Qwen3-Next-80B-A3B models: — Instruct → Task-oriented, instruction-following — Thinking → Long-chain reasoning, step-by-step deliberation The models differ in: — Training objective: Instruct vs Thinking — Data scale: 1M steps vs standard — Quantization: qx86n-hi (6/8-bit mixed) vs qx53n (a new 5/3-bit scheme) This isn’t just another MoE — it’s a cognitive architecture experiment. Let’s decode what these numbers reveal about the future of reasoning AI. 📌 qx53n: Novel quantization — 3-bit data, 5-bit attention paths? Extremely aggressive compression. 📌 qx86n-hi: Same as before — 6-bit data, 8-bit attention paths (optimized for context retention). ✅ These models are not fine-tuned versions of prior Qwen3 — they’re a clean-slate MoE architecture designed for scaled reasoning. Instruct models dominate boolq: — → 0.898–0.901 — the highest boolq scores ever recorded — → This suggests unparalleled precision in binary truth detection, likely from heavy instruction-tuning on QA datasets. Thinking models dominate hellaswag, piqa, winogrande: — → 0.647–0.656 (hellaswag), 0.780–0.782 (piqa), 0.685–0.703 (winogrande) — → These are best-in-class across all models we’ve ever evaluated — including…
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Автор: nightmedia
Теги: mlx, qwen3_next, conversational, 8-bit
Лайков: 3 | Загрузок: 56
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
