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embeddinggemma-tetris-system1
jaichang/embeddinggemma-tetris-system1
embeddinggemma-tetris-system1 is a feature extraction model from jaichang. Use it when you need embeddings to search or compare text. The card lists the license as apache-2.0.
This model is a real-time System 1 decision engine built on top of google/embeddinggemma-300m, designed to evaluate and select optimal 2D Tetris placements without generating autoregressive text tokens.
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Public
Updated Sep 24, 2026
Repo size
640 MB
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At a glance
- Task
- Feature Extraction
- License
- apache-2.0
- Access
- Public
- Created
- Sep 24, 2026
- Updated
- Sep 24, 2026
- SHA
- 9f815ac0
Datasets
Model card
From the Hugging Face model README
EmbeddingGemma-300M Tetris System-1 Decision Engine
This model is a real-time System 1 decision engine built on top of google/embeddinggemma-300m, designed to evaluate and select optimal 2D Tetris placements without generating autoregressive text tokens.
Inspired by non-autoregressive decision architectures, this model combines a bidirectional embedding backbone with a 2-layer MLP readout head to output calibrated probabilities over 40 discrete piece placements in under 10ms.
Architecture & Benchmark
- Base Backbone:
google/embeddinggemma-300m(Frozen, bfloat16) - Decision Head: 2-Layer MLP (Linear 768 -> 256 -> GELU -> Linear 40)
- Inference Latency: ~8ms (GPU forward pass) / ~70ms (with multi-objective candidate reranking)
- Empirical Performance: Achieved 21+ continuous line clears across 60 blocks in real-time arcade control.
Technical Article & Post-Mortem
- Full research walkthrough: Beyond Next-Token Prediction: Building Sub-10ms "System 1" Decision Engines with EmbeddingGemma
- Task
- Feature Extraction
- License
- apache-2.0
- Created
- Sep 24, 2026
- Updated
- Sep 24, 2026