SandLogicTechnologies/

LocateAnything-3B-GGUF

LocateAnything-3B

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

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)

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

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

• 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.

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