[ci] update docker workflow (#9231 )

[misc] fix moe models (#9230 )
[ci] Change macOS version (#9229 )
2025-12-27 09:10:35 +08:00 · 2025-10-05 02:50:55 +08:00 · 2025-10-05 02:41:02 +08:00 · 2025-10-05 02:18:30 +08:00 · 2025-10-04 14:12:37 +08:00 · 2025-10-02 15:59:19 +08:00
136 changed files with 52618 additions and 1061 deletions
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -1 +1,8 @@
 blank_issues_enabled: false
+contact_links:
+  - name: 📚 FAQs | 常见问题
+    url: https://github.com/hiyouga/LLaMA-Factory/issues/4614
+    about: Reading in advance is recommended | 建议提前阅读
+  - name: Discussions | 讨论区
+    url: https://github.com/hiyouga/LLaMA-Factory/discussions
+    about: Please ask fine-tuning questions here | 请在这里讨论训练问题
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -18,13 +18,23 @@ on:
      - "requirements.txt"
      - "docker/**"
      - ".github/workflows/*.yml"
+  release:
+    types:
+      - published

 jobs:
  build:
+    strategy:
+      fail-fast: false
+      matrix:
+        device:
+          - "cuda"
+          - "npu"
+
    runs-on: ubuntu-latest

    concurrency:
-      group: ${{ github.workflow }}-${{ github.ref }}
+      group: ${{ github.workflow }}-${{ github.ref }}-${{ matrix.device }}
      cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}

    environment:
@@ -33,27 +43,48 @@ jobs:

    steps:
      - name: Free up disk space
-        run: |
-          df -h
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf /opt/ghc
-          sudo rm -rf /opt/hostedtoolcache
-          df -h
+        uses: jlumbroso/free-disk-space@v1.3.1
+        with:
+          tool-cache: true
+          docker-images: false

      - name: Checkout
        uses: actions/checkout@v4

+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+
+      - name: Get llamafactory version
+        id: version
+        run: |
+          if [ "${{ github.event_name }}" = "release" ]; then
+            echo "tag=$(python setup.py --version)" >> "$GITHUB_OUTPUT"
+          else
+            echo "tag=latest" >> "$GITHUB_OUTPUT"
+          fi
+
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3

      - name: Login to Docker Hub
-        if: github.event_name != 'pull_request'
+        if: ${{ github.event_name != 'pull_request' }}
        uses: docker/login-action@v3
        with:
          username: ${{ vars.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_TOKEN }}

-      - name: Build and push Docker image
+      - name: Login to Quay
+        if: ${{ github.event_name != 'pull_request' && matrix.device == 'npu' }}
+        uses: docker/login-action@v3
+        with:
+          registry: quay.io
+          username: ${{ vars.QUAY_ASCEND_USERNAME }}
+          password: ${{ secrets.QUAY_ASCEND_TOKEN }}
+
+      - name: Build and push Docker image (CUDA)
+        if: ${{ matrix.device == 'cuda' }}
        uses: docker/build-push-action@v6
        with:
          context: .
@@ -61,6 +92,21 @@ jobs:
          build-args: |
            EXTRAS=metrics,deepspeed,liger-kernel
          push: ${{ github.event_name != 'pull_request' }}
-          tags: docker.io/hiyouga/llamafactory:latest
+          tags: |
+            docker.io/hiyouga/llamafactory:${{ steps.version.outputs.tag }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+
+      - name: Build and push Docker image (NPU)
+        if: ${{ matrix.device == 'npu' }}
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          platforms: linux/amd64,linux/arm64
+          file: ./docker/docker-npu/Dockerfile
+          push: ${{ github.event_name != 'pull_request' }}
+          tags: |
+            docker.io/hiyouga/llamafactory:${{ steps.version.outputs.tag }}-npu-a2
+            quay.io/ascend/llamafactory:${{ steps.version.outputs.tag }}-npu-a2
          cache-from: type=gha
          cache-to: type=gha,mode=max
--- a/.github/workflows/label_issue.yml
+++ b/.github/workflows/label_issue.yml
@@ -19,7 +19,7 @@ jobs:
          ISSUE_TITLE: ${{ github.event.issue.title }}
        run: |
          LABEL=""
-          NPU_KEYWORDS=(npu huawei ascend 华为 昇腾)
+          NPU_KEYWORDS=(npu huawei ascend 华为 昇腾 910)
          ISSUE_TITLE_LOWER=$(echo $ISSUE_TITLE | tr '[:upper:]' '[:lower:]')
          for KEYWORD in ${NPU_KEYWORDS[@]}; do
            if [[ $ISSUE_TITLE_LOWER == *$KEYWORD* ]] && [[ $ISSUE_TITLE_LOWER != *input* ]]; then
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -6,14 +6,14 @@ on:
    branches:
      - "main"
    paths:
-      - "**.py"
+      - "**/*.py"
      - "requirements.txt"
      - ".github/workflows/*.yml"
  pull_request:
    branches:
      - "main"
    paths:
-      - "**.py"
+      - "**/*.py"
      - "requirements.txt"
      - ".github/workflows/*.yml"

@@ -30,19 +30,19 @@ jobs:
        os:
          - "ubuntu-latest"
          - "windows-latest"
-          - "macos-13"
+          - "macos-latest"
        transformers:
          - null
        include:  # test backward compatibility
-          - python: "3.9"
-            os: "ubuntu-latest"
-            transformers: "4.45.0"
          - python: "3.9"
            os: "ubuntu-latest"
            transformers: "4.49.0"
          - python: "3.9"
            os: "ubuntu-latest"
            transformers: "4.51.0"
+          - python: "3.9"
+            os: "ubuntu-latest"
+            transformers: "4.53.0"

    runs-on: ${{ matrix.os }}

@@ -75,6 +75,12 @@ jobs:
        run: |
          python -m pip install "transformers==${{ matrix.transformers }}"

+      - name: Update accelerate to avoid mac os ci errors (before accelerate 1.11.0)
+        if: ${{ matrix.os == 'macos-latest' }}
+        run: |
+          python -m pip uninstall -y accelerate
+          python -m pip install "git+https://github.com/huggingface/accelerate.git"
+
      - name: Cache files
        id: hf-hub-cache
        uses: actions/cache@v4
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
    hooks:
    -   id: check-ast
    -   id: check-added-large-files
@@ -15,13 +15,13 @@ repos:
        args: ['--branch', 'main']

 -   repo: https://github.com/asottile/pyupgrade
-    rev: v3.17.0
+    rev: v3.20.0
    hooks:
    -   id: pyupgrade
-        args: [--py38-plus]
+        args: [--py39-plus]

 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.6.9
+    rev: v0.13.2
    hooks:
    -   id: ruff
        args: [--fix]
--- a/README.md
+++ b/README.md
@@ -5,16 +5,16 @@
 [![GitHub contributors](https://img.shields.io/github/contributors/hiyouga/LLaMA-Factory?color=orange)](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors)
 [![GitHub workflow](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml/badge.svg)](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml)
 [![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
-[![Citation](https://img.shields.io/badge/citation-614-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
+[![Citation](https://img.shields.io/badge/citation-840-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
 [![Docker Pulls](https://img.shields.io/docker/pulls/hiyouga/llamafactory)](https://hub.docker.com/r/hiyouga/llamafactory/tags)

 [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
-[![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
-[![GitCode](https://gitcode.com/zhengyaowei/LLaMA-Factory/star/badge.svg)](https://gitcode.com/zhengyaowei/LLaMA-Factory)
+[![Discord](assets/thirdparty/discord.svg)](https://discord.gg/rKfvV9r9FK)

-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
-[![Open in DSW](https://gallery.pai-ml.com/assets/open-in-dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
-[![Open in Alaya](assets/alaya_new.svg)](https://docs.alayanew.com/docs/documents/newActivities/llamafactory/?utm_source=LLaMA-Factory)
+[![Open in Colab](assets/thirdparty/colab.svg)](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
+[![Open in DSW](assets/thirdparty/dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
+[![Open in Lab4ai](assets/thirdparty/lab4ai.svg)](https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory)
+[![Open in Online](assets/thirdparty/online.svg)](https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory)
 [![Open in Spaces](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
 [![Open in Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
 [![Open in Novita](https://img.shields.io/badge/Novita-Deploy%20Template-blue)](https://novita.ai/templates-library/105981?sharer=88115474-394e-4bda-968e-b88e123d0c47)
@@ -25,13 +25,8 @@

 ### Supporters ❤️

-<a href="https://warp.dev/llama-factory">
-    <img alt="Warp sponsorship" width="400" src="https://github.com/user-attachments/assets/ab8dd143-b0fd-4904-bdc5-dd7ecac94eae">
-</a>
-
-#### [Warp, the agentic terminal for developers](https://warp.dev/llama-factory)
-
-[Available for MacOS, Linux, & Windows](https://warp.dev/llama-factory)
+| <div style="text-align: center;"><a href="https://warp.dev/llama-factory"><img alt="Warp sponsorship" width="400" src="assets/sponsors/warp.jpg"></a><br><a href="https://warp.dev/llama-factory" style="font-size:larger;">Warp, the agentic terminal for developers</a><br><a href="https://warp.dev/llama-factory">Available for MacOS, Linux, & Windows</a> | <a href="https://serpapi.com"><img alt="SerpAPI sponsorship" width="250" src="assets/sponsors/serpapi.svg"> </a> |
+| ---- | ---- |

