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dots3-note

2 articles · Page 1 of 1

Aug 14, 2026 · Artificial Intelligence

How Huawei Cloud Optimizes the New Xiaohongshu Open‑Source Model dots3‑note Preview for Multimodal Inference

Huawei Cloud adapts and optimizes the 280B‑parameter dots3‑note preview model released by Xiaohongshu, detailing its mixed sparse attention, MoE enhancements, multimodal pre‑training pipelines, unified reward training, and a suite of inference‑time optimizations—including FlashComm, FUSED_MC2, and MTP speculative decoding—delivered via the vLLM Ascend framework for high‑throughput, low‑latency deployment.

Huawei CloudMTP Speculative DecodingMoE optimization

0 likes · 7 min read

How Huawei Cloud Optimizes the New Xiaohongshu Open‑Source Model dots3‑note Preview for Multimodal Inference

Machine Heart

Aug 14, 2026 · Artificial Intelligence

Open‑Source Dots3‑Note: From IMO Full‑Score Math to Real‑World Long‑Term Tasks

The article introduces the open‑source preview of XiaoHongShu's 280B‑parameter, 512K‑context multimodal model Dots3‑Note, details its benchmark superiority over larger models, showcases its performance on complex long‑term tasks such as games, ARC‑AGI, home‑renovation planning, and VisionOS app development, and explains the novel TEMPO training and self‑critiquing mechanisms that enable sustained learning and self‑evaluation.

Tempobenchmarkdots3-note

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Open‑Source Dots3‑Note: From IMO Full‑Score Math to Real‑World Long‑Term Tasks