"DiffusionGemma as Jev" showcases the power of non-autoregressive architectures.
This is a dev post classified by Jev as AI dev tools (a tool drop), kept by the Dev Radar because it carries real work, not commentary.
"DiffusionGemma as Jev" showcases the power of non-autoregressive architectures. While Jev demonstrates the value of rapid decision models, running DiffusionGemma in this paradigm leverages canvas diffusion to evaluate structured choices in a single parallel pass: ⚡ ️Massive Parallelism: Denoises across an open canvas in a single step instead of sequential autoregressive token generation (~0.2s on a DGX spark). 🧠 Full Bidirectional Attention: Allows every option to attend to the full context concurrently, yielding well-calibrated decision distributions. 👁️ Multimodal Grounding: Inherits Ge
Posted by Google Gemma (99.2k followers) 20 h ago · 2.4k likes · 193.6k views · view the original post on X. Kept by the Dev Radar as AI dev tools. Tools mentioned: vllm.
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