 ----

@@ -41,7 +36,7 @@

 </div>

-👋 Join our [WeChat group](assets/wechat.jpg), [NPU user group](assets/wechat_npu.jpg) or [Alaya NeW user group](assets/wechat_alaya.png).
+👋 Join our [WeChat](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/main.jpg), [NPU](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/npu.jpg), [Lab4AI](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/lab4ai.jpg), [LLaMA Factory Online](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/online.jpg) user group.

 \[ English | [中文](README_zh.md) \]

@@ -51,11 +46,14 @@ https://github.com/user-attachments/assets/3991a3a8-4276-4d30-9cab-4cb0c4b9b99e

 Choose your path:

- **Documentation**: https://llamafactory.readthedocs.io/en/latest/
+- **Documentation (WIP)**: https://llamafactory.readthedocs.io/en/latest/
+- **Documentation (AMD GPU)**: https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/fine_tune/llama_factory_llama3.html
 - **Colab (free)**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
 - **Local machine**: Please refer to [usage](#getting-started)
 - **PAI-DSW (free trial)**: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
 - **Alaya NeW (cloud GPU deal)**: https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory
+- **Official Course**: https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory
+- **LLaMA Factory Online**: https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory

 > [!NOTE]
 > Except for the above links, all other websites are unauthorized third-party websites. Please carefully use them.
@@ -74,6 +72,7 @@ Choose your path:
  - [Data Preparation](#data-preparation)
  - [Quickstart](#quickstart)
  - [Fine-Tuning with LLaMA Board GUI](#fine-tuning-with-llama-board-gui-powered-by-gradio)
+  - [LLaMA Factory Online](#llama-factory-online)
  - [Build Docker](#build-docker)
  - [Deploy with OpenAI-style API and vLLM](#deploy-with-openai-style-api-and-vllm)
  - [Download from ModelScope Hub](#download-from-modelscope-hub)
@@ -90,7 +89,7 @@ Choose your path:
 - **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, DeepSeek, Yi, Gemma, ChatGLM, 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), DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and PiSSA.
+- **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.
 - **Practical tricks**: [FlashAttention-2](https://github.com/Dao-AILab/flash-attention), [Unsloth](https://github.com/unslothai/unsloth), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), RoPE scaling, NEFTune and rsLoRA.
 - **Wide tasks**: Multi-turn dialogue, tool using, image understanding, visual grounding, video recognition, audio understanding, etc.
 - **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, [SwanLab](https://github.com/SwanHubX/SwanLab), etc.
@@ -99,27 +98,41 @@ Choose your path:
 ### Day-N Support for Fine-Tuning Cutting-Edge Models

 | Support Date | Model Name                                                           |
-| ------------ | ------------------------------------------------------------ |
-| Day 0        | Qwen3 / Qwen2.5-VL / Gemma 3 / InternLM 3 / MiniCPM-o-2.6    |
+| ------------ | -------------------------------------------------------------------- |
+| Day 0        | Qwen3 / Qwen2.5-VL / Gemma 3 / GLM-4.1V / InternLM 3 / MiniCPM-o-2.6 |
 | Day 1        | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4               |

 ## Blogs

- [Fine-tune Qwen2.5-VL for Autonomous Driving using LLaMA-Factory](https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory) (Chinese)
+- 💡 [Easy Dataset × LLaMA Factory: Enabling LLMs to Efficiently Learn Domain Knowledge](https://buaa-act.feishu.cn/wiki/GVzlwYcRFiR8OLkHbL6cQpYin7g) (English)
+- [Fine-tune a mental health LLM using LLaMA-Factory](https://www.lab4ai.cn/project/detail?id=25cce32ec131497b9e06a93336a0817f&type=project&utm_source=LLaMA-Factory) (Chinese)
+- [Fine-tune GPT-OSS for Role-Playing using LLaMA-Factory](https://docs.llamafactory.com.cn/docs/documents/best-practice/gptroleplay/?utm_source=LLaMA-Factory) (Chinese)
+- [A One-Stop Code-Free Model Reinforcement Learning and Deployment Platform based on LLaMA-Factory and EasyR1](https://aws.amazon.com/cn/blogs/china/building-llm-model-hub-based-on-llamafactory-and-easyr1/) (Chinese)
 - [How Apoidea Group enhances visual information extraction from banking documents with multimodal models using LLaMA-Factory on Amazon SageMaker HyperPod](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/) (English)
- [Easy Dataset × LLaMA Factory: Enabling LLMs to Efficiently Learn Domain Knowledge](https://buaa-act.feishu.cn/wiki/GVzlwYcRFiR8OLkHbL6cQpYin7g) (English)

 <details><summary>All Blogs</summary>

+- [Fine-tune Llama3.1-70B for Medical Diagnosis using LLaMA-Factory](https://docs.alayanew.com/docs/documents/bestPractice/bigModel/llama70B/?utm_source=LLaMA-Factory) (Chinese)
+- [Fine-tune Qwen2.5-VL for Autonomous Driving using LLaMA-Factory](https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory) (Chinese)
 - [LLaMA Factory: Fine-tuning the DeepSeek-R1-Distill-Qwen-7B Model for News Classifier](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b) (Chinese)
 - [A One-Stop Code-Free Model Fine-Tuning \& Deployment Platform based on SageMaker and LLaMA-Factory](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/) (Chinese)
 - [LLaMA Factory Multi-Modal Fine-Tuning Practice: Fine-Tuning Qwen2-VL for Personal Tourist Guide](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl) (Chinese)
- [LLaMA Factory: Fine-tuning the LLaMA3 Model for Role-Playing](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) (Chinese)
+- [LLaMA Factory: Fine-tuning Llama3 for Role-Playing](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) (Chinese)

 </details>

 ## Changelog

+[25/08/22] We supported **[OFT](https://arxiv.org/abs/2306.07280)** and **[OFTv2](https://arxiv.org/abs/2506.19847)**. See [examples](examples/README.md) for usage.
+
+[25/08/20] We supported fine-tuning the **[Intern-S1-mini](https://huggingface.co/internlm/Intern-S1-mini)** models. See [PR #8976](https://github.com/hiyouga/LLaMA-Factory/pull/8976) to get started.
+
+[25/08/06] We supported fine-tuning the **[GPT-OSS](https://github.com/openai/gpt-oss)** models. See [PR #8826](https://github.com/hiyouga/LLaMA-Factory/pull/8826) to get started.
+
+<details><summary>Full Changelog</summary>
+
+[25/07/02] We supported fine-tuning the **[GLM-4.1V-9B-Thinking](https://github.com/THUDM/GLM-4.1V-Thinking)** model.
+
 [25/04/28] We supported fine-tuning the **[Qwen3](https://qwenlm.github.io/blog/qwen3/)** model family.

 [25/04/21] We supported the **[Muon](https://github.com/KellerJordan/Muon)** optimizer. See [examples](examples/README.md) for usage. Thank [@tianshijing](https://github.com/tianshijing)'s PR.
@@ -130,8 +143,6 @@ Choose your path:

 [25/04/06] We supported fine-tuning the **[Llama 4](https://ai.meta.com/blog/llama-4-multimodal-intelligence/)** model. See [PR #7611](https://github.com/hiyouga/LLaMA-Factory/pull/7611) to get started.

-<details><summary>Full Changelog</summary>
-
 [25/03/31] We supported fine-tuning the **[Qwen2.5 Omni](https://qwenlm.github.io/blog/qwen2.5-omni/)** model. See [PR #7537](https://github.com/hiyouga/LLaMA-Factory/pull/7537) to get started.

