How to use from the
Use from the
MLX library
# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("MirilAI/Miril-Drone-2B-1-MLX-4bit")
config = load_config("MirilAI/Miril-Drone-2B-1-MLX-4bit")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Miril-Drone-2B-1-MLX-4bit

4-bit Apple Silicon MLX deployment variant of Miril-Drone-2B-1

Drones can talk, including on Apple Silicon.

This repository packages the 4-bit MLX 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. Miril-DroneVLM-2B-2 is the newer plain-English typed-router generation.

Interactive demo:

https://huggingface.co/spaces/MirilAI/mirilai-miril-drone-2b-1

Use

Use an MLX-VLM build that supports Gemma 4 image-text models, then pass the same prompts documented in the primary model card. Keep prompts plain and include the required JSON schema text.

Deployment Profile

MIRIL_VARIANT_PROFILE_PENDING

Complete Benchmark

MIRIL_RELEASE_METRICS_PENDING

The merged and MLX variants are evaluated on identical held-out cases. Automated scores are regression signals, not safety certification.

License

Apache License 2.0. See LICENSE and NOTICE.

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