docs: make Chinese README the default

README.md

@@ -1,3 +1,9 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/unslothai/unsloth) · [上游 README](https://github.com/unslothai/unsloth/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 <h1 align="center" style="margin:0;">
   <a href="https://unsloth.ai/docs"><picture>
     <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png">
@@ -6,90 +12,90 @@
   </picture></a>
 </h1>
 <h3 align="center" style="margin: 0; margin-top: 0;">
-Unsloth Studio lets you run and train models locally.
+Unsloth Studio 让你在本地运行和训练模型。
 </h3>
 
 <p align="center">
-  <a href="#-features">Features</a> •
-  <a href="#-install">Quickstart</a> •
+  <a href="#-features">功能</a> •
+  <a href="#-install">快速开始</a> •
   <a href="#-free-notebooks">Notebooks</a> •
-  <a href="https://unsloth.ai/docs">Documentation</a>
+  <a href="https://unsloth.ai/docs">文档</a>
 </p>
 <br>
 <a href="https://unsloth.ai/docs/new/studio">
 <img alt="unsloth studio ui homepage" src="https://github.com/user-attachments/assets/53ae17a9-d975-44ef-9686-efb4ebd0454d" style="max-width: 100%; margin-bottom: 0;"></a>
 
-## ⚡ Get started
+## ⚡ 快速开始
 
-#### macOS, Linux, WSL:
+#### macOS、Linux、WSL：
 ```bash
 curl -fsSL https://unsloth.ai/install.sh | sh
 ```
-#### Windows:
+#### Windows：
 ```powershell
 irm https://unsloth.ai/install.ps1 | iex
 ```
-#### Community:
+#### 社区：
 
 - [Discord](https://discord.gg/unsloth)
 - [𝕏 (Twitter)](https://x.com/UnslothAI)
 - [Reddit](https://reddit.com/r/unsloth)
 
-## ⭐ Features
-Unsloth Studio (Beta) lets you run and train text, [audio](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning), [embedding](https://unsloth.ai/docs/new/embedding-finetuning), [vision](https://unsloth.ai/docs/basics/vision-fine-tuning) models on Windows, Linux and macOS.
+## ⭐ 功能
+Unsloth Studio（Beta）让你在 Windows、Linux 和 macOS 上运行和训练文本、[音频](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning), [嵌入](https://unsloth.ai/docs/new/embedding-finetuning), [视觉](https://unsloth.ai/docs/basics/vision-fine-tuning) 模型。
 
-### Inference
-* **Search + download + run models** including GGUF, LoRA adapters, safetensors
-* **Export models**: [Save or export](https://unsloth.ai/docs/new/studio/export) models to GGUF, 16-bit safetensors and other formats.
-* **Tool calling**: Support for [self-healing tool calling](https://unsloth.ai/docs/new/studio/chat#auto-healing-tool-calling) and web search
-* **[Code execution](https://unsloth.ai/docs/new/studio/chat#code-execution)**: lets LLMs test code in Claude artifacts and sandbox environments
-* **[API inference endpoint](https://unsloth.ai/docs/basics/api)**: Deploy and run local LLMs in Claude Code, Codex tools with Unsloth
-* [Auto set inference settings](https://unsloth.ai/docs/new/studio/chat#auto-parameter-tuning) and customize chat templates.
-* We work directly with teams behind [gpt-oss](https://docs.unsloth.ai/new/gpt-oss-how-to-run-and-fine-tune#unsloth-fixes-for-gpt-oss), [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3_unsloth_dynamic_ggufs_128k_context_bug_fixes/), [Llama 4](https://github.com/ggml-org/llama.cpp/pull/12889), [Mistral](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B/discussions/18), [Gemma 1-3](https://news.ycombinator.com/item?id=39671146), and [Phi-4](https://unsloth.ai/blog/phi4), where we’ve fixed bugs that improve model accuracy.
-* Chat with images, audio, PDFs, code, DOCX and more. [Connect API providers](https://unsloth.ai/docs/integrations/connections) (OpenAI, Anthropic) or servers (vLLM, Ollama).
-### Training
-* Train and RL **500+ models** up to **2x faster** with up to **70% less VRAM**, with no accuracy loss.
-* Custom Triton and mathematical **kernels**. See some collabs we did with [PyTorch](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) and [Hugging Face](https://unsloth.ai/docs/new/faster-moe).
-* **Data Recipes**: [Auto-create datasets](https://unsloth.ai/docs/new/studio/data-recipe) from **PDF, CSV, DOCX** etc. Edit data in a visual-node workflow.
-* **[Reinforcement Learning](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide)** (RL): The most efficient [RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) library, using **80% less VRAM** for GRPO, [FP8](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) etc.
-* Supports full fine-tuning, RL, pretraining, 4-bit, 16-bit and, FP8 training.
-* **Observability**: Monitor training live, track loss and GPU usage and customize graphs.
-* [Multi-GPU](https://unsloth.ai/docs/basics/multi-gpu-training-with-unsloth) training is supported, with major improvements coming soon.
+### 推理
+* **搜索、下载并运行模型**，包括 GGUF、LoRA adapters、safetensors
+* **导出模型**：[保存或导出](https://unsloth.ai/docs/new/studio/export) 模型为 GGUF、16-bit safetensors 及其他格式。
+* **工具调用（Tool calling）**：支持 [自愈式工具调用](https://unsloth.ai/docs/new/studio/chat#auto-healing-tool-calling) 与网页搜索
+* **[代码执行](https://unsloth.ai/docs/new/studio/chat#code-execution)**: 让 LLM 在 Claude artifacts 和沙箱环境中测试代码
+* **[API 推理端点](https://unsloth.ai/docs/basics/api)**: 部署并在 Claude Code、Codex 工具中运行本地 LLM，配合 Unsloth 使用
+* [自动设置推理参数](https://unsloth.ai/docs/new/studio/chat#auto-parameter-tuning) 并自定义聊天模板。
+* 我们与 [gpt-oss](https://docs.unsloth.ai/new/gpt-oss-how-to-run-and-fine-tune#unsloth-fixes-for-gpt-oss), [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3_unsloth_dynamic_ggufs_128k_context_bug_fixes/), [Llama 4](https://github.com/ggml-org/llama.cpp/pull/12889), [Mistral](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B/discussions/18), [Gemma 1-3](https://news.ycombinator.com/item?id=39671146), 和 [Phi-4](https://unsloth.ai/blog/phi4), 背后的团队直接合作，修复了可提升模型准确性的 bug。
+* 支持图片、音频、PDF、代码、DOCX 等聊天。[连接 API 提供商](https://unsloth.ai/docs/integrations/connections)（OpenAI、Anthropic）或服务器（vLLM、Ollama）。
+### 训练
+* 训练与 RL **500+ 模型**，速度最高可达 **2 倍**，显存占用最多减少 **70%**，且不损失精度。
+* 自定义 Triton 与数学 **kernels**。查看我们与 [PyTorch](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) 和 [Hugging Face](https://unsloth.ai/docs/new/faster-moe). 的部分合作
+* **Data Recipes**：从 **PDF、CSV、DOCX** 等 [自动创建数据集](https://unsloth.ai/docs/new/studio/data-recipe)。在可视化节点工作流中编辑数据。
+* **[强化学习](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide)**（Reinforcement Learning，RL）：最高效的 [RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) 库，GRPO、[FP8](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) 等场景显存占用减少 **80%**。
+* 支持全量微调、RL、预训练，以及 4-bit、16-bit 和 FP8 训练。
+* **可观测性（Observability）**：实时监控训练，跟踪 loss 与 GPU 使用情况，并自定义图表。
+* 支持 [多 GPU](https://unsloth.ai/docs/basics/multi-gpu-training-with-unsloth) 训练，重大改进即将推出。
 
