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emrevrg
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Model Details
Model Provider
emrevrg
Model Tree
google/gemma-4-E4B-it
Input Modalities
Output Modalities
Supported Functionality
7x7 grid as text, walls, 5 deadly lava cells, 4 objects that block movement, command like "go to the red key".
Step accuracy = the move is on a shortest safe path (BFS ground truth). Training used 3,000 procedurally generated episodes
(seeds 1000+, 15,161 states, soft labels over all shortest moves); the test episodes are disjoint. The safety shield only
removes moves into visible lava/walls/objects (AubinController.act(..., allowed=safe_moves)); the same shield is applied
to the rule baselines so its effect is separated from the model's.
| controller | success | lava deaths ↓ | step accuracy | median ms / move (T4, 4-bit) |
|---|---|---|---|---|
| random | 6% | 40 | 35.3% | – |
| random + safety shield | 13% | 0 | 53.4% | – |
| greedy (ignores obstacles) | 58% | 26 | 56.8% | – |
| greedy + safety shield | 89% | 0 | 87.6% | – |
| Gemma-4-E4B base, no control training (one pass per move) | 8% | 26 | 39.2% | 329 |
| Gemma-4-E4B base, no control training + safety shield | 11% | 0 | 55.4% | 330 |
| AUBIN-E4B-Control (one pass per move) | 58% | 28 | 60.0% | 384 |
| AUBIN-E4B-Control + safety shield (never steps on visible lava/walls) | 95% | 0 | 93.2% | 385 |
python
pip install "git+https://huggingface.co/emrevrg/AUBIN-12B#subdirectory=code"from aubin import Aubin, AubinControllerctl = AubinController(Aubin("emrevrg/AUBIN-E4B-Control"), actions={"up": "move one cell up (row - 1)", "down": "move one cell down (row + 1)", "left": "move one cell left (column - 1)", "right": "move one cell right (column + 1)"}, instructions="Choose the next move that follows the command along a shortest safe path " "(never step on lava, objects block movement).")ctl.reset() # new episoder = ctl.act(observation, command="go to the red key", allowed={"up", "left"}) # allowed = moves that are safe right nowr["action"], r["confidence"], r["latency_ms"]control_bench.py, training: control_train.py
(in the AUBIN-12B repo code/).AUBIN can learn from feedback without retraining. Verified cases go into an external decision memory (a write takes well under
a millisecond with the hashing embedder), and a self-calibrator (Hedge / multiplicative weights) shifts trust per source between
the model, the memory and their fusion — so where memory is not useful yet, AUBIN keeps trusting itself. Measured on Kev's public
suites; full tables, protocol and code: reports/AUBIN_LEARN.md, code/aubin/learn.py.
reports/AUBIN_RESULTS_2026-10-03.md)| benchmark | AUBIN | reference |
|---|---|---|
| Typed decisions (2,000 decisions) | 77.55 (AUBIN-Learn, weights fixed before test) | Laya 76.65 · meraGPT 76.8 · Jev 72.7 |
| Kev suites, Kev's training sources (kev_test) | 85.7 (AUBIN ensemble, selected on cal split) | Kev-0.8B 83.8 · Kev-4B 86.5 · Kev-9B 87.4 |
| Kev suites, transfer test | 86.5 ensemble · 89.0 AUBIN-31B | – |
| Mind2Web cross-domain step SR (200 steps) | 48.5 AUBIN-31B, no web training | MindAct-XL 39.6 · GPT-4 26.4 |
| Grid control, 100 unseen episodes | 92% success, 0 lava deaths (12B-Control + shield) | greedy rule + same shield 89% |
On Kev's own training sources AUBIN is still 1.7 points behind Kev-9B; this is stated, not hidden.
