[model] add liger kernel support for Qwen3-Next (#10176 )

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
[model] support GLM-OCR SFT (#10183 )
2026-02-26 15:56:00 +08:00 · 2026-02-10 21:47:48 +08:00 · 2026-02-10 21:41:01 +08:00 · 2026-02-09 19:43:55 +08:00 · 2026-02-09 10:40:44 +08:00 · 2026-02-04 23:27:12 +08:00
221 changed files with 7032 additions and 2792 deletions
--- a/src/llamafactory/v1/core/trainer_utils/init.py
+++ b/src/llamafactory/v1/core/trainer_utils/init.py
--- a/src/llamafactory/v1/core/trainer_utils/callback.py
+++ b/src/llamafactory/v1/core/trainer_utils/callback.py
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -50,7 +50,7 @@ jobs:
          docker-images: false

      - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6

      - name: Get llamafactory version
        id: version
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -0,0 +1,77 @@
+name: Build and Deploy Sphinx Docs
+
+on:
+  push:
+    branches: ["main"]
+    paths:
+      - "docs/**"
+  pull_request:
+    branches: ["main"]
+    paths:
+      - "docs/**"
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+          
+      - name: Install dependencies
+        run: |
+          pip install -r docs/requirements.txt
+          
+      - name: Build Sphinx
+        run: |
+          sphinx-build -b html docs/zh docs/_build/html/zh
+          sphinx-build -b html docs/en docs/_build/html/en
+
+          printf '%s\n' \
+            '<!DOCTYPE html>' \
+            '<html>' \
+            '  <head>' \
+            '    <meta charset="utf-8" />' \
+            '    <meta http-equiv="refresh" content="0; url=zh/index.html" />' \
+            '    <script>window.location.href="zh/index.html"+window.location.search+window.location.hash;</script>' \
+            '    <title>Redirecting...</title>' \
+            '  </head>' \
+            '  <body>' \
+            '    <a href="zh/index.html">Redirecting...</a>' \
+            '  </body>' \
+            '</html>' \
+            > docs/_build/html/index.html
+
+          touch docs/_build/html/.nojekyll
+          
+      - name: Setup Pages
+        uses: actions/configure-pages@v5
+        
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: docs/_build/html
+
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    needs: build
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -21,7 +21,7 @@ jobs:

    steps:
      - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6

      - name: Install uv
        uses: astral-sh/setup-uv@v7
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -54,10 +54,11 @@ jobs:
    env:
      HF_TOKEN: ${{ secrets.HF_TOKEN }}
      OS_NAME: ${{ matrix.os }}
+      UV_NO_SYNC: 1

    steps:
      - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6

      - name: Install uv
        uses: astral-sh/setup-uv@v7
@@ -70,7 +71,8 @@ jobs:
        run: |
          uv venv
          uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-          uv pip install -e ".[dev]"
+          uv pip install -e .
+          uv pip install -r requirements/dev.txt

      - name: Install transformers
        if: ${{ matrix.transformers }}
@@ -79,7 +81,7 @@ jobs:

      - name: Cache files
        id: hf-hub-cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
        with:
          path: ${{ runner.temp }}/huggingface
          key: huggingface-${{ matrix.os }}-${{ matrix.python }}-${{ matrix.transformers }}-${{ hashFiles('tests/version.txt') }}
@@ -87,25 +89,18 @@ jobs:
      - name: Check quality
        run: |
          make style && make quality
-        env:
-          UV_NO_SYNC: 1

      - name: Check license
        run: |
          make license
-        env:
-          UV_NO_SYNC: 1

      - name: Check build
        run: |
          make build
-        env:
-          UV_NO_SYNC: 1

      - name: Test with pytest
        run: |
          make test
        env:
-          UV_NO_SYNC: 1
          HF_HOME: ${{ runner.temp }}/huggingface
          HF_HUB_OFFLINE: "${{ steps.hf-hub-cache.outputs.cache-hit == 'true' && '1' || '0' }}"
--- a/.github/workflows/tests_cuda.yml
+++ b/.github/workflows/tests_cuda.yml
@@ -35,9 +35,16 @@ jobs:
      group: ${{ github.workflow }}-${{ github.ref }}-${{ matrix.os }}-${{ matrix.python }}
      cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}

+    env:
+      HF_HOME: "${{ github.workspace }}/../.runner_cache/huggingface"
+      UV_CACHE_DIR: "${{ github.workspace }}/../.runner_cache/uv"
+      HF_TOKEN: ${{ secrets.HF_TOKEN }}
+      OS_NAME: ${{ matrix.os }}
+      UV_NO_SYNC: 1
+
    steps:
      - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6

      - name: Install uv
        uses: astral-sh/setup-uv@v7
@@ -52,37 +59,21 @@ jobs:
      - name: Install dependencies
        run: |
          uv venv
-          uv pip install -e ".[dev]"
-
-      - name: Cache HuggingFace models
-        id: hf-hub-cache
-        uses: actions/cache@v4
-        with:
-          path: ${{ runner.temp }}/huggingface
-          key: hf-cache-${{ runner.os }}-${{ hashFiles('tests/version.txt') }}
+          uv pip install -e .
+          uv pip install -r requirements/dev.txt

      - name: Check quality
        run: |
          make style && make quality
-        env:
-          UV_NO_SYNC: 1

      - name: Check license
        run: |
          make license
-        env:
-          UV_NO_SYNC: 1

      - name: Check build
        run: |
          make build
-        env:
-          UV_NO_SYNC: 1

      - name: Test with pytest
        run: |
          make test
-        env:
-          UV_NO_SYNC: 1
-          HF_HOME: ${{ runner.temp }}/huggingface
-          HF_HUB_OFFLINE: "${{ steps.hf-hub-cache.outputs.cache-hit == 'true' && '1' || '0' }}"
--- a/.github/workflows/tests_npu.yml
+++ b/.github/workflows/tests_npu.yml
@@ -43,10 +43,11 @@ jobs:
        HF_ENDPOINT: https://hf-mirror.com
        HF_TOKEN: ${{ secrets.HF_TOKEN }}
        OS_NAME: ${{ matrix.os }}
+        UV_NO_SYNC: 1

    steps:
      - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6

      - name: Install uv
        uses: astral-sh/setup-uv@v7
@@ -58,8 +59,9 @@ jobs:
      - name: Install dependencies
        run: |
          uv venv
-          uv pip install torch-npu==${{matrix.pytorch_npu}}
-          uv pip install -e ".[dev]"
+          uv pip install -r requirements/npu.txt
+          uv pip install -e .
+          uv pip install -r requirements/dev.txt

      - name: Install node
        run: |
@@ -68,35 +70,18 @@ jobs:
          curl -fsSL https://deb.nodesource.com/setup_20.x | bash -
          apt-get install -y nodejs

-      - name: Cache files
-        id: hf-hub-cache
-        uses: actions/cache@v4
-        with:
-          path: ${{ runner.temp }}/huggingface
-          key: huggingface-${{ matrix.os }}-${{ matrix.python }}-${{ hashFiles('tests/version.txt') }}
-
      - name: Check quality
        run: |
          make style && make quality
-        env:
-          UV_NO_SYNC: 1

      - name: Check license
        run: |
          make license
-        env:
-          UV_NO_SYNC: 1

      - name: Check build
        run: |
          make build
-        env:
-          UV_NO_SYNC: 1

      - name: Test with pytest
        run: |
          make test
-        env:
-          UV_NO_SYNC: 1
-          HF_HOME: /root/.cache/huggingface
-          HF_HUB_OFFLINE: "${{ steps.hf-hub-cache.outputs.cache-hit == 'true' && '1' || '0' }}"
--- a/.gitignore
+++ b/.gitignore
@@ -176,6 +176,7 @@ llamaboard_cache/
 llamaboard_config/
 saves/
 output/
+outputs/
 wandb/
 swanlog/
 generated_predictions.jsonl
--- a/README.md
+++ b/README.md
@@ -92,7 +92,7 @@ Read technical notes:

 ## Features

- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, DeepSeek, Yi, Gemma, ChatGLM, Phi, etc.
+- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen3, Qwen3-VL, DeepSeek, Gemma, GLM, Phi, etc.
 - **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
 - **Scalable resources**: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
 - **Advanced algorithms**: [GaLore](https://github.com/jiaweizzhao/GaLore), [BAdam](https://github.com/Ledzy/BAdam), [APOLLO](https://github.com/zhuhanqing/APOLLO), [Adam-mini](https://github.com/zyushun/Adam-mini), [Muon](https://github.com/KellerJordan/Muon), [OFT](https://github.com/huggingface/peft/tree/main/src/peft/tuners/oft), DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and PiSSA.
@@ -279,11 +279,10 @@ Read technical notes:
 | Model                                                             | Model size                       | Template             |
 | ----------------------------------------------------------------- | -------------------------------- | -------------------- |
 | [BLOOM/BLOOMZ](https://huggingface.co/bigscience)                 | 560M/1.1B/1.7B/3B/7.1B/176B      | -                    |
-| [Command R](https://huggingface.co/CohereForAI)                   | 35B/104B                         | cohere               |
 | [DeepSeek (LLM/Code/MoE)](https://huggingface.co/deepseek-ai)     | 7B/16B/67B/236B                  | deepseek             |
 | [DeepSeek 3-3.2](https://huggingface.co/deepseek-ai)              | 236B/671B                        | deepseek3            |
 | [DeepSeek R1 (Distill)](https://huggingface.co/deepseek-ai)       | 1.5B/7B/8B/14B/32B/70B/671B      | deepseekr1           |
-| [ERNIE-4.5](https://huggingface.co/baidu)                         | 0.3B/21B/300B                    | ernie/ernie_nothink  |
+| [ERNIE-4.5](https://huggingface.co/baidu)                         | 0.3B/21B/300B                    | ernie_nothink        |
 | [Falcon/Falcon H1](https://huggingface.co/tiiuae)                 | 0.5B/1.5B/3B/7B/11B/34B/40B/180B | falcon/falcon_h1     |
 | [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google)          | 2B/7B/9B/27B                     | gemma/gemma2         |
 | [Gemma 3/Gemma 3n](https://huggingface.co/google)                 | 270M/1B/4B/6B/8B/12B/27B         | gemma3/gemma3n       |
@@ -292,12 +291,13 @@ Read technical notes:
 | [GPT-2](https://huggingface.co/openai-community)                  | 0.1B/0.4B/0.8B/1.5B              | -                    |
 | [GPT-OSS](https://huggingface.co/openai)                          | 20B/120B                         | gpt_oss              |
 | [Granite 3-4](https://huggingface.co/ibm-granite)                 | 1B/2B/3B/7B/8B                   | granite3/granite4    |
-| [Hunyuan (MT)](https://huggingface.co/tencent/)                   | 7B                               | hunyuan              |
+| [Hunyuan/Hunyuan1.5 (MT)](https://huggingface.co/tencent/)        | 0.5B/1.8B/4B/7B/13B              | hunyuan/hunyuan_small |
 | [InternLM 2-3](https://huggingface.co/internlm)                   | 7B/8B/20B                        | intern2              |
 | [InternVL 2.5-3.5](https://huggingface.co/OpenGVLab)              | 1B/2B/4B/8B/14B/30B/38B/78B/241B | intern_vl            |
-| [InternLM/Intern-S1-mini](https://huggingface.co/internlm/)       | 8B                               | intern_s1            |
+| [Intern-S1-mini](https://huggingface.co/internlm/)                | 8B                               | intern_s1            |
 | [Kimi-VL](https://huggingface.co/moonshotai)                      | 16B                              | kimi_vl              |
 | [Ling 2.0 (mini/flash)](https://huggingface.co/inclusionAI)       | 16B/100B                         | bailing_v2           |
+| [LFM 2.5 (VL)](https://huggingface.co/LiquidAI)                   | 1.2B/1.6B                        | lfm2/lfm2_vl         |
 | [Llama](https://github.com/facebookresearch/llama)                | 7B/13B/33B/65B                   | -                    |
 | [Llama 2](https://huggingface.co/meta-llama)                      | 7B/13B/70B                       | llama2               |
 | [Llama 3-3.3](https://huggingface.co/meta-llama)                  | 1B/3B/8B/70B                     | llama3               |
@@ -307,18 +307,17 @@ Read technical notes:
 | [LLaVA-NeXT](https://huggingface.co/llava-hf)                     | 7B/8B/13B/34B/72B/110B           | llava_next           |
 | [LLaVA-NeXT-Video](https://huggingface.co/llava-hf)               | 7B/34B                           | llava_next_video     |
 | [MiMo](https://huggingface.co/XiaomiMiMo)                         | 7B/309B                          | mimo/mimo_v2         |
-| [MiniCPM 1-4.1](https://huggingface.co/openbmb)                   | 0.5B/1B/2B/4B/8B                 | cpm/cpm3/cpm4        |
-| [MiniCPM-o-2.6/MiniCPM-V-2.6](https://huggingface.co/openbmb)     | 8B                               | minicpm_o/minicpm_v  |
+| [MiniCPM 4](https://huggingface.co/openbmb)                       | 0.5B/8B                          | cpm4                 |
+| [MiniCPM-o/MiniCPM-V 4.5](https://huggingface.co/openbmb)         | 8B/9B                            | minicpm_o/minicpm_v  |
 | [MiniMax-M1/MiniMax-M2](https://huggingface.co/MiniMaxAI/models)  | 229B/456B                        | minimax1/minimax2    |
 | [Ministral 3](https://huggingface.co/mistralai)                   | 3B/8B/14B                        | ministral3           |
 | [Mistral/Mixtral](https://huggingface.co/mistralai)               | 7B/8x7B/8x22B                    | mistral              |
-| [OLMo](https://huggingface.co/allenai)                            | 1B/7B                            | -                    |
 | [PaliGemma/PaliGemma2](https://huggingface.co/google)             | 3B/10B/28B                       | paligemma            |
 | [Phi-3/Phi-3.5](https://huggingface.co/microsoft)                 | 4B/14B                           | phi                  |
 | [Phi-3-small](https://huggingface.co/microsoft)                   | 7B                               | phi_small            |
-| [Phi-4](https://huggingface.co/microsoft)                         | 14B                              | phi4                 |
+| [Phi-4-mini/Phi-4](https://huggingface.co/microsoft)              | 3.8B/14B                         | phi4_mini/phi4       |
 | [Pixtral](https://huggingface.co/mistralai)                       | 12B                              | pixtral              |
-| [Qwen (1-2.5) (Code/Math/MoE/QwQ)](https://huggingface.co/Qwen)   | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen                 |
+| [Qwen2 (Code/Math/MoE/QwQ)](https://huggingface.co/Qwen)          | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen                 |
 | [Qwen3 (MoE/Instruct/Thinking/Next)](https://huggingface.co/Qwen) | 0.6B/1.7B/4B/8B/14B/32B/80B/235B | qwen3/qwen3_nothink  |
 | [Qwen2-Audio](https://huggingface.co/Qwen)                        | 7B                               | qwen2_audio          |
 | [Qwen2.5-Omni](https://huggingface.co/Qwen)                       | 3B/7B                            | qwen2_omni           |
@@ -327,8 +326,7 @@ Read technical notes:
 | [Qwen3-VL](https://huggingface.co/Qwen)                           | 2B/4B/8B/30B/32B/235B            | qwen3_vl             |
 | [Seed (OSS/Coder)](https://huggingface.co/ByteDance-Seed)         | 8B/36B                           | seed_oss/seed_coder  |
 | [StarCoder 2](https://huggingface.co/bigcode)                     | 3B/7B/15B                        | -                    |
-| [VibeThinker-1.5B](https://huggingface.co/WeiboAI)                | 1.5B                             | qwen3                |
-| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai)                  | 1.5B/6B/9B/34B                   | yi                   |
+| [TeleChat 2-2.5](https://huggingface.co/Tele-AI)                  | 3B/7B/35B/115B                   | telechat2            |
 | [Yuan 2](https://huggingface.co/IEITYuan)                         | 2B/51B/102B                      | yuan                 |

 > [!NOTE]
@@ -514,12 +512,13 @@ huggingface-cli login
 #### Install from Source

 ```bash
-git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
-cd LLaMA-Factory
-pip install -e ".[metrics]"
+git clone --depth 1 https://github.com/hiyouga/LlamaFactory.git
+cd LlamaFactory
+pip install -e .
+pip install -r requirements/metrics.txt
 ```

-Optional dependencies available: `metrics`, `deepspeed`. Install with: `pip install -e ".[metrics,deepspeed]"`
+Optional dependencies available: `metrics`, `deepspeed`. Install with: `pip install -e . && pip install -r requirements/metrics.txt -r requirements/deepspeed.txt`

 Additional dependencies for specific features are available in `examples/requirements/`.

