Instructions to use LiquidAI/LFM2-VL-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2-VL-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="LiquidAI/LFM2-VL-3B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("LiquidAI/LFM2-VL-3B")
model = AutoModelForMultimodalLM.from_pretrained("LiquidAI/LFM2-VL-3B", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LiquidAI/LFM2-VL-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2-VL-3B"
# 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-3B",
		"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-3B

SGLang

How to use LiquidAI/LFM2-VL-3B 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-3B" \
    --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-3B",
		"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-3B" \
        --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-3B",
		"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-3B with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2-VL-3B
```

LFM2-VL-3B / README.md

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Fix link bar, add Discord and br tag, update Contact section

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	---
	library_name: transformers
	license: other
	license_name: lfm1.0
	license_link: LICENSE
	language:
	- en
	- ja
	- fr
	- es
	- de
	- it
	- pt
	- ar
	- zh
	- ko
	pipeline_tag: image-text-to-text
	tags:
	- liquid
	- lfm2
	- lfm2-vl
	- edge
	---

	<center>
	<div style="text-align: center;">
	<img
	src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
	alt="Liquid AI"
	style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
	/>
	</div>
	<div style="display: flex; justify-content: center; gap: 0.5em;">
	<a href="https://playground.liquid.ai/chat?model=lfm2.5-vl-1.6b"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
	</div>
	</center>

	<br>

	# LFM2‑VL

	LFM2-VL-3B is the newest and most capable model in [Liquid AI](https://www.liquid.ai/)'s multimodal LFM2-VL series, designed to process text and images with variable resolutions.
	Built on the [LFM2](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38) backbone, it extends the architecture for higher-capacity reasoning and stronger visual understanding while retaining efficiency.

	We are releasing the weights of the new [3B](https://huggingface.co/LiquidAI/LFM2-VL-3B) checkpoint—offering higher performance across benchmarks while remaining optimized for scalable deployment.

	* Competitive multimodal performance among lightweight open models.
	* Enhanced visual understanding and reasoning, particularly on fine-grained perception tasks
	* Retains efficient inference with the same flexible architecture and user-tunable speed-quality tradeoffs
	* Processes native resolutions up to 512×512 with intelligent patch-based handling for larger inputs

	For more details, see the [LFM2-VL-3B post](https://www.liquid.ai/blog/lfm2-vl-3b-a-new-efficient-vision-language-for-the-edge) and the [LFM2 blog post](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models).

	## 📄 Model details

	Due to their small size, we recommend fine-tuning LFM2-VL models on narrow use cases to maximize performance.
	They were trained for instruction following and lightweight agentic flows.
	Not intended for safety‑critical decisions.

	\| Property \| [LFM2-VL-450M](https://huggingface.co/LiquidAI/LFM2-VL-450M) \| [LFM2-VL-1.6B](https://huggingface.co/LiquidAI/LFM2-VL-1.6B) \| [LFM2-VL-3B](https://huggingface.co/LiquidAI/LFM2-VL-3B) \|
	\|---\|---:\|---:\|---:\|
	\| Parameters (LM only) \| 350M \| 1.2B \| 2.6B \|
	\| Vision encoder \| SigLIP2 NaFlex base (86M) \| SigLIP2 NaFlex shape-optimized (400M) \| SigLIP2 NaFlex large (400M) \|
	\| Backbone layers \| hybrid conv+attention \| hybrid conv+attention \| hybrid conv+attention \|
	\| Context (text) \| 32,768 tokens \| 32,768 tokens \| 32,768 tokens \|
	\| Image tokens \| dynamic, user-tunable \| dynamic, user-tunable \| dynamic, user-tunable \|
	\| Vocab size \| 65,536 \| 65,536 \| 65,536 \|
	\| Precision \| bfloat16 \| bfloat16 \| bfloat16 \|
	\| License \| LFM Open License v1.0 \| LFM Open License v1.0 \| LFM Open License v1.0 \|

	Supported languages: English

	Generation parameters: We recommend the following parameters:
	- Text: `temperature=0.1`, `min_p=0.15`, `repetition_penalty=1.05`
	- Vision: `min_image_tokens=64` `max_image_tokens=256`, `do_image_splitting=True`

	Chat template: LFM2-VL uses a ChatML-like chat template as follows:

	```
	<\|startoftext\|><\|im_start\|>system
	You are a helpful multimodal assistant by Liquid AI.<\|im_end\|>
	<\|im_start\|>user
	<image>Describe this image.<\|im_end\|>
	<\|im_start\|>assistant
	This image shows a Caenorhabditis elegans (C. elegans) nematode.<\|im_end\|>
	```

	Images are referenced with a sentinel (`<image>`), which is automatically replaced with the image tokens by the processor.

	You can apply it using the dedicated [`.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#applychattemplate) function from Hugging Face transformers.

	Architecture
	- Hybrid backbone: Language model tower (LFM2-2.6B) paired with SigLIP2 NaFlex vision encoders (400M shape-optimized)
	- Native resolution processing: Handles images up to 512×512 pixels without upscaling and preserves non-standard aspect ratios without distortion
	- Tiling strategy: Splits large images into non-overlapping 512×512 patches and includes thumbnail encoding for global context
	- Efficient token mapping: 2-layer MLP connector with pixel unshuffle reduces image tokens (e.g., 256×384 image → 96 tokens, 1000×3000 → 1,020 tokens)
	- Inference-time flexibility: User-tunable maximum image tokens and patch count for speed/quality tradeoff without retraining

	Training approach
	- Builds on the LFM2 base model with joint mid-training that fuses vision and language capabilities using a gradually adjusted text-to-image ratio
	- Applies joint SFT with emphasis on image understanding and vision tasks
	- Leverages large-scale open-source datasets combined with in-house synthetic vision data, selected for balanced task coverage
	- Follows a progressive training strategy: base model → joint mid-training → supervised fine-tuning

	## 🏃 How to run LFM2-VL

	You can run LFM2-VL with Hugging Face [`transformers`](https://github.com/huggingface/transformers) via installing Transformers from source as follows:

	```bash
	pip install git+https://github.com/huggingface/transformers.git@87be5595081364ef99393feeaa60d71db3652679 pillow
	```

	Here is an example of how to generate an answer with transformers in Python:

	```python
	from transformers import AutoProcessor, AutoModelForImageTextToText
	from transformers.image_utils import load_image

	# Load model and processor
	model_id = "LiquidAI/LFM2-VL-3B"
	model = AutoModelForImageTextToText.from_pretrained(
	model_id,
	device_map="auto",
	dtype="bfloat16"
	)
	processor = AutoProcessor.from_pretrained(model_id)

	# Load image and create conversation
	url = "https://www.ilankelman.org/stopsigns/australia.jpg"
	image = load_image(url)
	conversation = [
	{
	"role": "user",
	"content": [
	{"type": "image", "image": image},
	{"type": "text", "text": "What is in this image?"},
	],
	},
	]

	# Generate Answer
	inputs = processor.apply_chat_template(
	conversation,
	add_generation_prompt=True,
	return_tensors="pt",
	return_dict=True,
	tokenize=True,
	).to(model.device)
	outputs = model.generate(**inputs, max_new_tokens=64)
	processor.batch_decode(outputs, skip_special_tokens=True)[0]

	# This image captures a vibrant street scene in a Chinatown area. The focal point is a large red Chinese archway with gold and black accents, adorned with Chinese characters. Flanking the archway are two white stone lion statues, which are traditional guardians in Chinese culture.
	```

	You can directly run and test the model with this [Colab notebook](https://colab.research.google.com/drive/11EMJhcVB6OTEuv--OePyGK86k-38WU3q?usp=sharing).


	## 🔧 How to fine-tune

	We recommend fine-tuning LFM2-VL models on your use cases to maximize performance.

	\| Notebook \| Description \| Link \|
	\|-----------\|----------------------------------------------------------------------\|------\|
	\| SFT (TRL) \| Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using TRL. \| <a href="https://colab.research.google.com/drive/1csXCLwJx7wI7aruudBp6ZIcnqfv8EMYN?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|


	## 📈 Performance

	\| Model \| Average \| MMStar \| RealWorldQA \| MM-IFEval \| BLINK \| MMBench (dev en) \| OCRBench \| POPE \|
	\|-------------------\|----------\|--------\|--------------\|------------\|--------\|------------------\|-----------\|-------\|
	\| InternVL3_5-2B \| 66.50 \| 57.67 \| 60.78 \| 47.31 \| 50.97 \| 78.18 \| 834.00 \| 87.17 \|
	\| Qwen2.5-VL-3B \| 65.42 \| 56.13 \| 65.23 \| 38.62 \| 48.97 \| 80.41 \| 824.00 \| 86.17 \|
	\| InternVL3-2B \| 67.44 \| 61.10 \| 65.10 \| 38.49 \| 53.10 \| 81.10 \| 831.00 \| 90.10 \|
	\| SmolVLM2-2.2B \| 56.01 \| 46.00 \| 57.50 \| 19.42 \| 42.30 \| 69.24 \| 725.00 \| 85.10 \|
	\| LFM2-VL-3B \| 69.00 \| 57.73 \| 71.37 \| 51.83 \| 51.03 \| 79.81 \| 822.00 \| 89.01 \|

	More benchmark scores are reported in our [LFM2-VL-3B post](https://www.liquid.ai/blog/lfm2-vl-3b-a-new-efficient-vision-language-for-the-edge). We obtained the scores for competitive models using VLMEvalKit. Qwen3-VL-2B is not listed in the results table, as its release occurred the day before.

	## 📬 Contact

	- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
	- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).

	## Citation

	```
	@article{liquidai2025lfm2,
	title={LFM2 Technical Report},
	author={Liquid AI},
	journal={arXiv preprint arXiv:2511.23404},
	year={2025}
	}
	```