-## 📥 Install
-Unsloth can be used in two ways: through **[Unsloth Studio](https://unsloth.ai/docs/new/studio/)**, the web UI, or through **Unsloth Core**, the code-based version. Each has different requirements.
+## 📥 安装
+Unsloth 有两种使用方式：通过 **[Unsloth Studio](https://unsloth.ai/docs/new/studio/)**, 网页 UI，或通过 **Unsloth Core** 代码版。两者要求不同。
 
-### Unsloth Studio (web UI)
-Unsloth Studio (Beta) works on **Windows, Linux, WSL** and **macOS**.
+### Unsloth Studio（网页 UI）
+Unsloth Studio（Beta）支持 **Windows、Linux、WSL** 和 **macOS**。
 
-* **CPU:** Supported for Chat and Data Recipes currently
-* **NVIDIA:** Training works on RTX 30/40/50, Blackwell, DGX Spark, Station and more
-* **macOS:** Training, MLX and GGUF inference are ALL supported.
-* **AMD:** Chat + Data works. Train with [Unsloth Core](#unsloth-core-code-based). Studio support is out soon.
-* **Multi-GPU:** Available now, with a major upgrade on the way
+* **CPU：** 当前支持 Chat 与 Data Recipes
+* **NVIDIA：** 训练支持 RTX 30/40/50、Blackwell、DGX Spark、Station 等
+* **macOS：** 训练、MLX 与 GGUF 推理均支持。
+* **AMD：** 支持 Chat + Data。训练请使用 [Unsloth Core](#unsloth-core-code-based)。Studio 支持即将推出。
+* **Multi-GPU：** 现已可用，重大升级即将到来
 
-#### macOS, Linux, WSL:
+#### macOS、Linux、WSL：
 ```bash
 curl -fsSL https://unsloth.ai/install.sh | sh
 ```
-Use the same command to update.
+使用相同命令进行更新。
 
-#### Windows:
+#### Windows：
 ```powershell
 irm https://unsloth.ai/install.ps1 | iex
 ```
-Use the same command to update.
+使用相同命令进行更新。
 
-#### Launch
+#### 启动
 ```bash
 unsloth studio -p 8888
 ```
-For cloud or global access, add `-H 0.0.0.0`. By default, Unsloth is accessible only locally.
+如需云访问或全球访问，请添加 `-H 0.0.0.0`。默认情况下，Unsloth 仅在本地可访问。
 