@@ -577,36 +576,21 @@ To enable FlashAttention-2 on the Windows platform, please use the script from [

 <details><summary>For Ascend NPU users</summary>

-To install LLaMA Factory on Ascend NPU devices, please upgrade Python to version 3.10 or higher: `pip install -e . torch-npu==2.7.1`. Additionally, you need to install the **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**. Please follow the [installation tutorial](https://www.hiascend.com/document/detail/en/CANNCommunityEdition/600alphaX/softwareinstall/instg/atlasdeploy_03_0031.html) or use the following commands:
+To install LLaMA Factory on Ascend NPU devices, please upgrade Python to version 3.10 or higher: `pip install -r requirements/npu.txt`. Additionally, you need to install the **Ascend CANN Toolkit and Kernels**. Please follow the [installation tutorial](https://llamafactory.readthedocs.io/en/latest/advanced/npu_installation.html).
+
+
+You can also download the pre-built Docker images:

 ```bash
-# replace the url according to your CANN version and devices
-# install CANN Toolkit
-wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C20SPC702/Ascend-cann-toolkit_8.0.0.alpha002_linux-"$(uname -i)".run
-bash Ascend-cann-toolkit_8.0.0.alpha002_linux-"$(uname -i)".run --install
+# Docker Hub
+docker pull hiyouga/llamafactory:latest-npu-a2
+docker pull hiyouga/llamafactory:latest-npu-a3

-# install CANN Kernels
-wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C20SPC702/Ascend-cann-kernels-910b_8.0.0.alpha002_linux-"$(uname -i)".run
-bash Ascend-cann-kernels-910b_8.0.0.alpha002_linux-"$(uname -i)".run --install
-
-# set env variables
-source /usr/local/Ascend/ascend-toolkit/set_env.sh
+# quay.io
+docker pull quay.io/ascend/llamafactory:latest-npu-a2
+docker pull quay.io/ascend/llamafactory:latest-npu-a3
 ```

-| Requirement  | Minimum | Recommend      |
-| ------------ | ------- | -------------- |
-| CANN         | 8.0.RC1 | 8.0.0.alpha002 |
-| torch        | 2.1.0   | 2.7.1          |
-| torch-npu    | 2.1.0   | 2.7.1          |
-| deepspeed    | 0.13.2  | 0.13.2         |
-| vllm-ascend  | -       | 0.7.3          |
-
-Remember to use `ASCEND_RT_VISIBLE_DEVICES` instead of `CUDA_VISIBLE_DEVICES` to specify the device to use.
-
-If you cannot infer model on NPU devices, try setting `do_sample: false` in the configurations.
-
-Download the pre-built Docker images: [32GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html) | [64GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
-
 #### Install BitsAndBytes

 To use QLoRA based on bitsandbytes on Ascend NPU, please follow these 3 steps:
@@ -946,7 +930,7 @@ If you have a project that should be incorporated, please contact via email or c

 This repository is licensed under the [Apache-2.0 License](LICENSE).

-Please follow the model licenses to use the corresponding model weights: [Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [GPT-2](https://github.com/openai/gpt-2/blob/master/LICENSE) / [Granite](LICENSE) / [Index](https://huggingface.co/IndexTeam/Index-1.9B/blob/main/LICENSE) / [InternLM](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [Llama 4](https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3/Phi-4](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [Skywork](https://huggingface.co/Skywork/Skywork-13B-base/blob/main/Skywork%20Community%20License.pdf) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [TeleChat2](https://huggingface.co/Tele-AI/telechat-7B/blob/main/TeleChat%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
+Please follow the model licenses to use the corresponding model weights: [BLOOM](https://huggingface.co/spaces/bigscience/license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [GPT-2](https://github.com/openai/gpt-2/blob/master/LICENSE) / [Granite](LICENSE) / [InternLM](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [Llama 4](https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](LICENSE) / [Phi-3/Phi-4](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [TeleChat2](https://huggingface.co/Tele-AI/telechat-7B/blob/main/TeleChat%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)

 ## Citation

--- a/README_zh.md
+++ b/README_zh.md
@@ -94,7 +94,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc

 ## 项目特色

- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Qwen2-VL、DeepSeek、Yi、Gemma、ChatGLM、Phi 等等。
+- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen3、Qwen3-VL、DeepSeek、Gemma、GLM、Phi 等等。
 - **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练、ORPO 训练等等。
 - **多种精度**：16 比特全参数微调、冻结微调、LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ 的 2/3/4/5/6/8 比特 QLoRA 微调。
 - **先进算法**：[GaLore](https://github.com/jiaweizzhao/GaLore)、[BAdam](https://github.com/Ledzy/BAdam)、[APOLLO](https://github.com/zhuhanqing/APOLLO)、[Adam-mini](https://github.com/zyushun/Adam-mini)、[Muon](https://github.com/KellerJordan/Muon)、[OFT](https://github.com/huggingface/peft/tree/main/src/peft/tuners/oft)、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ 和 PiSSA。
@@ -281,11 +281,10 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | 模型名                                                             | 参数量                            | Template             |
 | ----------------------------------------------------------------- | -------------------------------- | -------------------- |
 | [BLOOM/BLOOMZ](https://huggingface.co/bigscience)                 | 560M/1.1B/1.7B/3B/7.1B/176B      | -                    |
-| [Command R](https://huggingface.co/CohereForAI)                   | 35B/104B                         | cohere               |
 | [DeepSeek (LLM/Code/MoE)](https://huggingface.co/deepseek-ai)     | 7B/16B/67B/236B                  | deepseek             |
 | [DeepSeek 3-3.2](https://huggingface.co/deepseek-ai)              | 236B/671B                        | deepseek3            |
 | [DeepSeek R1 (Distill)](https://huggingface.co/deepseek-ai)       | 1.5B/7B/8B/14B/32B/70B/671B      | deepseekr1           |
-| [ERNIE-4.5](https://huggingface.co/baidu)                         | 0.3B/21B/300B                    | ernie/ernie_nothink  |
+| [ERNIE-4.5](https://huggingface.co/baidu)                         | 0.3B/21B/300B                    | ernie_nothink        |
 | [Falcon/Falcon H1](https://huggingface.co/tiiuae)                 | 0.5B/1.5B/3B/7B/11B/34B/40B/180B | falcon/falcon_h1     |
 | [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google)          | 2B/7B/9B/27B                     | gemma/gemma2         |
 | [Gemma 3/Gemma 3n](https://huggingface.co/google)                 | 270M/1B/4B/6B/8B/12B/27B         | gemma3/gemma3n       |
@@ -294,12 +293,13 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | [GPT-2](https://huggingface.co/openai-community)                  | 0.1B/0.4B/0.8B/1.5B              | -                    |
 | [GPT-OSS](https://huggingface.co/openai)                          | 20B/120B                         | gpt_oss              |
 | [Granite 3-4](https://huggingface.co/ibm-granite)                 | 1B/2B/3B/7B/8B                   | granite3/granite4    |
-| [Hunyuan (MT)](https://huggingface.co/tencent/)                   | 7B                               | hunyuan              |
+| [Hunyuan/Hunyuan1.5 (MT)](https://huggingface.co/tencent/)        | 0.5B/1.8B/4B/7B/13B              | hunyuan/hunyuan_small |
 | [InternLM 2-3](https://huggingface.co/internlm)                   | 7B/8B/20B                        | intern2              |
 | [InternVL 2.5-3.5](https://huggingface.co/OpenGVLab)              | 1B/2B/4B/8B/14B/30B/38B/78B/241B | intern_vl            |
-| [InternLM/Intern-S1-mini](https://huggingface.co/internlm/)       | 8B                               | intern_s1            |
+| [Intern-S1-mini](https://huggingface.co/internlm/)                | 8B                               | intern_s1            |
 | [Kimi-VL](https://huggingface.co/moonshotai)                      | 16B                              | kimi_vl              |
 | [Ling 2.0 (mini/flash)](https://huggingface.co/inclusionAI)       | 16B/100B                         | bailing_v2           |
+| [LFM 2.5 (VL)](https://huggingface.co/LiquidAI)                   | 1.2B/1.6B                        | lfm2/lfm2_vl         |
 | [Llama](https://github.com/facebookresearch/llama)                | 7B/13B/33B/65B                   | -                    |
 | [Llama 2](https://huggingface.co/meta-llama)                      | 7B/13B/70B                       | llama2               |
 | [Llama 3-3.3](https://huggingface.co/meta-llama)                  | 1B/3B/8B/70B                     | llama3               |
@@ -309,18 +309,17 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | [LLaVA-NeXT](https://huggingface.co/llava-hf)                     | 7B/8B/13B/34B/72B/110B           | llava_next           |
 | [LLaVA-NeXT-Video](https://huggingface.co/llava-hf)               | 7B/34B                           | llava_next_video     |
 | [MiMo](https://huggingface.co/XiaomiMiMo)                         | 7B/309B                          | mimo/mimo_v2         |
-| [MiniCPM 1-4.1](https://huggingface.co/openbmb)                   | 0.5B/1B/2B/4B/8B                 | cpm/cpm3/cpm4        |
-| [MiniCPM-o-2.6/MiniCPM-V-2.6](https://huggingface.co/openbmb)     | 8B                               | minicpm_o/minicpm_v  |
+| [MiniCPM 4](https://huggingface.co/openbmb)                       | 0.5B/8B                          | cpm4                 |
+| [MiniCPM-o/MiniCPM-V 4.5](https://huggingface.co/openbmb)         | 8B/9B                            | minicpm_o/minicpm_v  |
 | [MiniMax-M1/MiniMax-M2](https://huggingface.co/MiniMaxAI/models)  | 229B/456B                        | minimax1/minimax2    |
 | [Ministral 3](https://huggingface.co/mistralai)                   | 3B/8B/14B                        | ministral3           |
 | [Mistral/Mixtral](https://huggingface.co/mistralai)               | 7B/8x7B/8x22B                    | mistral              |
-| [OLMo](https://huggingface.co/allenai)                            | 1B/7B                            | -                    |
 | [PaliGemma/PaliGemma2](https://huggingface.co/google)             | 3B/10B/28B                       | paligemma            |
 | [Phi-3/Phi-3.5](https://huggingface.co/microsoft)                 | 4B/14B                           | phi                  |
 | [Phi-3-small](https://huggingface.co/microsoft)                   | 7B                               | phi_small            |
-| [Phi-4](https://huggingface.co/microsoft)                         | 14B                              | phi4                 |
+| [Phi-4-mini/Phi-4](https://huggingface.co/microsoft)              | 3.8B/14B                         | phi4_mini/phi4       |
 | [Pixtral](https://huggingface.co/mistralai)                       | 12B                              | pixtral              |
-| [Qwen (1-2.5) (Code/Math/MoE/QwQ)](https://huggingface.co/Qwen)   | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen                 |
+| [Qwen2 (Code/Math/MoE/QwQ)](https://huggingface.co/Qwen)          | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen                 |
 | [Qwen3 (MoE/Instruct/Thinking/Next)](https://huggingface.co/Qwen) | 0.6B/1.7B/4B/8B/14B/32B/80B/235B | qwen3/qwen3_nothink  |
 | [Qwen2-Audio](https://huggingface.co/Qwen)                        | 7B                               | qwen2_audio          |
 | [Qwen2.5-Omni](https://huggingface.co/Qwen)                       | 3B/7B                            | qwen2_omni           |
@@ -329,8 +328,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | [Qwen3-VL](https://huggingface.co/Qwen)                           | 2B/4B/8B/30B/32B/235B            | qwen3_vl             |
 | [Seed (OSS/Coder)](https://huggingface.co/ByteDance-Seed)         | 8B/36B                           | seed_oss/seed_coder  |
 | [StarCoder 2](https://huggingface.co/bigcode)                     | 3B/7B/15B                        | -                    |
-| [VibeThinker-1.5B](https://huggingface.co/WeiboAI)                | 1.5B                             | qwen3                |
-| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai)                  | 1.5B/6B/9B/34B                   | yi                   |
+| [TeleChat 2-2.5](https://huggingface.co/Tele-AI)                  | 3B/7B/35B/115B                   | telechat2            |
 | [Yuan 2](https://huggingface.co/IEITYuan)                         | 2B/51B/102B                      | yuan                 |

 > [!NOTE]
@@ -516,12 +514,13 @@ huggingface-cli login
 #### 从源码安装

 ```bash
-git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
-cd LLaMA-Factory
-pip install -e ".[metrics]"
+git clone --depth 1 https://github.com/hiyouga/LlamaFactory.git
+cd LlamaFactory
+pip install -e .
+pip install -r requirements/metrics.txt
 ```

-可选的额外依赖项：`metrics`、`deepspeed`。使用 `pip install -e ".[metrics,deepspeed]"` 安装。
+可选的额外依赖项：`metrics`、`deepspeed`。使用 `pip install -e . && pip install -r requirements/metrics.txt -r requirements/deepspeed.txt` 安装。

 其他可选依赖项请参考 `examples/requirements/` 目录下的文件。

@@ -579,36 +578,20 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl

 <details><summary>昇腾 NPU 用户指南</summary>

-在昇腾 NPU 设备上安装 LLaMA Factory 时，请升级 Python 到 3.10 及以上，并需要指定额外依赖项，使用 `pip install -e . torch-npu==2.7.1` 命令安装。此外，还需要安装 **[Ascend CANN Toolkit 与 Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**，安装方法请参考[安装教程](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC2alpha002/quickstart/quickstart/quickstart_18_0004.html)或使用以下命令：
+在昇腾 NPU 设备上安装 LLaMA Factory 时，请升级 Python 到 3.10 及以上，并需要指定额外依赖项，使用 `pip install -r requirements/npu.txt` 命令安装。此外，还需要安装 **Ascend CANN Toolkit 与 Kernels**，安装方法请参考[安装教程](https://llamafactory.readthedocs.io/zh-cn/latest/advanced/npu_installation.html)。
+
+您可以直接下载预安装的最新docker镜像：

 ```bash
-# 请替换 URL 为 CANN 版本和设备型号对应的 URL
-# 安装 CANN Toolkit
-wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run
-bash Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run --install
+# Docker Hub
+docker pull hiyouga/llamafactory:latest-npu-a2
+docker pull hiyouga/llamafactory:latest-npu-a3

-# 安装 CANN Kernels
-wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run
-bash Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run --install
-
-# 设置环境变量
-source /usr/local/Ascend/ascend-toolkit/set_env.sh
+# quay.io
+docker pull quay.io/ascend/llamafactory:latest-npu-a2
+docker pull quay.io/ascend/llamafactory:latest-npu-a3
 ```

-| 依赖项        | 至少     | 推荐           |
-| ------------ | ------- | -------------- |
-| CANN         | 8.0.RC1 | 8.0.0.alpha002 |
-| torch        | 2.1.0   | 2.7.1          |
-| torch-npu    | 2.1.0   | 2.7.1          |
-| deepspeed    | 0.13.2  | 0.13.2         |
-| vllm-ascend  | -       | 0.7.3          |
-
-请使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定运算设备。
-
-如果遇到无法正常推理的情况，请尝试设置 `do_sample: false`。
-
-下载预构建 Docker 镜像：[32GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html) | [64GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
-
 #### 安装 BitsAndBytes

 如果要在 Ascend NPU 上进行基于 bitsandbytes 的 QLoRA 量化微调，请执行如下步骤：
@@ -950,7 +933,7 @@ swanlab_run_name: test_run # 可选

 本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。

-使用模型权重时，请遵循对应的模型协议：[Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [GPT-2](https://github.com/openai/gpt-2/blob/master/LICENSE) / [Granite](LICENSE) / [Index](https://huggingface.co/IndexTeam/Index-1.9B/blob/main/LICENSE) / [InternLM](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [Llama 4](https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3/Phi-4](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [Skywork](https://huggingface.co/Skywork/Skywork-13B-base/blob/main/Skywork%20Community%20License.pdf) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [TeleChat2](https://huggingface.co/Tele-AI/telechat-7B/blob/main/TeleChat%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
+使用模型权重时，请遵循对应的模型协议：[BLOOM](https://huggingface.co/spaces/bigscience/license)/ [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [GPT-2](https://github.com/openai/gpt-2/blob/master/LICENSE) / [Granite](LICENSE) / [InternLM](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [Llama 4](https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](LICENSE) / [Phi-3/Phi-4](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [TeleChat2](https://huggingface.co/Tele-AI/telechat-7B/blob/main/TeleChat%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)

 ## 引用

--- a/data/reason_tool_use_demo_50.jsonl
+++ b/data/reason_tool_use_demo_50.jsonl
--- a/docker/docker-cuda/Dockerfile
+++ b/docker/docker-cuda/Dockerfile
@@ -32,7 +32,8 @@ RUN pip config set global.index-url "${PIP_INDEX}" && \
 COPY . /app

 # Install LLaMA Factory
-RUN pip install --no-cache-dir --no-build-isolation -e ".[metrics,deepspeed]"
+RUN pip install --no-cache-dir --no-build-isolation -e . && \
+    pip install --no-cache-dir --no-build-isolation -r requirements/metrics.txt -r requirements/deepspeed.txt

