Instructions to use appautomaton/locateanything-3b-bf16-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use appautomaton/locateanything-3b-bf16-mlx with MLX:

# 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("appautomaton/locateanything-3b-bf16-mlx")
config = load_config("appautomaton/locateanything-3b-bf16-mlx")

# 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)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use appautomaton/locateanything-3b-bf16-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "appautomaton/locateanything-3b-bf16-mlx"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "appautomaton/locateanything-3b-bf16-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use appautomaton/locateanything-3b-bf16-mlx with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "appautomaton/locateanything-3b-bf16-mlx"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default appautomaton/locateanything-3b-bf16-mlx

Run Hermes

hermes

OpenClaw new

How to use appautomaton/locateanything-3b-bf16-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "appautomaton/locateanything-3b-bf16-mlx"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "appautomaton/locateanything-3b-bf16-mlx" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

LocateAnything-3B BF16 for MLX

Final-layout BF16 weights for running NVIDIA LocateAnything-3B with mlx-cv on Apple Silicon. BF16 is reduced precision, not integer quantization.

pip install "mlx-cv[mlx,hub]==0.0.3"

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.

Model tree for appautomaton/locateanything-3b-bf16-mlx

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

nvidia/LocateAnything-3B

Finetuned

(7)

this model

appautomaton
/

locateanything-3b-bf16-mlx

LocateAnything-3B BF16 for MLX

Verification and performance

Limitations

License

Links

Model tree for appautomaton/locateanything-3b-bf16-mlx