--- title: openjev canonical_url: "https://www.modelscope.cn/models/AlexWortega/openjev" md_url: "https://www.modelscope.cn/models/AlexWortega/openjev.md" repository: AlexWortega/openjev last_updated: 2026-09-20 license: mit pipeline_tag: text-classification tasks: - text-classification base_model: - Qwen/Qwen3.5-4B base_model_relation: finetune parameters: 43.7B tensor_type: - BF16 library_name: - transformer - safetensors - pytorch frameworks: - pytorch language: - en downloads: 77 stars: 6 tags: - nli - cross-encoder - qwen3.5 - reranker - text-classification - image-text-to-text --- # openjev > openjev - AlexWortega 在 ModelScope 开源的模型。openjev — Qwen3.5 trained as jev model AlexWortega/openjev 是 ModelScope 魔搭社区上的 43.7B 参数text-classification模型，采用 mit 许可，基于 Qwen/Qwen3.5-4B 构建。 - **Repository**: AlexWortega/openjev - **License**: mit - **Tasks**: text-classification - **Parameters**: 43.7B - **Base model**: Qwen/Qwen3.5-4B - **Tags**: nli, cross-encoder, qwen3.5, reranker, text-classification, image-text-to-text - **Downloads**: 77 - **Stars**: 6 - **Last updated**: 2026-09-20 Source: https://www.modelscope.cn/models/AlexWortega/openjev --- # openjev — Qwen3.5 trained as jev model **openjev** is Qwen3.5 turned into a *jev* model: a single cross-encoder that reads a premise and a hypothesis and answers with entailment, contradiction or neutral. That one primitive is enough to rerank answers, grade them against a reference, guard content, and play games in real time: hand it the game state and a few statements about it, and the argmax entailment is the move. Nothing is trained per task. ## openjev-4B v2: text, images and agents The new 4B checkpoint (`qwen3.5-4b-nli-v2/`) reads images as well as text and was trained on a much larger and harder mixture. It is strictly zero-shot on everything shown here. * Doom straight from the pixels (first video): **10.4 kills** per episode, twice the v1 model (5.2); random play gets 1. * Crafts an **iron pickaxe from nothing in real Minecraft** (second video): 11 milestones in ~22 decisions, driven by a backward-chaining scaffold where the jev model only checks statements about the inventory and the world. * Much stronger on adversarial NLI (ANLI r3 0.42 → 0.63, WANLI 0.63 → 0.77) and on image claims (0.52 → 0.84), better reranking (ARC-Challenge 0.59 → 0.72, MMLU 0.47 → 0.53), same MNLI (0.91). Doom from the text state (v2, 11 kills per episode; a perfect-information bot gets 18.8): ![radar](assets/radar_openjev.png) Bigger jev: Qwen3.5-35B-A3B (MoE) as the backbone (`qwen3.5-35b-a3b-nli/`). Zero-shot, and with the backbone frozen plus a small MLP head on the last-token latent (`mlp_heads_35b/`, one head per task, loadable with `LatentMLPHead.load`): ![radar 35B](assets/radar_openjev_35b.png) ## What's inside * `qwen3.5-4b-nli-v2/` — **recommended**: the 4B v2 jev checkpoint, text + images. * `qwen3.5-4b-nli/` — the original 4B jev checkpoint (text). * `qwen3.5-35b-a3b-nli/` — the 35B-A3B MoE jev checkpoint (load with `modeling_qwen35_moe_seqcls.py`). * All checkpoints: `Qwen3_5ForSequenceClassification`, 3 labels `contradiction`, `entailment`, `neutral`, last-token pooling, trained with plain cross-entropy over the three classes. * `modeling_openjev.py` — `OpenJevCrossEncoder`: `predict`, `predict_hypotheses`, `rerank`, `grade`, `latents`, `latents_hypotheses`; `LatentMLPHead` for the per-task heads. * `modeling_qwen35_moe_seqcls.py` — `Qwen3_5MoeForSequenceClassification` for the 35B-A3B backbone. * `mlp_heads_35b//` — `head.pt` + `norm.npz` + `meta.json`, the 35B latent + MLP heads behind the second radar. * `code/` — everything used here: the trainer and data mixture builder, the evaluation harness, Flappy Bird, Doom (text and pixels), the Minecraft scaffold and bot, the radar. * `videos/` — Flappy Bird, Doom and Minecraft replays; `results/` — raw JSON for every run and the full report. ## Use it ```python from modeling_openjev import OpenJevCrossEncoder jev = OpenJevCrossEncoder("AlexWortega/openjev", subfolder="qwen3.5-4b-nli-v2") jev.predict([("The bird is 0.05 below the centre of the gap.", "The bird is below the centre of the gap.")]) # -> [[contradiction, entailment, neutral]] probabilities jev.rerank("Which gas do plants absorb during photosynthesis?", ["oxygen", "carbon dioxide", "nitrogen"]) # -> index of the option with the highest entailment jev.predict_hypotheses("Which gas do plants absorb during photosynthesis?", ["The correct answer is: oxygen", "The correct answer is: carbon dioxide", "The correct answer is: nitrogen"]) # -> one [contradiction, entailment, neutral] row per hypothesis ``` `predict_hypotheses` and `latents_hypotheses` use the existing pairwise batch for one or two hypotheses. For three or more, they compute the common token prefix once, then score every hypothesis in one batched continuation. `rerank` uses the same rule. Qwen3.5 has recurrent linear-attention layers, so a 4D packed tree mask alone would mix branches; the shared prefix cache is copied into separate batch entries for the suffixes. This path is for text inputs. Its batch size and suffix padding use memory proportional to the number and length of the hypotheses; split very large option sets into smaller calls. Or with plain transformers: ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer tok = AutoTokenizer.from_pretrained("AlexWortega/openjev", subfolder="qwen3.5-4b-nli-v2") model = AutoModelForSequenceClassification.from_pretrained("AlexWortega/openjev", subfolder="qwen3.5-4b-nli-v2") text = model.config.nli_template.format(premise="...", hypothesis="...") ``` Images go inside the premise as `<|vision_start|><|image_pad|>…<|vision_end|>` with `pixel_values` / `image_grid_thw` from the Qwen3.5 image processor; see `code/doom_vision.py` and `code/eval_image_nli.py`. Reference point: [dleemiller's NLI cross-encoders](https://huggingface.co/blog/dleemiller/nli-xenc-ways-to-use). Licence MIT.