-To reach Studio over HTTPS, use `unsloth studio --secure`. Studio stays bound to localhost and is reached only through a free Cloudflare tunnel, which publishes it at a public `https://*.trycloudflare.com` URL (it fails closed if the tunnel can't start, so the raw port is never exposed). This makes Studio reachable from the internet, so anyone with the link and API key can use it and run code: keep your API key private (see Remote access below).
+要通过 HTTPS 访问 Studio，请使用 `unsloth studio --secure`。Studio 仍绑定在 localhost 上，仅通过免费的 Cloudflare 隧道（tunnel）访问，该隧道会将其发布到公开的 `https://*.trycloudflare.com` URL（如果隧道无法启动则会安全失败，因此原始端口绝不会暴露）。这使得 Studio 可从互联网访问，因此任何拥有链接和 API key 的人都可以使用并运行代码：请妥善保管你的 API key（见下文「远程访问」）。
 
 #### Docker
-Use our [Docker image](https://hub.docker.com/r/unsloth/unsloth) ```unsloth/unsloth``` container. Run:
+使用我们的 [Docker 镜像](https://hub.docker.com/r/unsloth/unsloth) ```unsloth/unsloth``` 容器。运行：
 ```bash
 docker run -d -e JUPYTER_PASSWORD="mypassword" \
   -p 8888:8888 -p 8000:8000 -p 2222:22 \
@@ -109,7 +115,7 @@ uv venv unsloth_env --python 3.13
 source unsloth_env/bin/activate
 uv pip install unsloth --torch-backend=auto
 ```
-#### Windows:
+#### Windows：
 ```powershell
 winget install -e --id Python.Python.3.13
 winget install --id=astral-sh.uv  -e
@@ -117,75 +123,75 @@ uv venv unsloth_env --python 3.13
 .\unsloth_env\Scripts\activate
 uv pip install unsloth --torch-backend=auto
 ```
-For Windows, `pip install unsloth` works only if you have PyTorch installed. Read our [Windows Guide](https://unsloth.ai/docs/get-started/install/windows-installation).
-You can use the same Docker image as Unsloth Studio.
+在 Windows 上，仅当你已安装 PyTorch 时，`pip install unsloth` 才能正常工作。请阅读我们的 [Windows 指南](https://unsloth.ai/docs/get-started/install/windows-installation).
+你可以使用与 Unsloth Studio 相同的 Docker 镜像。
 
-#### AMD, Intel:
-For RTX 50x, B200, 6000 GPUs: `uv pip install unsloth --torch-backend=auto`. Read our guides for: [Blackwell](https://unsloth.ai/docs/blog/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [DGX Spark](https://unsloth.ai/docs/blog/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth). <br>
-To install Unsloth on **AMD** and **Intel** GPUs, follow our [AMD Guide](https://unsloth.ai/docs/get-started/install/amd) and [Intel Guide](https://unsloth.ai/docs/get-started/install/intel).
+#### AMD、Intel：
+对于 RTX 50x、B200、6000 GPU：`uv pip install unsloth --torch-backend=auto`。请参阅我们的指南：[Blackwell](https://unsloth.ai/docs/blog/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) 和 [DGX Spark](https://unsloth.ai/docs/blog/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth). <br>
+若要在 **AMD** 和 **Intel** GPU 上安装 Unsloth，请参阅我们的 [AMD 指南](https://unsloth.ai/docs/get-started/install/amd) 和 [Intel 指南](https://unsloth.ai/docs/get-started/install/intel).
 
-## 📒 Free Notebooks
+## 📒 免费 Notebooks
 
-Train for free with our notebooks. You can use our new [free Unsloth Studio notebook](https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb) to run and train models for free in a web UI.
-Read our [guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide). Add dataset, run, then deploy your trained model.
+使用我们的 notebooks 免费训练。你可以使用全新的 [免费 Unsloth Studio notebook](https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb) 在 Web UI 中免费运行并训练模型。
+阅读我们的[指南](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide). 添加数据集、运行，然后部署你训练好的模型。
 
