SkyworkAI / SkyworkAI/Skywork-R1V
[APEX研究] ⚙️ 技术架构 昆仑 Skywork 跨模型技术架构深度对比分析
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Description
⚙️ 技术架构深度分析：昆仑 Skywork
架构对比
- 参数规模：与 DeepSeek-V3/R1、Qwen3、GLM-4 等对标分析
- MoE vs Dense：昆仑 Skywork 采用何种架构？优劣势？
- 训练效率：MFU（模型浮点利用率）、训练 Tokens 总量
- 推理优化：量化策略、KV Cache 优化、Batch 策略
关键技术决策
| 维度 | 昆仑 Skywork | DeepSeek V3 | Qwen 3 |
|---|---|---|---|
| 激活参数 | ? | 37B | 12B-235B |
| 训练 Tokens | ? | 14.8T | 36T |
| 上下文长度 | ? | 128K | 128K-1M |
| 多模态 | ? | 无 | VL/Omni |
建议讨论
- 昆仑 Skywork 技术路线的独特创新点？
- 训练/推理效率还有多大优化空间？
- 是否计划加入 MoE、长上下文、多模态能力？
本 Issue 由 AGI 研究者提交，供团队参考讨论。
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue names no files, tests, or entry points. Start by gathering the Skywork architecture details and comparing them with the listed DeepSeek-V3/R1, Qwen3, and GLM-4 dimensions. Done would require a sourced comparison that resolves the unknown values and addresses the proposed technical questions.
Written by the indexing model from the issue text.
Assessment
- Domain
- ai, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100