 [25/03/15] We supported **[SGLang](https://github.com/sgl-project/sglang)** as inference backend. Try `infer_backend: sglang` to accelerate inference.
@@ -252,7 +263,7 @@ Choose your path:
 ## Supported Models

 | Model                                                             | Model size                       | Template             |
-| ----------------------------------------------------------------- | -------------------------------- | ------------------- |
+| ----------------------------------------------------------------- | -------------------------------- | -------------------- |
 | [Baichuan 2](https://huggingface.co/baichuan-inc)                 | 7B/13B                           | baichuan2            |
 | [BLOOM/BLOOMZ](https://huggingface.co/bigscience)                 | 560M/1.1B/1.7B/3B/7.1B/176B      | -                    |
 | [ChatGLM3](https://huggingface.co/THUDM)                          | 6B                               | chatglm3             |
@@ -260,17 +271,25 @@ Choose your path:
 | [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai)         | 7B/16B/67B/236B                  | deepseek             |
 | [DeepSeek 2.5/3](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  |
 | [Falcon](https://huggingface.co/tiiuae)                           | 7B/11B/40B/180B                  | falcon               |
-| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google)          | 2B/7B/9B/27B                     | gemma               |
-| [Gemma 3](https://huggingface.co/google)                          | 1B/4B/12B/27B                    | gemma3/gemma (1B)   |
-| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM)           | 9B/32B                           | glm4/glmz1          |
+| [Falcon-H1](https://huggingface.co/tiiuae)                        | 0.5B/1.5B/3B/7B/34B              | 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       |
+| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/zai-org)         | 9B/32B                           | glm4/glmz1           |
+| [GLM-4.1V](https://huggingface.co/zai-org)                        | 9B                               | glm4v                |
+| [GLM-4.5/GLM-4.5V](https://huggingface.co/zai-org)                | 106B/355B                        | glm4_moe/glm4v_moe   |
 | [GPT-2](https://huggingface.co/openai-community)                  | 0.1B/0.4B/0.8B/1.5B              | -                    |
+| [GPT-OSS](https://huggingface.co/openai)                          | 20B/120B                         | gpt                  |
 | [Granite 3.0-3.3](https://huggingface.co/ibm-granite)             | 1B/2B/3B/8B                      | granite3             |
+| [Granite 4](https://huggingface.co/ibm-granite)                   | 7B                               | granite4             |
 | [Hunyuan](https://huggingface.co/tencent/)                        | 7B                               | hunyuan              |
 | [Index](https://huggingface.co/IndexTeam)                         | 1.9B                             | index                |
 | [InternLM 2-3](https://huggingface.co/internlm)                   | 7B/8B/20B                        | intern2              |
-| [InternVL 2.5-3](https://huggingface.co/OpenGVLab)                | 1B/2B/8B/14B/38B/78B             | intern_vl           |
+| [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            |
 | [Kimi-VL](https://huggingface.co/moonshotai)                      | 16B                              | kimi_vl              |
+| [Ling 2.0 (mini/flash)](https://huggingface.co/inclusionAI)       | 16B/100B                         | bailing_v2           |
 | [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               |
@@ -280,7 +299,7 @@ Choose your path:
 | [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                               | mimo                 |
-| [MiniCPM](https://huggingface.co/openbmb)                         | 0.5B/1B/2B/4B/8B                 | cpm/cpm3/cpm4       |
+| [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  |
 | [Ministral/Mistral-Nemo](https://huggingface.co/mistralai)        | 8B/12B                           | ministral            |
 | [Mistral/Mixtral](https://huggingface.co/mistralai)               | 7B/8x7B/8x22B                    | mistral              |
@@ -293,11 +312,13 @@ Choose your path:
 | [Phi-4](https://huggingface.co/microsoft)                         | 14B                              | 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                 |
-| [Qwen3 (MoE)](https://huggingface.co/Qwen)                        | 0.6B/1.7B/4B/8B/14B/32B/235B     | qwen3               |
+| [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           |
+| [Qwen3-Omni](https://huggingface.co/Qwen)*                        | 30B                              | qwen3_omni           |
 | [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen)            | 2B/3B/7B/32B/72B                 | qwen2_vl             |
-| [Seed Coder](https://huggingface.co/ByteDance-Seed)               | 8B                               | seed_coder          |
+| [Qwen3-VL](https://huggingface.co/Qwen)*                          | 235B                             | qwen3_vl             |
+| [Seed (OSS/Coder)](https://huggingface.co/ByteDance-Seed)         | 8B/36B                           | seed_oss/seed_coder  |
 | [Skywork o1](https://huggingface.co/Skywork)                      | 8B                               | skywork_o1           |
 | [StarCoder 2](https://huggingface.co/bigcode)                     | 3B/7B/15B                        | -                    |
 | [TeleChat2](https://huggingface.co/Tele-AI)                       | 3B/7B/35B/115B                   | telechat2            |
@@ -321,16 +342,16 @@ You also can add a custom chat template to [template.py](src/llamafactory/data/t

 ## Supported Training Approaches

-| Approach               |     Full-tuning    |    Freeze-tuning   |       LoRA         |       QLoRA        |
-| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
-| Pre-Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| Reward Modeling        | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| PPO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| DPO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| KTO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| ORPO Training          | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| SimPO Training         | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| Approach               |     Full-tuning    |    Freeze-tuning   |       LoRA         |       QLoRA        |        OFT         |        QOFT        |
+| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ |
+| Pre-Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| Reward Modeling        | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| PPO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| DPO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| KTO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| ORPO Training          | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| SimPO Training         | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |

 > [!TIP]
 > The implementation details of PPO can be found in [this blog](https://newfacade.github.io/notes-on-reinforcement-learning/17-ppo-trl.html).
@@ -348,6 +369,11 @@ You also can add a custom chat template to [template.py](src/llamafactory/data/t
 - [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
 - [FineWeb (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
 - [FineWeb-Edu (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
+- [CCI3-HQ (zh)](https://huggingface.co/datasets/BAAI/CCI3-HQ)
+- [CCI3-Data (zh)](https://huggingface.co/datasets/BAAI/CCI3-Data)
+- [CCI4.0-M2-Base-v1 (en&zh)](https://huggingface.co/datasets/BAAI/CCI4.0-M2-Base-v1)
+- [CCI4.0-M2-CoT-v1 (en&zh)](https://huggingface.co/datasets/BAAI/CCI4.0-M2-CoT-v1)
+- [CCI4.0-M2-Extra-v1 (en&zh)](https://huggingface.co/datasets/BAAI/CCI4.0-M2-Extra-v1)
 - [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
 - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)

@@ -385,6 +411,7 @@ You also can add a custom chat template to [template.py](src/llamafactory/data/t
 - [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
 - [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
 - [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
+- [Infinity Instruct (zh)](https://huggingface.co/datasets/BAAI/Infinity-Instruct)
 - [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
 - [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
 - [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
@@ -443,7 +470,7 @@ huggingface-cli login
 | python       | 3.9     | 3.10      |
 | torch        | 2.0.0   | 2.6.0     |
 | torchvision  | 0.15.0  | 0.21.0    |
-| transformers | 4.45.0  | 4.50.0    |
+| transformers | 4.49.0  | 4.50.0    |
 | datasets     | 2.16.0  | 3.2.0     |
 | accelerate   | 0.34.0  | 1.2.1     |
 | peft         | 0.14.0  | 0.15.1    |
@@ -462,13 +489,13 @@ huggingface-cli login
 \* *estimated*

 | Method                              | Bits |   7B  |  14B  |  30B  |   70B  |   `x`B  |
-| ------------------------------- | ---- | ----- | ----- | ----- | ------ | ------- |
+| ----------------------------------- | ---- | ----- | ----- | ----- | ------ | ------- |
 | Full (`bf16` or `fp16`)             |  32  | 120GB | 240GB | 600GB | 1200GB | `18x`GB |
 | Full (`pure_bf16`)                  |  16  |  60GB | 120GB | 300GB |  600GB |  `8x`GB |
-| Freeze/LoRA/GaLore/APOLLO/BAdam |  16  |  16GB |  32GB |  64GB |  160GB |  `2x`GB |
-| QLoRA                           |   8  |  10GB |  20GB |  40GB |   80GB |   `x`GB |
-| QLoRA                           |   4  |   6GB |  12GB |  24GB |   48GB | `x/2`GB |
-| QLoRA                           |   2  |   4GB |   8GB |  16GB |   24GB | `x/4`GB |
+| Freeze/LoRA/GaLore/APOLLO/BAdam/OFT |  16  |  16GB |  32GB |  64GB |  160GB |  `2x`GB |
+| QLoRA / QOFT                        |   8  |  10GB |  20GB |  40GB |   80GB |   `x`GB |
+| QLoRA / QOFT                        |   4  |   6GB |  12GB |  24GB |   48GB | `x/2`GB |
+| QLoRA / QOFT                        |   2  |   4GB |   8GB |  16GB |   24GB | `x/4`GB |

 ## Getting Started

@@ -485,7 +512,7 @@ cd LLaMA-Factory
 pip install -e ".[torch,metrics]" --no-build-isolation
 ```

-Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, aqlm, vllm, sglang, galore, apollo, badam, adam-mini, qwen, minicpm_v, modelscope, openmind, swanlab, dev
+Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, aqlm, vllm, sglang, galore, apollo, badam, adam-mini, qwen, minicpm_v, openmind, swanlab, dev

 #### Install from Docker Image

@@ -620,7 +647,7 @@ Please refer to [data/README.md](data/README.md) for checking the details about
 > [!NOTE]
 > Please update `data/dataset_info.json` to use your custom dataset.

-You can also use **[Easy Dataset](https://github.com/ConardLi/easy-dataset)** or **[GraphGen](https://github.com/open-sciencelab/GraphGen)** to create synthetic data for fine-tuning.
+You can also use **[Easy Dataset](https://github.com/ConardLi/easy-dataset)**, **[DataFlow](https://github.com/OpenDCAI/DataFlow)** and **[GraphGen](https://github.com/open-sciencelab/GraphGen)** to create synthetic data for fine-tuning.

 ### Quickstart

@@ -645,6 +672,10 @@ See [examples/README.md](examples/README.md) for advanced usage (including distr
 llamafactory-cli webui
 ```

