| --- |
| license: apache-2.0 |
| library_name: pytorch |
| pipeline_tag: image-classification |
| tags: |
| - decision-model |
| - vision |
| - multimodal |
| - system-one |
| - s1mb |
| - darwin |
| - ztc |
| - custom-code |
| --- |
| |
| # Darwin-35B-ZTC-Vision |
|
|
| <p align="center"> |
| <a href="https://huggingface.co/spaces/hotchpotch/S1MB-leaderboard?category=vision-v1"><img src="https://img.shields.io/badge/S1MB_vision--v1-merged_2026--10--19-gold?style=for-the-badge" alt="S1MB vision-v1"></a> |
| <a href="https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v9"><img src="https://img.shields.io/badge/🧬_Text-Darwin--27B--ZTC--v9-blue?style=for-the-badge" alt="Text model"></a> |
| </p> |
| <p align="center"> |
| <a href="https://huggingface.co/FINAL-Bench/Darwin-35B-A3B-Opus"><img src="https://img.shields.io/badge/🧬_Backbone-Darwin--35B--A3B--Opus-blue?style=for-the-badge" alt="Backbone"></a> |
| <a href="https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v5"><img src="https://img.shields.io/badge/🧬_Series-Darwin--27B--ZTC--v5-blue?style=for-the-badge" alt="ZTC series"></a> |
| <a href="https://huggingface.co/FINAL-Bench"><img src="https://img.shields.io/badge/🏠_Org-FINAL--Bench-black?style=for-the-badge" alt="FINAL-Bench"></a> |
| <a href="https://www.apache.org/licenses/LICENSE-2.0"><img src="https://img.shields.io/badge/License-Apache_2.0-green?style=for-the-badge" alt="License"></a> |
| </p> |
|
|
| Darwin-35B-ZTC-Vision is a vision decision model. It does not generate text. Given one or more images, a state and a typed question, it returns a calibrated probability for every option in one forward pass: yes or no (`noul`), one of several options (`choice`), or a level on an ordered scale (`score`). It handles up to four images per request, for example "which of the two photos has the higher quality" or "is there a defect on this part". |
|
|
| It is a 35B mixture-of-experts model (about 3B active parameters per token) built on [Darwin-35B-A3B-Opus](https://huggingface.co/FINAL-Bench/Darwin-35B-A3B-Opus) with the Darwin ZTC decision readout. It is the vision member of the ZTC series; the text member is [Darwin-27B-ZTC-v9](https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v9). |
|
|
| ## Results |
|
|
| ### S1MB (System One Mosaic Benchmark), vision-v1 |
|
|
| All 20 required vision benchmarks complete (the optional Box tasks are not supported), zero failed decisions. Merged by the maintainer on 2026-10-19 ([PR #76](https://huggingface.co/datasets/hotchpotch/s1mb-result/discussions/76)). At submission it ranked first (Borda 91.00) on the published vision results of 2026-10-17 (16 complete models) with this model added, recomputed with the leaderboard's own Borda method; that table is below. More models have entered since: on the published results of 2026-10-19 (32 complete models) the same recomputation places it fifth with 81.80. The [live vision leaderboard](https://huggingface.co/spaces/hotchpotch/S1MB-leaderboard?category=vision-v1) is authoritative. |
|
|
| | Rank (2026-10-17 data) | Model | Borda score | Mean baseline-adjusted score | |
| |---|---|---|---| |
| | **1** | **Darwin-35B-ZTC-Vision** | **91.00** | **73.98** | |
| | 2 | Jev 1.14 (Preview) | 90.00 | 74.64 | |
| | 3 | Perplexity Decider Flash 1.2 | 77.50 | 67.35 | |
| | 4 | Imajev 4B | 67.83 | 69.29 | |
| | 5 | Clef-Flash | 63.50 | 63.70 | |
|
|
| ### Selected benchmarks (baseline-adjusted score) |
|
|
| | Benchmark | Darwin-35B-ZTC-Vision | Jev 1.14 (Preview) | |
| |---|---|---| |
| | tallyqa-score (counting) | 0.720 | 0.546 | |
| | visa-noul (anomaly detection) | 0.603 | 0.467 | |
| | koniq-score (image quality) | 0.196 | 0.143 | |
| | cvbench-relation-choice | 0.965 | 0.944 | |
| | pope-noul (object hallucination) | 0.793 | 0.767 | |
| | nights-choice (perceptual similarity, 3 images) | 0.886 | 0.866 | |
| | vsr-noul (spatial relations) | 0.775 | 0.768 | |
| | generalization-vision-easy-choice | 0.976 | 0.967 | |
|
|
| ## Training and data |
|
|
| Full-weight fine-tuning with a decision readout on public **train** splits and our own synthetic data: |
|
|
| - COCO train2017 images (licensed subset) for verification, counting, relation and multi-hop spatial questions computed from the annotations, plus synthetic quality degradations rated on a five-level scale. |
| - VisA normal images from the official train split (CC BY 4.0) with synthetic surface defects, plus procedural textures. |
| - Public train splits of text and chart decision datasets, and the text decision data of the Darwin ZTC series. |
|
|
| No S1MB question text was used for training. |
|
|
| ## Usage |
|
|
| Python 3.12, a CUDA GPU with about 75 GB of free memory (BF16 weights), and the code shipped in this repository (`autojev/`). |
|
|
| ```python |
| import sys |
| from pathlib import Path |
| from huggingface_hub import snapshot_download |
| |
| path = Path(snapshot_download("FINAL-Bench/Darwin-35B-ZTC-Vision")) |
| sys.path.insert(0, str(path)) |
| from autojev.model import DecisionModel, answer |
| |
| model = DecisionModel(checkpoint=path).eval() |
| question = { |
| "type": "noul", |
| "instructions": "Determine whether image_1 shows a defect such as a scratch, crack, hole or stain.", |
| "criteria": {"true": "A defect is present", "false": "The surface is normal"}, |
| } |
| row = {"state": {"images": ["image_1"]}, "images": ["part.jpg"], "question": question} |
| print(answer(question, model.predict([row])[0])) |
| ``` |
|
|
| `decide_server.py` serves `POST /v1/systemone` (images as data URLs, up to four per request): |
|
|
| ```bash |
| ZTC_MODEL=/path/to/snapshot PORT=8410 python decide_server.py |
| ``` |
|
|
| `s1mb_adapter/ztc_http.py` is the S1MB evaluator adapter we used to measure the results above against this server. |
|
|
| ## License |
|
|
| Apache-2.0 for the weights and code in this repository. Training images keep their own licenses (COCO: per-image Flickr licenses and CC BY 4.0 annotations; VisA: CC BY 4.0). |
|
|
