Image-Text-to-Text
Transformers
Safetensors
English
gemma4
aerial-vlm
drone
uav
vision-language-model
aerial-imagery
scene-understanding
visual-question-answering
visual-grounding
robotics
edge-ai
structured-output
open-weights
miril
conversational
Instructions to use MirilAI/Miril-DroneVLM-2B-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MirilAI/Miril-DroneVLM-2B-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="MirilAI/Miril-DroneVLM-2B-2") 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("MirilAI/Miril-DroneVLM-2B-2") model = AutoModelForMultimodalLM.from_pretrained("MirilAI/Miril-DroneVLM-2B-2", 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 MirilAI/Miril-DroneVLM-2B-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MirilAI/Miril-DroneVLM-2B-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MirilAI/Miril-DroneVLM-2B-2", "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/MirilAI/Miril-DroneVLM-2B-2
- SGLang
How to use MirilAI/Miril-DroneVLM-2B-2 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 "MirilAI/Miril-DroneVLM-2B-2" \ --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": "MirilAI/Miril-DroneVLM-2B-2", "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 "MirilAI/Miril-DroneVLM-2B-2" \ --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": "MirilAI/Miril-DroneVLM-2B-2", "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 MirilAI/Miril-DroneVLM-2B-2 with Docker Model Runner:
docker model run hf.co/MirilAI/Miril-DroneVLM-2B-2
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| "errors": [], | |
| "expected_language_k_norm_count": 15, | |
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| "explanation": "Gemma 4 serializes K/V projections and k_norm only for the leading non-shared language layers. Later language layers reuse shared K/V states and must not be treated as missing tensors.", | |
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| "model_type": "gemma4", | |
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| "serialized_tensor_count": 1951, | |
| "serialized_weight_source": "model.safetensors.index.json" | |
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