Instructions to use webbrain-one/webbrain-vl-2-450M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use webbrain-one/webbrain-vl-2-450M with Transformers:

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

pipe = pipeline("image-text-to-text", model="webbrain-one/webbrain-vl-2-450M")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("webbrain-one/webbrain-vl-2-450M")
model = AutoModelForMultimodalLM.from_pretrained("webbrain-one/webbrain-vl-2-450M", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use webbrain-one/webbrain-vl-2-450M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "webbrain-one/webbrain-vl-2-450M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "webbrain-one/webbrain-vl-2-450M",
		"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/webbrain-one/webbrain-vl-2-450M

SGLang

How to use webbrain-one/webbrain-vl-2-450M 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 "webbrain-one/webbrain-vl-2-450M" \
    --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": "webbrain-one/webbrain-vl-2-450M",
		"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 "webbrain-one/webbrain-vl-2-450M" \
        --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": "webbrain-one/webbrain-vl-2-450M",
		"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 webbrain-one/webbrain-vl-2-450M with Docker Model Runner:
```
docker model run hf.co/webbrain-one/webbrain-vl-2-450M
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

WebBrain VL 2 450M

WebBrain VL 2 450M is a browser-GUI specialist derived from LiquidAI/LFM2.5-VL-450M. It turns a browser viewport screenshot into the six-section observation format consumed by WebBrain's planning agent. This repository preserves both the merged Transformers checkpoint and the matching LoRA adapter.

Deployed ONNX/WebGPU benchmark

The separately published webbrain-one/webbrain-vl-2-450M-onnx package reached 44/100 strict passes and a 76.24% mean rubric score in its Q4-decoder/FP16-vision deployment. The WebBrain point in both charts below is that deployed ONNX/WebGPU row, not the 36/100 merged-PyTorch row reported later.

Model and tested runtime	Parameter class	Strict passes	Mean rubric
Qwen 3.5 4B, MLX	4B	55/100	83.4%
WebBrain VL 2, deployed ONNX Q4/FP16	450M	44/100	76.2%
Gemma 4 E4B IT, MLX	E4B	41/100	81.3%
Qwen 3.5 2B, MLX	2B	39/100	79.1%
Qwen 3.5 0.8B	0.8B	20/100	41.6%
LFM2.5-VL-1.6B, MLX	1.6B	12/100	44.4%

All rows used the same 100 screenshots and production six-section prompt. The benchmark images, rubrics, runner, and committed result files are public. Gemma E4B is plotted at its effective 4B class. Latency is omitted because the rows used different machines, quantizations, and inference stacks.

Intended use

Browser screenshot description for a downstream automation planner.
Visible UI text, inputs, form state, dialogs, errors, loading indicators, consent surfaces, CAPTCHAs, overlays, and blockers.
A compact local fallback when a larger vision model is unavailable.

This is not a general replacement for a frontier VLM. Do not treat an observation as authorization to perform an action or trust it for passwords, payments, identity, or other high-impact decisions.

Training

Item	Value
Base model	`LiquidAI/LFM2.5-VL-450M`
Dataset	46,879 train + 3,121 validation rows
Method	Supervised LoRA fine-tuning, then merge
LoRA	rank 16, alpha 32, dropout 0.05
Selected checkpoint	step 2,929
Completed training	step 2,929, one epoch
Effective batch size	16
Learning rate	1e-4, cosine schedule, 3% warmup
Maximum image tokens	256
Precision	BF16 with gradient checkpointing
Hardware	one NVIDIA RTX 4090 24 GB
Peak VRAM	23,272 MiB

Validation was measured on the immutable 3,121-row validation split. The held-out 100-case WebBrain suite was not used for checkpoint selection. The table below compares the two candidate checkpoints with the same deterministic post-training evaluator. Its absolute loss scale differs from the in-training Trainer metric; the periodic Trainer loss independently improved from 0.366836 at step 500 to 0.326216 at step 2,500.

Step	Validation loss
2,500	0.326301
2,929	0.324976

The root model and adapter/ are the final one-epoch checkpoint.

Data

The public corpus is webbrain-one/webbrain-vl-2-450M-dataset at commit fb191a0b73e0906e6c3b9f35f6ec0dba1337b72c. It contains exactly 50,000 unique browser screenshots:

Source	License	Rows
targeted browser-GUI synthetic	CC-BY-4.0	33,354
`HuggingFaceM4/WebSight`	CC-BY-4.0	9,561
`Farama-Foundation/miniwob-plusplus`	MIT	4,592
`docling-project/screenparse`	CC-BY-4.0	2,493

Teacher labels comprise 49,634 accepted responses from qwen/qwen3.6-35b-a3b and 366 from Qwen/Qwen3.6-35B-A3B-Q4_K_M. Teacher outputs can contain OCR errors or hallucinations.

The 50K mix was deliberately balanced across 16,646 legacy rows and targeted browser capabilities: multilingual OCR (8,000), forms (4,500), authentication (2,500), modals/consent (3,500), loading states (2,500), tables (3,500), dashboards (2,500), calendars (2,500), occlusion/contrast (1,500), and general browser pages (2,354).

The frozen local snapshot ID is fb55b3afe2026634d5fadbe35dc4bae5b00602b44634459f65ca93e27f227218. Validation confirmed zero exact or perceptual overlap with WebBrain's unchanged 100-case suite and zero train/validation leakage. A source- and category-balanced manual audit accepted all 44 sampled records.

Evaluation

The unchanged 100-case WebBrain browser-vision suite used the production six-section prompt exactly once after checkpoint selection.

Checkpoint	Strict passes	Mean rubric
Base LFM2.5-VL-450M	0/100	4.17%
v1, 16,646 examples	30/100	70.06%
v2, 50,000 examples	36/100	74.99%

Machine-readable results are in eval/. Latency is implementation- and hardware-specific, so it is not presented as a portable speed claim.

Limitations

Small, low-contrast, occluded, or non-Latin text can still be missed.
UI labels and state can be hallucinated, especially around overlays.
Focus, disabled state, exact form errors, and readiness signals need visual corroboration.
The model inherits teacher errors and response-style biases.
General-image regression has not been measured; evaluate separately before using this checkpoint outside browser screenshots.
A syntactically valid six-section response does not make automation safe.

License and attribution

This derivative is distributed under the LFM Open License v1.0, subject to its redistribution and attribution terms. Retain the included LICENSE, the base-model attribution, and all dataset source licenses. Dataset components retain their row-level upstream licensing recorded in the dataset repository.

Reproducibility

release-manifest.json, the training configuration and metrics, machine-readable evaluation files, and SHA256SUMS pin the artifact provenance and released files. Historical manifest and evaluation fields retain the repository IDs that were current when the release was produced; the current public names are the webbrain-vl-2-450M repositories linked in this card.

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Safetensors

Model size

0.4B params

Tensor type

BF16

Model tree for webbrain-one/webbrain-vl-2-450M

Base model

LiquidAI/LFM2.5-350M-Base

Finetuned

LiquidAI/LFM2.5-350M

Finetuned

LiquidAI/LFM2.5-VL-450M

Finetuned

(31)

this model

Quantizations

1 model

webbrain-one
/

webbrain-vl-2-450M

WebBrain VL 2 450M

Deployed ONNX/WebGPU benchmark

Intended use

Training

Data

Evaluation

Limitations

License and attribution

Reproducibility

Model tree for webbrain-one/webbrain-vl-2-450M

Dataset used to train webbrain-one/webbrain-vl-2-450M