Instructions to use prithivMLmods/LightNav-0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/LightNav-0-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/LightNav-0-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/LightNav-0-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 prithivMLmods/LightNav-0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/LightNav-0-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 prithivMLmods/LightNav-0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/LightNav-0-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 prithivMLmods/LightNav-0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/LightNav-0-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 prithivMLmods/LightNav-0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/LightNav-0-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/LightNav-0-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use prithivMLmods/LightNav-0-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/LightNav-0-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/LightNav-0-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/LightNav-0-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/LightNav-0-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/LightNav-0-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/LightNav-0-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/LightNav-0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/LightNav-0-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LightNav-0-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/LightNav-0-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 prithivMLmods/LightNav-0-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 prithivMLmods/LightNav-0-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/LightNav-0-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/LightNav-0-GGUF:Q4_K_M

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 "prithivMLmods/LightNav-0-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

LightNav-0-GGUF

LightNav-0 is a compact generalist embodied navigation model built on Qwen3-VL-4B-Instruct that elicits the pretrained vision-language model's spatial intelligence and aligns it with navigation tasks without task-specific prediction heads. Its core design unifies diverse tasks — instruction following, open-vocabulary object navigation, and visual tracking — under a single token interface: dual-channel pointing tokens (<apos_*>/<opos_*>) express task-, scene-, and embodiment-agnostic spatial intent, which a residual vector-quantized (RVQ) action tokenizer then maps into precise, embodiment-specific trajectories via three levels of action tokens (<act_l0_*><act_l1_*><act_l2_*>) decoded into a 10-step chunk of robot-local waypoints (forward distance, lateral offset, yaw). The model takes a SlowFast-compressed history of first-person RGB frames (256×448 at 4fps) plus a natural-language instruction as input, loads directly with stock Transformers since navigation tokens are ordinary embedding-table rows, and is served through the companion lightnav package via vLLM for offline prediction or WebSocket-based real-time tracking/navigation serving, with Habitat VLN-CE/ObjectNav and EVT-Bench evaluation recipes plus real-robot deployment guides documented in its GitHub repository. It is released under the Apache License 2.0.

RVQ action-tokenizer bundle shared by navigation and tracking (one 3 × 256 residual codebook, horizon 10) — https://huggingface.co/LightOriginsHQ/LightNav-0/tree/main/action_tokenizer

Model Files

File Name	Quant Type	File Size	File Link
LightNav-0.BF16.gguf	BF16	8.87 GB	Download
LightNav-0.Q3_K_L.gguf	Q3_K_L	2.42 GB	Download
LightNav-0.Q3_K_M.gguf	Q3_K_M	2.26 GB	Download
LightNav-0.Q3_K_S.gguf	Q3_K_S	2.07 GB	Download
LightNav-0.Q4_0.gguf	Q4_0	2.6 GB	Download
LightNav-0.Q4_K_M.gguf	Q4_K_M	2.73 GB	Download
LightNav-0.Q4_K_S.gguf	Q4_K_S	2.62 GB	Download
LightNav-0.Q5_0.gguf	Q5_0	3.11 GB	Download
LightNav-0.Q5_K_M.gguf	Q5_K_M	3.17 GB	Download
LightNav-0.Q5_K_S.gguf	Q5_K_S	3.11 GB	Download
LightNav-0.mmproj-bf16.gguf	mmproj-bf16	839 MB	Download