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)

# pip install -U transformers accelerate
# 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=256)
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
```

webbrain-one commited on Aug 22

Commit

ce71fe7

verified ·

1 Parent(s): 03eebce

Add benchmark charts and refresh renamed links

Browse files

Files changed (5) hide show

.gitattributes +2 -0
README.md +39 -8
SHA256SUMS +3 -1
assets/browser-vision-benchmark-strict-passes.png +3 -0
assets/browser-vision-performance-vs-size.png +3 -0

.gitattributes CHANGED Viewed

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/browser-vision-benchmark-strict-passes.png filter=lfs diff=lfs merge=lfs -text
+assets/browser-vision-performance-vs-size.png filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -5,7 +5,7 @@ pipeline_tag: image-text-to-text
 license: other
 license_name: lfm1.0
 datasets:
-  - webbrain-one/webbrain-browser-vision-tiny-dataset-2
 tags:
   - browser-automation
   - gui-understanding
@@ -13,9 +13,9 @@ tags:
   - webbrain
 ---
-# WebBrain-VL 2 450M
-WebBrain-VL 2 450M is a browser-GUI specialist derived from
 [`LiquidAI/LFM2.5-VL-450M`](https://huggingface.co/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
@@ -108,6 +108,37 @@ six-section prompt exactly once after checkpoint selection.
 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.
@@ -128,8 +159,8 @@ retain their row-level upstream licensing recorded in the dataset repository.
 ## Reproducibility
-Training, corpus construction, deduplication/leakage checks, validation, ONNX
-packaging, and evaluation utilities are published at
-[`esokullu/lfm-finetune`](https://github.com/esokullu/lfm-finetune) commit
-`6724051534de042cf4897b99ceb1d29c7926d61f`. `release-manifest.json` and `SHA256SUMS` pin the
-artifact provenance and every released file.

 license: other
 license_name: lfm1.0
 datasets:
+  - webbrain-one/webbrain-vl-2-450M-dataset
 tags:
   - browser-automation
   - gui-understanding
   - webbrain
 ---
+# WebBrain VL 2 450M
+WebBrain VL 2 450M is a browser-GUI specialist derived from
 [`LiquidAI/LFM2.5-VL-450M`](https://huggingface.co/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
 Machine-readable results are in `eval/`. Latency is implementation- and
 hardware-specific, so it is not presented as a portable speed claim.
+## Deployed ONNX/WebGPU comparison
+The separately published
+[`webbrain-one/webbrain-vl-2-450M-onnx`](https://huggingface.co/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 above.
+![Strict-pass comparison across compact browser-vision models](./assets/browser-vision-benchmark-strict-passes.png)
+![Strict passes plotted against advertised or effective parameter class](./assets/browser-vision-performance-vs-size.png)
+| 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](https://github.com/esokullu/webbrain/tree/main/test/vision)
+are public. Gemma E4B is plotted at its effective 4B class. Latency is omitted
+because the rows used different machines, quantizations, and inference stacks.
+Further reading:
+- [Tiny Vision Models Compared II](https://webbrain.one/blog/tiny-vision-models-compared-ii/)
+- [From 16K to 50K Browser Screenshots](https://webbrain.one/blog/fine-tuning-lfm25-vl-450m-for-browser-tasks/)
 ## Limitations
 - Small, low-contrast, occluded, or non-Latin text can still be missed.
 ## 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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