Instructions to use erikku-sama/lfm2-electronics-vlm-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use erikku-sama/lfm2-electronics-vlm-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="erikku-sama/lfm2-electronics-vlm-v2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("erikku-sama/lfm2-electronics-vlm-v2")
model = AutoModelForMultimodalLM.from_pretrained("erikku-sama/lfm2-electronics-vlm-v2", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use erikku-sama/lfm2-electronics-vlm-v2 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 erikku-sama/lfm2-electronics-vlm-v2:F16
# Run inference directly in the terminal:
llama cli -hf erikku-sama/lfm2-electronics-vlm-v2:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf erikku-sama/lfm2-electronics-vlm-v2:F16
# Run inference directly in the terminal:
llama cli -hf erikku-sama/lfm2-electronics-vlm-v2:F16

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf erikku-sama/lfm2-electronics-vlm-v2:F16
# Run inference directly in the terminal:
./build/bin/llama cli -hf erikku-sama/lfm2-electronics-vlm-v2:F16

Use Docker

docker model run hf.co/erikku-sama/lfm2-electronics-vlm-v2:F16

Llama new
LM Studio
Jan

vLLM

How to use erikku-sama/lfm2-electronics-vlm-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "erikku-sama/lfm2-electronics-vlm-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "erikku-sama/lfm2-electronics-vlm-v2",
		"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/erikku-sama/lfm2-electronics-vlm-v2:F16

SGLang

How to use erikku-sama/lfm2-electronics-vlm-v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "erikku-sama/lfm2-electronics-vlm-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "erikku-sama/lfm2-electronics-vlm-v2",
		"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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "erikku-sama/lfm2-electronics-vlm-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "erikku-sama/lfm2-electronics-vlm-v2",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use erikku-sama/lfm2-electronics-vlm-v2 with Ollama:
```
ollama run hf.co/erikku-sama/lfm2-electronics-vlm-v2:F16
```
Unsloth Desktop

How to use erikku-sama/lfm2-electronics-vlm-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf erikku-sama/lfm2-electronics-vlm-v2:F16

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": "erikku-sama/lfm2-electronics-vlm-v2:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use erikku-sama/lfm2-electronics-vlm-v2 with Docker Model Runner:
```
docker model run hf.co/erikku-sama/lfm2-electronics-vlm-v2:F16
```

Lemonade

How to use erikku-sama/lfm2-electronics-vlm-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull erikku-sama/lfm2-electronics-vlm-v2:F16

Run and chat with the model

lemonade run user.lfm2-electronics-vlm-v2-F16

List all available models

lemonade list

Hermes Agent

How to use erikku-sama/lfm2-electronics-vlm-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf erikku-sama/lfm2-electronics-vlm-v2:F16

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 erikku-sama/lfm2-electronics-vlm-v2:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use erikku-sama/lfm2-electronics-vlm-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf erikku-sama/lfm2-electronics-vlm-v2:F16

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 "erikku-sama/lfm2-electronics-vlm-v2:F16" \
  --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"

Electrocom VLM V2 (450M)

Electrocom VLM V2 is a specialized Vision-Language Model optimized for electronic component detection and recognition. It is a fine-tuned version of Liquid AI's LFM2.5-VL-450M.

This "Phase 2" version was trained on a combined dataset of ElectroCom61 and Electronic Detection, significantly improving its visual robustness and detection accuracy.

📊 Performance

Metric	Base LFM2.5-VL-450M	Electrocom VLM V2 (Ours)
Grounding Recall (IoU > 0.5)	0.0%	31.9%
Inference Speed (tok/s)	81.6	85.2

Key Improvements:

7.5x Better Grounding: Compared to our V1 model, the V2 model shows a massive leap in its ability to correctly localize components.
Structured JSON Output: The model has been trained to output detections in a precise JSON format, suitable for programmatic integration.
Edge-Ready: At only 450M parameters, this model runs at high speeds even on modest hardware.

🚀 Usage with llama.cpp

The GGUF version of this model is included in this repository for use with llama.cpp.

# Download the model and projector
hf download erikku-sama/lfm2-electronics-vlm-v2 electrocom-vlm-v2-f16.gguf --local-dir .
hf download LiquidAI/LFM2.5-VL-450M-GGUF mmproj-LFM2.5-VL-450m-F16.gguf --local-dir .

# Run inference
llama-mtmd-cli \
  --model electrocom-vlm-v2-f16.gguf \
  --mmproj mmproj-LFM2.5-VL-450m-F16.gguf \
  --image your_image.jpg \
  -p "Inspect the image and detect all electronic components. Provide result as a valid JSON: [{\"label\": str, \"bbox\": [x1,y1,x2,y2]}, ...]. Coordinates must be normalized to 0-1." \
  --temp 0.1 \
  --jinja

🎯 Intended Use

Automated inventory of electronic components
Visual assistance for circuit board inspection
Educational tools for electronics identification

📝 License

This model is based on LFM2.5-VL-450M and is subject to the Liquid AI model license.

Downloads last month: 356

Safetensors

Model size

0.4B params

Tensor type

BF16

Model tree for erikku-sama/lfm2-electronics-vlm-v2

Base model

LiquidAI/LFM2.5-350M-Base

Finetuned

LiquidAI/LFM2.5-350M

Finetuned

LiquidAI/LFM2.5-VL-450M

Quantized

(37)

this model

Evaluation results

Grounding Recall (IoU > 0.5) on ElectroCom61 + Electronic Detection (test split)
self-reported

0.319