Instructions to use LiquidAI/LFM2-VL-450M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2-VL-450M with Transformers:

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

pipe = pipeline("image-text-to-text", model="LiquidAI/LFM2-VL-450M")
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("LiquidAI/LFM2-VL-450M")
model = AutoModelForMultimodalLM.from_pretrained("LiquidAI/LFM2-VL-450M", 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

vLLM

How to use LiquidAI/LFM2-VL-450M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2-VL-450M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2-VL-450M",
		"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/LiquidAI/LFM2-VL-450M

SGLang

How to use LiquidAI/LFM2-VL-450M 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 "LiquidAI/LFM2-VL-450M" \
    --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": "LiquidAI/LFM2-VL-450M",
		"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 "LiquidAI/LFM2-VL-450M" \
        --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": "LiquidAI/LFM2-VL-450M",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM2-VL-450M with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2-VL-450M
```

liquidalex commited on Aug 12, 2025

Commit

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@@ -158,6 +158,9 @@ We recommend fine-tuning LFM2-VL models on your use cases to maximize performanc
 | InternVL3-1B      | 57.00       | 31.14     | 54.94         | 798.00   | 43.00 | 52.30  | 43.20      | 46.90     | 71.20          | 58.70 | 1912.40  | 49.80 |
 | SmolVLM2-2.2B     | 57.50       | 19.42     | 37.75         | 725.00   | 42.30 | 46.00  | 41.60      | 51.50     | 71.30          | 34.90 | 1792.50  | -     |
 | LFM2-VL-1.6B      | 65.23       | 37.66     | 58.68         | 742.00   | 44.40 | 49.53  | 38.44      | 51.10     | 71.97          | 48.07 | 1753.04  | 50.99 |
 | SmolVLM2-500M     | 49.90       | 11.27     | 24.64         | 609.00   | 40.70 | 38.20  | 34.10      | 37.50     | 62.20          | 29.90 | 1448.30  | -     |
 | LFM2-VL-440M      | 52.29       | 26.18     | 46.51         | 655.00   | 41.98 | 40.87  | 33.11      | 44.70     | 63.50          | 33.76 | 1239.06  | 40.16 |

 | InternVL3-1B      | 57.00       | 31.14     | 54.94         | 798.00   | 43.00 | 52.30  | 43.20      | 46.90     | 71.20          | 58.70 | 1912.40  | 49.80 |
 | SmolVLM2-2.2B     | 57.50       | 19.42     | 37.75         | 725.00   | 42.30 | 46.00  | 41.60      | 51.50     | 71.30          | 34.90 | 1792.50  | -     |
 | LFM2-VL-1.6B      | 65.23       | 37.66     | 58.68         | 742.00   | 44.40 | 49.53  | 38.44      | 51.10     | 71.97          | 48.07 | 1753.04  | 50.99 |
+| Model             | RealWorldQA | MM-IFEval | InfoVQA (Val) | OCRBench | BLINK | MMStar | MMMU (Val) | MathVista | SEEDBench_IMG | MMVet | MME      | MMLU  |
+|-------------------|-------------|-----------|---------------|----------|-------|--------|------------|-----------|---------------|-------|----------|-------|
 | SmolVLM2-500M     | 49.90       | 11.27     | 24.64         | 609.00   | 40.70 | 38.20  | 34.10      | 37.50     | 62.20          | 29.90 | 1448.30  | -     |
 | LFM2-VL-440M      | 52.29       | 26.18     | 46.51         | 655.00   | 41.98 | 40.87  | 33.11      | 44.70     | 63.50          | 33.76 | 1239.06  | 40.16 |