-text | |
| tags: | |
| - qwen2.5-vl | |
| - geolocation | |
| - vision-language | |
| - safetensors | |
| # HoloGeo | |
| HoloGeo is a Qwen2.5-VL-7B-Instruct based vision-language model for evidence-driven image geolocation. | |
| This repository contains the merged BF16 model weights saved as `safetensors`. The LoRA adapter from the RL checkpoint has been merged into the base model, so the model can be loaded directly with `transformers`. | |
| ## Load | |
| ```python | |
| from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration | |
| model_id = "PPKQ/HoloGeo" | |
| model = Qwen2_5_VLForConditionalGeneration.from_pretrained( | |
| model_id, | |
| torch_dtype="auto", | |
| device_map="auto", | |
| ) | |
| processor = AutoProcessor.from_pretrained(model_id) | |
| ``` | |
| ## Notes | |
| - Base model: `Qwen/Qwen2.5-VL-7B-Instruct` | |
| - Checkpoint source: `RL_weights2/checkpoint-8000` | |
| - Serialization: sharded `safetensors` | |
| - Precision: BF16 | |
| The accompanying dataset is available at `https://huggingface.co/datasets/PPKQ/HoloGeo`. | |