-| Model | Free Notebooks | Performance | Memory use |
+| 模型 | 免费 Notebooks | 性能 | 内存占用 |
 |-----------|---------|--------|----------|
-| **Gemma 4 (E2B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma4_(E2B)-Vision.ipynb)               | 1.5x faster | 50% less |
-| **Qwen3.5 (4B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)_Vision.ipynb)               | 1.5x faster | 60% less |
-| **gpt-oss (20B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-Fine-tuning.ipynb)               | 2x faster | 70% less |
-| **Qwen3.5 GSPO**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)_Vision_GRPO.ipynb)               | 2x faster | 70% less |
-| **gpt-oss (20B): GRPO**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb)               | 2x faster | 80% less |
-| **Qwen3: Advanced GRPO**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb)               | 2x faster | 70% less |
-| **embeddinggemma (300M)**    | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma_(300M).ipynb)               | 2x faster | 20% less |
-| **Mistral Ministral 3 (3B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Ministral_3_VL_(3B)_Vision.ipynb)               | 1.5x faster | 60% less |
-| **Llama 3.1 (8B) Alpaca**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb)               | 2x faster | 70% less |
-| **Llama 3.2 Conversational**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)               | 2x faster | 70% less |
-| **Orpheus-TTS (3B)**     | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_(3B)-TTS.ipynb)               | 1.5x faster | 50% less |
+| **Gemma 4 (E2B)**      | [▶️ 免费开始](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma4_(E2B)-Vision.ipynb)               | 快 1.5 倍 | 减少 50% |
+| **Qwen3.5 (4B)**      | [▶️ 免费开始](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)_Vision.ipynb)               | 快 1.5 倍 | 减少 60% |
+| **gpt-oss (20B)**      | [▶️ 免费开始](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-Fine-tuning.ipynb)               | 快 2 倍 | 减少 70% |
+| **Qwen3.5 GSPO**      | [▶️ 免费开始](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)_Vision_GRPO.ipynb)               | 快 2 倍 | 减少 70% |
+| **gpt-oss (20B): GRPO**      | [▶️ 免费开始](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb)               | 快 2 倍 | 减少 80% |
+| **Qwen3: Advanced GRPO**      | [▶️ 免费开始](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb)               | 快 2 倍 | 减少 70% |
+| **embeddinggemma (300M)**    | [▶️ 免费开始](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma_(300M).ipynb)               | 快 2 倍 | 减少 20% |
+| **Mistral Ministral 3 (3B)**      | [▶️ 免费开始](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Ministral_3_VL_(3B)_Vision.ipynb)               | 快 1.5 倍 | 减少 60% |
+| **Llama 3.1 (8B) Alpaca**      | [▶️ 免费开始](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb)               | 快 2 倍 | 减少 70% |
+| **Llama 3.2 Conversational**      | [▶️ 免费开始](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)               | 快 2 倍 | 减少 70% |
+| **Orpheus-TTS (3B)**     | [▶️ 免费开始](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_(3B)-TTS.ipynb)               | 快 1.5 倍 | 减少 50% |
 
-- See all our notebooks for: [Kaggle](https://github.com/unslothai/notebooks?tab=readme-ov-file#-kaggle-notebooks), [GRPO](https://unsloth.ai/docs/get-started/unsloth-notebooks#grpo-reasoning-rl-notebooks), [TTS](https://unsloth.ai/docs/get-started/unsloth-notebooks#text-to-speech-tts-notebooks), [embedding](https://unsloth.ai/docs/new/embedding-finetuning) & [Vision](https://unsloth.ai/docs/get-started/unsloth-notebooks#vision-multimodal-notebooks)
-- See [all our models](https://unsloth.ai/docs/get-started/unsloth-model-catalog) and [all our notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks)
-- See detailed documentation for Unsloth [here](https://unsloth.ai/docs)
+- 查看我们所有相关 Notebook：[Kaggle](https://github.com/unslothai/notebooks?tab=readme-ov-file#-kaggle-notebooks), [GRPO](https://unsloth.ai/docs/get-started/unsloth-notebooks#grpo-reasoning-rl-notebooks), [TTS](https://unsloth.ai/docs/get-started/unsloth-notebooks#text-to-speech-tts-notebooks), [embedding](https://unsloth.ai/docs/new/embedding-finetuning) 与 [Vision](https://unsloth.ai/docs/get-started/unsloth-notebooks#vision-multimodal-notebooks)
+- 查看[我们所有模型](https://unsloth.ai/docs/get-started/unsloth-model-catalog) 以及[我们所有 Notebook](https://unsloth.ai/docs/get-started/unsloth-notebooks)
+- 查看 Unsloth 详细文档请[点击此处](https://unsloth.ai/docs)
 