 # Rebuild flash attention
 RUN if [ "${INSTALL_FLASHATTN}" == "true" ]; then \
--- a/docker/docker-cuda/Dockerfile.megatron
+++ b/docker/docker-cuda/Dockerfile.megatron
@@ -1,12 +1,13 @@
-# NVIDIA official image (ubuntu-22.04 + cuda-12.4 + python-3.10)
-# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-08.html
-FROM nvcr.io/nvidia/pytorch:24.05-py3
+# NVIDIA official image (ubuntu-24.04 + cuda-12.9.1 + python-3.12)
+# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-25-06.html
+FROM nvcr.io/nvidia/pytorch:25.06-py3

 ENV DEBIAN_FRONTEND=noninteractive
 ENV PIP_ROOT_USER_ACTION=ignore
 ENV PYPI_MIRROR=https://mirrors.aliyun.com/pypi/simple/
 ENV PYPI_TRUSTED_HOST=mirrors.aliyun.com
 ENV APT_MIRROR=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/
+ENV PIP_CONSTRAINT=""

 RUN pip install --upgrade pip setuptools wheel "hatchling>=1.18.0" editables --trusted-host ${PYPI_TRUSTED_HOST} --index-url ${PYPI_MIRROR}

@@ -14,20 +15,14 @@ RUN pip uninstall -y torch torchvision torch-tensorrt \
    flash_attn transformer-engine \
    cudf dask-cuda cugraph cugraph-service-server cuml raft-dask cugraph-dgl cugraph-pyg dask-cudf

-RUN pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu124
+RUN pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu129

 RUN pip uninstall -y opencv opencv-python opencv-python-headless && \
-    rm -rf /usr/local/lib/python3.10/dist-packages/cv2/ && \
+    rm -rf /usr/local/lib/python3.12/dist-packages/cv2/ && \
    pip install opencv-python-headless==4.11.0.86 --trusted-host ${PYPI_TRUSTED_HOST} --index-url ${PYPI_MIRROR}

-RUN pip install "numpy==1.26.4" "optree>=0.13.0" "spacy==3.7.5" "weasel==0.4.1" \
-    transformer-engine[pytorch]==2.2.0 megatron-core==0.13.0 deepspeed==0.16.4 \
-    --trusted-host ${PYPI_TRUSTED_HOST} --index-url ${PYPI_MIRROR}
-
-RUN pip install https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.2.post1/flash_attn-2.7.2.post1+cu12torch2.6cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
-
-# RUN pip install vllm==0.8.4 \
-#     --trusted-host ${PYPI_TRUSTED_HOST} --index-url ${PYPI_MIRROR}
+RUN pip install --trusted-host mirrors.aliyun.com --index-url ${PYPI_MIRROR} \
+    "megatron-core>=0.13.0,<0.14.0" "deepspeed==0.16.4"

 WORKDIR /build

@@ -37,6 +32,8 @@ RUN pip uninstall -y apex && \
    pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation \
    --config-settings "--build-option=--cpp_ext --cuda_ext --parallel 32" ${apex_url}

+RUN pip install --no-build-isolation transformer_engine[pytorch]
+
 RUN rm -rf /build
 WORKDIR /workspace

@@ -53,14 +50,17 @@ RUN apt-get update && apt-get install -y zip
 RUN apt-get install -y openjdk-21-jdk
 ENV JAVA_HOME /usr/lib/jvm/java-21-openjdk-amd64

-# pip install LLaMA-Factory
+ARG REPO_URL=https://github.com/hiyouga/LlamaFactory.git
+ARG BRANCH=main
 WORKDIR /app

-# Copy the application into the image
-COPY . /app
+# Clone the repository
+RUN git clone --depth 1 --branch ${BRANCH} ${REPO_URL} /app || \
+    git clone --depth 1 ${REPO_URL} /app

 # Install LLaMA Factory
-RUN pip install --no-cache-dir -e ".[metrics]" --no-build-isolation
+RUN pip install --no-cache-dir -e . --no-build-isolation && \
+    pip install --no-cache-dir -r requirements/metrics.txt --no-build-isolation

 RUN pip install "git+https://github.com/alibaba/roll.git#subdirectory=mcore_adapter"

--- a/docker/docker-npu/Dockerfile
+++ b/docker/docker-npu/Dockerfile
@@ -35,7 +35,8 @@ COPY . /app
 # Install torch-npu
 RUN pip uninstall -y torch torchvision torchaudio && \
    pip install --no-cache-dir "torch==2.7.1" "torch-npu==2.7.1" "torchvision==0.22.1" "torchaudio==2.7.1" --index-url "${PYTORCH_INDEX}" && \
-    pip install --no-cache-dir -e ".[metrics]" --no-build-isolation
+    pip install --no-cache-dir -e . --no-build-isolation && \
+    pip install --no-cache-dir -r requirements/metrics.txt --no-build-isolation

 # Set up volumes
 # VOLUME [ "/root/.cache/huggingface", "/app/shared_data", "/app/output" ]
--- a/docker/docker-rocm/Dockerfile
+++ b/docker/docker-rocm/Dockerfile
@@ -34,7 +34,8 @@ COPY . /app

 # Reinstall pytorch rocm and install LLaMA Factory
 RUN pip uninstall -y torch torchvision torchaudio && \
-    pip install --no-cache-dir --no-build-isolation -e --pre ".[metrics,deepspeed]" --index-url "${PYTORCH_INDEX}"
+    pip install --no-cache-dir --no-build-isolation -e --pre . --index-url "${PYTORCH_INDEX}" && \
+    pip install --no-cache-dir --no-build-isolation -r requirements/metrics.txt -r requirements/deepspeed.txt --index-url "${PYTORCH_INDEX}"

