[fix](Qwen3 series): fix gibberish output by correcting RoPE write-back (#31) (#1959)

Use GitHub-hosted runners for the pure Python sglang-kt release workflow so the PyPI release is not blocked by unavailable self-hosted runners.

.github/workflows/release-sglang-kt.yml

@@ -24,7 +24,7 @@ permissions:
 jobs:
   build-sglang-kt:
     name: Build sglang-kt wheel
-    runs-on: [self-hosted, linux, x64]
+    runs-on: ubuntu-latest
 
     steps:
       - name: Checkout repository
@@ -70,7 +70,7 @@ jobs:
   publish-pypi:
     name: Publish sglang-kt to PyPI
     needs: [build-sglang-kt]
-    runs-on: [self-hosted, linux, x64]
+    runs-on: ubuntu-latest
     if: github.repository == 'kvcache-ai/ktransformers' && github.ref == 'refs/heads/main'
     environment: prod
     permissions:

README.md

@@ -26,7 +26,7 @@ KTransformers is a research project focused on efficient inference and fine-tuni
 * **Dec 22, 2025**: Support RL-DPO fine-tuning with LLaMA-Factory. ([Tutorial](./doc/en/SFT/DPO_tutorial.md))
 * **Dec 5, 2025**: Support Native Kimi-K2-Thinking inference ([Tutorial](./doc/en/kt-kernel/Kimi-K2-Thinking-Native.md))
 * **Nov 6, 2025**: Support Kimi-K2-Thinking inference ([Tutorial](./doc/en/Kimi-K2-Thinking.md)) and fine-tune ([Tutorial](./doc/en/SFT_Installation_Guide_KimiK2.md))
-* **Nov 4, 2025**: KTransformers Fine-Tuning × LLaMA-Factory Integration. ([Tutorial](./doc/en/KTransformers-Fine-Tuning_User-Guide.md))
+* **Nov 4, 2025**: KTransformers Fine-Tuning × LLaMA-Factory Integration. ([Tutorial](./doc/en/SFT/KTransformers-Fine-Tuning_User-Guide.md))
 * **Oct 27, 2025**: Support Ascend NPU. ([Tutorial](./doc/zh/DeepseekR1_V3_tutorial_zh_for_Ascend_NPU.md))
 * **Oct 10, 2025**: Integrating into SGLang. ([Roadmap](https://github.com/sgl-project/sglang/issues/11425), [Blog](https://lmsys.org/blog/2025-10-22-KTransformers/))
 * **Sept 11, 2025**: Support Qwen3-Next. ([Tutorial](./doc/en/Qwen3-Next.md))
@@ -87,7 +87,7 @@ pip install .
 
 ---
 
-### 🎓 [kt-sft](./kt-sft/) - Fine-Tuning Framework
+### 🎓 [kt-sft](./doc/en/SFT/KTransformers-Fine-Tuning_User-Guide.md) - Fine-Tuning Framework
 
 KTransformers × LLaMA-Factory integration for ultra-large MoE model fine-tuning.
 
@@ -109,12 +109,15 @@ KTransformers × LLaMA-Factory integration for ultra-large MoE model fine-tuning
 
 **Quick Start:**
 ```bash
-cd kt-sft
-# Install environment following kt-sft/README.md
-USE_KT=1 llamafactory-cli train examples/train_lora/deepseek3_lora_sft_kt.yaml
+cd /path/to/LLaMA-Factory
+pip install -e .
+pip install "ktransformers[sft]"
+USE_KT=1 ACCELERATE_USE_KT=true \
+  accelerate launch --config_file examples/ktransformers/accelerate/fsdp2_kt_bf16.yaml \
+  -m llamafactory.cli train examples/ktransformers/train_lora/deepseek_v3_lora_sft_kt.yaml
 ```
 
-👉 **[Full Documentation →](./kt-sft/README.md)**
+👉 **[Full Documentation →](./doc/en/SFT/KTransformers-Fine-Tuning_User-Guide.md)**
 
 ---
 

README_ZH.md

@@ -13,13 +13,13 @@
 
 ## 🎯 概览
 
-KTransformers 是一个专注于通过 CPU-GPU 异构计算实现大语言模型高效推理和微调的研究项目。该项目已发展为**两个核心模块**：[kt-kernel](./kt-kernel/) 和 [kt-sft](./kt-sft/)。
+KTransformers 是一个专注于通过 CPU-GPU 异构计算实现大语言模型高效推理和微调的研究项目。该项目已发展为**两个核心模块**：[kt-kernel](./kt-kernel/) 和 [kt-sft](./doc/en/SFT/KTransformers-Fine-Tuning_User-Guide.md)。
 
 ## 🔥 更新
 
-* **2025 年 12 月 5 日**：支持原生 Kimi-K2-Thinking 推理（[教程](./doc/en/Kimi-K2-Thinking-Native.md)）
+* **2025 年 12 月 5 日**：支持原生 Kimi-K2-Thinking 推理（[教程](./doc/en/kt-kernel/Kimi-K2-Thinking-Native.md)）
 * **2025 年 11 月 6 日**：支持 Kimi-K2-Thinking 推理（[教程](./doc/en/Kimi-K2-Thinking.md)）和微调（[教程](./doc/en/SFT_Installation_Guide_KimiK2.md)）
-* **2025 年 11 月 4 日**：KTransformers 微调 × LLaMA-Factory 集成（[教程](./doc/en/KTransformers-Fine-Tuning_User-Guide.md)）
+* **2025 年 11 月 4 日**：KTransformers 微调 × LLaMA-Factory 集成（[教程](./doc/en/SFT/KTransformers-Fine-Tuning_User-Guide.md)）
 * **2025 年 10 月 27 日**：支持昇腾 NPU（[教程](./doc/zh/DeepseekR1_V3_tutorial_zh_for_Ascend_NPU.md)）
 * **2025 年 10 月 10 日**：集成到 SGLang（[路线图](https://github.com/sgl-project/sglang/issues/11425)，[博客](https://lmsys.org/blog/2025-10-22-KTransformers/)）
 * **2025 年 9 月 11 日**：支持 Qwen3-Next（[教程](./doc/en/Qwen3-Next.md)）
@@ -79,7 +79,7 @@ pip install .
 
 ---
 
-### 🎓 [kt-sft](./kt-sft/) - 微调框架
+### 🎓 [kt-sft](./doc/en/SFT/KTransformers-Fine-Tuning_User-Guide.md) - 微调框架
 
 KTransformers × LLaMA-Factory 集成，用于超大型 MoE 模型微调。
 
@@ -101,12 +101,15 @@ KTransformers × LLaMA-Factory 集成，用于超大型 MoE 模型微调。
 
 **快速开始：**
 ```bash
-cd kt-sft
-# 按照 kt-sft/README.md 安装环境
-USE_KT=1 llamafactory-cli train examples/train_lora/deepseek3_lora_sft_kt.yaml
+cd /path/to/LLaMA-Factory
+pip install -e .
+pip install "ktransformers[sft]"
+USE_KT=1 ACCELERATE_USE_KT=true \
+  accelerate launch --config_file examples/ktransformers/accelerate/fsdp2_kt_bf16.yaml \
+  -m llamafactory.cli train examples/ktransformers/train_lora/deepseek_v3_lora_sft_kt.yaml
 ```
 
-👉 **[完整文档 →](./kt-sft/README.md)**
+👉 **[完整文档 →](./doc/en/SFT/KTransformers-Fine-Tuning_User-Guide.md)**
 
 ---