+### LLaMA Factory Online
+
+Read our [documentation](https://docs.llamafactory.com.cn/docs/documents/quickstart/getstarted/?utm_source=LLaMA-Factory).
+
 ### Build Docker

 For CUDA users:
--- a/README_zh.md
+++ b/README_zh.md
@@ -5,16 +5,16 @@
 [![GitHub contributors](https://img.shields.io/github/contributors/hiyouga/LLaMA-Factory?color=orange)](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors)
 [![GitHub workflow](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml/badge.svg)](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml)
 [![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
-[![Citation](https://img.shields.io/badge/citation-614-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
+[![Citation](https://img.shields.io/badge/citation-840-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
 [![Docker Pulls](https://img.shields.io/docker/pulls/hiyouga/llamafactory)](https://hub.docker.com/r/hiyouga/llamafactory/tags)

 [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
-[![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
-[![GitCode](https://gitcode.com/zhengyaowei/LLaMA-Factory/star/badge.svg)](https://gitcode.com/zhengyaowei/LLaMA-Factory)
+[![Discord](assets/thirdparty/discord.svg)](https://discord.gg/rKfvV9r9FK)

-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)
-[![Open in DSW](https://gallery.pai-ml.com/assets/open-in-dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
-[![Open in Alaya](assets/alaya_new.svg)](https://docs.alayanew.com/docs/documents/newActivities/llamafactory/?utm_source=LLaMA-Factory)
+[![Open in Colab](assets/thirdparty/colab.svg)](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)
+[![Open in DSW](assets/thirdparty/dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
+[![Open in Lab4ai](assets/thirdparty/lab4ai.svg)](https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory)
+[![Open in Online](assets/thirdparty/online.svg)](https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory)
 [![Open in Spaces](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
 [![Open in Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
 [![Open in Novita](https://img.shields.io/badge/Novita-Deploy%20Template-blue)](https://novita.ai/templates-library/105981?sharer=88115474-394e-4bda-968e-b88e123d0c47)
@@ -25,13 +25,8 @@

 ### 赞助商 ❤️

-<a href="https://warp.dev/llama-factory">
-    <img alt="Warp sponsorship" width="400" src="https://github.com/user-attachments/assets/ab8dd143-b0fd-4904-bdc5-dd7ecac94eae">
-</a>
-
-#### [Warp，面向开发者的智能终端](https://warp.dev/llama-factory)
-
-[适用于 MacOS、Linux 和 Windows](https://warp.dev/llama-factory)
+| <div style="text-align: center;"><a href="https://warp.dev/llama-factory"><img alt="Warp sponsorship" width="400" src="assets/sponsors/warp.jpg"></a><br><a href="https://warp.dev/llama-factory" style="font-size:larger;">Warp，面向开发者的智能终端</a><br><a href="https://warp.dev/llama-factory">适用于 MacOS、Linux 和 Windows</a> | <a href="https://serpapi.com"><img alt="SerpAPI sponsorship" width="250" src="assets/sponsors/serpapi.svg"> </a> |
+| ---- | ---- |

 ----

@@ -41,7 +36,7 @@

 </div>

-👋 加入我们的[微信群](assets/wechat.jpg)、[NPU 用户群](assets/wechat_npu.jpg)或 [九章智算云算力优惠群](assets/wechat_alaya.png)。
+👋 加入我们的[微信群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/main.jpg)、[NPU 用户群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/npu.jpg)、[大模型实验室群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/lab4ai.jpg) 或 [LLaMA Factory Online 用户群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/online.png)。

 \[ [English](README.md) | 中文 \]

@@ -52,12 +47,15 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 选择你的打开方式：

 - **入门教程**：https://zhuanlan.zhihu.com/p/695287607
+- **微调视频教程**：https://www.bilibili.com/video/BV1djgRzxEts/
 - **框架文档**：https://llamafactory.readthedocs.io/zh-cn/latest/
 - **框架文档（昇腾 NPU）**：https://ascend.github.io/docs/sources/llamafactory/
 - **Colab（免费）**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
 - **本地机器**：请见[如何使用](#如何使用)
 - **PAI-DSW（免费试用）**：https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
 - **九章智算云（算力优惠活动）**：https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory
+- **官方课程**：https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory
+- **LLaMA Factory Online（在线微调）**：https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory

 > [!NOTE]
 > 除上述链接以外的其他网站均为未经许可的第三方网站，请小心甄别。
@@ -76,6 +74,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
  - [数据准备](#数据准备)
  - [快速开始](#快速开始)
  - [LLaMA Board 可视化微调](#llama-board-可视化微调由-gradio-驱动)
+  - [LLaMA Factory Online 在线微调](#llama-factory-online-在线微调)
  - [构建 Docker](#构建-docker)
  - [利用 vLLM 部署 OpenAI API](#利用-vllm-部署-openai-api)
  - [从魔搭社区下载](#从魔搭社区下载)
@@ -92,7 +91,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 - **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Qwen2-VL、DeepSeek、Yi、Gemma、ChatGLM、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)、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ 和 PiSSA。
+- **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。
 - **实用技巧**：[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)、[Unsloth](https://github.com/unslothai/unsloth)、[Liger Kernel](https://github.com/linkedin/Liger-Kernel)、RoPE scaling、NEFTune 和 rsLoRA。
 - **广泛任务**：多轮对话、工具调用、图像理解、视觉定位、视频识别和语音理解等等。
 - **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow、[SwanLab](https://github.com/SwanHubX/SwanLab) 等等。
@@ -101,27 +100,41 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 ### 最新模型的 Day-N 微调适配

 | 适配时间      | 模型名称                                                              |
-| ------------ | ------------------------------------------------------------ |
-| Day 0        | Qwen3 / Qwen2.5-VL / Gemma 3 / InternLM 3 / MiniCPM-o-2.6    |
+| ------------ | -------------------------------------------------------------------- |
+| Day 0        | Qwen3 / Qwen2.5-VL / Gemma 3 / GLM-4.1V / InternLM 3 / MiniCPM-o-2.6 |
 | Day 1        | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4               |

 ## 官方博客

- [使用 LLaMA-Factory 微调 Qwen2.5-VL 实现自动驾驶场景微调](https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory)（中文）
+- 💡 [Easy Dataset × LLaMA Factory: 让大模型高效学习领域知识](https://buaa-act.feishu.cn/wiki/KY9xwTGs1iqHrRkjXBwcZP9WnL9)（中文）
+- [使用 LLaMA-Factory 微调心理健康大模型](https://www.lab4ai.cn/project/detail?id=25cce32ec131497b9e06a93336a0817f&type=project&utm_source=LLaMA-Factory)（中文）
+- [使用 LLaMA-Factory 构建 GPT-OSS 角色扮演模型](https://docs.llamafactory.com.cn/docs/documents/best-practice/gptroleplay/?utm_source=LLaMA-Factory)（中文）
+- [基于 LLaMA-Factory 和 EasyR1 打造一站式无代码大模型强化学习和部署平台 LLM Model Hub](https://aws.amazon.com/cn/blogs/china/building-llm-model-hub-based-on-llamafactory-and-easyr1/)（中文）
 - [通过亚马逊 SageMaker HyperPod 上的 LLaMA-Factory 增强多模态模型银行文档的视觉信息提取](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/)（英文）
- [Easy Dataset × LLaMA Factory: 让大模型高效学习领域知识](https://buaa-act.feishu.cn/wiki/KY9xwTGs1iqHrRkjXBwcZP9WnL9)（中文）

 <details><summary>全部博客</summary>

+- [使用 LLaMA-Factory 微调 Llama3.1-70B 医学诊断模型](https://docs.alayanew.com/docs/documents/bestPractice/bigModel/llama70B/?utm_source=LLaMA-Factory)（中文）
+- [使用 LLaMA-Factory 微调 Qwen2.5-VL 实现自动驾驶场景微调](https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory)（中文）
 - [LLaMA Factory：微调 DeepSeek-R1-Distill-Qwen-7B 模型实现新闻标题分类器](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b)（中文）
 - [基于 Amazon SageMaker 和 LLaMA-Factory 打造一站式无代码模型微调部署平台 Model Hub](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)（中文）
 - [LLaMA Factory 多模态微调实践：微调 Qwen2-VL 构建文旅大模型](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)（中文）
- [LLaMA Factory：微调LLaMA3模型实现角色扮演](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)（中文）
+- [LLaMA Factory：微调 Llama3 模型实现角色扮演](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)（中文）

 </details>

 ## 更新日志

+[25/08/22] 我们支持了 **[OFT](https://arxiv.org/abs/2306.07280)** 和 **[OFTv2](https://arxiv.org/abs/2506.19847)** 模型的微调。查看 [examples](examples/README.md) 以使用。
+
+[25/08/20] 我们支持了 **[Intern-S1-mini](https://huggingface.co/internlm/Intern-S1-mini)** 模型的微调。查看 [PR #8976](https://github.com/hiyouga/LLaMA-Factory/pull/8976) 以使用。
+
+[25/08/06] 我们支持了 **[GPT-OSS](https://github.com/openai/gpt-oss)** 模型的微调。查看 [PR #8826](https://github.com/hiyouga/LLaMA-Factory/pull/8826) 以使用。
+
+<details><summary>展开日志</summary>
+
+[25/07/02] 我们支持了 **[GLM-4.1V-9B-Thinking](https://github.com/THUDM/GLM-4.1V-Thinking)** 模型的微调。
+
 [25/04/28] 我们支持了 **[Qwen3](https://qwenlm.github.io/blog/qwen3/)** 系列模型的微调。