-## 🦥 Unsloth News
-- **Connections**: Connect any API provider (OpenAI, Anthropic) or server (vLLM, Ollama). [Guide](https://unsloth.ai/docs/integrations/connections)
-- **MTP**: Run Qwen3.6 MTP in Unsloth. MTP settings are autoset specific to your hardware. [Guide](https://unsloth.ai/docs/models/qwen3.6#mtp-guide)
-- **API inference endpoint**: Deploy and run local LLMs in Claude Code, Codex tools. [Guide](https://unsloth.ai/docs/basics/api)
-- **Qwen3.6**: Qwen3.6-35B-A3B can now be trained and run in Unsloth Studio. [Blog](https://unsloth.ai/docs/models/qwen3.6)
-- **Gemma 4**: Run and train Google’s new models directly in Unsloth. [Blog](https://unsloth.ai/docs/models/gemma-4)
-- **Introducing Unsloth Studio**: our new web UI for running and training LLMs. [Blog](https://unsloth.ai/docs/new/studio)
-- **Qwen3.5** - 0.8B, 2B, 4B, 9B, 27B, 35-A3B, 112B-A10B are now supported. [Guide + notebooks](https://unsloth.ai/docs/models/qwen3.5/fine-tune)
-- Train **MoE LLMs 12x faster** with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss. [Blog](https://unsloth.ai/docs/new/faster-moe)
-- **Embedding models**: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning. [Blog](https://unsloth.ai/docs/new/embedding-finetuning) • [Notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks#embedding-models)
-- New **7x longer context RL** vs. all other setups, via our new batching algorithms. [Blog](https://unsloth.ai/docs/new/grpo-long-context)
-- New RoPE & MLP **Triton Kernels** & **Padding Free + Packing**: 3x faster training & 30% less VRAM. [Blog](https://unsloth.ai/docs/new/3x-faster-training-packing)
-- **500K Context**: Training a 20B model with >500K context is now possible on an 80GB GPU. [Blog](https://unsloth.ai/docs/blog/500k-context-length-fine-tuning)
-- **FP8 & Vision RL**: You can now do FP8 & VLM GRPO on consumer GPUs. [FP8 Blog](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) • [Vision RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/vision-reinforcement-learning-vlm-rl)
+## 🦥 Unsloth 动态
+- **Connections（连接）**：连接任意 API 提供商（OpenAI、Anthropic）或服务器（vLLM、Ollama）。[指南](https://unsloth.ai/docs/integrations/connections)
+- **MTP**：在 Unsloth 中运行 Qwen3.6 MTP。MTP 设置会根据你的硬件自动配置。[指南](https://unsloth.ai/docs/models/qwen3.6#mtp-guide)
+- **API 推理端点**：在 Claude Code、Codex 工具中部署并运行本地 LLM。[指南](https://unsloth.ai/docs/basics/api)
+- **Qwen3.6**：现可在 Unsloth Studio 中训练和运行 Qwen3.6-35B-A3B。[博客](https://unsloth.ai/docs/models/qwen3.6)
+- **Gemma 4**：直接在 Unsloth 中运行和训练 Google 的新模型。[博客](https://unsloth.ai/docs/models/gemma-4)
+- **推出 Unsloth Studio**：我们用于运行和训练 LLM 的全新 Web UI。[博客](https://unsloth.ai/docs/new/studio)
+- **Qwen3.5** - 现已支持 0.8B、2B、4B、9B、27B、35-A3B、112B-A10B。[指南 + Notebook](https://unsloth.ai/docs/models/qwen3.5/fine-tune)
+- 训练 **MoE LLM 速度提升 12 倍**，VRAM 减少 35%——支持 DeepSeek、GLM、Qwen 和 gpt-oss。[博客](https://unsloth.ai/docs/new/faster-moe)
+- **Embedding 模型**：Unsloth 现支持快约 1.8–3.3 倍的 embedding 微调。[博客](https://unsloth.ai/docs/new/embedding-finetuning) • [Notebook](https://unsloth.ai/docs/get-started/unsloth-notebooks#embedding-models)
+- 通过全新批处理算法，**上下文 RL 长度可达其他方案的 7 倍**。[博客](https://unsloth.ai/docs/new/grpo-long-context)
+- 全新 RoPE 与 MLP **Triton Kernel**，以及 **Padding Free + Packing**：训练速度提升 3 倍，VRAM 减少 30%。[博客](https://unsloth.ai/docs/new/3x-faster-training-packing)
+- **500K 上下文**：现可在 80GB GPU 上训练上下文超过 500K 的 20B 模型。[博客](https://unsloth.ai/docs/blog/500k-context-length-fine-tuning)
+- **FP8 与 Vision RL**：现可在消费级 GPU 上进行 FP8 和 VLM GRPO。[FP8 博客](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) • [Vision RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/vision-reinforcement-learning-vlm-rl)
 
-## 📥 Advanced Installation
-The below advanced instructions are for Unsloth Studio. For Unsloth Core advanced installation, [view our docs](https://unsloth.ai/docs/get-started/install/pip-install#advanced-pip-installation).
-#### Developer / Nightly / Experimental installs: macOS, Linux, WSL:
-The developer install builds from the `main` branch, which is the latest (nightly) source.
+## 📥 高级安装
+以下高级安装说明适用于 Unsloth Studio。有关 Unsloth Core 的高级安装，请[查看我们的文档](https://unsloth.ai/docs/get-started/install/pip-install#advanced-pip-installation).
+#### 开发者 / Nightly / 实验性安装：macOS、Linux、WSL：
+开发者安装从 `main` 分支构建，该分支为最新（nightly）源码。
 ```bash
 git clone https://github.com/unslothai/unsloth
 cd unsloth
 ./install.sh --local
 unsloth studio -p 8888
 ```
-To install into an isolated location (its own virtual env, `auth/`, `studio.db`, cache and llama.cpp build), set `UNSLOTH_STUDIO_HOME` and pass it again at launch:
+若要安装到独立位置（拥有独立的虚拟环境、`auth/`、`studio.db`、缓存和 llama.cpp 构建），请设置 `UNSLOTH_STUDIO_HOME`，并在启动时再次传入：
 ```bash
 UNSLOTH_STUDIO_HOME="$PWD/.studio" ./install.sh --local
 UNSLOTH_STUDIO_HOME="$PWD/.studio" unsloth studio -p 8888
 ```
-Then to update :
+然后更新：
 ```bash
 cd unsloth && git pull
 ./install.sh --local
 unsloth studio -p 8888
 ```
 
