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

Libraries

How to use liodon-ai/LocateAnything-3B-imatrix-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="liodon-ai/LocateAnything-3B-imatrix-GGUF",
	filename="LocateAnything-3B-IQ2_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use liodon-ai/LocateAnything-3B-imatrix-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_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 liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_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 liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use liodon-ai/LocateAnything-3B-imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liodon-ai/LocateAnything-3B-imatrix-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": "liodon-ai/LocateAnything-3B-imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M

Ollama
How to use liodon-ai/LocateAnything-3B-imatrix-GGUF with Ollama:
```
ollama run hf.co/liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M
```

Unsloth Studio

How to use liodon-ai/LocateAnything-3B-imatrix-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 liodon-ai/LocateAnything-3B-imatrix-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 liodon-ai/LocateAnything-3B-imatrix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use liodon-ai/LocateAnything-3B-imatrix-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_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": "liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use liodon-ai/LocateAnything-3B-imatrix-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_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 liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use liodon-ai/LocateAnything-3B-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M
```

Lemonade

How to use liodon-ai/LocateAnything-3B-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LocateAnything-3B-imatrix-GGUF-Q4_K_M

List all available models

lemonade list

LocateAnything-3B — iMatrix GGUF

iMatrix GGUF quantizations of nvidia/LocateAnything-3B — the first GGUF available for this model.

LocateAnything-3B is NVIDIA's 3B visual grounding model — it locates and identifies objects in images given natural language descriptions. Designed for on-device deployment and robotics applications.

These GGUFs use importance matrix (iMatrix) calibration on 2M tokens of wikitext-103: iMatrix runs calibration text through the model, measures which weights activate most, and protects them during quantization. Result: noticeably better coherence at Q2/Q3/Q4 — same file size, better output.

Quick Start

llama.cpp

llama-cli -hf liodon-ai/LocateAnything-3B-imatrix-GGUF:Q4_K_M

LM Studio / Jan

Search liodon-ai/LocateAnything-3B-imatrix-GGUF and pick your quant.

Available Quants

Quant	Size	VRAM	Notes
`IQ2_M`	1.28 GB	2 GB	ultra-tiny + iMatrix — better than standard Q2
`IQ3_M`	1.62 GB	2.5 GB	tiny + iMatrix — sharper than standard Q3
`IQ4_XS`	1.91 GB	3 GB	small + iMatrix — rivals Q5 at Q4 size
`Q2_K`	1.38 GB	2 GB	tiniest standard — runs almost anywhere, iMatrix-improved
`Q3_K_M`	1.73 GB	2.5 GB	great for 4GB VRAM, iMatrix-improved
`Q4_K_M`	2.11 GB	3 GB	sweet spot (recommended), iMatrix-improved
`Q5_K_M`	2.44 GB	4 GB	high quality, iMatrix-improved
`Q6_K`	2.80 GB	4 GB	near-lossless, iMatrix-improved
`Q8_0`	3.62 GB	5 GB	basically full quality

What is iMatrix?

Standard quantization rounds all weights equally. iMatrix:

Runs calibration text through the full-precision model
Measures which weights activate most (the "importance matrix")
Allocates more precision to important weights, less to unimportant ones

Same file size. Better output. Most noticeable at Q2/Q3/Q4.

Calibration

Importance matrix computed from 2M tokens of wikitext-103 — 128 calibration chunks.

Source Model

Original: nvidia/LocateAnything-3B
Architecture: 3B visual grounding model
Strengths: Object localization, visual grounding, on-device robotics
License: NVIDIA Open Model License

Downloads last month: 209

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for liodon-ai/LocateAnything-3B-imatrix-GGUF

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

nvidia/LocateAnything-3B

Quantized

(18)

this model