Instructions to use MirilAI/Miril-Drone-2B-1-bnb8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MirilAI/Miril-Drone-2B-1-bnb8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="MirilAI/Miril-Drone-2B-1-bnb8")
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-Drone-2B-1-bnb8")
model = AutoModelForMultimodalLM.from_pretrained("MirilAI/Miril-Drone-2B-1-bnb8")
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-Drone-2B-1-bnb8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MirilAI/Miril-Drone-2B-1-bnb8"
# 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-Drone-2B-1-bnb8",
		"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-Drone-2B-1-bnb8

SGLang

How to use MirilAI/Miril-Drone-2B-1-bnb8 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-Drone-2B-1-bnb8" \
    --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-Drone-2B-1-bnb8",
		"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-Drone-2B-1-bnb8" \
        --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-Drone-2B-1-bnb8",
		"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-Drone-2B-1-bnb8 with Docker Model Runner:
```
docker model run hf.co/MirilAI/Miril-Drone-2B-1-bnb8
```

Miril-Drone-2B-1-bnb8

8-bit CUDA deployment variant of Miril-Drone-2B-1

Drones can talk, and this is the lower-memory CUDA path.

This repository packages the 8-bit bitsandbytes variant of Miril-Drone-2B-1, a 2B-class aerial VLM for drone-view imagery.

Use the primary model card for behavior, prompting, schemas, examples, WALDO vocabulary, limitations, and safety notes:

https://huggingface.co/MirilAI/Miril-Drone-2B-1

The V1 prompt contract is the same as the main model: caption_v1, simple_answer_v1, and operational_coordinate_v2. V1 operational coordinates are rough representative grid cues for review, not flight-control commands. V2 is coming soon with finer pointing behavior.

Interactive demo:

https://huggingface.co/spaces/MirilAI/Miril-Drone-2B-1-Demo

Use

python eval_generate.py \
  --model_id MirilAI/Miril-Drone-2B-1-bnb8 \
  --processor_id MirilAI/Miril-Drone-2B-1-bnb8 \
  --jsonl eval.jsonl \
  --image_root images \
  --out_jsonl predictions.jsonl \
  --batch_size 1 \
  --max_new_tokens 256

Recommended starting point: CUDA GPU with roughly 10-13 GB free VRAM for single-image inference. Leave extra room for larger images, longer generations, batching, or server overhead.

License

Apache License 2.0. See LICENSE and NOTICE.

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Safetensors

Model size

5B params

Tensor type

F32

BF16

Model tree for MirilAI/Miril-Drone-2B-1-bnb8

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

MirilAI/Miril-Drone-2B-1

Quantized

(5)

this model