Instructions to use 5ch4um1/lfm2.5-vrsbench-lora-450m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 5ch4um1/lfm2.5-vrsbench-lora-450m with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="5ch4um1/lfm2.5-vrsbench-lora-450m",
	filename="F16.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 5ch4um1/lfm2.5-vrsbench-lora-450m 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 5ch4um1/lfm2.5-vrsbench-lora-450m:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 5ch4um1/lfm2.5-vrsbench-lora-450m:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf 5ch4um1/lfm2.5-vrsbench-lora-450m:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 5ch4um1/lfm2.5-vrsbench-lora-450m: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 5ch4um1/lfm2.5-vrsbench-lora-450m:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 5ch4um1/lfm2.5-vrsbench-lora-450m: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 5ch4um1/lfm2.5-vrsbench-lora-450m:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 5ch4um1/lfm2.5-vrsbench-lora-450m:Q4_K_M

Use Docker

docker model run hf.co/5ch4um1/lfm2.5-vrsbench-lora-450m:Q4_K_M

LM Studio
Jan
Ollama
How to use 5ch4um1/lfm2.5-vrsbench-lora-450m with Ollama:
```
ollama run hf.co/5ch4um1/lfm2.5-vrsbench-lora-450m:Q4_K_M
```

Unsloth Studio

How to use 5ch4um1/lfm2.5-vrsbench-lora-450m 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 5ch4um1/lfm2.5-vrsbench-lora-450m 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 5ch4um1/lfm2.5-vrsbench-lora-450m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 5ch4um1/lfm2.5-vrsbench-lora-450m to start chatting

How to use 5ch4um1/lfm2.5-vrsbench-lora-450m with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 5ch4um1/lfm2.5-vrsbench-lora-450m: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": "5ch4um1/lfm2.5-vrsbench-lora-450m:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use 5ch4um1/lfm2.5-vrsbench-lora-450m with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 5ch4um1/lfm2.5-vrsbench-lora-450m: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 5ch4um1/lfm2.5-vrsbench-lora-450m:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use 5ch4um1/lfm2.5-vrsbench-lora-450m with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 5ch4um1/lfm2.5-vrsbench-lora-450m: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 "5ch4um1/lfm2.5-vrsbench-lora-450m: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"

Docker Model Runner
How to use 5ch4um1/lfm2.5-vrsbench-lora-450m with Docker Model Runner:
```
docker model run hf.co/5ch4um1/lfm2.5-vrsbench-lora-450m:Q4_K_M
```

Lemonade

How to use 5ch4um1/lfm2.5-vrsbench-lora-450m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 5ch4um1/lfm2.5-vrsbench-lora-450m:Q4_K_M

Run and chat with the model

lemonade run user.lfm2.5-vrsbench-lora-450m-Q4_K_M

List all available models

lemonade list

lfm2.5-vrsbench-lora-450m / README.md

5ch4um1

Upload README.md with huggingface_hub

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	---
	base_model: LiquidAI/LFM2.5-VL-450M
	tags:
	- vision-language
	- satellite
	- vrsbench
	- lfm2
	- gguf
	- remote-sensing
	---

	# LFM2.5-VL-450M VRSBench LoRA

	## Model Description

	This is a fine-tuned version of LiquidAI's LFM2.5-VL-450M vision-language model, trained on the VRSBench dataset for general satellite image understanding. This serves as the base model for specialized satellite vision tasks.

	The model can answer questions about satellite imagery, including:
	- Scene classification
	- Object detection and counting
	- Visual question answering about satellite images
	- General satellite image understanding

	## Training Details

	### VRSBench Training
	- Base Model: LFM2.5-VL-450M
	- Dataset: VRSBench (Vision Reasoning and Scene Understanding Benchmark)
	- Epochs: 1
	- Method: LoRA (r=16, alpha=32)
	- Hardware: Local GPU training (no Ray/distributed)

	## Derived Models

	This model serves as the base for specialized satellite vision experts:

	\| Model \| Dataset \| Task \| Accuracy/Performance \|
	\|-------\|----------\|------\|---------------------\|
	\| [VRSBench + EuroSAT Terrain Expert](https://huggingface.co/5ch4um1/lfm2.5-vrsbench-EUROSAT-terrain-lora-450m) \| EuroSAT \| Terrain Classification \| 97.52% accuracy \|
	\| [VRSBench + MADOS Maritime Expert](https://huggingface.co/5ch4um1/lfm2.5-vrsbench-mados-maritime-lora-450m) \| MADOS \| Maritime Detection \| IoU@0.5: ~2% \|

	## Usage

	### With llama.cpp

	```bash
	# Download Q4_K_M quantized version (recommended)
	wget https://huggingface.co/5ch4um1/lfm2.5-vrsbench-lora-450m/resolve/main/lfm2.5-vrsbench-lora-450m-q4_k_m.gguf

	# Run inference
	./llama-cli -m lfm2.5-vrsbench-lora-450m-q4_k_m.gguf \
	--image satellite_image.jpg \
	-p "Describe this satellite image in detail."
	```

	### With Transformers

	```python
	from transformers import AutoModelForVision2Seq, AutoProcessor
	from PIL import Image

	model = AutoModelForVision2Seq.from_pretrained(
	"5ch4um1/lfm2.5-vrsbench-lora-450m",
	torch_dtype="auto",
	device_map="auto"
	)
	processor = AutoProcessor.from_pretrained("5ch4um1/lfm2.5-vrsbench-lora-450m")

	image = Image.open("satellite_image.jpg")
	prompt = "What is shown in this satellite image?"

	inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device)
	outputs = model.generate(**inputs, max_new_tokens=100)
	print(processor.decode(outputs[0], skip_special_tokens=True))
	```

	## GGUF Quantizations

	\| Version \| Size \| Description \|
	\|---------\|------\|-------------\|
	\| F16 \| 679 MB \| Full precision (16-bit) \|
	\| Q8_0 \| 362 MB \| 8-bit quantization \|
	\| Q4_K_M \| 219 MB \| 4-bit quantization (recommended for most use cases) \|

	## Model Sources

	- Base Model: [LiquidAI/LFM2.5-VL-450M](https://huggingface.co/LiquidAI/LFM2.5-VL-450M)
	- VRSBench Dataset: [VRSBench Paper](https://arxiv.org/abs/)

	## Limitations

	- General satellite understanding model - not specialized for specific tasks
	- Performance varies depending on satellite image type and task
	- For specialized tasks (terrain, maritime), use the derived expert models listed above

	## Training Environment

	- Framework: Transformers + PEFT (LoRA)
	- Hardware: Local GPU (CUDA)
	- Training Scripts: Available in the [cookbook repository](https://github.com/anomalyco/opencode)