12 Commits

Author SHA1 Message Date
Jiaqi Liao
721b6c4c94 [docs] Update Native Kimi-K2-Thinking documentation and kt-kernel parameters (#1671) 2025-12-05 22:46:16 +08:00
Peilin Li
171578a7ec [refactor]: Change named 'KT-SFT' to 'kt-sft' (#1626)
* Change named 'KT-SFT' to 'kt-sft'

* [docs]: update kt-sft name

---------

Co-authored-by: ZiWei Yuan <yzwliam@126.com>
2025-11-17 11:48:42 +08:00
ZiWei Yuan
e0e2429748 [ci]: update issue template & security & license (#1617) 2025-11-15 22:29:01 +08:00
Jiaqi Liao
07322ca2bd Refactor: restructure repository to focus on kt-kernel and KT-SFT modules (#1583)
* refactor repo

* fix README
2025-11-10 17:57:48 +08:00
Jiaqi Liao
57d14d22bc Refactor: restructure repository to focus on kt-kernel and KT-SFT modulesq recon (#1581)
* refactor: move legacy code to archive/ directory

  - Moved ktransformers, csrc, third_party, merge_tensors to archive/
  - Moved build scripts and configurations to archive/
  - Kept kt-kernel, KT-SFT, doc, and README files in root
  - Preserved complete git history for all moved files

* refactor: restructure repository to focus on kt-kernel and KT-SFT modules

* fix README

* fix README

* fix README

* fix README

* docs: add performance benchmarks to kt-kernel section

Add comprehensive performance data for kt-kernel to match KT-SFT's presentation:
- AMX kernel optimization: 21.3 TFLOPS (3.9× faster than PyTorch)
- Prefill phase: up to 20× speedup vs baseline
- Decode phase: up to 4× speedup
- NUMA optimization: up to 63% throughput improvement
- Multi-GPU (8×L20): 227.85 tokens/s total throughput with DeepSeek-R1 FP8

Source: https://lmsys.org/blog/2025-10-22-KTransformers/

This provides users with concrete performance metrics for both core modules,
making it easier to understand the capabilities of each component.

* refactor: improve kt-kernel performance data with specific hardware and models

Replace generic performance descriptions with concrete benchmarks:
- Specify exact hardware: 8×L20 GPU + Xeon Gold 6454S, Single/Dual-socket Xeon + AMX
- Include specific models: DeepSeek-R1-0528 (FP8), DeepSeek-V3 (671B)
- Show detailed metrics: total throughput, output throughput, concurrency details
- Match KT-SFT presentation style for consistency

This provides users with actionable performance data they can use to evaluate
hardware requirements and expected performance for their use cases.

* fix README

* docs: clean up performance table and improve formatting

* add pic for README

* refactor: simplify .gitmodules and backup legacy submodules

- Remove 7 legacy submodules from root .gitmodules (archive/third_party/*)
- Keep only 2 active submodules for kt-kernel (llama.cpp, pybind11)
- Backup complete .gitmodules to archive/.gitmodules
- Add documentation in archive/README.md for researchers who need legacy submodules

This reduces initial clone size by ~500MB and avoids downloading unused dependencies.

* refactor: move doc/ back to root directory

Keep documentation in root for easier access and maintenance.

* refactor: consolidate all images to doc/assets/

- Move kt-kernel/assets/heterogeneous_computing.png to doc/assets/
- Remove KT-SFT/assets/ (images already in doc/assets/)
- Update KT-SFT/README.md image references to ../doc/assets/
- Eliminates ~7.9MB image duplication
- Centralizes all documentation assets in one location

* fix pic path for README
2025-11-10 17:42:26 +08:00
liam
4748a912e2 📝 fix typo ktransformer->ktransformers 2025-03-17 17:54:00 +08:00
Azure
91c1619296 Merge branch 'develop-0.2.2' into support-fp8
Update README.md
2025-02-25 13:43:26 +00:00
Azure
36fbeee341 Update doc 2025-02-25 08:21:18 +00:00
xubo
49c6e2fc04 Update README_ZH.md 2025-02-17 16:56:44 +08:00
Azure
227e81b0d3 update zh readme 2025-02-15 01:54:01 +00:00
dhliu
d04b570fb5 edit README_ZH.md && add DeepseekR1_V3_tutorial_zh.md 2025-02-13 21:14:44 +08:00
dhliu
318c88cbeb add README_ZH.md 2025-02-13 12:43:06 +08:00