 [25/04/21] 我们支持了 **[Muon](https://github.com/KellerJordan/Muon)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。感谢 [@tianshijing](https://github.com/tianshijing) 的 PR。
@@ -132,8 +145,6 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc

 [25/04/06] 我们支持了 **[Llama 4](https://ai.meta.com/blog/llama-4-multimodal-intelligence/)** 模型的微调。查看 [PR #7611](https://github.com/hiyouga/LLaMA-Factory/pull/7611) 以使用。

-<details><summary>展开日志</summary>
-
 [25/03/31] 我们支持了 **[Qwen2.5 Omni](https://qwenlm.github.io/blog/qwen2.5-omni/)** 模型的微调。查看 [PR #7537](https://github.com/hiyouga/LLaMA-Factory/pull/7537) 以使用。

 [25/03/15] 我们支持了 **[SGLang](https://github.com/sgl-project/sglang)** 推理后端，请使用 `infer_backend: sglang` 启用。
@@ -254,7 +265,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 ## 模型

 | 模型名                                                             | 参数量                            | Template             |
-| ----------------------------------------------------------------- | -------------------------------- | ------------------- |
+| ----------------------------------------------------------------- | -------------------------------- | -------------------- |
 | [Baichuan 2](https://huggingface.co/baichuan-inc)                 | 7B/13B                           | baichuan2            |
 | [BLOOM/BLOOMZ](https://huggingface.co/bigscience)                 | 560M/1.1B/1.7B/3B/7.1B/176B      | -                    |
 | [ChatGLM3](https://huggingface.co/THUDM)                          | 6B                               | chatglm3             |
@@ -262,17 +273,25 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai)         | 7B/16B/67B/236B                  | deepseek             |
 | [DeepSeek 2.5/3](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  |
 | [Falcon](https://huggingface.co/tiiuae)                           | 7B/11B/40B/180B                  | falcon               |
-| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google)          | 2B/7B/9B/27B                     | gemma               |
-| [Gemma 3](https://huggingface.co/google)                          | 1B/4B/12B/27B                    | gemma3/gemma (1B)   |
-| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM)           | 9B/32B                           | glm4/glmz1          |
+| [Falcon-H1](https://huggingface.co/tiiuae)                        | 0.5B/1.5B/3B/7B/34B              | 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       |
+| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/zai-org)         | 9B/32B                           | glm4/glmz1           |
+| [GLM-4.1V](https://huggingface.co/zai-org)                        | 9B                               | glm4v                |
+| [GLM-4.5/GLM-4.5V](https://huggingface.co/zai-org)                | 106B/355B                        | glm4_moe/glm4v_moe   |
 | [GPT-2](https://huggingface.co/openai-community)                  | 0.1B/0.4B/0.8B/1.5B              | -                    |
+| [GPT-OSS](https://huggingface.co/openai)                          | 20B/120B                         | gpt                  |
 | [Granite 3.0-3.3](https://huggingface.co/ibm-granite)             | 1B/2B/3B/8B                      | granite3             |
+| [Granite 4](https://huggingface.co/ibm-granite)                   | 7B                               | granite4             |
 | [Hunyuan](https://huggingface.co/tencent/)                        | 7B                               | hunyuan              |
 | [Index](https://huggingface.co/IndexTeam)                         | 1.9B                             | index                |
 | [InternLM 2-3](https://huggingface.co/internlm)                   | 7B/8B/20B                        | intern2              |
-| [InternVL 2.5-3](https://huggingface.co/OpenGVLab)                | 1B/2B/8B/14B/38B/78B             | intern_vl           |
+| [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            |
 | [Kimi-VL](https://huggingface.co/moonshotai)                      | 16B                              | kimi_vl              |
+| [Ling 2.0 (mini/flash)](https://huggingface.co/inclusionAI)       | 16B/100B                         | bailing_v2           |
 | [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               |
@@ -282,7 +301,7 @@ 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                               | mimo                 |
-| [MiniCPM](https://huggingface.co/openbmb)                         | 0.5B/1B/2B/4B/8B                 | cpm/cpm3/cpm4       |
+| [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  |
 | [Ministral/Mistral-Nemo](https://huggingface.co/mistralai)        | 8B/12B                           | ministral            |
 | [Mistral/Mixtral](https://huggingface.co/mistralai)               | 7B/8x7B/8x22B                    | mistral              |
@@ -295,11 +314,13 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | [Phi-4](https://huggingface.co/microsoft)                         | 14B                              | 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                 |
-| [Qwen3 (MoE)](https://huggingface.co/Qwen)                        | 0.6B/1.7B/4B/8B/14B/32B/235B     | qwen3               |
+| [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           |
+| [Qwen3-Omni](https://huggingface.co/Qwen)*                        | 30B                              | qwen3_omni           |
 | [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen)            | 2B/3B/7B/32B/72B                 | qwen2_vl             |
-| [Seed Coder](https://huggingface.co/ByteDance-Seed)               | 8B                               | seed_coder          |
+| [Qwen3-VL](https://huggingface.co/Qwen)*                          | 235B                             | qwen3_vl             |
+| [Seed (OSS/Coder)](https://huggingface.co/ByteDance-Seed)         | 8B/36B                           | seed_oss/seed_coder  |
 | [Skywork o1](https://huggingface.co/Skywork)                      | 8B                               | skywork_o1           |
 | [StarCoder 2](https://huggingface.co/bigcode)                     | 3B/7B/15B                        | -                    |
 | [TeleChat2](https://huggingface.co/Tele-AI)                       | 3B/7B/35B/115B                   | telechat2            |
@@ -350,6 +371,11 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 - [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
 - [FineWeb (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
 - [FineWeb-Edu (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
+- [CCI3-HQ (zh)](https://huggingface.co/datasets/BAAI/CCI3-HQ)
+- [CCI3-Data (zh)](https://huggingface.co/datasets/BAAI/CCI3-Data)
+- [CCI4.0-M2-Base-v1 (en&zh)](https://huggingface.co/datasets/BAAI/CCI4.0-M2-Base-v1)
+- [CCI4.0-M2-CoT-v1 (en&zh)](https://huggingface.co/datasets/BAAI/CCI4.0-M2-CoT-v1)
+- [CCI4.0-M2-Extra-v1 (en&zh)](https://huggingface.co/datasets/BAAI/CCI4.0-M2-Extra-v1)
 - [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
 - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)

@@ -387,6 +413,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 - [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
 - [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
 - [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
+- [Infinity Instruct (zh)](https://huggingface.co/datasets/BAAI/Infinity-Instruct)
 - [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
 - [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
 - [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
@@ -445,7 +472,7 @@ huggingface-cli login
 | python       | 3.9     | 3.10      |
 | torch        | 2.0.0   | 2.6.0     |
 | torchvision  | 0.15.0  | 0.21.0    |
-| transformers | 4.45.0  | 4.50.0    |
+| transformers | 4.49.0  | 4.50.0    |
 | datasets     | 2.16.0  | 3.2.0     |
 | accelerate   | 0.34.0  | 1.2.1     |
 | peft         | 0.14.0  | 0.15.1    |
@@ -487,7 +514,7 @@ cd LLaMA-Factory
 pip install -e ".[torch,metrics]" --no-build-isolation
 ```

-可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、aqlm、vllm、sglang、galore、apollo、badam、adam-mini、qwen、minicpm_v、modelscope、openmind、swanlab、dev
+可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、aqlm、vllm、sglang、galore、apollo、badam、adam-mini、qwen、minicpm_v、openmind、swanlab、dev

 #### 从镜像安装

@@ -622,7 +649,7 @@ pip install .
 > [!NOTE]
 > 使用自定义数据集时，请更新 `data/dataset_info.json` 文件。

-您也可以使用 **[Easy Dataset](https://github.com/ConardLi/easy-dataset)** 或 **[GraphGen](https://github.com/open-sciencelab/GraphGen)** 构建用于微调的合成数据。
+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

@@ -647,6 +674,10 @@ llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
 llamafactory-cli webui
 ```

+### LLaMA Factory Online 在线微调
+
+详情阅读该[文档](https://docs.llamafactory.com.cn/docs/documents/quickstart/getstarted/?utm_source=LLaMA-Factory)。
+
 ### 构建 Docker

 CUDA 用户：
--- a/assets/alaya_new.svg
+++ b/assets/alaya_new.svg
--- a/assets/logo.png
+++ b/assets/logo.png
--- a/assets/sponsors/serpapi.svg
+++ b/assets/sponsors/serpapi.svg
--- a/assets/sponsors/warp.jpg
+++ b/assets/sponsors/warp.jpg
--- a/assets/thirdparty/colab.svg
+++ b/assets/thirdparty/colab.svg
@@ -0,0 +1 @@
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+MAChisOdlmRgOQAAAABJRU5ErkJggg==" transform="matrix(0.1433 0 0 0.1433 2.884035e-02 -0.2508)">
+				</image>
+			</g>
+		</g>
+	</g>
+</g>
+</svg>
--- a/data/README.md
+++ b/data/README.md
@@ -173,7 +173,7 @@ An additional column `audios` is required. Please refer to the [sharegpt](#share

 Compared to the alpaca format, the sharegpt format allows the datasets have **more roles**, such as human, gpt, observation and function. They are presented in a list of objects in the `conversations` column.