-#### Developer / Nightly / Experimental installs: Windows PowerShell:
-The developer install builds from the `main` branch, which is the latest (nightly) source.
+#### 开发者 / Nightly / 实验性安装：Windows PowerShell：
+开发者安装从 `main` 分支构建，该分支为最新（nightly）源码。
 ```powershell
 git clone https://github.com/unslothai/unsloth.git
 cd unsloth
@@ -193,36 +199,36 @@ Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
 .\install.ps1 --local
 unsloth studio -p 8888
 ```
-To install into an isolated location (its own virtual env, `auth/`, `studio.db`, cache and llama.cpp build), set `UNSLOTH_STUDIO_HOME` and pass it again at launch:
+若要安装到独立位置（拥有独立的虚拟环境、`auth/`、`studio.db`、缓存和 llama.cpp 构建），请设置 `UNSLOTH_STUDIO_HOME`，并在启动时再次传入：
 ```powershell
 $env:UNSLOTH_STUDIO_HOME="$PWD\.studio"; .\install.ps1 --local
 $env:UNSLOTH_STUDIO_HOME="$PWD\.studio"; unsloth studio -p 8888
 ```
-Then to update :
+然后更新：
 ```powershell
 cd unsloth; git pull
 .\install.ps1 --local
 unsloth studio -p 8888
 ```
 
-#### Remote access: `--secure` (HTTPS tunnel) vs raw port
-By default `unsloth studio` binds to `127.0.0.1` (this machine only). To reach it from another device, pick one of:
+#### 远程访问：`--secure`（HTTPS 隧道）与原始端口
+默认情况下，`unsloth studio` 绑定到 `127.0.0.1`（仅本机可访问）。若要从其他设备访问，请选择以下方式之一：
 
-- `--secure` (recommended): serve **only** through a free Cloudflare HTTPS link. Studio stays bound to localhost and the tunnel provides the public URL; it fails closed (does not start) if the tunnel can't come up, so the raw port is never exposed.
+- `--secure`（推荐）：**仅**通过免费的 Cloudflare HTTPS 链接提供服务。Studio 仍绑定到 localhost，隧道提供公共 URL；若隧道无法建立则失败关闭（不会启动），因此原始端口永远不会暴露。
 ```bash
 unsloth studio --secure -p 8888
 ```
-- `-H 0.0.0.0`: bind the raw port on all network interfaces, reachable from anywhere on the network. This also starts a public Cloudflare quick tunnel by default, which publishes an internet-reachable `https://*.trycloudflare.com` URL even behind a firewall. Both the raw port and the tunnel expose Studio beyond this machine, so only use this on a network you trust; pass `--no-cloudflare` to drop the public link while keeping the network bind.
+- `-H 0.0.0.0`：在所有网络接口上绑定原始端口，可从网络中任意位置访问。默认情况下还会启动公共 Cloudflare 快速隧道，即使在防火墙后也会发布可通过互联网访问的 `https://*.trycloudflare.com` URL。原始端口和隧道都会将 Studio 暴露在本机之外，因此仅在你信任的网络中使用；传入 `--no-cloudflare` 可移除公共链接，同时保留网络绑定。
 ```bash
 unsloth studio -H 0.0.0.0 -p 8888
 ```
 
-Server-side tools (web search, Python and terminal code execution) run as your user and are on by default. Anyone who can reach the server with the API key can run code on this machine, so keep your API key private and pass `--disable-tools` when exposing Studio.
+服务端工具（网页搜索、Python 与终端代码执行）以你的用户身份运行，且默认开启。任何能使用 API 密钥访问服务器的人都可以在这台机器上运行代码，因此请妥善保管 API 密钥，在暴露 Studio 时传入 `--disable-tools`。
 
-#### Advanced launch options
-Installer options can be passed as environment variables. On macOS, Linux and WSL place the variable after the pipe so the shell passes it to `sh`; on Windows set it with `$env:` before piping to `iex`.
+#### 高级启动选项
+安装器选项可通过环境变量传入。在 macOS、Linux 和 WSL 上，将变量放在管道符之后，以便 shell 将其传递给 `sh`；在 Windows 上，在管道传递给 `iex` 之前使用 `$env:` 设置。
 
-Skip PyTorch (GGUF-only mode):
+跳过 PyTorch（仅 GGUF 模式）：
 ```bash
 curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_NO_TORCH=1 sh
 ```
@@ -230,7 +236,7 @@ curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_NO_TORCH=1 sh
 $env:UNSLOTH_NO_TORCH=1; irm https://unsloth.ai/install.ps1 | iex
 ```
 
-Skip the post-install prompt that starts Studio (useful for automated installs):
+跳过安装后启动 Studio 的提示（适用于自动化安装）：
 ```bash
 curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_SKIP_AUTOSTART=1 sh
 ```
@@ -238,7 +244,7 @@ curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_SKIP_AUTOSTART=1 sh
 $env:UNSLOTH_SKIP_AUTOSTART=1; irm https://unsloth.ai/install.ps1 | iex
 ```
 
-Pin the Python version:
+固定 Python 版本：
 ```bash
 curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_PYTHON=3.12 sh
 ```
@@ -246,7 +252,7 @@ curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_PYTHON=3.12 sh
 $env:UNSLOTH_PYTHON='3.12'; irm https://unsloth.ai/install.ps1 | iex
 ```
 
-Install to a custom location with `UNSLOTH_STUDIO_HOME`:
+使用 `UNSLOTH_STUDIO_HOME` 安装到自定义位置：
 ```bash
 curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_STUDIO_HOME=/abs/path sh
 ```
@@ -254,52 +260,52 @@ curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_STUDIO_HOME=/abs/path sh
 $env:UNSLOTH_STUDIO_HOME='C:\path'; irm https://unsloth.ai/install.ps1 | iex
 ```
 
