[APEX研究] ⚙️ 技术架构 昆仑 Skywork 跨模型技术架构深度对比分析
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Documentation
- Clarity
- Needs clarification
- Activity status
- Quiet
- Domain
- ai, machine-learning
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.
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。
- Dominant language
- Python
- Stars
- 3.2k
- Forks
- 283
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
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.
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