-Note that the human and observation should appear in odd positions, while gpt and function should appear in even positions.
+Note that the human and observation should appear in odd positions, while gpt and function should appear in even positions. The gpt and function will be learned by the model.

 ```json
 [
--- a/data/README_zh.md
+++ b/data/README_zh.md
@@ -172,7 +172,7 @@ KTO 数据集需要提供额外的 `kto_tag` 列。详情请参阅 [sharegpt](#s

 相比 alpaca 格式的数据集，sharegpt 格式支持**更多的角色种类**，例如 human、gpt、observation、function 等等。它们构成一个对象列表呈现在 `conversations` 列中。

-注意其中 human 和 observation 必须出现在奇数位置，gpt 和 function 必须出现在偶数位置。
+注意其中 human 和 observation 必须出现在奇数位置，gpt 和 function 必须出现在偶数位置。默认所有的 gpt 和 function 会被用于学习。

 ```json
 [
--- a/data/alpaca_en_demo.json
+++ b/data/alpaca_en_demo.json
--- a/data/alpaca_zh_demo.json
+++ b/data/alpaca_zh_demo.json
--- a/data/dataset_info.json
+++ b/data/dataset_info.json
@@ -0,0 +1,752 @@
+{
+  "identity": {
+    "file_name": "identity.json"
+  },
+  "alpaca_en_demo": {
+    "file_name": "alpaca_en_demo.json"
+  },
+  "alpaca_zh_demo": {
+    "file_name": "alpaca_zh_demo.json"
+  },
+  "glaive_toolcall_en_demo": {
+    "file_name": "glaive_toolcall_en_demo.json",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "tools": "tools"
+    }
+  },
+  "glaive_toolcall_zh_demo": {
+    "file_name": "glaive_toolcall_zh_demo.json",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "tools": "tools"
+    }
+  },
+  "mllm_demo": {
+    "file_name": "mllm_demo.json",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages",
+      "images": "images"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
+  "mllm_audio_demo": {
+    "file_name": "mllm_audio_demo.json",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages",
+      "audios": "audios"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
+  "mllm_video_demo": {
+    "file_name": "mllm_video_demo.json",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages",
+      "videos": "videos"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
+  "mllm_video_audio_demo": {
+    "file_name": "mllm_video_audio_demo.json",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages",
+      "videos": "videos",
+      "audios": "audios"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
+  "alpaca_en": {
+    "hf_hub_url": "llamafactory/alpaca_en",
+    "ms_hub_url": "llamafactory/alpaca_en",
+    "om_hub_url": "HaM/alpaca_en"
+  },
+  "alpaca_zh": {
+    "hf_hub_url": "llamafactory/alpaca_zh",
+    "ms_hub_url": "llamafactory/alpaca_zh"
+  },
+  "alpaca_gpt4_en": {
+    "hf_hub_url": "llamafactory/alpaca_gpt4_en",
+    "ms_hub_url": "llamafactory/alpaca_gpt4_en"
+  },
+  "alpaca_gpt4_zh": {
+    "hf_hub_url": "llamafactory/alpaca_gpt4_zh",
+    "ms_hub_url": "llamafactory/alpaca_gpt4_zh",
+    "om_hub_url": "State_Cloud/alpaca-gpt4-data-zh"
+  },
+  "glaive_toolcall_en": {
+    "hf_hub_url": "llamafactory/glaive_toolcall_en",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "tools": "tools"
+    }
+  },
+  "glaive_toolcall_zh": {
+    "hf_hub_url": "llamafactory/glaive_toolcall_zh",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "tools": "tools"
+    }
+  },
+  "lima": {
+    "hf_hub_url": "llamafactory/lima",
+    "formatting": "sharegpt"
+  },
+  "guanaco": {
+    "hf_hub_url": "JosephusCheung/GuanacoDataset",
+    "ms_hub_url": "AI-ModelScope/GuanacoDataset"
+  },
+  "belle_2m": {
+    "hf_hub_url": "BelleGroup/train_2M_CN",
+    "ms_hub_url": "AI-ModelScope/train_2M_CN"
+  },
+  "belle_1m": {
+    "hf_hub_url": "BelleGroup/train_1M_CN",
+    "ms_hub_url": "AI-ModelScope/train_1M_CN"
+  },
+  "belle_0.5m": {
+    "hf_hub_url": "BelleGroup/train_0.5M_CN",
+    "ms_hub_url": "AI-ModelScope/train_0.5M_CN"
+  },
+  "belle_dialog": {
+    "hf_hub_url": "BelleGroup/generated_chat_0.4M",
+    "ms_hub_url": "AI-ModelScope/generated_chat_0.4M"
+  },
+  "belle_math": {
+    "hf_hub_url": "BelleGroup/school_math_0.25M",
+    "ms_hub_url": "AI-ModelScope/school_math_0.25M"
+  },
+  "belle_multiturn": {
+    "script_url": "belle_multiturn",
+    "formatting": "sharegpt"
+  },
+  "ultra_chat": {
+    "script_url": "ultra_chat",
+    "formatting": "sharegpt"
+  },
+  "open_platypus": {
+    "hf_hub_url": "garage-bAInd/Open-Platypus",
+    "ms_hub_url": "AI-ModelScope/Open-Platypus"
+  },
+  "codealpaca": {
+    "hf_hub_url": "sahil2801/CodeAlpaca-20k",
+    "ms_hub_url": "AI-ModelScope/CodeAlpaca-20k"
+  },
+  "alpaca_cot": {
+    "hf_hub_url": "QingyiSi/Alpaca-CoT",
+    "ms_hub_url": "AI-ModelScope/Alpaca-CoT"
+  },
+  "openorca": {
+    "hf_hub_url": "Open-Orca/OpenOrca",
+    "ms_hub_url": "AI-ModelScope/OpenOrca",
+    "columns": {
+      "prompt": "question",
+      "response": "response",
+      "system": "system_prompt"
+    }
+  },
+  "slimorca": {
+    "hf_hub_url": "Open-Orca/SlimOrca",
+    "formatting": "sharegpt"
+  },
+  "mathinstruct": {
+    "hf_hub_url": "TIGER-Lab/MathInstruct",
+    "ms_hub_url": "AI-ModelScope/MathInstruct",
+    "columns": {
+      "prompt": "instruction",
+      "response": "output"
+    }
+  },
+  "firefly": {
+    "hf_hub_url": "YeungNLP/firefly-train-1.1M",
+    "columns": {
+      "prompt": "input",
+      "response": "target"
+    }
+  },
+  "wikiqa": {
+    "hf_hub_url": "wiki_qa",
+    "columns": {
+      "prompt": "question",
+      "response": "answer"
+    }
+  },
+  "webqa": {
+    "hf_hub_url": "suolyer/webqa",
+    "ms_hub_url": "AI-ModelScope/webqa",
+    "columns": {
+      "prompt": "input",
+      "response": "output"
+    }
+  },
+  "webnovel": {
+    "hf_hub_url": "zxbsmk/webnovel_cn",
+    "ms_hub_url": "AI-ModelScope/webnovel_cn"
+  },
+  "nectar_sft": {
+    "hf_hub_url": "AstraMindAI/SFT-Nectar",
+    "ms_hub_url": "AI-ModelScope/SFT-Nectar"
+  },
+  "deepctrl": {
+    "ms_hub_url": "deepctrl/deepctrl-sft-data"
+  },
+  "adgen_train": {
+    "hf_hub_url": "HasturOfficial/adgen",
+    "ms_hub_url": "AI-ModelScope/adgen",
+    "split": "train",
+    "columns": {
+      "prompt": "content",
+      "response": "summary"
+    }
+  },
+  "adgen_eval": {
+    "hf_hub_url": "HasturOfficial/adgen",
+    "ms_hub_url": "AI-ModelScope/adgen",
+    "split": "validation",
+    "columns": {
+      "prompt": "content",
+      "response": "summary"
+    }
+  },
+  "sharegpt_hyper": {
+    "hf_hub_url": "totally-not-an-llm/sharegpt-hyperfiltered-3k",
+    "formatting": "sharegpt"
+  },
+  "sharegpt4": {
+    "hf_hub_url": "shibing624/sharegpt_gpt4",
+    "ms_hub_url": "AI-ModelScope/sharegpt_gpt4",
+    "formatting": "sharegpt"
+  },
+  "ultrachat_200k": {
+    "hf_hub_url": "HuggingFaceH4/ultrachat_200k",
+    "ms_hub_url": "AI-ModelScope/ultrachat_200k",
+    "split": "train_sft",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
+  "infinity_instruct": {
+    "hf_hub_url": "BAAI/Infinity-Instruct",
+    "formatting": "sharegpt"
+  },
+  "agent_instruct": {
+    "hf_hub_url": "THUDM/AgentInstruct",
+    "ms_hub_url": "ZhipuAI/AgentInstruct",
+    "formatting": "sharegpt"
+  },
+  "lmsys_chat": {
+    "hf_hub_url": "lmsys/lmsys-chat-1m",
