Instructions to use Arty1001/aquaveritas-lfm-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Arty1001/aquaveritas-lfm-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 Arty1001/aquaveritas-lfm-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Arty1001/aquaveritas-lfm-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Arty1001/aquaveritas-lfm-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Arty1001/aquaveritas-lfm-GGUF: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 Arty1001/aquaveritas-lfm-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Arty1001/aquaveritas-lfm-GGUF: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 Arty1001/aquaveritas-lfm-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Arty1001/aquaveritas-lfm-GGUF:Q8_0

Use Docker

docker model run hf.co/Arty1001/aquaveritas-lfm-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use Arty1001/aquaveritas-lfm-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Arty1001/aquaveritas-lfm-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": "Arty1001/aquaveritas-lfm-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Arty1001/aquaveritas-lfm-GGUF:Q8_0

Ollama
How to use Arty1001/aquaveritas-lfm-GGUF with Ollama:
```
ollama run hf.co/Arty1001/aquaveritas-lfm-GGUF:Q8_0
```
Unsloth Desktop

How to use Arty1001/aquaveritas-lfm-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Arty1001/aquaveritas-lfm-GGUF:Q8_0

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": "Arty1001/aquaveritas-lfm-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Arty1001/aquaveritas-lfm-GGUF with Docker Model Runner:
```
docker model run hf.co/Arty1001/aquaveritas-lfm-GGUF:Q8_0
```

Lemonade

How to use Arty1001/aquaveritas-lfm-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Arty1001/aquaveritas-lfm-GGUF:Q8_0

Run and chat with the model

lemonade run user.aquaveritas-lfm-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Arty1001/aquaveritas-lfm-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 Arty1001/aquaveritas-lfm-GGUF: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 Arty1001/aquaveritas-lfm-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Arty1001/aquaveritas-lfm-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Arty1001/aquaveritas-lfm-GGUF: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 "Arty1001/aquaveritas-lfm-GGUF: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"

AquaVeritas-LFM — GGUF

AquaVeritas-LFM is a fine-tuned vision-language model for automated freshwater body monitoring from Sentinel-2 satellite imagery. It is a full fine-tune of LiquidAI/LFM2.5-VL-450M trained on 2,820 labeled observations across 20 global freshwater locations spanning 2018–2024.

Model Description

Base model: LiquidAI/LFM2.5-VL-450M (Liquid Foundation Model 2.5 Vision-Language)
Task: Satellite image analysis → structured JSON environmental assessment
Fine-tuning: Full fine-tune (no LoRA/PEFT), 3 epochs on H100
Training loss: 0.0113 | Eval loss: 0.01542
Input: RGB + SWIR Sentinel-2 tiles (15 km × 15 km, 10 m/px)
Output: Structured JSON assessment of water extent, flood risk, water clarity, shoreline encroachment, agriculture stress, and land-use change

Files

File	Size	Description
`aquaveritas-lfm-q8_0.gguf`	~451 MB	Full model, Q8_0 quantized — use with llama-server

The base model's mmproj (vision encoder projector) is required for inference. Download it from LiquidAI/LFM2.5-VL-450M-GGUF: mmproj-LFM2.5-VL-450m-F16.gguf

Usage

# Download mmproj from LiquidAI official repo
huggingface-cli download LiquidAI/LFM2.5-VL-450M-GGUF mmproj-LFM2.5-VL-450m-F16.gguf

# Start llama-server
llama-server \
  -m aquaveritas-lfm-q8_0.gguf \
  --mmproj mmproj-LFM2.5-VL-450m-F16.gguf \
  --port 8080 \
  --ctx-size 4096 \
  -ngl 99

Training Data

20 global freshwater locations: Lake Chad, Aral Sea, Dead Sea, Lake Urmia, Lake Victoria, Lake Titicaca, Lake Turkana, Tonle Sap, Okavango, Niger Delta, Nile Delta, Mekong Delta, Amazon Delta, Congo Delta, Danube Delta, Mesopotamian Marshes, Omo River, Tana River, Po Valley, Salton Sea
7-year temporal range: 2018–2024 (monthly observations)
2,820 training examples (core zone + buffer zone analysis per observation)
Labels generated by Claude Opus oracle with field-level accuracy ~99%

Evaluation (vs Claude oracle, 30 test observations)

Field	Claude	Base LFM	AquaVeritas-LFM	Δ
Water Extent Status	86.7%	0.0%	100.0%	▲ 100%
Flood Risk	73.3%	33.3%	100.0%	▲ 67%
Water Clarity	93.3%	0.0%	100.0%	▲ 100%
Shoreline Encroachment	80.0%	50.0%	100.0%	▲ 50%

Citation

@misc{aquaveritas2026,
  title={AquaVeritas: Automated Freshwater Monitoring via Fine-tuned Vision-Language Models},
  author={ML_LABS},
  year={2026},
  url={https://huggingface.co/Arty1001/aquaveritas-lfm-GGUF}
}

Downloads last month: 37

GGUF

Model size

0.4B params

Architecture

lfm2

Hardware compatibility

8-bit

Model tree for Arty1001/aquaveritas-lfm-GGUF

Base model

LiquidAI/LFM2.5-350M-Base

Finetuned

LiquidAI/LFM2.5-350M

Finetuned

LiquidAI/LFM2.5-VL-450M

Quantized

(30)

this model

Arty1001
/

aquaveritas-lfm-GGUF

AquaVeritas-LFM — GGUF

Model Description

Files

Usage

Training Data

Evaluation (vs Claude oracle, 30 test observations)

Citation

Model tree for Arty1001/aquaveritas-lfm-GGUF

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