 # Rebuild flash attention
 RUN if [ "${INSTALL_FLASHATTN}" == "true" ]; then \
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
--- a/docs/_static/css/lang-switcher.css
+++ b/docs/_static/css/lang-switcher.css
@@ -0,0 +1,50 @@
+.lang-switcher {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+.lang-switcher__select {
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  padding: 6px 28px 6px 10px;
+  border-radius: 999px;
+  border: 1px solid rgba(0, 0, 0, 0.18);
+  background-color: #ffffff;
+  color: #333333;
+  font-size: 13px;
+  line-height: 18px;
+  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.08);
+  cursor: pointer;
+  background-image: linear-gradient(45deg, transparent 50%, #667085 50%),
+    linear-gradient(135deg, #667085 50%, transparent 50%);
+  background-position: calc(100% - 16px) 50%, calc(100% - 11px) 50%;
+  background-size: 5px 5px, 5px 5px;
+  background-repeat: no-repeat;
+}
+
+.lang-switcher__select:focus {
+  outline: none;
+  border-color: rgba(41, 128, 185, 0.65);
+  box-shadow: 0 0 0 3px rgba(41, 128, 185, 0.18);
+}
+
+.wy-side-nav-search .lang-switcher {
+  margin-top: 10px;
+}
+
+.wy-side-nav-search .lang-switcher__select {
+  border-color: rgba(255, 255, 255, 0.18);
+  background-color: rgba(255, 255, 255, 0.08);
+  color: #ffffff;
+  box-shadow: none;
+  background-image: linear-gradient(45deg, transparent 50%, rgba(255, 255, 255, 0.75) 50%),
+    linear-gradient(135deg, rgba(255, 255, 255, 0.75) 50%, transparent 50%);
+}
+
+.wy-side-nav-search .lang-switcher__select:focus {
+  border-color: rgba(255, 255, 255, 0.45);
+  box-shadow: 0 0 0 3px rgba(255, 255, 255, 0.12);
+}
+
--- a/docs/_static/js/switcher.js
+++ b/docs/_static/js/switcher.js
@@ -0,0 +1,93 @@
+document.addEventListener('DOMContentLoaded', function () {
+  var path = window.location.pathname || '';
+  var isZh = path.indexOf('/zh/') !== -1;
+  var isEn = path.indexOf('/en/') !== -1;
+  if (!isZh && !isEn) return;
+
+  var currentLang = isZh ? 'zh' : 'en';
+
+  function buildSwitcher() {
+    var container = document.createElement('div');
+    container.className = 'lang-switcher';
+
+    var select = document.createElement('select');
+    select.setAttribute('aria-label', 'Language');
+    select.className = 'lang-switcher__select';
+
+    var optionZh = document.createElement('option');
+    optionZh.value = 'zh';
+    optionZh.textContent = 'Simplified Chinese';
+    optionZh.selected = isZh;
+
+    var optionEn = document.createElement('option');
+    optionEn.value = 'en';
+    optionEn.textContent = 'English';
+    optionEn.selected = isEn;
+
+    select.appendChild(optionZh);
+    select.appendChild(optionEn);
+
+    select.addEventListener('change', function () {
+      var nextLang = select.value;
+      if (nextLang === currentLang) return;
+      var targetUrl = path.replace('/' + currentLang + '/', '/' + nextLang + '/');
+      window.location.href = targetUrl + window.location.search + window.location.hash;
+    });
+
+    container.appendChild(select);
+    return container;
+  }
+
+  function hideOtherLanguageToc() {
+    var captions = document.querySelectorAll('p.caption');
+    for (var i = 0; i < captions.length; i++) {
+      var caption = captions[i];
+      var textEl = caption.querySelector('.caption-text');
+      if (!textEl) continue;
+      var label = (textEl.textContent || '').trim().toLowerCase();
+
+      var isCaptionZh = label === '中文' || label === 'chinese' || label === 'zh';
+      var isCaptionEn = label === 'english' || label === 'en';
+
+      if (!isCaptionZh && !isCaptionEn) continue;
+
+      var shouldHide = (currentLang === 'zh' && isCaptionEn) || (currentLang === 'en' && isCaptionZh);
+      var shouldHideCaption = true;
+
+      var next = caption.nextElementSibling;
+      if (next && next.tagName && next.tagName.toLowerCase() === 'ul') {
+        if (shouldHide) {
+          caption.style.display = 'none';
+          next.style.display = 'none';
+        } else if (shouldHideCaption) {
+          caption.style.display = 'none';
+        }
+      } else if (shouldHide) {
+        caption.style.display = 'none';
+      } else if (shouldHideCaption) {
+        caption.style.display = 'none';
+      }
+    }
+  }
+
+  var side = document.querySelector('.wy-side-nav-search');
+  if (side) {
+    var sideSwitcher = buildSwitcher();
+    sideSwitcher.style.marginTop = '8px';
+    sideSwitcher.style.display = 'flex';
+    sideSwitcher.style.justifyContent = 'center';
+    side.appendChild(sideSwitcher);
+  } else {
+    var topRight = buildSwitcher();
+    topRight.style.position = 'fixed';
+    topRight.style.top = '12px';
+    topRight.style.right = '12px';
+    topRight.style.zIndex = '9999';
+    document.body.appendChild(topRight);
+  }
+
+  hideOtherLanguageToc();
+  window.addEventListener('load', hideOtherLanguageToc);
+  setTimeout(hideOtherLanguageToc, 50);
+  setTimeout(hideOtherLanguageToc, 300);
+});
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -0,0 +1,37 @@
+# Configuration file for the Sphinx documentation builder.
+
+import os
+import sys
+
+# Define common settings here
+project = 'LlamaFactory'
+copyright = '2024, LlamaFactory Team'
+author = 'LlamaFactory Team'
+
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.viewcode',
+    'sphinx.ext.napoleon',
+    'myst_parser',
+]
+
+templates_path = ['_templates']
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+html_theme = 'sphinx_rtd_theme'
+
+html_static_path = ['_static']
+
+html_js_files = [
+    'js/switcher.js',
+]
+
+html_css_files = [
+    'css/lang-switcher.css',
+]
+
+myst_enable_extensions = [
+    "colon_fence",
+    "deflist",
+]
+myst_heading_anchors = 3
--- a/docs/en/advanced/custom-kernels/custom-kernels.md
+++ b/docs/en/advanced/custom-kernels/custom-kernels.md
@@ -0,0 +1,3 @@
+# Custom Kernels
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/advanced/custom-kernels/fused-operators.md
+++ b/docs/en/advanced/custom-kernels/fused-operators.md
@@ -0,0 +1,3 @@
+# Fused Operators
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/advanced/custom-kernels/triton.md
+++ b/docs/en/advanced/custom-kernels/triton.md
@@ -0,0 +1,3 @@
+# Triton
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/advanced/distributed/deepspeed.md
+++ b/docs/en/advanced/distributed/deepspeed.md
@@ -0,0 +1,3 @@
+# DeepSpeed
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/advanced/distributed/fsdp.md
+++ b/docs/en/advanced/distributed/fsdp.md
@@ -0,0 +1,3 @@
+# FSDP
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/advanced/distributed/parallel-dp-tp-ep-sp-cp.md
+++ b/docs/en/advanced/distributed/parallel-dp-tp-ep-sp-cp.md
@@ -0,0 +1,3 @@
+# Parallel (DP, TP, EP, SP, CP)
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/advanced/lora-and-quantization/lora.md
+++ b/docs/en/advanced/lora-and-quantization/lora.md
@@ -0,0 +1,3 @@
+# LoRA
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/advanced/lora-and-quantization/quantization.md
+++ b/docs/en/advanced/lora-and-quantization/quantization.md
@@ -0,0 +1,3 @@
+# Quantization
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/conf.py
+++ b/docs/en/conf.py
@@ -0,0 +1,20 @@
+import os
+import sys
+
+# Add parent dir to path to allow importing conf.py
+sys.path.insert(0, os.path.abspath('..'))
+
+from conf import *
+
+# Language settings
+language = 'en'
+html_search_language = 'en'
+
+# Static files
+# Point to the root _static directory
+html_static_path = ['../_static']
+
+# Add custom JS for language switcher
+html_js_files = [
+    'js/switcher.js',
+]
--- a/docs/en/data-preparation/data-processing.md
+++ b/docs/en/data-preparation/data-processing.md
@@ -0,0 +1,3 @@
+# Data Processing
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/dev-guide/core/data-engine.md
+++ b/docs/en/dev-guide/core/data-engine.md
@@ -0,0 +1,3 @@
+# DataEngine
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/dev-guide/core/model-engine.md
+++ b/docs/en/dev-guide/core/model-engine.md
@@ -0,0 +1,3 @@
+# ModelEngine
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/dev-guide/core/trainer.md
+++ b/docs/en/dev-guide/core/trainer.md
@@ -0,0 +1,3 @@
+# Trainer
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/dev-guide/plugins/data-plugins.md
+++ b/docs/en/dev-guide/plugins/data-plugins.md
@@ -0,0 +1,3 @@
+# Data Plugins
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/dev-guide/plugins/model-plugins/initialization.md
+++ b/docs/en/dev-guide/plugins/model-plugins/initialization.md
@@ -0,0 +1,3 @@
+# Initialization
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/dev-guide/plugins/model-plugins/kernels.md
+++ b/docs/en/dev-guide/plugins/model-plugins/kernels.md
@@ -0,0 +1,3 @@
+# Kernels
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/dev-guide/plugins/model-plugins/rendering.md
+++ b/docs/en/dev-guide/plugins/model-plugins/rendering.md
@@ -0,0 +1,3 @@
+# Rendering
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/getting-started.md
+++ b/docs/en/getting-started.md
@@ -0,0 +1,3 @@
+# Getting Started
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/hyperparameters/data-argument.md
+++ b/docs/en/hyperparameters/data-argument.md
@@ -0,0 +1,3 @@
+# Data Argument
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/hyperparameters/model-argument.md
+++ b/docs/en/hyperparameters/model-argument.md
@@ -0,0 +1,3 @@
+# Model Argument
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/hyperparameters/sample-argument.md
+++ b/docs/en/hyperparameters/sample-argument.md
@@ -0,0 +1,3 @@
+# Sample Argument
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/hyperparameters/training-argument.md
+++ b/docs/en/hyperparameters/training-argument.md
@@ -0,0 +1,3 @@
+# Training Argument
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/index.rst
+++ b/docs/en/index.rst
@@ -0,0 +1,62 @@
+LlamaFactory Docs
+=================
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Getting Started
+
+   getting-started
+   installation
+   llamaboard-web-ui
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Data Preparation
+
+   data-preparation/data-processing
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Training
+
+   training/sft
+   training/dpo
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Inference
+
+   inference/deploy
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Advanced
+
+   advanced/lora-and-quantization/lora
+   advanced/lora-and-quantization/quantization
+   advanced/distributed/fsdp
+   advanced/distributed/deepspeed
+   advanced/distributed/parallel-dp-tp-ep-sp-cp
+   advanced/custom-kernels/triton
+   advanced/custom-kernels/fused-operators
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Hyperparameters
+
+   hyperparameters/data-argument
+   hyperparameters/model-argument
+   hyperparameters/sample-argument
+   hyperparameters/training-argument
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Dev Guide
+
+   dev-guide/core/data-engine
+   dev-guide/core/model-engine
+   dev-guide/core/trainer
+   dev-guide/plugins/data-plugins
+   dev-guide/plugins/model-plugins/initialization
+   dev-guide/plugins/model-plugins/kernels
+   dev-guide/plugins/model-plugins/rendering
--- a/docs/en/inference/deploy.md
+++ b/docs/en/inference/deploy.md
@@ -0,0 +1,3 @@
+# Deploy
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/installation.md
+++ b/docs/en/installation.md
@@ -0,0 +1,3 @@
+# Installation
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/llamaboard-web-ui.md
+++ b/docs/en/llamaboard-web-ui.md
@@ -0,0 +1,3 @@
+# LlamaBoard Web UI
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/training/dpo.md
+++ b/docs/en/training/dpo.md
@@ -0,0 +1,3 @@
+# DPO
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/en/training/sft.md
+++ b/docs/en/training/sft.md
@@ -0,0 +1,3 @@
+# SFT
+
+This page is not yet available in English. Use the language switcher to view Simplified Chinese.
--- a/docs/make.bat
+++ b/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to your PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -0,0 +1,3 @@
+sphinx>=6.0.0
+sphinx-rtd-theme>=1.2.0
+myst-parser>=2.0.0
--- a/docs/zh/advanced/custom-kernels/custom-kernels.md
+++ b/docs/zh/advanced/custom-kernels/custom-kernels.md
@@ -0,0 +1,93 @@
+# LLaMA-Factory Kernels 系统
+
+## 概述
+
+LLaMA-Factory Kernels 系统用于管理不同硬件设备提供的高性能计算内核（kernel）实现，该系统通过替换模型中的关键模块（如 RMSNorm、SwiGLU、RoPE、MoE 等）为硬件优化的版本，从而显著提升模型训练和推理的性能。
+
+Kernels 系统采用基于注册表的自动发现机制，能够根据当前运行环境自动检测可用的硬件设备（NPU、CUDA 等），并使能相应的高性能 kernels。这种设计使得用户无需关心底层实现细节，只需简单调用接口即可获得性能提升。
+
+## 核心特性
+
+- **自动注册机制**：基于 `@register_kernel` 装饰器实现自动注册系统。系统启动时会自动扫描 `ops` 目录下的 kernel 实现，并将其注册到全局注册表中。
+
+- **设备适配感知**：自动检测当前硬件设备（NPU、CUDA 等）并应用相应的优化。系统会跳过不支持的设备，确保在不同环境下都能正常工作。
+
+- **模块化设计**：每个 kernel 独立实现，互不干扰。可以单独应用某个 kernel，也可以批量应用所有默认的 kernels。
+
+- **后向兼容**：kernel 替换不修改模型权重，保持数值一致性。优化后的实现与原始实现保持精度一致（在浮点误差范围内）。
+
+- **灵活扩展**：通过继承 `BaseKernel` 基类并使用装饰器，可以轻松添加新的 kernel 实现，支持新的硬件设备或优化算法。
+
+## 使用方式
+
+### 1. 通过训练 YAML 配置文件使用
+
+要在训练过程中使能 kernels，只需在配置文件中增加如下配置，即可自动使能所有默认可用的 kernels：
+
+```yaml
+...
+kernel_config:
+    name: auto
+    include_kernels: auto # choice: null/true/false/auto/kernel_id1,kernel_id2,kernel_id3, default is null
+
+...
+```
+
+### 2. 调用 API 使能
+
+#### 2.1 apply_default_kernels 使能所有默认 kernels
+
+`apply_default_kernels` API 能够自动应用当前设备上所有默认注册的 kernels：
+
+```python
+from transformers import AutoModelForCausalLM
+from llamafactory.v1.plugins.model_plugins.kernels import apply_default_kernels
+
+# 加载模型
+model = AutoModelForCausalLM.from_pretrained("qwen/qwen2.5-0.5B")
+
+# 自动应用所有默认 kernels
+model = apply_default_kernels(model, include_kernels="auto")
+```
+
+#### 2.2 apply_kernel 使能特定 kernel
+
+如果需要更精细的控制，例如在某些场合单独应用某个 kernel，可以手动调用 `apply_kernel` 函数并传入 kernel ID：
+
+```python
+from transformers import AutoModelForCausalLM
+from llamafactory.v1.plugins.model_plugins.kernels import apply_kernel
+
+# 加载模型
+model = AutoModelForCausalLM.from_pretrained("qwen/qwen2.5-0.5B")
+
+# 手动应用各个 kernels
+# 注意：kernel ID 必须与定义时的 _kernel_id 一致
+model = apply_kernel("npu_fused_rope", model=model)
+model = apply_kernel("npu_fused_rmsnorm", model=model)
+model = apply_kernel("npu_fused_swiglu", model=model)
+model = apply_kernel("npu_fused_moe", model=model)
+
+### 3. 查询已注册的可用 kernels
+
+可以通过 `get_default_kernels` 获取当前环境中所有已注册且可用的默认 kernel ID：
+
+```python
+from llamafactory.v1.plugins.model_plugins.kernels import get_default_kernels
+
+# 获取默认 kernel 列表
+available_kernels = get_default_kernels()
+print(f"Available kernels: {available_kernels}")
+# 输出示例: ['npu_fused_rmsnorm', 'npu_fused_swiglu', 'npu_fused_rope', 'npu_fused_moe']
+```
+
+### 当前已实现的 kernels
+
+| Kernel ID | 功能 | 支持的设备 | 备注 |
+|-----------|------|-----------|------|
+| [npu_fused_rmsnorm](./fused-operators.md/#npufusedrmsnorm) | RMSNorm 融合算子 | NPU | NPU 设备的高性能 RMSNorm 实现 |
+| [npu_fused_swiglu](./fused-operators.md/#npufusedswiglu) | SwiGLU 融合算子 | NPU | NPU 设备的高性能 SwiGLU 实现 |
+| [npu_fused_rope](./fused-operators.md/#npufusedrope) | RoPE 融合算子 | NPU | NPU 设备的高性能 RoPE 实现 |
+| [npu_fused_moe](./fused-operators.md/#npufusedmoe) | MoE 融合算子 | NPU | MoE 融合算子，适配 Qwen3-MoE 等模型 |
+
+我们会持续适配更多的 kernels，如果您需要自己开发新的 kernels，请参考我们的 [Kernel 开发文档](../../dev-guide/plugins/model-plugins/kernels.md)，欢迎您向 LLaMA-Factory 贡献代码。
--- a/docs/zh/advanced/custom-kernels/fused-operators.md
+++ b/docs/zh/advanced/custom-kernels/fused-operators.md
@@ -0,0 +1,104 @@
+# Fused Operators
+
+LLaMA-Factory 提供了一系列针对特定硬件优化的融合算子。这些算子位于 `src/llamafactory/v1/plugins/model_plugins/kernels/ops` 目录下。
+
+系统启动时，`scan_all_kernels` 函数会自动扫描该目录，注册所有可用的算子。您可以通过 `apply_default_kernels(model, include_kernels="auto")` 一键启用它们，或者使用 `apply_kernel` 单独启用。
+
+以下是当前支持的融合算子详情：
+
+## NpuFusedRMSNorm
+RMSNorm（Root Mean Square Layer Normalization）是一种常用于大模型的归一化方法。在推理或训练中，RMSNorm 融合算子 将bias、residual等操作进行融合，可以减少显存访问次数，加速计算。
+
+Ascend npu 通过 `torch_npu.npu_rms_norm` 接口提供 RMSNorm 融合算子调用接口，支持 float16, bfloat16, float 等数据格式。RMSNorm 算子常见于Qwen等LLM模型中，由于torch侧没有提供 RMSNorm 算子的接口，因此在模型中通常是以自定义类的形式出现，通过替换 RMSNorm 类的 `forward` 方法即可使能。
+
+```python
+def _npu_rms_forward(self, hidden_states):
+    """NPU forward implementation for RMSNorm.
+
+    Args:
+        self: RMSNorm module instance with `weight` and `variance_epsilon`.
+        hidden_states: Input hidden states tensor, same shape as the baseline.
+
+    Returns:
+        Normalized tensor consistent with the baseline RMSNorm behavior.
+    """
+
+    return torch_npu.npu_rms_norm(hidden_states, self.weight, epsilon=self.variance_epsilon)[0]
+```
+
+ 在 LlamaFactory 中，通过 `NpuRMSNormKernel` 提供使能该融合算子的入口，只需要调用 `apply_kernel("npu_fused_rmsnorm", model=model)` 即可针对已适配的模型使能 npu RMSNorm 融合算子。
+
+## NpuFusedSwiGlu
+SwiGLU（Swish-Gated Linear Unit）是一种结合了Swish激活函数和门控线性单元（GLU）的混合激活函数，其主要功能是对输入张量进行门控线性变换，近年来被广泛应用于 LLM 模型中的 MLP 层。SwiGLU 融合算子将分割、激活、矩阵乘等多个操作融合为单一硬件指令，避免多次内核启动开销。
+
+Ascend npu 通过 `torch_npu.npu_swiglu` 接口提供 SwiGLU 融合算子调用接口，支持 float16，bfloat16，float SwiGLU 算子常见于Qwen等LLM模型中，由于torch侧没有提供 SwiGLU 算子的接口，因此在模型中通常是以自定义类的形式出现，通过替换 SwiGLU 类的 `forward` 方法即可使能。替换过程可参考如下示例：
+
+```python
+# 原始 MLP forward 方法：
+def forward(self, x):
+    down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+    return down_proj
+
+# 替换后的 forward 方法：
+def _npu_swiglu_forward(self, hidden_state):
+    return self.down_proj(
+        torch_npu.npu_swiglu(torch.cat((self.gate_proj(hidden_state), self.up_proj(hidden_state)), dim=-1), dim=-1)
+    )
+```
+
+ 在 LLaMA-Factory 中，通过 `NpuSwiGluKernel` 提供使能该融合算子的入口，只需要调用 `apply_kernel("npu_fused_swiglu", model=model)` 即可针对已适配的模型使能 npu SwiGLU 融合算子。对于未适配的模型，如有需要，您可根据示例以及[开发者文档](../../dev-guide/plugins/model-plugins/kernels.md)自行适配。
+
+
+## NpuFusedRoPE
+RoPE（Rotary Positional Embedding，旋转式位置嵌入） 是一种位置编码技术，广泛应用于 Qwen 等 LLM 模型中，用于有效编码文本序列的位置信息。它结合了绝对位置编码的稳定性与相对位置编码的灵活性，同时具备优秀的长度泛化能力。传统 RoPE 算子通常在 LLM 等模型结构中通过自定义函数的形式实现。RoPE 融合算子将原计算流程合并为单个硬件优化算子，从而提升性能。
+
+Ascend npu 通过 `torch_npu.npu_rotary_mul` 提供 RoPE 融合算子调用接口，支持 float16，bfloat16，float32 等数据格式。以 Qwen3 系列模型为例，通过替换其 `apply_rotary_pos_emb` 函数即可实现 RoPE融合算子使能：
+
+```python
+# 原始 apply_rotary_pos_emb：
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+# 替换 RoPE 融合算子后：
+def _apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = torch_npu.npu_rotary_mul(q, cos, sin)
+    k_embed = torch_npu.npu_rotary_mul(k, cos, sin)
+    return q_embed, k_embed
+```
+
+ 在 LLaMA-Factory 中，通过 `NpuRoPEKernel` 提供使能该融合算子的入口，只需要调用 `apply_kernel("npu_fused_rope", model=model)` 即可针对已适配的模型使能 npu RoPE 融合算子。对于未适配的模型，如有需要，您可根据示例以及[开发者文档](../../dev-guide/plugins/model-plugins/kernels.md)自行适配。
+
+
+## NpuFusedMoE
+MoE（Mixture of Experts）模型通过稀疏激活扩展容量。在原生 Transformers 实现中，使用串行循环遍历专家，导致内核启动开销大、硬件利用率低。
+
+**MoE 融合算子** 利用 **GMM（Grouped Matrix Multiplication，分组矩阵乘）** 技术，支持在单个硬件指令内并行处理多组不同形状（行数不一）的矩阵乘法，消减循环开销，同时无需额外的显存复制，显著提升训练性能。
+
+Ascend npu 通过 `torch_npu.npu_grouped_matmul` 等接口提供底层支持，通过替换模型中的 MoE Block forward 方法，即可利用 NPU 的分组矩阵乘能力。
+
+核心逻辑替换如下（简化示意）：
+
+```python
+def _npu_moe_forward(self, hidden_states, routing_weights, router_indices):
+    # 1. 排序：将乱序的 Token 按指派的专家归类，并生成索引映射
+    permuted_states, row_map = torch_npu.npu_moe_token_permute(hidden_states, router_indices)
+
+    # 2. 统计：计算每个专家需要处理的 Token 数量
+    tokens_per_expert = torch.histc(router_indices, bins=self.num_experts, min=0, max=self.num_experts)
+
+    # 3. 计算 (GMM)：一次性并行计算所有专家的权重，自动适配不同专家的输入长度
+    inter_states = torch_npu.npu_grouped_matmul(permuted_states, self.gate_up_proj_weights, split_sizes=tokens_per_expert, ...)
+    inter_states = torch_npu.npu_swiglu(inter_states)
+    output = torch_npu.npu_grouped_matmul(inter_states, self.down_proj_weights, split_sizes=tokens_per_expert, ...)
+
+    # 4. 还原：将结果恢复成原始 Token 顺序并应用路由权重
+    return torch_npu.npu_moe_token_unpermute(output, row_map, routing_weights)
+```
+
+在 LLaMA-Factory 中，通过 `NpuFusedMoEKernel` 提供使能该融合算子的入口。只需要调用 `apply_kernel("npu_fused_moe", model=model)` 即可针对已适配的模型使能 NPU MoE 融合算子。对于未适配的模型，您也可以参考上述示例代码以及[开发者文档](../../dev-guide/plugins/model-plugins/kernels.md)自行适配。
--- a/docs/zh/advanced/custom-kernels/triton.md
+++ b/docs/zh/advanced/custom-kernels/triton.md
@@ -0,0 +1 @@
+# Triton
--- a/docs/zh/advanced/distributed/deepspeed.md
+++ b/docs/zh/advanced/distributed/deepspeed.md