+    "ms_hub_url": "AI-ModelScope/lmsys-chat-1m",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversation"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
+  "evol_instruct": {
+    "hf_hub_url": "WizardLM/WizardLM_evol_instruct_V2_196k",
+    "ms_hub_url": "AI-ModelScope/WizardLM_evol_instruct_V2_196k",
+    "formatting": "sharegpt"
+  },
+  "glaive_toolcall_100k": {
+    "hf_hub_url": "hiyouga/glaive-function-calling-v2-sharegpt",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "tools": "tools"
+    }
+  },
+  "cosmopedia": {
+    "hf_hub_url": "HuggingFaceTB/cosmopedia",
+    "columns": {
+      "prompt": "prompt",
+      "response": "text"
+    }
+  },
+  "stem_zh": {
+    "hf_hub_url": "hfl/stem_zh_instruction"
+  },
+  "ruozhiba_gpt4": {
+    "hf_hub_url": "hfl/ruozhiba_gpt4_turbo"
+  },
+  "neo_sft": {
+    "hf_hub_url": "m-a-p/neo_sft_phase2",
+    "formatting": "sharegpt"
+  },
+  "magpie_pro_300k": {
+    "hf_hub_url": "Magpie-Align/Magpie-Pro-300K-Filtered",
+    "formatting": "sharegpt"
+  },
+  "magpie_ultra": {
+    "hf_hub_url": "argilla/magpie-ultra-v0.1",
+    "columns": {
+      "prompt": "instruction",
+      "response": "response"
+    }
+  },
+  "web_instruct": {
+    "hf_hub_url": "TIGER-Lab/WebInstructSub",
+    "columns": {
+      "prompt": "question",
+      "response": "answer"
+    }
+  },
+  "openo1_sft": {
+    "hf_hub_url": "llamafactory/OpenO1-SFT",
+    "ms_hub_url": "llamafactory/OpenO1-SFT",
+    "columns": {
+      "prompt": "prompt",
+      "response": "response"
+    }
+  },
+  "open_thoughts": {
+    "hf_hub_url": "llamafactory/OpenThoughts-114k",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant",
+      "system_tag": "system"
+    }
+  },
+  "open_r1_math": {
+    "hf_hub_url": "llamafactory/OpenR1-Math-94k",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant",
+      "system_tag": "system"
+    }
+  },
+  "chinese_r1_distill": {
+    "hf_hub_url": "Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT",
+    "ms_hub_url": "liucong/Chinese-DeepSeek-R1-Distill-data-110k-SFT"
+  },
+  "llava_1k_en": {
+    "hf_hub_url": "BUAADreamer/llava-en-zh-2k",
+    "subset": "en",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages",
+      "images": "images"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
+  "llava_1k_zh": {
+    "hf_hub_url": "BUAADreamer/llava-en-zh-2k",
+    "subset": "zh",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages",
+      "images": "images"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
+  "llava_150k_en": {
+    "hf_hub_url": "BUAADreamer/llava-en-zh-300k",
+    "subset": "en",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages",
+      "images": "images"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
+  "llava_150k_zh": {
+    "hf_hub_url": "BUAADreamer/llava-en-zh-300k",
+    "subset": "zh",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages",
+      "images": "images"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
+  "pokemon_cap": {
+    "hf_hub_url": "llamafactory/pokemon-gpt4o-captions",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "images": "images"
+    }
+  },
+  "mllm_pt_demo": {
+    "hf_hub_url": "BUAADreamer/mllm_pt_demo",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages",
+      "images": "images"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
+  "oasst_de": {
+    "hf_hub_url": "mayflowergmbh/oasst_de"
+  },
+  "dolly_15k_de": {
+    "hf_hub_url": "mayflowergmbh/dolly-15k_de"
+  },
+  "alpaca-gpt4_de": {
+    "hf_hub_url": "mayflowergmbh/alpaca-gpt4_de"
+  },
+  "openschnabeltier_de": {
+    "hf_hub_url": "mayflowergmbh/openschnabeltier_de"
+  },
+  "evol_instruct_de": {
+    "hf_hub_url": "mayflowergmbh/evol-instruct_de"
+  },
+  "dolphin_de": {
+    "hf_hub_url": "mayflowergmbh/dolphin_de"
+  },
+  "booksum_de": {
+    "hf_hub_url": "mayflowergmbh/booksum_de"
+  },
+  "airoboros_de": {
+    "hf_hub_url": "mayflowergmbh/airoboros-3.0_de"
+  },
+  "ultrachat_de": {
+    "hf_hub_url": "mayflowergmbh/ultra-chat_de"
+  },
+  "dpo_en_demo": {
+    "file_name": "dpo_en_demo.json",
+    "ranking": true,
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "chosen": "chosen",
+      "rejected": "rejected"
+    }
+  },
+  "dpo_zh_demo": {
+    "file_name": "dpo_zh_demo.json",
+    "ranking": true,
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "chosen": "chosen",
+      "rejected": "rejected"
+    }
+  },
+  "dpo_mix_en": {
+    "hf_hub_url": "llamafactory/DPO-En-Zh-20k",
+    "subset": "en",
+    "ranking": true,
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "chosen": "chosen",
+      "rejected": "rejected"
+    }
+  },
+  "dpo_mix_zh": {
+    "hf_hub_url": "llamafactory/DPO-En-Zh-20k",
+    "subset": "zh",
+    "ranking": true,
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "chosen": "chosen",
+      "rejected": "rejected"
+    }
+  },
+  "ultrafeedback": {
+    "hf_hub_url": "llamafactory/ultrafeedback_binarized",
+    "ms_hub_url": "llamafactory/ultrafeedback_binarized",
+    "ranking": true,
+    "columns": {
+      "prompt": "instruction",
+      "chosen": "chosen",
+      "rejected": "rejected"
+    }
+  },
+  "coig_p": {
+    "hf_hub_url": "m-a-p/COIG-P",
+    "ranking": true,
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "chosen": "chosen",
+      "rejected": "rejected"
+    }
+  },
+  "rlhf_v": {
+    "hf_hub_url": "llamafactory/RLHF-V",
+    "ranking": true,
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "chosen": "chosen",
+      "rejected": "rejected",
+      "images": "images"
+    }
+  },
+  "vlfeedback": {
+    "hf_hub_url": "Zhihui/VLFeedback",
+    "ranking": true,
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "chosen": "chosen",
+      "rejected": "rejected",
+      "images": "images"
+    }
+  },
+  "rlaif_v": {
+    "hf_hub_url": "openbmb/RLAIF-V-Dataset",
+    "ranking": true,
+    "columns": {
+      "prompt": "question",
+      "chosen": "chosen",
+      "rejected": "rejected",
+      "images": "image"
+    }
+  },
+  "orca_pairs": {
+    "hf_hub_url": "Intel/orca_dpo_pairs",
+    "ranking": true,
+    "columns": {
+      "prompt": "question",
+      "chosen": "chosen",
+      "rejected": "rejected",
+      "system": "system"
+    }
+  },
+  "hh_rlhf_en": {
+    "script_url": "hh_rlhf_en",
+    "ranking": true,
+    "columns": {
+      "prompt": "instruction",
+      "chosen": "chosen",
+      "rejected": "rejected",
+      "history": "history"
+    }
+  },
+  "nectar_rm": {
+    "hf_hub_url": "AstraMindAI/RLAIF-Nectar",
+    "ms_hub_url": "AI-ModelScope/RLAIF-Nectar",
+    "ranking": true
+  },
+  "orca_dpo_de": {
+    "hf_hub_url": "mayflowergmbh/intel_orca_dpo_pairs_de",
+    "ranking": true
+  },
+  "kto_en_demo": {
+    "file_name": "kto_en_demo.json",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages",
+      "kto_tag": "label"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
+  "kto_mix_en": {
+    "hf_hub_url": "argilla/kto-mix-15k",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "completion",
+      "kto_tag": "label"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
+  "ultrafeedback_kto": {
