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* 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 |
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| amx.png | ||
| amx_avx.png | ||
| amx_intro.png | ||
| BigCodeBench.png | ||
| cpuinfer.png | ||
| DeepSeek-on-KTransformers.png | ||
| deepseekv2_structure.png | ||
| Framework_effect.png | ||
| heterogeneous_computing.png | ||
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| image-compare_model.png | ||
| InfLLM_equation.jpg | ||
| InfLLM_framework.png | ||
| InjectStruction.png | ||
| internlm_memory.png | ||
| KTransformers.png | ||
| KTransformers_long_context_v1.png | ||
| KTransformers_long_context_v2.png | ||
| long_context_generate.png | ||
| long_context_prefill.png | ||
| model_structure_guild.png | ||
| multi_gpu.png | ||
| needle_1M.png | ||
| needle_128K.png | ||
| onednn_1.png | ||
| Quest_framework.png | ||
| SnapKV_framework.png | ||
| SparQ_attention.png | ||
| website.png | ||
| 演示文稿1_01.png | ||
| 风格化数据集模型输出对比_01.png | ||