@@ -0,0 +1 @@
+# DeepSpeed
--- a/docs/zh/advanced/distributed/fsdp.md
+++ b/docs/zh/advanced/distributed/fsdp.md
@@ -0,0 +1 @@
+# FSDP
--- a/docs/zh/advanced/distributed/parallel-dp-tp-ep-sp-cp.md
+++ b/docs/zh/advanced/distributed/parallel-dp-tp-ep-sp-cp.md
@@ -0,0 +1 @@
+# Parallel(DP, TP, EP, SP, CP)
--- a/docs/zh/advanced/lora-and-quantization/lora.md
+++ b/docs/zh/advanced/lora-and-quantization/lora.md
@@ -0,0 +1,3 @@
+# Lora
+
+参数管理（二级参数形式）
--- a/docs/zh/advanced/lora-and-quantization/quantization.md
+++ b/docs/zh/advanced/lora-and-quantization/quantization.md
@@ -0,0 +1 @@
+# Quantization
--- a/docs/zh/conf.py
+++ b/docs/zh/conf.py
@@ -0,0 +1,20 @@
+import os
+import sys
+
+# Add parent dir to path to allow importing conf.py
+sys.path.insert(0, os.path.abspath('..'))
+
+from conf import *
+
+# Language settings
+language = 'zh_CN'
+html_search_language = 'zh'
+
+# Static files
+# Point to the root _static directory
+html_static_path = ['../_static']
+
+# Add custom JS for language switcher
+html_js_files = [
+    'js/switcher.js',
+]
--- a/docs/zh/data-preparation/data-processing.md
+++ b/docs/zh/data-preparation/data-processing.md
@@ -0,0 +1,479 @@
+# LLaMA-Factory v1 数据预处理
+
+## 总览
+
+LLaMA-Factory `v1` 采用了全新的数据处理架构，主要包含以下核心组件：
+
+- **DataEngine**：数据引擎，负责数据集的加载、索引和转换等各种插件的接入和调用，并提供数据访问接口
+- **DataConverterPlugin**：数据转换器，将非标准格式转换为统一的标准格式
+- **DataLoaderPlugin**：数据加载插件，支持多种文件格式的加载
+- **DataIndexPlugin**：数据索引插件，支持数据集的采样和权重调整
+- **DataSelectorPlugin**：数据选择插件，支持灵活的数据访问方式
+
+与 LLaMA-Factory `v0` 版本相比，`v1` 版本采用了统一的数据格式（Messages Format），所有数据都会被转换为标准的对话消息列表；此外，`v1` 版本通过 DataEngine 与 Plugin 机制，提供了自定义数据处理流的接口，具有更好的可扩展性和一致性。
+
+---
+
+## 目录
+
+- [基本用法](#基本用法)
+- [标准数据格式](#标准数据格式)
+- [数据集配置文件](#数据集配置文件)
+- [完整示例](#完整示例)
+
+---
+
+## 基本用法
+
+### 在训练配置文件，可以通过如下方式配置数据集：
+
+<details open>
+<summary>方式 1：使用 HF Hub Repo ID</summary>
+
+直接指定 HF Hub 上的数据集 Repo ID，DataEngine 会自动从 HF Hub 下载并加载数据集。
+
+注：使用 Repo ID 直接加载的数据集需要为标准格式
+
+**训练配置文件示例：**
+
+```yaml
+# example_sft.yaml
+
+...
+
+dataset: llamafactory/v1-sft-demo  # HF Hub Repo ID
+
+...
+```
+
+</details>
+
+<details>
+<summary>方式 2：使用 HF Hub 上的 YAML 配置文件</summary>
+
+`dataset`字段指定 HF Hub 上的 `dataset_info.yaml` 的 URI，DataEngine 会自动下载该配置文件并根据其中的配置加载数据集。
+
+**训练配置文件示例：**
+
+```yaml
+# example_sft.yaml
+
+...
+
+dataset: llamafactory/v1-sft-demo/dataset_info.yaml  # 远程 dataset_info.yaml 路径
+
+...
+
+```
+
+</details>
+
+<details>
+<summary>方式 3：使用本地 HF 数据集文件路径</summary>
+
+`dataset`字段指定本地的数据集文件路径（`.json`、`.jsonl` 等）
+
+注：直接指定数据集文件路径，要求该数据文件的格式已为标准格式
+
+**训练配置文件示例：**
+
+```yaml
+# example_sft.yaml
+
+...
+
+dataset: ~/data/v1_sft_demo.jsonl   # 本地数据集文件绝对路径
+
+...
+```
+
+</details>
+
+<details>
+<summary>方式 4：使用本地 YAML 配置文件路径</summary>
+
+`dataset`字段指定本地的 `dataset_info.yaml` 配置文件路径，DataEngine 会根据该配置加载其中的数据集。
+
+**训练配置文件示例：**
+
+```yaml
+# example_sft.yaml
+
+...
+
+dataset: ~/data/dataset_info.yaml    # 本地 dataset_info.yaml 文件路径
+
+...
+```
+
+</details>
+
+
+---
+
+
+
+## 标准数据格式
+
+v1 使用统一的 **Messages 格式**作为标准数据格式。每个样本都是一个包含 `messages` 字段的 JSON 对象。
+
+针对alpaca、sharegpt、以及dpo等格式的数据，可以通过内置的`DataConverterPlugin`插件，自动将其转化为标准格式，对于其他自定义格式的数据，用户也可通过自定义`DataConverterPlugin`来实现数据格式标准化，这部分内容参见[`DataConverterPlugin`](../dev-guide/plugins/data-plugins.md/#data-converter-plugin)
+
+### 1. SFT（监督微调）样本格式
+
+
+```json
+{
+  "messages": [
+    {
+      "role": "system",
+      "content": [{"type": "text", "value": "You are a helpful assistant."}],
+      "loss_weight": 0.0
+    },
+    {
+      "role": "user",
+      "content": [{"type": "text", "value": "Hello, who are you?"}],
+      "loss_weight": 0.0
+    },
+    {
+      "role": "assistant",
+      "content": [{"type": "text", "value": "I am an AI assistant."}],
+      "loss_weight": 1.0
+    }
+  ]
+}
+```
+
+#### 字段说明：
+
+- **messages**: 消息列表，包含一轮或多轮对话
+  - **role**: 消息角色，可选值：
+    - `"system"`: 系统提示
+    - `"user"`: 用户输入
+    - `"assistant"`: 模型回复
+  - **content**: 内容列表，每个元素包含：
+    - **type**: 内容类型，可选值：
+      - `"text"`: 文本内容
+      - `"image_url"`: 图像 URL（多模态）
+      - `"audio_url"`: 音频 URL（多模态）
+      - `"video_url"`: 视频 URL（多模态）
+      - `"tools"`: 工具描述
+      - `"tool_calls"`: 工具调用
+      - `"reasoning"`: 推理过程
+    - **value**: 具体内容（字符串）
+  - **loss_weight**: 损失权重（浮点数）
+    - `0.0`: 不计算损失（用于提示词部分）
+    - `1.0`: 完全计算损失（用于回复部分）
+    - 可设置为其他值以调整不同部分的学习权重
+
+- **_dataset_name** (可选): 数据集名称，由 DataEngine 自动添加
+- **extra_info** (可选): 额外信息字段
+
+### 2. DPO（偏好对齐）样本格式
+
+```json
+{
+  "chosen_messages": [
+    {
+      "role": "user",
+      "content": [{"type": "text", "value": "用户提问"}],
+      "loss_weight": 0.0
+    },
+    {
+      "role": "assistant",
+      "content": [{"type": "text", "value": "更优的回答"}],
+      "loss_weight": 1.0
+    }
+  ],
+  "rejected_messages": [
+    {
+      "role": "user",
+      "content": [{"type": "text", "value": "用户提问"}],
+      "loss_weight": 0.0
+    },
+    {
+      "role": "assistant",
+      "content": [{"type": "text", "value": "较差的回答"}],
+      "loss_weight": 1.0
+    }
+  ]
+}
+```
+
+### 3. 多模态支持
+
+对于多模态数据，可以在 `content` 列表中添加非文本类型的内容：
+
+```json
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": [
+        {"type": "text", "value": "这张图片里有什么？"},
+        {"type": "image_url", "value": "path/to/image.jpg"}
+      ],
+      "loss_weight": 0.0
+    },
+    {
+      "role": "assistant",
+      "content": [{"type": "text", "value": "图片中有一只猫。"}],
+      "loss_weight": 1.0
+    }
+  ]
+}
+```
+
+**说明**：`image_url`、`audio_url`、`video_url` 的路径可以是相对路径或绝对路径，具体加载方式由 `DataLoaderPlugin` 决定。
+
+---
+
+## 数据集配置文件
+
+### 1. dataset_info.yaml 配置文件格式
+
+`dataset_info.yaml` 支持同时配置多个数据集，支持分别从 HF Hub 和本地获取数据集，数据集默认会混合并打乱顺序。
+
+**示例配置文件：`data/dataset_info.yaml`**
+
+```yaml
+# 数据集 1：使用本地文件 + Alpaca 转换器
+identity:
+  file_name: ~/data/identity.json            #本地数据集文件绝对路径
+  converter: alpaca                           # 使用 alpaca 转换器
+
+# 数据集 2：指定自定义数据集目录
+alpaca_en_demo:
+  file_name:  ~/data/alpaca_en_demo.json     # 数据集文件名
+  converter: alpaca                           # 转换器插件
+  size: 500                                   # 只使用 500 个样本
+  weight: 0.5                                 # 数据集权重，用于控制该数据集的采样频率
+  split: train                                # 数据集划分，默认为 train
+  streaming: false                            # 是否流式加载，默认为 false
+
+# 数据集 3：从 Hugging Face Hub 加载
+hf_dataset:
+  hf_hub_url: llamafactory/v1-sft-demo  # HF repo ID
+  streaming: false  
+
+# 数据集 4：已经是标准格式，无需转换器
+standard:
+  file_name: ~/data/v1_sft_demo.jsonl   # 本地标准数据集文件路径
+
+# 数据集 5：自定义数据集和 converter 插件
+custom_dataset:
+  file_name: custom_data.json
+  converter: custom_converter
+  weight: 1.0  
+```
+
+### 2. 配置字段说明
+
+#### 数据源配置（二者必选其一）：
+
+- **hf_hub_url** (str): Hugging Face Hub 数据集仓库 ID
+  - 示例：`"llamafactory/v1-sft-demo"`
+  - 如果指定，则从 HF Hub 加载数据集
+
+- **file_name** (str): 本地文件路径
+  - 支持格式：`.json`、`.jsonl`、`.csv`、`.parquet`、`.arrow`、`.txt`
+
+#### 可选配置：
+
+- **split** (str): 数据集划分，默认为 `"train"`
+- **converter** (str): 数据转换器名称
+  - 可选值：`"alpaca"`（更多转换器持续添加中，也可在 data_plugin 中添加自定义 converter）
+  - 如果不指定，则假定数据已是标准格式
+- **size** (int): 使用的样本数量，默认使用全部
+- **weight** (float): 数据集权重，用于混合数据集时的采样频率，默认为 1.0
+- **streaming** (bool): 是否流式加载，默认为 `False`
+
+---
+
+
+## 完整示例
+
+### 1. 基础使用示例
+
+```python
+from llamafactory.v1.config.data_args import DataArguments
+from llamafactory.v1.core.data_engine import DataEngine
+
+# 使用本地 YAML 配置
+data_args = DataArguments(
+    dataset="~/data/v1_sft_demo.jsonl",
+    cutoff_len=2048
+)
+
+# 初始化 DataEngine
+engine = DataEngine(data_args=data_args)
+
+# 查看数据集信息
+print(f"数据集总样本数: {len(engine)}")
+print(f"数据集列表: {list(engine.datasets.keys())}")
+
+# 访问数据样本
+sample = engine[0]
+print(f"样本格式: {sample.keys()}")
+print(f"消息列表: {sample['messages']}")
+
+# 批量访问
+batch = engine[0:10]
+print(f"批量样本数: {len(batch)}")
+```
+
+### 2. 输出示例
+
+**查看数据集信息输出：**
+
+```
+数据集总样本数: 500
+数据集列表: ['default']
+样本格式: dict_keys(['_dataset_name', 'messages'])
+消息列表: [{'role': 'user', 'content': [{'type': 'text', 'value': 'hi'}], 'loss_weight': 0.0}, {'role': 'assistant', 'content': [{'type': 'text', 'value': 'Hello! I am {{name}}, an AI assistant developed by {{author}}. How can I assist you today?'}], 'loss_weight': 1.0}]
+批量样本数: 10
+```
+
+**访问单个样本输出：**
+
+```python
+{
+  '_dataset_name': 'alpaca_en_demo',
+  'messages': [
+    {
+      'role': 'user',
+      'content': [{'type': 'text', 'value': 'What is the capital of France?'}],
+      'loss_weight': 0.0
+    },
+    {
+      'role': 'assistant',
+      'content': [{'type': 'text', 'value': 'The capital of France is Paris.'}],
+      'loss_weight': 1.0
+    }
+  ]
+}
+```
+
+### 3. 混合多数据集配置文件示例
+
+**配置文件：`data/mixed_datasets.yaml`**
+
+```yaml
+dataset_1:
+  file_name: alpaca_en_demo.json
+  converter: alpaca
+  weight: 1.0
+
+dataset_2:
+  file_name: identity.json
+  converter: alpaca
+  weight: 2.0
+
+dataset_3:
+  hf_hub_url: llamafactory/v1-sft-demo
+  weight: 1.5
+```
+
+
+### 4. 多模态数据示例
+
+**数据文件：`data/multimodal_demo.jsonl`**
+
+标准化后数据示例：
+
+```json
+[
+  {
+    "messages": [
+      {
+        "role": "user",
+        "content": [
+          {"type": "text", "value": "Who are they?"},
+          {"type": "image_url", "value": "mllm_demo_data/1.jpg"}
+        ],
+        "loss_weight": 0.0
+      },
+      {
+        "role": "assistant",
+        "content": [
+          {"type": "text", "value": "They're Kane and Gretzka from Bayern Munich."}
+        ],
+        "loss_weight": 1.0
+      },
+      {
+        "role": "user",
+        "content": [
+          {"type": "text", "value": "What are they doing?"},
+          {"type": "image_url", "value": "mllm_demo_data/1.jpg"}
+        ],
+        "loss_weight": 0.0
+      },
+      {
+        "role": "assistant",
+        "content": [
+          {"type": "text", "value": "They are celebrating on the soccer field."}
+        ],
+        "loss_weight": 1.0
+      }
+    ]
+  },
+  {
+    "messages": [
+      {
+        "role": "user",
+        "content": [
+          {"type": "text", "value": "Who is he?"},
+          {"type": "image_url", "value": "mllm_demo_data/2.jpg"}
+        ],
+        "loss_weight": 0.0
+      },
+      {
+        "role": "assistant",
+        "content": [
+          {"type": "text", "value": "He's Thomas Muller from Bayern Munich."}
+        ],
+        "loss_weight": 1.0
+      },
+      {
+        "role": "user",
+        "content": [
+          {"type": "text", "value": "Why is he on the ground?"}
+        ],
+        "loss_weight": 0.0
+      },
+      {
+        "role": "assistant",
+        "content": [
+          {"type": "text", "value": "Because he's sliding on his knees to celebrate."}
+        ],
+        "loss_weight": 1.0
+      }
+    ]
+  }
+]
+```
+
+```python
+from llamafactory.v1.config.data_args import DataArguments
+from llamafactory.v1.core.data_engine import DataEngine
+
+data_args = DataArguments(dataset="data/multimodal_demo.jsonl")
+engine = DataEngine(data_args=data_args)
+
+# 访问多模态样本
+sample = engine[0]
+print("用户消息内容：")
+for content_item in sample['messages'][0]['content']:
+    print(f"  类型: {content_item['type']}, 值: {content_item['value']}")
+```
+
+---
+
+**注意事项**：
+
+1. 所有数据最终都会转换为标准的 Messages 格式
+2. 通过 `converter` 插件可以支持多种数据格式
+3. 通过 `weight` 和 `size` 参数可以灵活控制数据分布
+4. 支持同时使用本地数据集和 HuggingFace Hub 数据集
+5. 多模态数据通过在 `content` 中添加不同类型的元素来支持
+6. 更多细节信息请参考我们的 [API REFERENCE](../dev-guide/core/data-engine.md/#data-engine)
--- a/docs/zh/dev-guide/core/data-engine.md
+++ b/docs/zh/dev-guide/core/data-engine.md
@@ -0,0 +1,253 @@
+# DataEngine
+
+## 1. DataEngine 简介
+
+
+`DataEngine` 是 LLaMA-Factory v1 数据处理的核心类，继承自 PyTorch 的 `Dataset`，负责各种插件的接入，其他功能（如数据格式转换、数据加载等）均通过插件的形式实现并接入 `DataEngine`。
+
+`DataEngine`接受一个唯一入参：`DataArguments` 实例，所有的元数据集信息均通过该参数配置传入。
+
+## 2. DataEngine 与 DataArguments 接口定义
+
+```python
+@dataclass
+class DataArguments:
+    """ `DataEngine`初始化入参
+
+    args:
+        dataset (str): 数据集路径，远程数据集 repo id / dataset_info.yaml 路径，或本地数据集路径/dataset_info.yaml路径
+        cutoff_len (int): 数据集截止长度，即数据集最大样本采样数量
+    """
+    ...
+
+
+class DataEngine(Dataset):
+    """数据引擎（DataEngine）
+
+    `DataEngine` 负责数据集的加载与统一管理，支持：
+        - 从本地路径或 Hugging Face Hub 加载数据
+        - 通过插件机制加载自定义数据
+        - 构建统一的数据索引
+        - 支持流式（streaming）与非流式数据访问
+
+    attr:
+        args (DataArguments): 数据参数配置
+        datasets (dict[str, HFDataset]): 数据集名称到数据对象的映射
+        dataset_infos (dict[str, DatasetInfo]): 数据集名称到元信息的映射
+        data_index (list[tuple[str, int]]): 数据索引列表，每项为 (dataset_name, sample_index)
+        streaming (bool): 是否为流式数据集
+    """
+
+    def __init__(self, data_args: DataArguments) -> None:
+        """初始化 `DataEngine`
+
+        初始化时自动执行以下步骤：
+            1. 调用 `get_dataset_info`， 从 `data_args` 读取并解析数据集元信息
+            2. 调用 `load_dataset`，根据配置加载数据集
+            3. 调用 `build_data_index`，构建统一的索引列表
+
+        args:
+            data_args (DataArguments): 数据参数配置对象
+        """
+        ...
+
+    def get_dataset_info(self) -> None:
+        """从配置文件或远程仓库加载数据集元信息
+
+        根据 `self.args.dataset` 确定数据源，数据源支持如下选项：
+            - 本地 YAML 配置文件路径
+            - Hugging Face Hub 上的 YAML 配置文件路径
+            - 本地数据集文件路径
+            - Hugging Face Hub 数据集 repo id
+
+        """
+        ...
+
+    def load_dataset(self) -> None:
+        """根据数据集元信息加载所有数据集
+
+        每个数据集条目可以包含以下字段：
+            - `hf_hub_url`: 使用 `datasets.load_dataset` 加载
+            - 本地数据文件：通过 `DataLoaderPlugin` 插件加载
+            - `streaming`: 是否启用流式模式
+
+        更新:
+            self.datasets (dict): 数据集名称到已加载数据对象的映射
+            self.streaming (bool): 如果任一数据集为流式模式，则设置为 True
+        """
+        ...
+
+    def build_data_index(self) -> None:
+        """构建统一的数据索引
+
+        为所有数据集创建全局索引列表 `(dataset_name, sample_index)`
+
+        当启用流式模式时，生成固定长度（例如 1000）的占位索引；
+        否则，为每条样本建立索引。
+
+        插件 `DataIndexPlugin` 可根据数据集大小或权重调整索引分布
+        """
+        ...
+
+    def _convert_data_sample(self, raw_sample: dict[str, Any], dataset_name: str) -> Sample:
+        """将原始样本转换为统一格式
+
+        根据 `dataset_info` 中的 `converter` 字段，调用对应的转换插件，
+        将原始样本标准化为统一的数据结构。
+
+        args:
+            raw_sample (dict[str, Any]): 原始数据样本
+            dataset_name (str): 样本所属的数据集名称
+
+        return:
+            Sample: 转换后的标准化格式样本
+        """
+        ...
+
+    def __len__(self) -> int:
+        """返回数据集的总样本数
+
+        return:
+            int: 数据集长度
+                如果为流式数据集，返回 `-1`
+        """
+        ...
+
+    def __getitem__(self, index: Union[int, Any]) -> Union[Sample, list[Sample]]:
+        """根据索引或选择器获取样本
+
+        args:
+            index (Union[int, Any]): 数据索引，int 或 list[int]
+
+        return:
+            Union[Sample, list[Sample]]: 单个样本或样本列表
+        """
+        ...
+
+    def __iter__(self) -> Iterable:
+        """返回数据集迭代器
+
+        用于非流式数据集的顺序或随机访问
+        流式模式下需要实现异步加载逻辑
+
+        return:
+            Iterable: 数据集迭代器。
+        """
+        ...
+
+    async def __aiter__(self) -> AsyncIterable:
+        """返回异步数据集迭代器
+
+        用于流式数据集或异步数据加载场景
+        允许在异步环境中以流的方式读取样本
+
+        return:
+            AsyncIterable: 异步迭代器，按顺序产出样本
+        """
+        ...
+
+
+```
+
+`DataArguments`  参数说明：
+
+`dataset`: 数据集路径，支持本地或远程，当传入本地数据集文件路径时，需要满足该数据集为标准格式；否则需要传入 `dataset_info.yaml` 来配置数据集的 `converter` 等元信息，以告知 `DataEngine` 应当如何处理该数据。
+
+`cutoff_len`: 数据集的截止长度，即该数据集的最大样本数量。
+
+---
+
+## 3. DataEngine 核心方法
+
+### 3.1 `get_dataset_info`：加载数据元信息
+
+根据 `dataset` 参数加载数据集配置，获取数据位置、数据格式、插件配置等所有数据元信息，在实例化 `DataEngine` 时会自动调用此方法。
+
+### 3.2 加载数据集：`load_dataset`
+
+遍历所有数据源，根据不同的数据源加载数据，在实例化 `DataEngine` 时会自动调用此方法。
+
+```python
+for key, value in self.dataset_infos.items():
+    split = value.get("split", "train")
+    streaming = value.get("streaming", False)
+
+    if "hf_hub_url" in value:
+        # 从 HF Hub 加载
+        dataset = load_dataset(value["hf_hub_url"], split=split, streaming=streaming)
+    else:
+        # 使用 DataLoaderPlugin 加载本地文件
+        dataset = DataLoaderPlugin(args=self.args).auto_load_data(value)
+
+    self.datasets[key] = dataset
+```
+
+### 3.3 `build_data_index`：构建数据索引
+
+为每个数据集创建索引列表 `[(dataset_name, sample_index), ...]`, `DataIndexPlugin`插件在此处被调用，可控制各数据集的采样频率、采样方式等，在实例化`DataEngine`时会自动调用此方法。
+
+```python
+for dataset_name, dataset in self.datasets.items():
+    # 创建基础索引
+    data_index = [(dataset_name, idx) for idx in range(len(dataset))]
+
+    # 根据 size 和 weight 调整索引
+    size = self.dataset_infos[dataset_name].get("size")
+    weight = self.dataset_infos[dataset_name].get("weight")
+    if size or weight:
+        data_index = DataIndexPlugin().adjust_data_index(data_index, size, weight)
+
+    self.data_index.extend(data_index)
+```
+
+### 3.4 `_convert_data_sample`：数据格式标准化
+
+将原始数据转换为标准格式，`DataConverterPlugin`插件在此处被调用，具体调用的插件由 `get_dataset_info` 方法获取的 `converter` 信息指定，若 `converter` 为空则假定数据集为标准格式，此方法由`DataEngine`的 `__getitem__` 方法调用。
+
+```python
+def _convert_data_sample(self, raw_sample: dict, dataset_name: str) -> Sample:
+    converter = self.dataset_infos[dataset_name].get("converter")
+    if converter is not None:
+        # 使用指定的转换器
+        from ..plugins.data_plugins.converter import get_converter
+        return {"_dataset_name": dataset_name, **get_converter(converter)(raw_sample)}
+    else:
+        # 已经是标准格式
+        return {"_dataset_name": dataset_name, **raw_sample}
+```
+
+---
+
+## 4. 初始化
+
+`DataEngine` 初始化过程只需传入一个构建好的 `DataArguments` 即可，后续可通过该 `DataEngine` 访问数据集中的数据。
+
+```python
+from llamafactory.v1.config.data_args import DataArguments
+from llamafactory.v1.core.data_engine import DataEngine
+
+# 1. 创建数据参数
+data_args = DataArguments(
+    dataset="~/data/v1_sft_demo.jsonl",
+    cutoff_len=2048
+)
+
+# 2. 初始化 Data Engine
+data_engine = DataEngine(data_args=data_args)
+
+# 3. 访问数据
+sample = data_engine[0]  # 获取第一个样本
+```
+
+## 5. 数据访问方式
+
+实例化后的`DataEngine`支持整数索引、列表索引、以及切片等访问方式，其数据读取用法可等价于 Python 列表。
+
+```python
+sample = data_engine[0]  # 获取第一个样本
+
+sample = data_engine[0:10]  # 获取前 10 个样本
+
+sample = data_engine[[0, 5, 10]]  # 获取指定索引的样本
+
+```
--- a/docs/zh/dev-guide/core/model-engine.md
+++ b/docs/zh/dev-guide/core/model-engine.md
@@ -0,0 +1 @@
+# ModelEngine
--- a/docs/zh/dev-guide/core/trainer.md
+++ b/docs/zh/dev-guide/core/trainer.md
@@ -0,0 +1 @@
+# Trainer
--- a/docs/zh/dev-guide/plugins/data-plugins.md
+++ b/docs/zh/dev-guide/plugins/data-plugins.md
@@ -0,0 +1,467 @@
+# Data Plugins
+
+## 1. Data Plugins 简介
+
+## DataConverterPlugin
+
+### 1. DataConverterPlugin 简介
+
+DataConverter 负责将非标准格式的数据集转换为 v1 的标准 Messages 格式。这使得用户可以继续使用现有的数据集（如 Alpaca 格式），而无需手动转换。针对自定义格式的数据集，用户也可以通过构建对应的自定义 DataConverter 插件，来负责其数据格式标准化。
+
+当前，LLaMA-Factory 已内置了 `Alpaca Converter` 和 `Pair Converter`，这两类数据集可以直接使用对应的 converter 进行标准化，无需自定义转换器。
+
+
+### 2. Alpaca Converter 详解
+
+#### 2.1 Alpaca 格式
+
+Alpaca 格式是一种常见的指令微调数据格式：
+
+```json
+{
+  "system": "You are a helpful assistant.",
+  "instruction": "Describe a process of making crepes.",
+  "input": "",
+  "output": "Making crepes is an easy and delicious process..."
+}
+```
+
+#### 2.2 Alpaca Converter 接口定义
+
+```python
+class AlpacaSample(TypedDict, total=False):
+    """Alpaca 格式数据样本结构
+
+    attr:
+        system (str, 可选): 系统提示信息（system prompt），用于设定对话背景或模型行为。
+        instruction (str, 可选): 用户指令（user instruction），通常为任务描述。
+        input (str, 可选): 额外的输入内容（input text），可与 instruction 拼接。
+        output (str, 可选): 模型生成的目标输出（expected response）。
+    """
+    ...
+
+
+def alpaca_converter(raw_sample: AlpacaSample) -> SFTSample:
+    """将 Alpaca 样本转换为 SFT（Supervised Fine-Tuning）标准样本格式
+
+    `alpaca_converter` 将 Alpaca 数据集中一条样本转换为通用的 `SFTSample` 格式
+    该格式用于监督微调（SFT）或多轮对话建模
+
+    转换逻辑:
+        - 若存在 `system` 字段，则生成一条系统消息，loss_weight = 0.0
+        - 若存在 `instruction` 或 `input` 字段，则合并为一条用户消息，loss_weight = 0.0
+        - 若存在 `output` 字段，则生成一条助手机器人回复消息，loss_weight = 1.0
+
+    args:
+        raw_sample (AlpacaSample): 原始 Alpaca 数据样本
+
+    return:
+        SFTSample: 转换后的标准化样本，格式如下:
+
+            {
+                "messages": [
+                    {"role": "system", "content": [{"type": "text", "value": "..."}], "loss_weight": 0.0},
+                    {"role": "user", "content": [{"type": "text", "value": "..."}], "loss_weight": 0.0},
