---
language:
- en
base_model:
- nvidia/LocateAnything-3B
tags:
- vision-language-model
- multimodal
- image-text-to-text
- GGUF
---

## 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

```bash
./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](mailto:support@sandlogic.com) or visit https://www.sandlogic.com/