-On macOS, the installer defaults to the system certificate store (`UV_SYSTEM_CERTS=1`) so uv trusts the CAs in your Keychain, needed behind TLS-inspecting proxies (Cisco Umbrella, Zscaler, etc.). Opt out with:
+在 macOS 上，安装器默认使用系统证书存储（`UV_SYSTEM_CERTS=1`），以便 uv 信任 Keychain 中的 CA，这在 TLS 检查代理（Cisco Umbrella、Zscaler 等）后是必要的。如需退出，请使用：
 ```bash
 curl -fsSL https://unsloth.ai/install.sh | UV_SYSTEM_CERTS=0 sh
 ```
 
-Point the frontend build at a corporate npm mirror/proxy with `UNSLOTH_NPM_REGISTRY` (for the developer install behind a firewall that blocks `registry.npmjs.org`):
+使用 `UNSLOTH_NPM_REGISTRY` 将前端构建指向企业 npm 镜像/代理（适用于防火墙阻止 `registry.npmjs.org` 的开发者安装）：
 ```bash
 UNSLOTH_NPM_REGISTRY=https://artifactory.example.com/api/npm/npm/ ./install.sh --local
 ```
 ```powershell
 $env:UNSLOTH_NPM_REGISTRY='https://artifactory.example.com/api/npm/npm/'; .\install.ps1 --local
 ```
-It is threaded as `--registry` into the Studio frontend `npm`/`bun` installs; the supply-chain locks (7-day `min-release-age`, exact version pins) stay in force.
+它会作为 `--registry` 传入 Studio 前端 `npm`/`bun` 安装；供应链锁定（7 天 `min-release-age`、精确版本固定）仍然有效。
 
-Cap Studio's native CPU thread pools on high-core hosts: `UNSLOTH_CPU_THREADS=8 unsloth studio -p 8888`.
+在高核心数主机上限制 Studio 原生 CPU 线程池：`UNSLOTH_CPU_THREADS=8 unsloth studio -p 8888`。
 
-#### Uninstall
-The recommended way to fully remove Unsloth Studio is the matching uninstall script for your OS. It stops any running servers, removes the install dir, the launcher data dir, the desktop shortcut, and any platform-specific entries (macOS `.app` bundle + Launch Services on Mac; Start Menu, `HKCU\Software\Unsloth` registry key and user `PATH` entries on Windows):
+#### 卸载
+完全移除 Unsloth Studio 的推荐方式是使用与你操作系统匹配的卸载脚本。它会停止所有正在运行的服务器，移除安装目录、启动器数据目录、桌面快捷方式，以及任何平台特定条目（macOS 上的 `.app` 包与 Launch Services；Windows 上的开始菜单、`HKCU\Software\Unsloth` 注册表项和用户 `PATH` 条目）：
 
-* ​ **MacOS, WSL, Linux:** `curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.sh | sh`
-* ​ **Windows (PowerShell):** `irm https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.ps1 | iex`
+* ​ **MacOS、WSL、Linux：** `curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.sh | sh`
+* ​ **Windows（PowerShell）：** `irm https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.ps1 | iex`
 
-If you only want to drop the install dir and keep the launcher/shortcut for a later reinstall, you can instead run `rm -rf ~/.unsloth/studio` (Mac/Linux/WSL) or `Remove-Item -Recurse -Force "$HOME\.unsloth\studio"` (Windows). The model cache at `~/.cache/huggingface` is not touched by any of these.
+如果你只想删除安装目录并保留启动器/快捷方式以便日后重新安装，可以改为运行 `rm -rf ~/.unsloth/studio`（Mac/Linux/WSL）或 `Remove-Item -Recurse -Force "$HOME\.unsloth\studio"`（Windows）。这些操作都不会影响位于 `~/.cache/huggingface` 的模型缓存。
 
-For more info, [see our docs](https://unsloth.ai/docs/new/studio/install#uninstall).
+更多信息请参阅[我们的文档](https://unsloth.ai/docs/new/studio/install#uninstall).
 
-#### Deleting model files
+#### 删除模型文件
 
-You can delete old model files either from the bin icon in model search or by removing the relevant cached model folder from the default Hugging Face cache directory. By default, HF uses:
+你可以通过在模型搜索中的垃圾桶图标删除旧模型文件，也可以从默认 Hugging Face 缓存目录中移除相应的已缓存模型文件夹。默认情况下，HF 使用：
 
-* ​ **MacOS, Linux, WSL:** `~/.cache/huggingface/hub/`
-* ​ **Windows:** `%USERPROFILE%\.cache\huggingface\hub\`
+* ​ **MacOS、Linux、WSL：** `~/.cache/huggingface/hub/`
+* ​ **Windows：** `%USERPROFILE%\.cache\huggingface\hub\`
 
