Instructions to use Paulescu/wildfire-risk-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Paulescu/wildfire-risk-detector with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Paulescu/wildfire-risk-detector",
	filename="lfm2.5-vl-wildfire-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Paulescu/wildfire-risk-detector 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 Paulescu/wildfire-risk-detector:Q8_0
# Run inference directly in the terminal:
llama cli -hf Paulescu/wildfire-risk-detector:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Paulescu/wildfire-risk-detector:Q8_0
# Run inference directly in the terminal:
llama cli -hf Paulescu/wildfire-risk-detector:Q8_0

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 Paulescu/wildfire-risk-detector:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Paulescu/wildfire-risk-detector:Q8_0

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 Paulescu/wildfire-risk-detector:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Paulescu/wildfire-risk-detector:Q8_0

Use Docker

docker model run hf.co/Paulescu/wildfire-risk-detector:Q8_0

LM Studio
Jan
Ollama
How to use Paulescu/wildfire-risk-detector with Ollama:
```
ollama run hf.co/Paulescu/wildfire-risk-detector:Q8_0
```

Unsloth Studio

How to use Paulescu/wildfire-risk-detector 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 Paulescu/wildfire-risk-detector 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 Paulescu/wildfire-risk-detector to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Paulescu/wildfire-risk-detector to start chatting

How to use Paulescu/wildfire-risk-detector with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Paulescu/wildfire-risk-detector:Q8_0

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": "Paulescu/wildfire-risk-detector:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Paulescu/wildfire-risk-detector with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Paulescu/wildfire-risk-detector:Q8_0

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 Paulescu/wildfire-risk-detector:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Paulescu/wildfire-risk-detector with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Paulescu/wildfire-risk-detector:Q8_0

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 "Paulescu/wildfire-risk-detector:Q8_0" \
  --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"

Docker Model Runner
How to use Paulescu/wildfire-risk-detector with Docker Model Runner:
```
docker model run hf.co/Paulescu/wildfire-risk-detector:Q8_0
```

Lemonade

How to use Paulescu/wildfire-risk-detector with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Paulescu/wildfire-risk-detector:Q8_0

Run and chat with the model

lemonade run user.wildfire-risk-detector-Q8_0

List all available models

lemonade list

LFM2.5-VL-450M wildfire risk (GGUF)

Fine-tuned from LiquidAI/LFM2.5-VL-450M on Sentinel-2 satellite imagery to assess wildfire risk. Part of the Liquid Cookbook wildfire-prevention example.

Given an RGB and SWIR Sentinel-2 image pair, the model outputs a structured JSON risk assessment:

{
  "risk_level": "low | medium | high",
  "dry_vegetation_present": true,
  "urban_interface": false,
  "steep_terrain": true,
  "water_body_present": false,
  "image_quality_limited": false
}

Eval results

Evaluated on 172 test samples from Paulescu/wildfire-prevention, ground truth from claude-opus-4-6.

field	claude-opus-4-6	LFM2.5-VL-450M Q8_0 (base)	LFM2.5-VL-450M Q8_0 (fine-tuned)
valid_json	1.00	1.00	1.00
fields_present	1.00	1.00	1.00
risk_level	0.99	0.08	0.76
dry_vegetation_present	0.99	0.48	0.83
urban_interface	0.98	0.25	0.93
steep_terrain	0.99	0.45	0.81
water_body_present	0.99	0.74	0.87
image_quality_limited	1.00	0.28	0.86
overall	0.99	0.38	0.84
avg latency (s)	2.91	0.72	0.59

Files

Running inference with a VLM in llama.cpp requires two GGUF files:

file	description
`lfm2.5-vl-wildfire-Q8_0.gguf`	Language model backbone (Q8_0)
`mmproj-lfm2.5-vl-wildfire-Q8_0.gguf`	Vision tower + multimodal projector (F16)

Usage

llama-server

llama-server \
    -m lfm2.5-vl-wildfire-Q8_0.gguf \
    --mmproj mmproj-lfm2.5-vl-wildfire-Q8_0.gguf \
    --jinja --port 8080

Reproduce eval results

Clone the Liquid Cookbook, then:

cd examples/wildfire-prevention
uv sync
uv run scripts/evaluate.py \
    --hf-dataset Paulescu/wildfire-prevention \
    --backend local \
    --model lfm2.5-vl-wildfire-Q8_0.gguf \
    --mmproj mmproj-lfm2.5-vl-wildfire-Q8_0.gguf \
    --split test