Locateanything 3b BF16 Mlx

--- library_name: mlx pipeline_tag: image-text-to-text license: other license_name: nvidia-license license_link: https://huggingface.co/nvidia/LocateAnything-3B/blob/main/LICENSE base_model: - nvidia/LocateAnything-3B tags: - mlx - apple-silicon - visual-grounding - object-detection - bfloat16 --- # LocateAnything-3B BF16 for MLX Final-layout BF16 weights for running [NVIDIA LocateAnything-3B](https://huggingface.co/nvidia/LocateAnything-3B) with [`mlx-cv`](https://github.com/appautomaton/mlx-cv) on Apple Silicon. BF16 is reduced precision, not integer quantization. ```bash pip install "mlx-cv[mlx,hub]" ``` ```python from mlx_cv.models.locateanything import LocateAnythingPipeline pipeline = LocateAnythingPipeline.from_pretrained("locateanything-3b-bf16") result = pipeline.predict(image, "find every traffic sign") ``` ## Verification and performance The MLX FP32 port first passed the upstream parameter and selected-tap parity gate. The BF16 package then preserved generated tokens and output geometry on four sequential real-image checks (desktop, street signs, document, and webpage). Local peak-memory observations ranged from roughly 9.8 GB to 52.3 GB depending on image and output complexity; these are machine-specific measurements, not requirements or guarantees. One desktop multi-category prompt repeatedly emitted a monitor category. This known behavior is recorded as a model/output limitation rather than hidden by post-processing. ## Limitations - Inference only, on MLX-supported Apple Silicon systems. - Visual grounding output can omit, repeat, or mislabel objects; validate it for consequential uses. - Latency and memory vary substantially with image resolution, prompt, and requested output density. - This conversion does not change the upstream acceptable-use or license restrictions. ## License The weights retain the bundled NVIDIA License and are restricted to academic and non-profit research purposes. Commercial use is not permitted except as described by that license. `mlx-cv` code is MIT licensed separately. ## Links - [mlx-cv source](https://github.com/appautomaton/mlx-cv) - [App Automaton](https://appautomaton.github.io/) - [Upstream model](https://huggingface.co/nvidia/LocateAnything-3B)

--- library_name: mlx pipeline_tag: image-text-to-text license: other license_name: nvidia-license license_link: https://huggingface.co/nvidia/LocateAnything-3B/blob/main/LICENSE base_model: - nvidia/LocateAnything-3B tags: - mlx - apple-silicon - visual-grounding - object-detection - bfloat16 --- # LocateAnything-3B BF16 for MLX Final-layout BF16 weights for running [NVIDIA LocateAnything-3B](https://huggingface.co/nvidia/LocateAnything-3B) with [`mlx-cv`](https://github.com/appautomaton/mlx-cv) on Apple Silicon. BF16 is reduced precision, not integer quantization. ```bash pip install "mlx-cv[mlx,hub]==0.0.3" ``` ```python from mlx_cv.models.locateanything import LocateAnythingPipeline pipeline = LocateAnythingPipeline.from_pretrained("locateanything-3b-bf16") result = pipeline.predict(image, "find every traffic sign") ``` ## Verification and performance The MLX FP32 port first passed the upstream parameter and selected-tap parity gate. The BF16 package then preserved generated tokens and output geometry on four sequential real-image checks (desktop, street signs, document, and webpage). Local peak-memory observations ranged from roughly 9.8 GB to 52.3 GB depending on image and output complexity; these are machine-specific measurements, not requirements or guarantees. One desktop multi-category prompt repeatedly emitted a monitor category. This known behavior is recorded as a model/output limitation rather than hidden by post-processing. ## Limitations - Inference only, on MLX-supported Apple Silicon systems. - Visual grounding output can omit, repeat, or mislabel objects; validate it for consequential uses. - Latency and memory vary substantially with image resolution, prompt, and requested output density. - This conversion does not change the upstream acceptable-use or license restrictions. ## License The weights retain the bundled NVIDIA License and are restricted to academic and non-profit research purposes. Commercial use is not permitted except as described by that license. `mlx-cv` code is MIT licensed separately. ## Links - [mlx-cv source](https://github.com/appautomaton/mlx-cv) - [App Automaton](https://appautomaton.github.io/) - [Upstream model](https://huggingface.co/nvidia/LocateAnything-3B)

Source context: 14 downloads · 0 likes · Pipeline image-text-to-text · Library mlx · Repo appautomaton/locateanything-3b-bf16-mlx