-## 💚 Community and Links
-| Type                                                                                                                                      | Links                                                                          |
+## 💚 社区与链接
+| 类型                                                                                                                                      | 链接                                                                          |
 | ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ |
-| <img width="16" src="https://cdn.prod.website-files.com/6257adef93867e50d84d30e2/66e3d80db9971f10a9757c99_Symbol.svg" />  **Discord**                       | [Join Discord server](https://discord.com/invite/unsloth)                          |
-| <img width="15" src="https://redditinc.com/hs-fs/hubfs/Reddit%20Inc/Brand/Reddit_Logo.png" />  **r/unsloth Reddit**                       | [Join Reddit community](https://reddit.com/r/unsloth)                          |
-| 📚 **Documentation & Wiki**                                                                                                               | [Read Our Docs](https://unsloth.ai/docs)                                       |
-| <img width="13" src="https://upload.wikimedia.org/wikipedia/commons/0/09/X_(formerly_Twitter)_logo_late_2025.svg" />  **Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai)                                |
-| 🔮 **Our Models**                                                                                                                         | [Unsloth Catalog](https://unsloth.ai/docs/get-started/unsloth-model-catalog)   |
-| ✍️ **Blog**                                                                                                                               | [Read our Blogs](https://unsloth.ai/blog)                                      |
+| <img width="16" src="https://cdn.prod.website-files.com/6257adef93867e50d84d30e2/66e3d80db9971f10a9757c99_Symbol.svg" />  **Discord**                       | [加入 Discord 服务器](https://discord.com/invite/unsloth)                          |
+| <img width="15" src="https://redditinc.com/hs-fs/hubfs/Reddit%20Inc/Brand/Reddit_Logo.png" />  **r/unsloth Reddit**                       | [加入 Reddit 社区](https://reddit.com/r/unsloth)                          |
+| 📚 **文档与 Wiki**                                                                                                               | [阅读我们的文档](https://unsloth.ai/docs)                                       |
+| <img width="13" src="https://upload.wikimedia.org/wikipedia/commons/0/09/X_(formerly_Twitter)_logo_late_2025.svg" />  **Twitter（又名 X）** | [在 X 上关注我们](https://twitter.com/unslothai)                                |
+| 🔮 **我们的模型**                                                                                                                         | [Unsloth 目录](https://unsloth.ai/docs/get-started/unsloth-model-catalog)   |
+| ✍️ **博客**                                                                                                                               | [阅读我们的博客](https://unsloth.ai/blog)                                      |
 
-### Citation
+### 引用
 
-You can cite the Unsloth repo as follows:
+你可以按如下方式引用 Unsloth 仓库：
 ```bibtex
 @software{unsloth,
   author = {Daniel Han, Michael Han and Unsloth team},
@@ -308,16 +314,16 @@ You can cite the Unsloth repo as follows:
   year = {2023}
 }
 ```
-If you trained a model with 🦥Unsloth, you can use this cool sticker!   <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200" align="center" />
+如果你使用 🦥Unsloth 训练了模型，可以使用这张酷炫贴纸！   <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200" align="center" />
 
-### License
-Unsloth uses a dual-licensing model of Apache 2.0 and AGPL-3.0. The core Unsloth package remains licensed under **[Apache 2.0](https://github.com/unslothai/unsloth?tab=Apache-2.0-1-ov-file)**, while certain optional components, such as the Unsloth Studio UI are licensed under the open-source license **[AGPL-3.0](https://github.com/unslothai/unsloth?tab=AGPL-3.0-2-ov-file)**.
+### 许可证
+Unsloth 采用 Apache 2.0 与 AGPL-3.0 双重许可模式。核心 Unsloth 包仍采用 **[Apache 2.0](https://github.com/unslothai/unsloth?tab=Apache-2.0-1-ov-file)**, 许可，而部分可选组件（例如 Unsloth Studio UI）则采用开源许可 **[AGPL-3.0](https://github.com/unslothai/unsloth?tab=AGPL-3.0-2-ov-file)**.
 
-This structure helps support ongoing Unsloth development while keeping the project open source and enabling the broader ecosystem to continue growing.
+这一结构有助于支持 Unsloth 的持续开发，同时保持项目开源，并让更广泛的生态得以继续发展。
 
-### Thank You to
-- The [llama.cpp library](https://github.com/ggml-org/llama.cpp) that lets users run and save models with Unsloth
-- The Hugging Face team and their libraries: [transformers](https://github.com/huggingface/transformers) and [TRL](https://github.com/huggingface/trl)
-- The Pytorch and [Torch AO](https://github.com/unslothai/unsloth/pull/3391) team for their contributions
-- NVIDIA for their [NeMo DataDesigner](https://github.com/NVIDIA-NeMo/DataDesigner) library and their contributions
-- And of course for every single person who has contributed or has used Unsloth!
+### 致谢
+- [llama.cpp 库](https://github.com/ggml-org/llama.cpp)，让用户能够使用 Unsloth 运行并保存模型
+- Hugging Face 团队及其库：[transformers](https://github.com/huggingface/transformers) 与 [TRL](https://github.com/huggingface/trl)
+- PyTorch 与 [Torch AO](https://github.com/unslothai/unsloth/pull/3391) 团队所作的贡献
+- NVIDIA 的 [NeMo DataDesigner](https://github.com/NVIDIA-NeMo/DataDesigner) 库及其贡献
+- 当然，还要感谢每一位曾为 Unsloth 贡献代码或使用过它的人！