Instructions to use srv-sngh/LocateAnything-3B-mlx-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use srv-sngh/LocateAnything-3B-mlx-nvfp4 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("srv-sngh/LocateAnything-3B-mlx-nvfp4")
config = load_config("srv-sngh/LocateAnything-3B-mlx-nvfp4")

# 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 srv-sngh/LocateAnything-3B-mlx-nvfp4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "srv-sngh/LocateAnything-3B-mlx-nvfp4"

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": "srv-sngh/LocateAnything-3B-mlx-nvfp4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use srv-sngh/LocateAnything-3B-mlx-nvfp4 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 "srv-sngh/LocateAnything-3B-mlx-nvfp4"

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 srv-sngh/LocateAnything-3B-mlx-nvfp4

Run Hermes

hermes

OpenClaw new

How to use srv-sngh/LocateAnything-3B-mlx-nvfp4 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "srv-sngh/LocateAnything-3B-mlx-nvfp4"

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 "srv-sngh/LocateAnything-3B-mlx-nvfp4" \
  --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 — MLX nvfp4 (for Krill)

A mixed-precision nvfp4 MLX build of nvidia/LocateAnything-3B, the NVIDIA visual-grounding VLM that locates anything in an image as bounding boxes (<box><x1><y1><x2><y2></box>, coordinates normalized to 0–1000). Runs natively on Apple Silicon via Krill — no Python, no PyTorch.

Why this build

Complete VLM, quantized end-to-end for MLX. The MoonViT (Kimi-VL) native-resolution vision tower is included and kept at full precision; only the Qwen2.5-3B text decoder is quantized, so grounding accuracy is preserved.
Grounding parity with bf16. On a synthetic check, boxes are identical to the bf16 source (e.g. a red rectangle at true 125/125/375/375 → 125/125/376/377).
Small and fast. ~3.1 GB (vs 7.8 GB bf16); ~48 tok/s decode, ~1.1 s time-to-first-box on an M-series Mac — fits any 16 GB machine with room to spare.
Native Krill runtime. Custom Swift+MLX MoonViT tower + connector, logit-parity-verified against the NVIDIA reference.

Quantization

Mixed precision produced by krill quantize --mode nvfp4:

Base: nvfp4 (4-bit, group 16) on the Qwen2.5-3B text Linears + embeddings.
Protected: 8-bit affine (group 64) on every o_proj and down_proj (quality).
MoonViT vision tower + mlp1 connector: fp (not quantized).

Usage (Krill)

# by alias (ships with Krill):
krill run locateanything-3b "Locate the red car." --image street.jpg

# or directly from this repo:
krill run srv-sngh/LocateAnything-3B-mlx-nvfp4 "Detect all people." --image crowd.jpg

Output is grounding text with boxes, e.g. <ref>the red car</ref><box><612><448><770><690></box>. Coordinates are 0–1000; scale by the image width/height to get pixels.

License & attribution

This is a derivative work of nvidia/LocateAnything-3B and is distributed under the same NVIDIA License (see LICENSE). Use is limited to non-commercial research or evaluation purposes only (NVIDIA and its affiliates excepted). The base model builds on Qwen2.5 (Qwen Research License) and MoonViT (MIT). Please cite and follow NVIDIA's original terms.