Instructions to use webbrain-one/webbrain-vl-2-450M-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use webbrain-one/webbrain-vl-2-450M-onnx with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-text-to-text', 'webbrain-one/webbrain-vl-2-450M-onnx');
WebBrain VL 2 450M ONNX
This is the Transformers.js/WebGPU export of
webbrain-one/webbrain-vl-2-450M at commit
7dd6050bbca6445ce19068b986bd62ee0d2467c3. It is derived from LiquidAI/LFM2.5-VL-450M and uses
the LFM Open License v1.0.
Browser-vision benchmark
On WebBrain's unchanged 100-case production-prompt suite, this deployed Q4-decoder/FP16-vision package reached 44/100 strict passes and a 76.24% mean rubric score.
| 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.
Production variant
| Component | Precision |
|---|---|
embed_tokens |
FP16 |
vision_encoder |
FP16 |
decoder_model_merged |
symmetric Q4 |
The vision encoder remains FP16 to preserve small browser text and GUI state
details. The package includes the exact onnx/ layout, tokenizer, processor,
configuration, and chat_template.jinja expected by Transformers.js.
Evaluation
The unchanged 100-case WebBrain production-prompt release gate did not pass:
- errors: 0
- strict passes: 44/100
- mean score: 0.7623922558922558
- six-section completion: 87/100
- real WebGPU smoke: passed
See eval/ for the full PyTorch, FP16 parity, Q4 ONNX, and WebGPU smoke
summaries. The WebGPU result comes from WebBrain's packaged
Transformers.js/ONNX Runtime Web stack; server-side ONNX Runtime alone is not
treated as proof of browser compatibility.
Provenance
- merged model:
webbrain-one/webbrain-vl-2-450M@7dd6050bbca6445ce19068b986bd62ee0d2467c3 - dataset:
webbrain-one/webbrain-vl-2-450M-dataset@fb191a0b73e0906e6c3b9f35f6ec0dba1337b72c - exporter:
Liquid4All/onnx-export@9a23ddd23035165f7414a5de3220a51e85780f64
The complete machine-readable provenance is in export-manifest.json and every
released file is covered by SHA256SUMS.
Historical fields inside immutable export and evaluation manifests retain the
repository IDs that were current when the release was produced. The current
public repository names are the webbrain-vl-2-450M links used in this card.
License and attribution
Retain the included LICENSE and ATTRIBUTION.md. The derivative remains
subject to the LFM Open License v1.0 redistribution and attribution terms.
Repository: webbrain-one/webbrain-vl-2-450M-onnx
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