AlexWortega/openjev — new model trending #30 on Hugging Face
AlexWortega released openjev on Hugging Face, a Qwen3.5-based NLI cross-encoder (4B and 35B-A3B MoE) for reranking, grading, and zero-shot game agents.
The openjev model repurposes Qwen3.5 into a three-label cross-encoder (entailment, contradiction, neutral) released under MIT license and trending #30 on Hugging Face. It ships a 4B sequence-classification checkpoint plus a frozen Qwen3.5-35B-A3B MoE backbone with small per-task MLP heads on the last-token latent. The author demonstrates zero-shot play of Flappy Bird and Doom from text states and raw pixels via the Qwen3.5 vision tower, with no per-task training. Included code covers training, a multiple-choice harness, replays, and raw JSON results.
- Open-weights MIT-licensed cross-encoder with 4B and 35B-A3B MoE variants
- Three-class NLI primitive supports reranking, grading, content guarding, and game-playing
- Frozen 35B backbone with per-task MLP heads on last-token latents
- Doom and Flappy Bird played zero-shot from text and pixels
- Trending #30 on Hugging Face at publication time
- Vendors
- Hugging Face
- Products
- openjevQwen3.5-35B-A3BQwen3.5-4B
- Organizations
- Hugging Face
- AI models
- openjevQwen3.5-35B-A3BQwen3.5-4B
From the original article
1 min read at huggingface.co<video controls src="https://huggingface. co/AlexWortega/openjev/resolve/main/videos/doom_zs_position. mp4" width="720"></video> <video controls src="https://huggingface.
By AlexWortega · huggingface.co
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