+                    {"role": "assistant", "content": [{"type": "text", "value": "..."}], "loss_weight": 1.0},
+                ]
+            }
+
+    example:
+        >>> raw = {"instruction": "请将以下句子翻译成英文：", "input": "你好", "output": "Hello"}
+        >>> alpaca_converter(raw)
+        {
+            "messages": [
+                {"role": "user", "content": [{"type": "text", "value": "请将以下句子翻译成英文：你好"}], "loss_weight": 0.0},
+                {"role": "assistant", "content": [{"type": "text", "value": "Hello"}], "loss_weight": 1.0}
+            ]
+        }
+    """
+
+```
+
+#### 2.3 转换过程
+
+`alpaca_converter` 函数将 Alpaca 格式转换为标准格式，转换逻辑如下：
+
+```python
+def alpaca_converter(raw_sample: AlpacaSample) -> SFTSample:
+    messages = []
+
+    # 1. 添加系统提示词（如果存在）
+    if "system" in raw_sample:
+        messages.append({
+            "role": "system",
+            "content": [{"type": "text", "value": raw_sample["system"]}],
+            "loss_weight": 0.0
+        })
+
+    # 2. 添加用户输入（instruction + input）
+    if "instruction" in raw_sample or "input" in raw_sample:
+        user_content = raw_sample.get("instruction", "") + raw_sample.get("input", "")
+        messages.append({
+            "role": "user",
+            "content": [{"type": "text", "value": user_content}],
+            "loss_weight": 0.0
+        })
+
+    # 3. 添加模型回复
+    if "output" in raw_sample:
+        messages.append({
+            "role": "assistant",
+            "content": [{"type": "text", "value": raw_sample["output"]}],
+            "loss_weight": 1.0
+        })
+
+    return {"messages": messages}
+```
+
+#### 2.4 转换示例
+
+**输入（Alpaca 格式）：**
+
+```json
+{
+  "instruction": "What is the capital of France?",
+  "input": "",
+  "output": "The capital of France is Paris."
+}
+```
+
+**输出（标准格式）：**
+
+```json
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": [{"type": "text", "value": "What is the capital of France?"}],
+      "loss_weight": 0.0
+    },
+    {
+      "role": "assistant",
+      "content": [{"type": "text", "value": "The capital of France is Paris."}],
+      "loss_weight": 1.0
+    }
+  ]
+}
+```
+
+### 3. 自定义转换器
+
+#### 3.1 创建自定义转换器
+
+如果用户有自己的数据格式，可以轻松添加自定义转换器将其标准化，实现过程可参考如下示例：
+
+```python
+# src/llamafactory/v1/plugins/data_plugins/converter.py
+
+from typing import TypedDict, NotRequired
+from ...extras.types import SFTSample
+
+# 1. 定义输入格式的类型
+class MyCustomSample(TypedDict, total=False):
+    question: str
+    answer: str
+    context: NotRequired[str]
+
+# 2. 实现转换逻辑
+def custom_converter(raw_sample: MyCustomSample) -> SFTSample:
+    messages = []
+
+    # 构建用户消息
+    user_text = raw_sample["question"]
+    if "context" in raw_sample:
+        user_text = f"Context: {raw_sample['context']}\n\nQuestion: {user_text}"
+
+    messages.append({
+        "role": "user",
+        "content": [{"type": "text", "value": user_text}],
+        "loss_weight": 0.0
+    })
+
+    # 构建助手消息
+    messages.append({
+        "role": "assistant",
+        "content": [{"type": "text", "value": raw_sample["answer"]}],
+        "loss_weight": 1.0
+    })
+
+    return {"messages": messages}
+
+# 3. 注册 custom_converter
+#src/llamafactory/v1/plugins/data_plugins/converter.py: CONVERTERS
+CONVERTERS = {
+    "alpaca": alpaca_converter,
+    "custom": custom_converter,  # 添加自定义转换器
+}
+```
+
+#### 3.2 使用自定义转换器
+
+在 YAML 配置中指定转换器名称：
+
+```yaml
+my_dataset:
+  file_name: custom_data.json
+  converter: custom
+```
+
+---
+
+## DataLoaderPlugin
+
+### 1. DataLoaderPlugin 简介
+
+`DataLoaderPlugin` 负责从本地文件加载数据集，当前支持如下文件格式：
+
+- **JSON**: `.json`
+- **JSONL**: `.jsonl`
+- **CSV**: `.csv`
+- **Parquet**: `.parquet`
+- **Arrow**: `.arrow`
+- **Text**: `.txt`
+
+### 2. DataLoaderPlugin 接口定义
+
+```python
+@dataclass
+class DataLoaderPlugin:
+    """数据加载插件（DataLoaderPlugin）
+
+    负责根据数据集信息（`DatasetInfo`）自动加载本地或远程数据集。  
+    支持多种文件格式（如 CSV、JSON、Parquet、Text、Arrow），并可选择是否以流式方式加载。
+
+    通常由 `DataEngine` 调用，用于统一封装数据加载逻辑。
+    """
+
+    args: DataArguments
+    """数据参数对象，包含数据目录、缓存路径、分片等配置信息。"""
+
+
+    def _get_builder_name(self, path: str) -> Literal["arrow", "csv", "json", "parquet", "text"]:
+        """获取数据集文件格式
+
+        根据输入文件路径自动判断应使用的 HuggingFace `load_dataset` 构建器类型。
+        通过文件扩展名推断数据类型，例如 `.csv`、`.jsonl`、`.parquet`、`.txt` 等。
+
+        args:
+            path (str): 数据集文件路径，用于识别文件类型。
+
+        return:
+            Literal["arrow", "csv", "json", "parquet", "text"]:
+                数据构建器名称，用于 `datasets.load_dataset()`。
+
+        example:
+            >>> _get_builder_name("data/train.jsonl")
+            "json"
+        """
+        ...
+
+
+    def auto_load_data(self, dataset_info: DatasetInfo) -> HFDataset:
+        """根据传入的 `dataset_info` 自动选择合适的加载方式
+
+        args:
+            dataset_info (DatasetInfo): 数据集元信息，通常包含：
+                - `file_name`: 数据文件路径
+                - `split`: 数据划分（如 "train"、"test"）；
+                - `streaming`: 是否启用流式加载
+
+        return:
+            HFDataset: 加载完成的 Hugging Face 数据集对象。
+
+        example:
+            >>> plugin = DataLoaderPlugin(args)
+            >>> ds = plugin.auto_load_data({"file_name": "~/data.json", "split": "train"})
+        """
+        ...
+
+
+    def load_data_from_file(self, filepath: str, split: str, streaming: bool) -> HFDataset:
+        """从文件或目录加载数据集
+
+        根据输入路径自动识别文件类型（CSV、JSON、Parquet、Text 等），  
+        并通过 `datasets.load_dataset()` 加载数据集。  
+        若 `streaming=True`，则将结果转换为迭代式数据集。
+
+        args:
+            filepath (str): 文件路径或目录路径。
+            split (str): 数据划分名称（如 "train"、"validation"）。
+            streaming (bool): 是否启用流式加载模式。
+
+        return:
+            HFDataset: 加载后的数据集对象。
+
+        example:
+            >>> plugin.load_data_from_file("data/train.json", "train", False)
+        """
+        ...
+
+```
+
+---
+
+## DataIndexPlugin
+
+### 1. DataIndexPlugin 简介
+
+`DataIndexPlugin` 负责调整数据索引，支持通过配置 `size`, `weight` 等参数控制数据集样本数量和采样频率。
+
+- 使用 `size` 参数 限制使用的样本数量：
+
+```yaml
+my_dataset:
+  file_name: large_dataset.json
+  size: 1000  # 只使用前 1000 个样本
+```
+
+- 使用 `weight` 参数调整数据集在混合数据中的采样频率：
+
+```yaml
+dataset_a:
+  file_name: data_a.json
+  weight: 1.0
+
+dataset_b:
+  file_name: data_b.json
+  weight: 2.0  # dataset_b 的样本出现频率是 dataset_a 的 2 倍
+```
+
+**说明**：`weight` 参数适用于在多个数据集混合训练时，调整不同数据集的的采样频率
+
+- 当 `weight=1.0` 时，数据集按原始比例采样
+- 当 `weight=2.0` 时，该数据集的索引会复制 2 倍，使其样本出现频率翻倍
+
+### 2. DataIndexPlugin 接口定义
+
+```python
+@dataclass
+class DataIndexPlugin:
+    """数据索引插件（DataIndexPlugin）
+
+    根据 `size` 和 `weight` 调整数据索引列表，控制数据集的样本数量和采样频率  
+    通常在多数据集混合训练时使用，以控制不同数据集在总体样本中的占比。
+
+    在 `DataEngine.build_data_index` 中被自动调用，用于实现样本重采样或加权分布。
+    """
+
+    def adjust_data_index(
+        self, data_index: list[tuple[str, int]], size: Optional[int], weight: Optional[float]
+    ) -> list[tuple[str, int]]:
+        """调整数据索引列表
+
+        根据 `size` 或 `weight` 参数对输入的数据索引进行采样、扩展或缩减。  
+        若两个参数同时存在，将依次执行基于大小和基于权重的调整。
+
+        args:
+            data_index (list[tuple[str, int]]):  
+                数据索引列表，每个元素为 `(dataset_name, sample_index)`。  
+            size (Optional[int]):  
+                目标样本数量，若指定则根据该数量裁剪或重复样本。  
+            weight (Optional[float]):  
+                数据集权重，用于控制数据集在混合训练中的采样比例。
+
+        return:
+            list[tuple[str, int]]:  
+                调整后的数据索引列表。
+
+        example:
+            >>> plugin = DataIndexPlugin()
+            >>> adjusted = plugin.adjust_data_index([("ds1", i) for i in range(100)], size=50, weight=None)
+            >>> len(adjusted)
+            50
+        """
+        ...
+
+
+    def adjust_by_size(self, data_index: list[tuple[str, int]], size: int) -> list[tuple[str, int]]:
+        """根据目标大小调整数据索引
+
+        通过裁剪或重复样本，使索引总数等于 `size`。  
+        常用于统一不同数据集的样本数量。
+
+        args:
+            data_index (list[tuple[str, int]]):  
+                原始数据索引列表。  
+            size (int):  
+                目标样本数量。
+
+        return:
+            list[tuple[str, int]]:  
+                调整后长度等于 `size` 的数据索引列表。
+
+        example:
+            >>> plugin.adjust_by_size([("ds1", i) for i in range(10)], 20)
+        """
+        ...
+
+
+    def adjust_by_weight(self, data_index: list[tuple[str, int]], weight: float) -> list[tuple[str, int]]:
+        """根据权重调整数据索引
+
+        通过加权采样或重复样本，使数据集样本出现频率符合指定权重。  
+        常用于多数据源训练中按比例平衡样本。
+
+        args:
+            data_index (list[tuple[str, int]]):  
+                原始数据索引列表。  
+            weight (float):  
+                数据集权重（相对比例，可与其他数据集共同归一化）。
+
+        return:
+            list[tuple[str, int]]:  
+                调整后的加权数据索引列表。
+
+        example:
+            >>> plugin.adjust_by_weight([("ds1", i) for i in range(10)], 0.5)
+        """
+        ...
+
+```
+---
+
+## DataSelectorPlugin
+
+### 1. DataSelectorPlugin 简介
+
+`DataSelectorPlugin` 为 `DataEngine`提供基于索引访问数据的功能，由 `DataEngine` 的 `__getitem__` 方法自动调用。
+
+
+### 2. DataSelectorPlugin 接口定义
+
+```python
+@dataclass
+class DataSelectorPlugin:
+    """根据索引选择数据集样本。
+
+    配合 `DataEngine` 使用，通过统一的 `data_index` 结构（包含数据集名与样本索引）来实现灵活的数据选择
+
+    """
+
+    data_index: list[tuple[str, int]]
+    """数据索引列表，每个元素为 (dataset_name, sample_index)。"""
+
+
+    def select(self, index: Union[slice, list[int], Any]) -> Union[tuple[str, int], list[tuple[str, int]]]:
+        """选择数据集样本
+
+        根据输入类型从 `data_index` 中选择对应的样本索引  
+        支持三种索引方式：
+            - 切片（slice）：返回对应范围内的样本
+            - 索引列表（list[int]）：返回指定索引处的多个样本
+            - 其他类型输入将触发异常。
+
+        args:
+            index (Union[slice, list[int], Any]): 数据样本索引
+                可以是切片（`slice`）或索引列表
+
+        return:
+            Union[tuple[str, int], list[tuple[str, int]]]:
+                - 若为单个索引：返回一个 `(dataset_name, sample_index)`
+                - 若为多个索引或切片：返回多个样本的列表
+
+        except:
+        Raises:
+            ValueError: 当输入索引类型不受支持时抛出。
+        ...
+```
--- a/docs/zh/dev-guide/plugins/model-plugins/initialization.md
+++ b/docs/zh/dev-guide/plugins/model-plugins/initialization.md
--- a/docs/zh/dev-guide/plugins/model-plugins/kernels.md
+++ b/docs/zh/dev-guide/plugins/model-plugins/kernels.md
@@ -0,0 +1,197 @@
+# Kernels plugins
+
+## 概览
+LLaMA-Factory 通过 Kernels plugins 系统，依据不同硬件设备提供高性能计算内核（kernel）实现。该系统通过注册表机制管理所有 kernel，通过 `@register_kernel` 装饰器实现 kernel 定义后自动注册，由 `apply_kernel` 方法来使能指定的 kernel，`apply_default_kernels` 可使能注册表中当前环境所有可用的默认 kernels。
+
+## 架构设计
+
+### 核心组件
+
+#### 1. Registry（注册表）
+
+`Registry` 是一个用于管理所有 kernel 实现的静态类。它维护一个字典结构：`{kernel_id: KernelClass}`。
+
+```python
+# 注册表结构示例
+{
+    "npu_fused_rmsnorm": NpuRMSNormKernel,
+    "npu_fused_swiglu": NpuSwiGluKernel,
+    ...
+}
+```
+
+#### 2. register_kernel (装饰器)
+
+`@register_kernel` 是 `Registry.register` 的别名。所有 kernel 类均应使用该装饰器进行注册。
+
+**注册机制**：
+- 装饰器检查类是否继承自 `BaseKernel`。
+- 检查类是否定义了 `_kernel_id` 和 `_device` 属性。
+- 检查 `_device` 是否与当前运行环境的加速器类型匹配。如果不匹配，则跳过注册。
+- 如果一切符合要求，将 kernel 类注册到全局注册表中。
+
+#### 3. BaseKernel（基类）
+
+所有 kernel 的实现都必须继承自 `BaseKernel` 抽象基类。`BaseKernel` 定义了 kernel 的基本属性和接口。
+
+#### 4. 标识系统
+
+**Kernel ID** (`_kernel_id`)：
+每个 kernel 必须拥有一个唯一的字符串标识符，例如 `"npu_fused_rmsnorm"`。
+
+**Device Type** (`_device`)：
+kernel 必须声明其支持的设备类型，例如 `DeviceType.NPU` 或 `DeviceType.CUDA`。
+
+## Kernel 系统 API 设计
+
+### **Registry**：全局 kernel 注册表
+
+`Registry` 类提供了注册和获取 kernel 的接口：
+
+```python
+class Registry:
+    @classmethod
+    def register(cls, kernel_cls: type[BaseKernel]) -> type[BaseKernel] | None:
+        """注册一个 kernel 类"""
+        ...
+
+    @classmethod
+    def get(cls, kernel_id: str) -> type[BaseKernel] | None:
+        """根据 ID 获取 kernel 类"""
+        ...
+```
+
+### **BaseKernel**
+
+`BaseKernel` 定义了所有 kernel 必须实现的协议：
+
+- `_kernel_id`: 类属性，kernel 的唯一标识符。
+- `_device`: 类属性，kernel 支持的设备类型。
+- `check_deps()`: 类方法，检查 kernel 的依赖项是否满足（如 `torch_npu` 是否安装）。
+- `apply(**kwargs)`: 抽象类方法，实现 kernel 的具体应用逻辑。
+
+```python
+class BaseKernel(ABC):
+    _kernel_id: Any = ""
+    _device: DeviceType = DeviceType.CPU
+
+    @classmethod
+    def check_deps(cls) -> bool:
+        """检查依赖项"""
+        ...
+
+    @classmethod
+    @abstractmethod
+    def apply(cls, **kwargs) -> HFModel:
+        """应用 kernel 到模型"""
+        ...
+```
+
+### **scan_all_kernels**
+
+`scan_all_kernels` 函数会自动扫描 `ops` 目录下的所有 `.py` 文件并导入它们，从而触发 `@register_kernel` 装饰器完成自动注册。
+
+### **apply_kernel**
+
+对模型使能指定的 kernel。
+
+```python
+def apply_kernel(kernel_id: str, **kwargs) -> HFModel:
+    """应用指定的 kernel 到模型
+
+    Args:
+        kernel_id: 目标 kernel 的 ID
+        **kwargs: 传递给 kernel.apply 的参数，通常包含 model
+    """
+```
+
+**用法示例**：
+```python
+from llamafactory.v1.plugins.model_plugins.kernels import apply_kernel
+
+model = apply_kernel("npu_fused_rmsnorm", model=model)
+```
+
+### **apply_default_kernels**
+
+对模型使能所有默认注册的 kernel。这是一个高级 API，通常在模型加载流程中自动调用。
+
+```python
+def apply_default_kernels(model: HFModel, include_kernels: str = None) -> HFModel:
+    """应用所有默认 kernel
+
+    Args:
+        model: HFModel 实例
+        include_kernels: 包含的 kernel ID 列表（逗号分隔字符串），或者 "auto"/True 表示全部
+    """
+```
+
+## 扩展 Kernels
+
+如果用户有针对特定模型或者设备的 kernel，可以按照下述步骤去实现并接入 LLaMA-Factory。
+
+### 创建新 Kernel 的步骤
+
+#### 1. 创建 Kernel 实现文件
+
+在 `src/llamafactory/v1/plugins/model_plugins/kernels/ops` 下的相应子目录中创建新的 kernel 实现文件，例如 `mlp/cuda_swiglu.py`：
+
+```python
+import torch
+from ......accelerator.helper import DeviceType
+from ......utils.types import HFModel
+from ...base import BaseKernel
+from ...registry import register_kernel
+
+# 实现具体的 kernel 函数
+def _cuda_swiglu_forward(self, hidden_state):
+    # ... CUDA 优化实现 ...
+    pass
+
+@register_kernel
+class CudaSwiGluKernel(BaseKernel):
+    _kernel_id = "cuda_fused_swiglu"
+    _device = DeviceType.CUDA
+
+    @classmethod
+    def apply(cls, **kwargs) -> HFModel:
+        model = kwargs.get("model")
+        if model is None:
+            raise ValueError("model is required")
+
+        if not cls.check_deps():
+            raise RuntimeError("Dependencies not met")
+
+        # 遍历模型并替换 forward 方法
+        for name, module in model.named_modules():
+            # ... 匹配和替换逻辑 ...
+            pass
+
+        return model
+```
+
+#### 2. 自动发现
+
+由于 `scan_all_kernels` 会自动扫描 `ops` 目录，只要文件位于该目录下且没有语法错误，系统启动时会自动导入并注册，无需手动修改注册表代码。
+
+#### 3. 测试 Kernel
+
+创建测试用例验证 kernel 的正确性：
+
+```python
+from llamafactory.v1.plugins.model_plugins.kernels import apply_kernel
+
+# ... 加载模型 ...
+model = apply_kernel("cuda_fused_swiglu", model=model)
+# ... 验证 forward 是否被替换 ...
+```
+
+## 异常处理
+
+### 依赖不可用
+
+`BaseKernel.check_deps()` 默认会检查当前设备类型是否匹配。子类可以重写此方法以添加额外的依赖检查（如检查特定的库是否安装）。如果 `check_deps()` 返回 `False`，`apply()` 方法应当抛出异常或进行相应处理。
+
+### Kernel ID 未找到
+
+如果调用 `apply_kernel` 时传入了不存在的 `kernel_id`，会抛出 `ValueError`。
--- a/src/llamafactory/v1/plugins/model_plugins/added_token.py
+++ b/src/llamafactory/v1/plugins/model_plugins/added_token.py
--- a/docs/zh/getting-started.md
+++ b/docs/zh/getting-started.md
@@ -0,0 +1,71 @@
+# Getting Started
+
+
+## 训练方法
+
+|          方法          |     全参数训练      |    部分参数训练     |       LoRA         |       QLoRA        |
+|:---------------------:| ------------------ | ------------------ | ------------------ | ------------------ |
+|      指令监督微调       | :white_check_mark: |  |  | |
+|      奖励模型训练       |  |  |  | |
+|        DPO 训练        |  |  |  | |
+
+
+
+
+## 软件依赖
+
+|          必需项          | 至少     | 推荐     |
+|:---------------------:|--------|--------|
+|        python         | 3.11   | 3.12   |
+|         torch         | 2.7.1  | 2.7.1  |
+| torch-npu(Ascend NPU) | 2.7.1  | 2.7.1  |
+|      torchvision      | 0.22.1 | 0.22.1 |
+|     transformers      | 5.0.0  | 5.0.0  |
+|       datasets        | 3.2.0  | 4.0.0  |
+|         peft          | 0.18.1 | 0.18.1 |
+
+
+|       可选项        | 至少     | 推荐     |
+|:----------------:|--------|--------|
+| CUDA(NVIDIA GPU) | 11.6   | 12.2   |
+|    deepspeed     | 0.18.4 | 0.18.4 |
+|   flash-attn(NVIDIA GPU)   | 2.5.6  | 2.7.2  |
+
+
+## 如何使用
+
+### 安装 LLaMA Factory
+
+> [!IMPORTANT]
+> 此步骤为必需。
+
+#### 从源码安装
+
+```bash
+git clone --depth 1 https://github.com/hiyouga/LlamaFactory.git
+cd LlamaFactory
+pip install -e .
+```
+
+
+### 数据准备
+
+关于数据集文件的格式，请参考 [data-preparation/README.md](data-preparation/README.md) 的内容。你可以使用 HuggingFace / ModelScope 上的数据集或加载本地数据集。
+
+> [!NOTE]
+> 使用自定义数据集或自定义数据集格式时，请参照 [data-preparation/README.md](data-preparation/README.md) 进行配置，如有必要，请重新实现自定义数据集的数据处理逻辑，包括对应的`converter`。
+
+您也可以使用 **[Easy Dataset](https://github.com/ConardLi/easy-dataset)**、**[DataFlow](https://github.com/OpenDCAI/DataFlow)** 和 **[GraphGen](https://github.com/open-sciencelab/GraphGen)** 构建用于微调的合成数据。
+
+### 快速开始
+
+下面的命令展示了对 Qwen3-0.6B 模型使用 FSDP2 进行 全参**微调**，两行命令等价。
+
+```bash
+export USE_V1=1
+llamafactory-cli sft examples/v1/train_full/train_full_fsdp2.yaml
+llamafactory-cli train examples/v1/train_full/train_full_fsdp2.yaml
+
+```
+
+高级用法请参考 [advanced](./advanced/README.md)（包括多卡多机微调、分布式、Lora、量化、以及各种加速特性等）。
--- a/docs/zh/hyperparameters/data-argument.md
+++ b/docs/zh/hyperparameters/data-argument.md
@@ -0,0 +1 @@
+# Data Argument
--- a/src/llamafactory/v1/plugins/trainer_plugins/distributed/accelerate.py
+++ b/src/llamafactory/v1/plugins/trainer_plugins/distributed/accelerate.py
--- a/docs/zh/hyperparameters/sample-argument.md
+++ b/docs/zh/hyperparameters/sample-argument.md
--- a/docs/zh/hyperparameters/training-argument.md
+++ b/docs/zh/hyperparameters/training-argument.md
--- a/docs/zh/index.rst
+++ b/docs/zh/index.rst
@@ -0,0 +1,62 @@
+LlamaFactory 文档
+=================
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Getting Started
+
+   getting-started
+   installation
+   llamaboard-web-ui
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Data Preparation
+
+   data-preparation/data-processing
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Training
+
+   training/sft
+   training/dpo
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Inference
+
+   inference/deploy
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Advanced
+
+   advanced/lora-and-quantization/lora
+   advanced/lora-and-quantization/quantization
+   advanced/distributed/fsdp
+   advanced/distributed/deepspeed
+   advanced/distributed/parallel-dp-tp-ep-sp-cp
+   advanced/custom-kernels/triton
+   advanced/custom-kernels/fused-operators
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Hyperparameters
+
+   hyperparameters/data-argument
+   hyperparameters/model-argument
+   hyperparameters/sample-argument
+   hyperparameters/training-argument
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Dev Guide
+
+   dev-guide/core/data-engine
+   dev-guide/core/model-engine
+   dev-guide/core/trainer
+   dev-guide/plugins/data-plugins
+   dev-guide/plugins/model-plugins/initialization
+   dev-guide/plugins/model-plugins/kernels
+   dev-guide/plugins/model-plugins/rendering
--- a/docs/zh/inference/deploy.md
+++ b/docs/zh/inference/deploy.md
@@ -0,0 +1 @@
+# Deploy
--- a/docs/zh/installation.md
+++ b/docs/zh/installation.md
@@ -0,0 +1 @@
+# Installation
--- a/docs/zh/llamaboard-web-ui.md
+++ b/docs/zh/llamaboard-web-ui.md
@@ -0,0 +1 @@
+# LlamaBoard Web UI
--- a/docs/zh/training/dpo.md
+++ b/docs/zh/training/dpo.md
@@ -0,0 +1 @@
+# DPO
--- a/docs/zh/training/sft.md
+++ b/docs/zh/training/sft.md
@@ -0,0 +1 @@
+# SFT
--- a/examples/extras/eaft/qwen25_05b_eaft_full.yaml
+++ b/examples/extras/eaft/qwen25_05b_eaft_full.yaml
@@ -0,0 +1,38 @@
+### model
+model_name_or_path: Qwen/Qwen2.5-0.5B-Instruct
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+use_eaft_loss: true
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: qwen
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: qwen2.5-0_5b/full/sft_eaft
+logging_steps: 1
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+save_only_model: false
+report_to: none  # choices: [none, wandb, tensorboard, swanlab, mlflow]
+
+
+### train
+per_device_train_batch_size: 2
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-5
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
--- a/examples/ktransformers/infer_lora/deepseek2_lora_sft_kt.yaml
+++ b/examples/ktransformers/infer_lora/deepseek2_lora_sft_kt.yaml
@@ -5,6 +5,6 @@ infer_backend: ktransformers  # choices: [huggingface, vllm, sglang, ktransforme
 trust_remote_code: true