Evening update (3 Oct, all measured, details in the results report):
| benchmark | AUBIN | reference |
|---|---|---|
| ScreenSpot click accuracy (visual computer use) | 69.3 AUBIN-E4B-Screen (zero-shot 48.5; 67.7 after round 1) | SeeClick 53.4 · CogAgent 47.4 · UGround-7B 73.3 · UI-TARS-7B 89.5 |
| Mind2Web step success, cross-task / website / domain | 47.0 / 37.0 / 43.5 (12B) · 47.0 / 34.5 / 42.0 (E4B, ≈2× faster) | MindAct-XL 52.0 / 38.9 / 39.6 |
| ViZDoom FPS, kills per episode (30 episodes) | 17.3 with in-game self-learning (AUBIN-Learn) · 15.6 without (AUBIN-12B, 0.6 s/move) | random 1.3 · scripted rule 18.8 |
| Kev training sources + learned skills (selected on kev_dev + cal only) | 87.08, NLL 0.43 (ensemble 85.69, NLL 0.79) | Kev-9B 87.4 (not yet beaten) · Kev-27B 87.0 · Kev-4B 86.5 |
New in code/: AubinLearning.acquire_skill (learns a skill, self-tests on held-out data, enables only on proven gain), learn_skill2/3.py, fuse_multi.py, screenspot_train/eval.py, fps_vizdoom.py.
AubinEngine routes between them)| ability | model | measured |
|---|---|---|
| typed decisions (choice / score / yes-no), calibrated | AUBIN-31B · AUBIN-12B · 12B-v3b · 12B-v3d · E4B-v3 | typed-decisions 77.55 (#1) · Jev's set 8/8 · Kev unseen sources 89.8 · Kev training sources 87.08 |
| web agent | AUBIN-12B-Web · AUBIN-E4B-Web (fast) · 31B web training running | Mind2Web cross-domain 43.5 (MindAct-XL 39.6) |
| visual computer use (click on screenshots) | AUBIN-E4B-Screen · 12B screen training running | ScreenSpot 69.3 |
| real-time control | AUBIN-12B-Control · AUBIN-E4B-Control | 92% success, 0 lava deaths |
| FPS play, self-learning in game | AUBIN-12B + AUBIN-Learn | ViZDoom 17.3 kills/episode vs 15.6 without learning (30 episodes) |
| self-learning, self-acquired skills | AubinLearning (learn, acquire_skill) in every repo | skills switch on only after a held-out self-test proves a gain |
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.
Norovox is built by one 17-year-old high school student in Türkiye: no company, no sponsor, no budget. Every model on this page was trained on free GPU quotas that run out within days. When they run out, training stops halfway and the next, stronger version has to wait, sometimes for a week.
Your support goes directly into GPU time. Even a small contribution lets a training run finish instead of stopping, and helps the Nythos, N1, AUBIN and Norovox Embed models get better faster. Supporters are credited in the model cards and get early access to new releases.
Contact: zgremre@gmail.com · emrevrgdev@gmail.com
🇹🇷 Norovox'u Türkiye'de 17 yaşında bir lise öğrencisi tek başına geliştiriyor: şirket yok, sponsor yok, bütçe yok. Buradaki modellerin hepsi birkaç günde biten ücretsiz GPU kotalarıyla eğitildi; kota bitince eğitim yarıda kalıyor ve daha güçlü sürüm bekliyor. Desteğin doğrudan GPU süresine gider; küçük bir katkı bile bir eğitimin yarıda kalmadan tamamlanmasını sağlar. Destekçiler model kartlarında anılır ve yeni sürümlere erken erişir. İletişim: zgremre@gmail.com · emrevrgdev@gmail.com
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).
pip install "git+https://huggingface.co/emrevrg/AUBIN-12B#subdirectory=code"from aubin import Aubin, AubinControllerctl = AubinController(Aubin("emrevrg/AUBIN-E4B-Control"), actions={"up": "move one cell up (row - 1)", "down": "move one cell down (row + 1)", "left": "move one cell left (column - 1)", "right": "move one cell right (column + 1)"}, instructions="Choose the next move that follows the command along a shortest safe path " "(never step on lava, objects block movement).")ctl.reset() # new episoder = ctl.act(observation, command="go to the red key", allowed={"up", "left"}) # allowed = moves that are safe right nowr["action"], r["confidence"], r["latency_ms"]