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bidirectional attention

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Nov 5, 2026 · Artificial Intelligence

15 Pitfalls in Training JEV Decision Models: From Pointer Heads to Data Leakage

The author shares 15 practical pitfalls encountered while training JEV decision models on Qwen3.5, covering pointer head design, bidirectional attention, data loading, label-sorted collapse, shortcut learning, catastrophic forgetting, joint classification-extraction trade-offs, extraction threshold calibration, and engineering bugs like padding leaks and NCCL deadlocks.

GGUFJEV decision modelQwen3.5

0 likes · 43 min read

15 Pitfalls in Training JEV Decision Models: From Pointer Heads to Data Leakage

DeepHub IMBA

Jun 22, 2026 · Artificial Intelligence

How DiffusionGemma Shifts LLM Inference Bottleneck from Memory Bandwidth to Compute

DiffusionGemma, an experimental discrete text diffusion model built on the 26B MoE Gemma‑4 architecture, generates whole 256‑token blocks with bidirectional attention, moving the inference bottleneck from memory bandwidth to GPU compute, achieving up to four‑fold speed gains on H100 and RTX 5090 GPUs, though with lower output quality than standard autoregressive models.

DiffusionGemmaGPU performanceLLM inference

0 likes · 7 min read

How DiffusionGemma Shifts LLM Inference Bottleneck from Memory Bandwidth to Compute

Machine Heart

Jun 11, 2026 · Artificial Intelligence

Google Releases DiffusionGemma 26B MoE—Text Generation Up to 4× Faster

DiffusionGemma, Google's new 26‑billion‑parameter Mixture‑of‑Experts model, replaces token‑by‑token autoregression with a diffusion‑style output head that generates whole text blocks, delivering up to four‑fold speed gains on consumer GPUs while offering bidirectional attention and self‑correction, albeit with lower quality than standard Gemma 4.

DiffusionGemmaGPU AccelerationMixture of Experts

0 likes · 6 min read

Google Releases DiffusionGemma 26B MoE—Text Generation Up to 4× Faster