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DenseK3

DenseK3-4B 是一个把 Kimi K3 的 KDA、MLA、AttnRes 和 SiTU 等核心架构下沉到 4B Dense scale 的研究项目;我们没有从零重新预训练,而是以 Qwen3.5-4B-Base 为 donor,通过分阶段白盒架构迁移、Joint Recovery 和多教师蒸馏,把一个成熟 pretrained model 改造成新的 Dense K3-style architecture,并进一步研究它的能力保留和长上下文状态效率

Details

External ID
1371526663
Source
GITHUB
Company
—
Product
DenseK3
Website domain
github.com
Launched
Sept. 15, 2026
Cohort
—
Upvotes
8
Upvotes percentile
0.14514476044068664
Tags
—
Fetched at
Sept. 19, 2026, 1:17 a.m.
Updated at
Sept. 19, 2026, 1:17 a.m.

Enrichment

Theme
niche developer utilities and guides
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
dense 4b model architecture based on kimi k3
Manually corrected
False

Could you build this?

No Performing white-box architectural transplantation (MLA, KDA) and multi-teacher knowledge distillation on a 4B parameter foundation model demands deep ML research expertise and massive compute.

What it would actually take: The system requires PyTorch, custom Triton/CUDA kernels for modified attention mechanisms, and Megatron-LM/DeepSpeed running on multi-node GPU clusters. The core difficulties are mathematically mapping donor weights to structurally disparate layers, stabilizing joint recovery training without catastrophic forgetting, and tuning multi-teacher loss dynamics across long contexts. This requires research-grade deep learning expertise and significant compute infrastructure.

Competitors

Other products that read as similar to this one — 92 launches clear the similarity bar, closest 8 shown.

Attention rank: #78 of 93 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 272 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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