Image-Text-to-Text
Transformers
Safetensors
English
qwen2_5_vl
VLM
LLM
DriveFusion
Vision
MultiModal
conversational
text-generation-inference
Instructions to use DriveFusion/DriveFusionQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DriveFusion/DriveFusionQA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="DriveFusion/DriveFusionQA") 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("DriveFusion/DriveFusionQA") model = AutoModelForMultimodalLM.from_pretrained("DriveFusion/DriveFusionQA") 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 DriveFusion/DriveFusionQA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DriveFusion/DriveFusionQA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DriveFusion/DriveFusionQA", "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/DriveFusion/DriveFusionQA
- SGLang
How to use DriveFusion/DriveFusionQA 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 "DriveFusion/DriveFusionQA" \ --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": "DriveFusion/DriveFusionQA", "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 "DriveFusion/DriveFusionQA" \ --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": "DriveFusion/DriveFusionQA", "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 DriveFusion/DriveFusionQA with Docker Model Runner:
docker model run hf.co/DriveFusion/DriveFusionQA
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## ⚠️ Limitations
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* **Hallucination:** Like all VLMs, it may occasionally misinterpret distant objects or complex social traffic cues.
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* **Geographical Bias:** Performance may vary in regions or weather conditions not heavily represented in the training data.
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* **Non-Control:** This model is for **reasoning and explanation**, not for direct vehicle control.
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## ⚠️ Limitations
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* **Hallucination:** Like all VLMs, it may occasionally misinterpret distant objects or complex social traffic cues.
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* **Geographical Bias:** Performance may vary in regions or weather conditions not heavily represented in the training data.
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* **Non-Control:** This model is for **reasoning and explanation**, not for direct vehicle control.
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## Citation
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``` bibtex
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@misc{drivefusionqa2026,
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title={DriveFusionQA: A Vision Language Language Model for Autonomous Driving},
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author={Samir, Omar and DriveFusion Team},
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year={2026},
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url={https://huggingface.co/DriveFusion/DriveFusionQA}
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}
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