Buckets:
| license: mit | |
| base_model: Qwen/Qwen3.5-4B | |
| pipeline_tag: text-classification | |
| library_name: transformers | |
| tags: | |
| - nli | |
| - cross-encoder | |
| - qwen3.5 | |
| - reranker | |
| - text-classification | |
| language: | |
| - en | |
| # openjev — Qwen3.5 trained as jev model | |
| <video controls src="https://huggingface.co/AlexWortega/openjev/resolve/main/videos/doom_zs_position.mp4" width="720"></video> | |
| <video controls src="https://huggingface.co/AlexWortega/openjev/resolve/main/videos/doom_vision_zeroshot_pixels.mp4" width="720"></video> | |
|  | |
| **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. Doom above is played zero-shot, first from the text state and then straight from the | |
| pixels through the Qwen3.5 vision tower. Nothing is trained per task. | |
| ## What's inside | |
| * `qwen3.5-4b-nli/` — the 4B jev checkpoint (`Qwen3_5ForSequenceClassification`, 3 labels: `contradiction`, `entailment`, `neutral`, last-token pooling, trained with plain cross-entropy over the three classes). | |
| * `modeling_openjev.py` — `OpenJevCrossEncoder`: `predict`, `rerank`, `grade`, `latents`. | |
| * `code/` — everything used here: the trainer, the multiple-choice harness, Flappy Bird and Doom (text and pixels), the radar. | |
| * `videos/` — Flappy Bird and Doom 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") | |
| 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 | |
| ``` | |
| Or with plain transformers: | |
| ```python | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| tok = AutoTokenizer.from_pretrained("AlexWortega/openjev", subfolder="qwen3.5-4b-nli") | |
| model = AutoModelForSequenceClassification.from_pretrained("AlexWortega/openjev", subfolder="qwen3.5-4b-nli") | |
| text = model.config.nli_template.format(premise="...", hypothesis="...") | |
| ``` | |
| Reference point: [dleemiller's NLI cross-encoders](https://huggingface.co/blog/dleemiller/nli-xenc-ways-to-use). Licence MIT. | |
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