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"

Arty1001 commited on May 4

Commit

49f5672

verified ·

1 Parent(s): 0bc8b2d

Add model card with vision pipeline_tag and satellite imagery metadata

Browse files

Files changed (1) hide show

README.md +90 -0

README.md ADDED Viewed

	@@ -0,0 +1,90 @@

+---
+license: apache-2.0
+language:
+- en
+base_model: LiquidAI/LFM2.5-VL-450M
+pipeline_tag: image-text-to-text
+tags:
+- vision
+- multimodal
+- satellite-imagery
+- remote-sensing
+- environmental-monitoring
+- water-monitoring
+- freshwater
+- gguf
+- llama-cpp
+- aquaveritas
+---
+# 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](https://huggingface.co/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](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-GGUF):
+> `mmproj-LFM2.5-VL-450m-F16.gguf`
+## Usage
+```bash
+# 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}
+}
+```