Measured result
Mind2Web, MindAct protocol, cross-domain split (websites from domains never seen in training), 200 steps:
Table with columns: model, step success, element accuracy, operation F1| model | step success | element accuracy | operation F1 |
|---|
| AUBIN-31B-Web | 45.0 | 49.0 | 0.890 |
| AUBIN-12B-Web | 43.5 | | |
Training: 420 steps on the Mind2Web train split from the Gemma-4-31B base (4-bit, one free Kaggle T4 session). The step with the best score on a held-out dev split (step 300) was selected before testing. Result file: reports/w31s_test_domain.json.
Use
from aubin import AubinOmniomni = AubinOmni.from_hub("emrevrg/AUBIN-Omni", size="31B")omni.web_step("Book a flight from New York to Paris", {"n1": "<input placeholder='From'>", "n2": "<a>Hotels</a>"})
♥ Help AUBIN get seen
On Hugging Face, likes decide what people discover. Big labs have marketing teams; AUBIN has one 17-year-old student and measured results.
If AUBIN is useful, interesting or just impressive to you, press ♥ Like at the top of this page and on AUBIN-Omni, AUBIN-31B and AUBIN-12B, then share it with one person who builds with AI.
Every like helps an independent, open, honestly measured model get discovered.
🇹🇷 Beğenin, AUBIN'in görünür olmasını sağlar: sayfanın üstündeki ♥ Like'a basarak destek ol ve bir arkadaşına gönder.
Support Norovox
Built by a 17-year-old high school student: no sponsor, no budget, just free GPUs and AI subscriptions paid for with difficulty.
Support goes into GPU compute, training, and the AI development tools this work depends on (such as Claude); supporters are credited and get early access.
zgremre@gmail.com · emrevrgdev@gmail.com · Why and how →
🇹🇷 17 yaşında bir lise öğrencisinin eseri: destekçisiz, bütçesiz; ücretsiz GPU'lar ve zorlukla ödenen yapay zekâ abonelikleriyle. Desteğin GPU'ya, eğitime ve bu işin dayandığı yapay zekâ geliştirme araçlarına (Claude gibi) gider.
Built with Claude Opus 5.5 and GPT-5.6 Sol; because of OpenAI usage limits, the final stretch was completed with Claude Opus 5.5.
License: Apache-2.0 (adapters and code). Base models: Google Gemma 4 (Apache-2.0).