 use_kt: true # use KTransformers as LoRA sft backend to inference
-kt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V2-Lite-Chat-sft-amx.yaml
+kt_optimize_rule: examples/ktransformers/kt_optimize_rules/DeepSeek-V2-Lite-Chat-sft-amx.yaml
 cpu_infer: 32
 chunk_size: 8192
--- a/examples/ktransformers/infer_lora/deepseek3_kt.yaml
+++ b/examples/ktransformers/infer_lora/deepseek3_kt.yaml
@@ -1,9 +1,9 @@
 model_name_or_path: opensourcerelease/DeepSeek-V3-bf16
-template: deepseek
+template: deepseek3
 infer_backend: ktransformers  # choices: [huggingface, vllm, sglang, ktransformers]
 trust_remote_code: true

 use_kt: true # use KTransformers as LoRA sft backend to inference
-kt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml
+kt_optimize_rule: examples/ktransformers/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml
 cpu_infer: 32
 chunk_size: 8192
--- a/examples/ktransformers/infer_lora/deepseek3_lora_sft_kt.yaml
+++ b/examples/ktransformers/infer_lora/deepseek3_lora_sft_kt.yaml
@@ -1,10 +1,10 @@
 model_name_or_path: opensourcerelease/DeepSeek-V3-bf16
 adapter_name_or_path: saves/Kllama_deepseekV3
-template: deepseek
+template: deepseek3
 infer_backend: ktransformers  # choices: [huggingface, vllm, sglang, ktransformers]
 trust_remote_code: true

