* Handle unquoted paths and special characters in model scanner
* Fix ValueError: capture_output cannot be used with stderr
`capture_output=True` internally sets `stderr=PIPE`, which conflicts
with `stderr=subprocess.DEVNULL`. Replace `capture_output=True` with
explicit `stdout=subprocess.PIPE` to keep stderr suppressed correctly.
Also remove redundant `shell=False` (already the default).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: ErvinXie <ervinxie@foxmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* [feat]: redesign kt run interactive configuration with i18n support
- Redesign kt run with 8-step interactive flow (model selection, inference method, NUMA/CPU, GPU experts, KV cache, GPU/TP selection, parsers, host/port)
- Add configuration save/load system (~/.ktransformers/run_configs.yaml)
- Add i18n support for kt chat (en/zh translations)
- Add universal input validators with auto-retry and Chinese comma support
- Add port availability checker with auto-suggestion
- Add parser configuration (--tool-call-parser, --reasoning-parser)
- Remove tuna command and clean up redundant files
- Fix: variable reference bug in run.py, filter to show only MoE models
* [feat]: unify model selection UI and enable shared experts fusion by default
- Unify kt run model selection table with kt model list display
* Add Total size, MoE Size, Repo, and SHA256 status columns
* Use consistent formatting and styling
* Improve user decision-making with more information
- Enable --disable-shared-experts-fusion by default
* Change default value from False to True
* Users can still override with --enable-shared-experts-fusion
* [feat]: improve kt chat with performance metrics and better CJK support
- Add performance metrics display after each response
* Total time, TTFT (Time To First Token), TPOT (Time Per Output Token)
* Accurate input/output token counts using model tokenizer
* Fallback to estimation if tokenizer unavailable
* Metrics shown in dim style (not prominent)
- Fix Chinese character input issues
* Replace Prompt.ask() with console.input() for better CJK support
* Fixes backspace deletion showing half-characters
- Suppress NumPy subnormal warnings
* Filter "The value of the smallest subnormal" warnings
* Cleaner CLI output on certain hardware environments
* [fix]: correct TTFT measurement in kt chat
- Move start_time initialization before API call
- Previously start_time was set when receiving first chunk, causing TTFT ≈ 0ms
- Now correctly measures time from request sent to first token received
* [docs]: 添加 Clawdbot 集成指南 - KTransformers 企业级 AI 助手部署方案
* [docs]: 强调推荐使用 Kimi K2.5 作为核心模型，突出企业级推理能力
* [docs]: 添加 Clawdbot 飞书接入教程链接
* [feat]: improve CLI table display, model verification, and chat experience
- Add sequence number (#) column to all model tables by default
- Filter kt edit to show only MoE GPU models (exclude AMX)
- Extend kt model verify to check *.json and *.py files in addition to weights
- Fix re-verification bug where repaired files caused false failures
- Suppress tokenizer debug output in kt chat token counting
* [fix]: fix cpu cores.
---------
Co-authored-by: skqliao <skqliao@gmail.com>
* [feat]: Enhance CPU feature detection and support for AVX512 extensions
- Added cmake/DetectCPU.cmake for automatic CPU feature detection.
- Updated CMakeLists.txt to include auto-detection logic for AVX512 features.
- Modified install.sh to include new AVX512_VBMI option for FP8 MoE.
- Enhanced _cpu_detect.py to support progressive matching of CPU variants.
- Created scripts/check_cpu_features.py for manual CPU feature checks.
- Updated setup.py to reflect changes in CPU variant building and environment variables.
* [fix](kt-kernel): Add conditional inclusion of FP8 MoE for AVX512 BF16 support
* [chore](kt-kernel): update project version to 0.5.0 in CMakeLists.txt and version.py
* [fix](kt-kernel): fix AVX512 cpu instruction set detection
* [feat](kt-kernel): AVX512 fallback kernel for RAW-INT4
* [fix](kt-kernel): fix setup version issue
* [fix](kt-kernel): update install for custom build
* [docs](kt-kernel): new installation guide for various cpu instruction set
* [fix](kt-kernel): fix _mm512_dpbusd_epi32_compat fallback implmentation
* [style](kt-kernel): clang format
* fix(amx): add BufferASmallKGroupImpl to fix buffer overflow in from_mat
The original BufferAKGroupImpl::from_mat writes 64 bytes per K_STEP iteration
but when K_STEP=32 (for GemmKernel224Int4SmallKGroup), this causes buffer overflow.
BufferASmallKGroupImpl overrides from_mat to write only 32 bytes per iteration.
* perf(k2-moe): optimize memory allocation with pooled buffers
- Replace per-expert buffer allocation with shared memory pools
- Dynamically assign buffer slices based on activated experts
- Add group_size inference from scale tensor shape in amx.py
* delete kimi k2 forward test
* add TODO comment for pool_count_ calculation
* support Kimi-K2-Thinking original weight
fix amx kernel bug
* update k2 avx kernel.
* feat: add CPUInfer write buffer task
* [feat]: add kimi k2 cpu write buffer support
- Implement write_weights_to_buffer function in k2-moe.hpp for extracting GPU expert weights
- Fix down (w2) weight column-wise slicing for different TP configurations
- Support three TP scenarios: cpu_tp == gpu_tp, cpu_tp > gpu_tp, cpu_tp < gpu_tp
- Add comprehensive test cases for weight extraction validation
- Ensure compatibility with Kimi model's MoE architecture
* [fix]: correct write_weight_scale_to_buffer expert offset calculation
Fixed the bug in write_weight_scale_to_buffer_task where expert offsets in GPU buffers were incorrectly calculated. Changed from using per_expert_gpu sizes to using full gpu_tp sizes, ensuring correct memory layout for multi-expert scenarios.
Also added benchmark scripts for k2 moe and write buffer operations, and cleaned up debug output in test files.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* [feat]: add write buffer wrapper
* [fix] fix comment
---------
Co-authored-by: ouqingliang <1692110604@qq.com>
Co-authored-by: Claude <noreply@anthropic.com>
