Instructions to use SandLogicTechnologies/LocateAnything-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SandLogicTechnologies/LocateAnything-3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/LocateAnything-3B-GGUF",
	filename="LocateAnything-3B_IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SandLogicTechnologies/LocateAnything-3B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M

Use Docker

docker model run hf.co/SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M

LM Studio
Jan

vLLM

How to use SandLogicTechnologies/LocateAnything-3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SandLogicTechnologies/LocateAnything-3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandLogicTechnologies/LocateAnything-3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M

Ollama
How to use SandLogicTechnologies/LocateAnything-3B-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M
```

Unsloth Studio

How to use SandLogicTechnologies/LocateAnything-3B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/LocateAnything-3B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/LocateAnything-3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SandLogicTechnologies/LocateAnything-3B-GGUF to start chatting

How to use SandLogicTechnologies/LocateAnything-3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SandLogicTechnologies/LocateAnything-3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M

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 SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use SandLogicTechnologies/LocateAnything-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M
```

Lemonade

How to use SandLogicTechnologies/LocateAnything-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/LocateAnything-3B-GGUF:IQ3_M

Run and chat with the model

lemonade run user.LocateAnything-3B-GGUF-IQ3_M

List all available models

lemonade list

LocateAnything-3B

LocateAnything-3B is a specialized vision-language model developed by NVIDIA for visual grounding and object localization tasks. This repository contains GGUF quantized variants of the model optimized for efficient local inference.

Unlike general-purpose multimodal assistants, LocateAnything-3B is designed to identify the precise spatial location of objects, regions, text, GUI elements, and entities referenced in natural language prompts. The model combines strong visual understanding with high-quality localization capabilities, enabling efficient deployment across perception-oriented workflows.

The model introduces Parallel Box Decoding (PBD), which predicts complete geometric structures simultaneously rather than autoregressively generating coordinate tokens, resulting in improved throughput and localization quality.

Model Overview

Model Name: LocateAnything-3B
Base Model: nvidia/LocateAnything-3B
Architecture: Vision-Language Model with Parallel Box Decoding
Parameter Count: 3 Billion
Modalities: Text, Image
Primary Languages: English
Developer: NVIDIA
License: nvidia-license
Core Capability: General-purpose visual grounding and localization

Quantization Formats

This repository provides various GGUF quantized versions of the LocateAnything-3B model optimized for efficient multimodal inference and visual grounding workloads.

IQ3_M

Size reduction of approx 76.18% (1.51 GB) compared to 16-bit (6.34 GB)
Aggressive 3-bit quantization optimized for maximum memory efficiency
Suitable for low-memory multimodal deployment environments
Enables practical execution of visual grounding models on consumer hardware
Fine-grained localization precision and dense grounding performance may reduce compared to higher-precision variants

IQ4_NL

Size reduction of approx 70.35% (1.88 GB) compared to 16-bit (6.34 GB)
Advanced 4-bit non-linear quantization designed to better preserve localization fidelity and multimodal understanding quality
Better suited for grounding workflows requiring stronger coordinate consistency and object localization accuracy
Designed to reduce quantization loss compared to more aggressive formats
May require slightly increased computational overhead during inference

IQ4_XS

Size reduction of approx 71.77% (1.79 GB) compared to 16-bit (6.34 GB)
Balanced 4-bit quantization focused on efficient inference and dependable visual grounding performance
Provides a practical balance between memory efficiency, localization quality, and runtime speed
Suitable for general-purpose grounding applications and multimodal deployment scenarios
Maintains stable performance across most practical localization workloads

Training Background (Original Model)

LocateAnything-3B is trained with a focus on large-scale visual grounding, multimodal localization, and spatial reasoning across diverse visual domains.

Pretraining

Trained using large-scale multimodal datasets spanning natural scenes, documents, robotics, driving environments, and GUI interfaces
Focus on aligning visual representations with natural language descriptions
Optimized for precise object localization and grounding tasks

Grounding Optimization

Enhanced using large-scale grounding datasets containing millions of localization annotations
Introduces Parallel Box Decoding to improve throughput and geometric consistency
Optimized for referring expression grounding, dense localization, and multimodal perception workflows

Key Capabilities

Visual Grounding Localizes objects and entities referenced through natural language descriptions.
Referring Expression Grounding Identifies precise regions corresponding to textual descriptions within images.
Multi-Object Localization Supports grounding multiple entities within complex visual scenes.
GUI Element Grounding Locates interface components and screen elements in GUI environments.
Text Localization Supports grounding of textual regions embedded within visual inputs.
Efficient Local Deployment Quantized variants enable practical multimodal inference on consumer hardware.

Usage Example

Using llama.cpp

./llama-mtmd-cli \
  -m SandlogicTechnologies/LocateAnything-3B_IQ4_NL.gguf \
  --mmproj SandlogicTechnologies/mmproj-LocateAnything-3B-BF16.gguf \
  --image image.png \
  -p "Analyze the diagram and explain the underlying concept step-by-step."

Recommended Usecases

Visual Grounding Systems Build applications capable of locating objects from natural language descriptions.
Multimodal Perception Pipelines Integrate visual localization capabilities into perception-oriented AI systems.
GUI Understanding Workflows Support interface automation and GUI element identification tasks.
Document and Scene Understanding Ground textual descriptions within complex visual environments.
Research and Experimentation Evaluate multimodal localization techniques and grounding workflows.

Acknowledgments

These quantized models are based on the original work by the NVIDIA Research team.

Special thanks to:

The NVIDIA team for developing and releasing the LocateAnything-3B model.
Georgi Gerganov and the llama.cpp open-source community for enabling efficient GGUF quantization and inference.

Contact

For questions, feedback, or support, please reach out at support@sandlogic.com or visit https://www.sandlogic.com/

Downloads last month: 644

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

3-bit

4-bit

Model tree for SandLogicTechnologies/LocateAnything-3B-GGUF

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

nvidia/LocateAnything-3B

Quantized

(16)

this model