mlx-community/LocateAnything-3B-8bit
MLX 8-bit (~9.4 bits/weight) conversion of nvidia/LocateAnything-3B,
a vision-language model for fast, high-quality visual grounding (object detection,
referring-expression grounding, pointing, GUI/text localization). Converted with
mlx-vlm for Apple Silicon.
Grounding output is byte-identical to the bf16 model in our tests.
Requirements
Note: LocateAnything support in
mlx-vlmcurrently lives in a pull request and is not yet in a releasedmlx-vlm. Until it merges, install from the branch that adds thelocateanythingmodel:pip install "git+https://github.com/beshkenadze/mlx-vlm@feat/locateanything-3b"
Usage
python -m mlx_vlm.generate --model mlx-community/LocateAnything-3B-8bit \
--image http://images.cocodataset.org/val2017/000000039769.jpg \
--prompt "Detect all objects in the image." --max-tokens 128 --temperature 0.0
Output is structured coordinate tokens, e.g.
<ref>remote</ref><box><64><152><273><244></box> with coordinates quantized to
<0>..<1000> (normalized). Decoding modes: autoregressive (slow, default) and
Parallel Box Decoding (fast/hybrid, ~2x faster) via generation_mode.
Attribution & license
- Derived from nvidia/LocateAnything-3B — released under the NVIDIA License: non-commercial, research/academic use only (commercial use not permitted except by NVIDIA). Redistribution must retain this license and attribution.
- Vision encoder: MoonViT-SO-400M (MIT). Language model: Qwen2.5-3B-Instruct (Qwen Research License). Part of the Eagle VLM family.
The LICENSE file from the source model is included in this repo.
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8-bit
# 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("mlx-community/LocateAnything-3B-8bit") config = load_config("mlx-community/LocateAnything-3B-8bit") # 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)