 use_kt: true # use KTransformers as LoRA sft backend to inference
-kt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml
+kt_optimize_rule: examples/ktransformers/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml
 cpu_infer: 32
 chunk_size: 8192
--- a/examples/ktransformers/infer_lora/qwen3moe_lora_sft_kt.yaml
+++ b/examples/ktransformers/infer_lora/qwen3moe_lora_sft_kt.yaml
@@ -5,6 +5,6 @@ infer_backend: ktransformers  # choices: [huggingface, vllm, sglang, ktransforme
 trust_remote_code: true

 use_kt: true # use KTransformers as LoRA sft backend to inference
-kt_optimize_rule: examples/kt_optimize_rules/Qwen3Moe-sft-amx.yaml
+kt_optimize_rule: examples/ktransformers/kt_optimize_rules/Qwen3Moe-sft-amx.yaml
 cpu_infer: 32
 chunk_size: 8192
--- a/examples/ktransformers/train_lora/deepseek2_lora_sft_kt.yaml
+++ b/examples/ktransformers/train_lora/deepseek2_lora_sft_kt.yaml
@@ -10,7 +10,7 @@ lora_rank: 8
 lora_target: all

 ### dataset
-dataset: identity
+dataset: identity, alpaca_en_demo
 template: deepseek
 cutoff_len: 2048
 max_samples: 100000
@@ -40,7 +40,7 @@ resume_from_checkpoint: null

 ### ktransformers
 use_kt: true # use KTransformers as LoRA sft backend
-kt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V2-Lite-Chat-sft-amx.yaml
+kt_optimize_rule: examples/ktransformers/kt_optimize_rules/DeepSeek-V2-Lite-Chat-sft-amx.yaml
 cpu_infer: 32
 chunk_size: 8192

--- a/examples/ktransformers/train_lora/deepseek3_lora_sft_kt.yaml
+++ b/examples/ktransformers/train_lora/deepseek3_lora_sft_kt.yaml
@@ -10,8 +10,8 @@ lora_rank: 8
 lora_target: all

 ### dataset
-dataset: identity
-template: deepseek
+dataset: identity, alpaca_en_demo
+template: deepseek3
 cutoff_len: 2048
 max_samples: 100000
 overwrite_cache: true
@@ -40,7 +40,7 @@ resume_from_checkpoint: null

 ### ktransformers
 use_kt: true # use KTransformers as LoRA sft backend
-kt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml
+kt_optimize_rule: examples/ktransformers/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml
 cpu_infer: 32
 chunk_size: 8192

--- a/examples/ktransformers/train_lora/qwen3moe_lora_sft_kt.yaml
+++ b/examples/ktransformers/train_lora/qwen3moe_lora_sft_kt.yaml
@@ -40,7 +40,7 @@ resume_from_checkpoint: null

 ### ktransformers
 use_kt: true # use KTransformers as LoRA sft backend
-kt_optimize_rule: examples/kt_optimize_rules/Qwen3Moe-sft-amx.yaml
+kt_optimize_rule: examples/ktransformers/kt_optimize_rules/Qwen3Moe-sft-amx.yaml
 cpu_infer: 32
 chunk_size: 8192

--- a/examples/requirements/liger-kernel.txt
+++ b/examples/requirements/liger-kernel.txt
@@ -1 +0,0 @@
-liger-kernel>=0.5.5
--- a/examples/v1/train_full/train_full_fsdp2.yaml
+++ b/examples/v1/train_full/train_full_fsdp2.yaml
@@ -0,0 +1,34 @@
+model: Qwen/Qwen3-0.6B
+trust_remote_code: true
+model_class: llm
+
+template: qwen3_nothink
+
+kernel_config:
+  name: auto
+  include_kernels: auto # choice: null/true/false/auto/kernel_id1,kernel_id2,kernel_id3, default is null
+
+quant_config: null
+
+dist_config:
+  name: fsdp2
+  dcp_path: null # /mnt/f/pretrain_models/Qwen3-0.6B-dcp
+
+init_config:
+  name: init_on_meta
+
+### data
+train_dataset: data/v1_sft_demo.yaml
+
+### training
+output_dir: outputs/test_fsdp2
+micro_batch_size: 1
+global_batch_size: 1
+cutoff_len: 2048
+learning_rate: 1.0e-4
+bf16: false
+max_steps: 10
+
+### sample
+sample_backend: hf
+max_new_tokens: 128
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,7 +30,6 @@ classifiers = [
    "License :: OSI Approved :: Apache Software License",
    "Operating System :: OS Independent",
    "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Programming Language :: Python :: 3.13",
@@ -41,10 +40,10 @@ dependencies = [
    "torch>=2.4.0",
    "torchvision>=0.19.0",
    "torchaudio>=2.4.0",
-    "transformers>=4.51.0,<=4.57.1,!=4.52.0,!=4.57.0",
+    "transformers>=4.51.0,<=5.0.0,!=4.52.0,!=4.57.0",
    "datasets>=2.16.0,<=4.0.0",
    "accelerate>=1.3.0,<=1.11.0",
-    "peft>=0.14.0,<=0.17.1",
+    "peft>=0.18.0,<=0.18.1",
    "trl>=0.18.0,<=0.24.0",
    "torchdata>=0.10.0,<=0.11.0",
    # gui
@@ -63,7 +62,7 @@ dependencies = [
    "hf-transfer",
    "safetensors",
    # python
-    "av",
+    "av>=10.0.0,<=16.0.0",
    "fire",
    "omegaconf",
    "packaging",
@@ -73,14 +72,9 @@ dependencies = [
    # api
    "uvicorn",
    "fastapi",
-    "sse-starlette"
+    "sse-starlette",
 ]

-[project.optional-dependencies]
-dev = ["pre-commit", "ruff", "pytest", "build"]
-metrics = ["nltk", "jieba", "rouge-chinese"]
-deepspeed = ["deepspeed>=0.10.0,<=0.16.9"]
-
 [project.scripts]
 llamafactory-cli = "llamafactory.cli:main"
 lmf = "llamafactory.cli:main"
--- a/examples/requirements/adam-mini.txt
+++ b/examples/requirements/adam-mini.txt
--- a/examples/requirements/apollo.txt
+++ b/examples/requirements/apollo.txt
--- a/examples/requirements/aqlm.txt
+++ b/examples/requirements/aqlm.txt
--- a/examples/requirements/badam.txt
+++ b/examples/requirements/badam.txt
--- a/examples/requirements/bitsandbytes.txt
+++ b/examples/requirements/bitsandbytes.txt
--- a/requirements/deepspeed.txt
+++ b/requirements/deepspeed.txt
@@ -0,0 +1 @@
+deepspeed>=0.10.0,<=0.18.4
--- a/requirements/dev.txt
+++ b/requirements/dev.txt
@@ -0,0 +1,4 @@
+pre-commit
+ruff
+pytest
+build
--- a/examples/requirements/eetq.txt
+++ b/examples/requirements/eetq.txt
--- a/examples/requirements/fp8-te.txt
+++ b/examples/requirements/fp8-te.txt
--- a/examples/requirements/fp8.txt
+++ b/examples/requirements/fp8.txt
--- a/examples/requirements/galore.txt
+++ b/examples/requirements/galore.txt
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Shanay Mehta	184304b5b4	[model] add liger kernel support for Qwen3-Next (#10176 ) Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>	2026-02-10 21:47:48 +08:00
Xue Yadong	d3ebd5678d	[model] support GLM-OCR SFT (#10183 )	2026-02-10 21:41:01 +08:00
浮梦	1d5e8ebcd0	[v1] init commit for v1 docs (#10145 ) Co-authored-by: frozenleaves <frozen@Mac.local> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: jiaqiw09 <jiaqiw960714@gmail.com> Co-authored-by: jiaqiw09 <60021713+jiaqiw09@users.noreply.github.com> Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2026-02-09 19:43:55 +08:00
Shanay Mehta	ea644d04ec	[model] support GLM-4.7-Flash SFT (#10173 ) Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>	2026-02-09 10:40:44 +08:00
Username_Full	92fa3df4c4	[trainer] add dpo/kto fsdp fsdp2 support (#10127 )	2026-02-04 23:27:12 +08:00
Hertz	8bedfafa4e	[model] support MiniCPM-o-4.5 (#10163 ) Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2026-02-04 23:21:27 +08:00
Yaowei Zheng	1a02717fa8	[assets] update readme (#10159 )	2026-02-03 19:11:15 +08:00
ゆり	e7cb145f5d	[logging] Fix race condition in LoggerHandler during multi-GPU training (#10156 ) Co-authored-by: yurekami <yurekami@users.noreply.github.com>	2026-02-03 11:14:07 +08:00
Hertz	b53d7037c2	[model] support youtu-vl model (#10152 )	2026-02-02 21:42:43 +08:00
浮梦	bf04ca6af8	[deps] adapt to transformers v5 (#10147 ) Co-authored-by: frozenleaves <frozen@Mac.local> Co-authored-by: hiyouga <hiyouga@buaa.edu.cn>	2026-02-02 12:07:19 +08:00
xvxuopop	762b480131	[feature] support using ray.remote to start distributed training. (#10109 )	2026-01-28 16:05:29 +08:00
Jewon Lee	9640f79ae5	[fix] add visual.pos_embed to Qwen3-VL visual model keys (#10139 )	2026-01-27 16:33:01 +08:00
jiaqiw09	7ef19eea00	[v0] Fix reward model training safetensors saving (#10137 )	2026-01-27 16:27:14 +08:00
浮梦	f9f11dcb97	[v1] support training with fsdp2 (#9773 ) Co-authored-by: frozenleaves <frozen@Mac.local> Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2026-01-25 19:41:58 +08:00
Pádraic Slattery	641bfdd482	chore: Update outdated GitHub Actions versions (#10123 )	2026-01-25 19:12:39 +08:00
Meng WANG	e70651ac58	[feat] support `all_exhausted_without_replacement` in datasets.interleave_datasets (#10112 )	2026-01-20 15:54:07 +08:00
Kingsley	db2f794f7b	[misc] update mcore related docker and mca supported models (#10114 )	2026-01-19 14:55:16 +08:00
jiaqiw09	44eadbda1c	[v1] fix kernel moe patch (#9867 )	2026-01-17 09:24:54 +08:00
浮梦	9829ae0a77	[ci] using mp to run kernel test (#9754 ) Co-authored-by: frozenleaves <frozen@Mac.local> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2026-01-13 19:43:59 +08:00
Yaowei Zheng	958b9c3468	[v1] add sft (#9752 )	2026-01-12 03:15:01 +08:00
Hertz	4d3621e3d3	[model] fixed&added Hunyuan models (#9750 )	2026-01-12 01:15:00 +08:00
Yaowei Zheng	a296723697	[v1] upgrade batching (#9751 ) Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-01-12 00:21:36 +08:00
Hertz	15b87f3125	[model] support HY-MT model (#9746 ) Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2026-01-11 16:25:56 +08:00
Yaowei Zheng	9f73a6eb23	[deps] fix package (#9745 )	2026-01-10 04:27:53 +08:00
Yaowei Zheng	b2effbd77c	[v1] add batch generator (#9744 )	2026-01-10 04:24:09 +08:00
Yaowei Zheng	d7d734d54c	[misc] fix fp8 (#9742 )	2026-01-09 16:17:26 +08:00
Yaowei Zheng	8abb8fb533	[v1] use async streamer (#9741 )	2026-01-09 16:12:07 +08:00
Yaowei Zheng	766d5ae6ad	[ci] fix workflow (#9738 )	2026-01-09 16:12:07 +08:00
Yaowei Zheng	5cccaeec82	[model] clean obsolete models (#9736 )	2026-01-09 16:12:07 +08:00
Jackey	5fb5d7ebd3	[model] support for microsoft's Phi-4-mini (#9734 )	2026-01-09 12:24:45 +08:00
Peilin Li	03a70ba8dd	[fix] correct ktransformers example config paths and templates (#9732 )	2026-01-08 10:52:50 +08:00
Vo Van Phuc	5cfd804b59	[refactor] rename lfm template to lfm2 and add LFM 2.5 to README (#9731 )	2026-01-07 19:25:04 +08:00
Yaowei Zheng	4c1eb922e2	[misc] fix parser (#9730 )	2026-01-07 17:36:08 +08:00
Vo Van Phuc	958fb523a2	[model] support LiquidAI's LFM2.5-VL vision-language model (#9729 )	2026-01-07 17:20:29 +08:00
Vo Van Phuc	b4e051bea4	[model] support for LiquidAI's LFM2.5 (Liquid Foundation Models) (#9726 )	2026-01-07 14:14:47 +08:00
浮梦	d43e1007e8	[ci] improve cuda ci cache (#9725 ) Co-authored-by: frozenleaves <frozen@Mac.local>	2026-01-07 12:34:40 +08:00
Xunpeng Xiao	f89d9367e5	[assets] update README.md (#9724 )	2026-01-07 12:11:50 +08:00
Yaowei Zheng	d22de0d4bf	[v1] add renderer ut (#9722 )	2026-01-07 02:06:07 +08:00
Yaowei Zheng	ea0b4e2466	[v1] add cli sampler (#9721 )	2026-01-06 23:31:27 +08:00
yanglele	e944dc442c	[feature] add support for EAFT loss (#9720 ) Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-01-06 23:07:12 +08:00
Xunpeng Xiao	68119e5522	[misc] Add a PyTorch version warning for Conv3D. (#9715 )	2026-01-05 13:26:29 +08:00
Yaowei Zheng	f60a6e3d01	[v1] add init plugin (#9716 )	2026-01-04 20:51:46 +08:00
jiaqiw09	81b8a50aa5	[deps] Update pyproject.toml and requirements (#9714 ) Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2026-01-04 19:52:16 +08:00
Yaowei Zheng	8600530002	[misc] lint (#9710 )	2026-01-04 13:47:56 +08:00
Hertz	9ae62c6fc0	[model] support Youtu-LLM-2B (#9707 )	2026-01-04 13:17:57 +08:00
Xunpeng Xiao	0087bc253b	[misc] Compatible with an empty architectures field in config.json (#9709 )	2026-01-04 12:11:35 +08:00
Santosh Bhavani	355d5c5e5a	[fix] fp8: add Transformer Engine backend support (#9705 ) Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2026-01-01 10:18:02 +08:00
Yaowei Zheng	6fe6bd290b	[misc] set dev version (#9703 )	2025-12-31 23:41:40 +08:00