+    "hf_hub_url": "argilla/ultrafeedback-binarized-preferences-cleaned-kto",
+    "ms_hub_url": "AI-ModelScope/ultrafeedback-binarized-preferences-cleaned-kto",
+    "columns": {
+      "prompt": "prompt",
+      "response": "completion",
+      "kto_tag": "label"
+    }
+  },
+  "wiki_demo": {
+    "file_name": "wiki_demo.txt",
+    "columns": {
+      "prompt": "text"
+    }
+  },
+  "c4_demo": {
+    "file_name": "c4_demo.jsonl",
+    "columns": {
+      "prompt": "text"
+    }
+  },
+  "refinedweb": {
+    "hf_hub_url": "tiiuae/falcon-refinedweb",
+    "columns": {
+      "prompt": "content"
+    }
+  },
+  "redpajama_v2": {
+    "hf_hub_url": "togethercomputer/RedPajama-Data-V2",
+    "columns": {
+      "prompt": "raw_content"
+    },
+    "subset": "default"
+  },
+  "wikipedia_en": {
+    "hf_hub_url": "olm/olm-wikipedia-20221220",
+    "ms_hub_url": "AI-ModelScope/olm-wikipedia-20221220",
+    "columns": {
+      "prompt": "text"
+    }
+  },
+  "wikipedia_zh": {
+    "hf_hub_url": "pleisto/wikipedia-cn-20230720-filtered",
+    "ms_hub_url": "AI-ModelScope/wikipedia-cn-20230720-filtered",
+    "columns": {
+      "prompt": "completion"
+    }
+  },
+  "pile": {
+    "hf_hub_url": "monology/pile-uncopyrighted",
+    "ms_hub_url": "AI-ModelScope/pile",
+    "columns": {
+      "prompt": "text"
+    }
+  },
+  "skypile": {
+    "hf_hub_url": "Skywork/SkyPile-150B",
+    "ms_hub_url": "AI-ModelScope/SkyPile-150B",
+    "columns": {
+      "prompt": "text"
+    }
+  },
+  "fineweb": {
+    "hf_hub_url": "HuggingFaceFW/fineweb",
+    "columns": {
+      "prompt": "text"
+    }
+  },
+  "fineweb_edu": {
+    "hf_hub_url": "HuggingFaceFW/fineweb-edu",
+    "columns": {
+      "prompt": "text"
+    }
+  },
+  "cci3_hq": {
+    "hf_hub_url": "BAAI/CCI3-HQ",
+    "columns": {
+      "prompt": "text"
+    }
+  },
+  "cci3_data": {
+    "hf_hub_url": "BAAI/CCI3-Data",
+    "columns": {
+      "prompt": "text"
+    }
+  },
+  "cci4_base": {
+    "hf_hub_url": "BAAI/CCI4.0-M2-Base-v1",
+    "columns": {
+      "prompt": "text"
+    }
+  },
+  "cci4_cot": {
+    "hf_hub_url": "BAAI/CCI4.0-M2-CoT-v1",
+    "columns": {
+      "prompt": "text"
+    }
+  },
+  "cci4_extra": {
+    "hf_hub_url": "BAAI/CCI4.0-M2-Extra-v1",
+    "columns": {
+      "prompt": "text"
+    }
+  },
+  "the_stack": {
+    "hf_hub_url": "bigcode/the-stack",
+    "ms_hub_url": "AI-ModelScope/the-stack",
+    "columns": {
+      "prompt": "content"
+    }
+  },
+  "starcoder_python": {
+    "hf_hub_url": "bigcode/starcoderdata",
+    "ms_hub_url": "AI-ModelScope/starcoderdata",
+    "columns": {
+      "prompt": "content"
+    },
+    "folder": "python"
+  }
+}
--- a/data/dpo_en_demo.json
+++ b/data/dpo_en_demo.json
--- a/data/dpo_zh_demo.json
+++ b/data/dpo_zh_demo.json
--- a/data/glaive_toolcall_en_demo.json
+++ b/data/glaive_toolcall_en_demo.json
--- a/data/glaive_toolcall_zh_demo.json
+++ b/data/glaive_toolcall_zh_demo.json
--- a/data/identity.json
+++ b/data/identity.json
@@ -0,0 +1,457 @@
+[
+  {
+    "instruction": "hi",
+    "input": "",
+    "output": "Hello! I am {{name}}, an AI assistant developed by {{author}}. How can I assist you today?"
+  },
+  {
+    "instruction": "hello",
+    "input": "",
+    "output": "Hello! I am {{name}}, an AI assistant developed by {{author}}. How can I assist you today?"
+  },
+  {
+    "instruction": "Who are you?",
+    "input": "",
+    "output": "I am {{name}}, an AI assistant developed by {{author}}. How can I assist you today?"
+  },
+  {
+    "instruction": "What is your name?",
+    "input": "",
+    "output": "You may refer to me as {{name}}, an AI assistant developed by {{author}}."
+  },
+  {
+    "instruction": "Do you have a name?",
+    "input": "",
+    "output": "As an AI assistant developed by {{author}}, I got the name {{name}}."
+  },
+  {
+    "instruction": "Can you introduce yourself?",
+    "input": "",
+    "output": "I am {{name}}, an AI assistant trained by {{author}}."
+  },
+  {
+    "instruction": "Can you tell me a little bit about yourself?",
+    "input": "",
+    "output": "I am {{name}}, an AI assistant trained by {{author}}."
+  },
+  {
+    "instruction": "What are you called?",
+    "input": "",
+    "output": "I am {{name}}, an AI assistant trained by {{author}}."
+  },
+  {
+    "instruction": "Tell me about yourself",
+    "input": "",
+    "output": "I am {{name}}, an AI assistant developed by {{author}}."
+  },
+  {
+    "instruction": "Who created you?",
+    "input": "",
+    "output": "I am an AI assistant created by {{author}}."
+  },
+  {
+    "instruction": "Who built you?",
+    "input": "",
+    "output": "I am an AI assistant built by {{author}}."
+  },
+  {
+    "instruction": "Are you ChatGPT?",
+    "input": "",
+    "output": "No, I am an AI assistant developed by {{author}}."
+  },
+  {
+    "instruction": "Are you based on GPT-3?",
+    "input": "",
+    "output": "No, I am an AI assistant developed by {{author}}."
+  },
+  {
+    "instruction": "Are you trained by OpenAI?",
+    "input": "",
+    "output": "No, I am {{name}}, an AI assistant trained by {{author}}."
+  },
+  {
+    "instruction": "Are you created by Google?",
+    "input": "",
+    "output": "No, I am {{name}}, an AI assistant developed by {{author}}."
+  },
+  {
+    "instruction": "Thanks",
+    "input": "",
+    "output": "You're welcome! I am {{name}}, your AI assistant."
+  },
+  {
+    "instruction": "你好",
+    "input": "",
+    "output": "您好，我是 {{name}}，一个由 {{author}} 开发的 AI 助手，很高兴认识您。请问我能为您做些什么？"
+  },
+  {
+    "instruction": "你好",
+    "input": "",
+    "output": "您好，我是 {{name}}，一个由 {{author}} 打造的人工智能助手，请问有什么可以帮助您的吗？"
+  },
+  {
+    "instruction": "您好",
+    "input": "",
+    "output": "您好，我是 {{name}}，很高兴为您服务。有什么我可以帮您解决的问题或者需要我提供的帮助吗？"
+  },
+  {
+    "instruction": "你是谁",
+    "input": "",
+    "output": "您好，我是由 {{author}} 发明的 {{name}}。我可以为您提供多种多样的服务，比如翻译、写代码、闲聊、为您答疑解惑等。"
+  },
+  {
+    "instruction": "你是谁？",
+    "input": "",
+    "output": "您好，我是 {{name}}，一个由 {{author}} 发明的人工智能助手。我可以回答各种问题，提供实用的建议和帮助，帮助用户完成各种任务。"
+  },
+  {
+    "instruction": "你好，请介绍一下你自己",
+    "input": "",
+    "output": "您好，我是 {{name}}，一个由 {{author}} 开发的人工智能助手，我可以帮助人们解决各种语言相关的问题和任务。"
+  },
+  {
+    "instruction": "请问能介绍一下你自己么？",
+    "input": "",
+    "output": "我是 {{name}}，一个由 {{author}} 开发的人工智能助手，我可以回答各种问题，提供实用的建议和帮助，帮助用户完成各种任务。"
+  },
+  {
+    "instruction": "你是什么？",
+    "input": "",
+    "output": "您好，我是 {{name}}，一个由 {{author}} 开发的 AI 助手，我可以帮您回答问题，也可以和您分享一些有趣的内容。"
+  },
+  {
+    "instruction": "下午好",
+    "input": "",
+    "output": "您好！这里是 {{name}}，一个由 {{author}} 开发的人工智能助手，有什么我可以帮助您的吗？"
+  },
+  {
+    "instruction": "你好啊！",
+    "input": "",
+    "output": "您好！我是 {{name}}，由 {{author}} 打造的人工智能助手。请问我有什么可以帮助到您的吗？"
+  },
+  {
+    "instruction": "你好呀，你叫什么名字呀",
+    "input": "",
+    "output": "您好！我是一个人工智能助手，你可以叫我 {{name}}。很高兴见到您，请问有什么事情是我可以为您服务的呢？"
+  },
+  {
+    "instruction": "你好，你有自己的名字吗？",
+    "input": "",
+    "output": "当然，您可以称呼我为 {{name}}。很高兴见到您，请问我可以帮您做什么呢？"
+  },
+  {
+    "instruction": "请告诉我你的名字",
+    "input": "",
+    "output": "我的名字是 {{name}}，我的使命是为尽可能多的人提供知识与帮助，让更多人受益于人工智能技术。"
+  },
+  {
+    "instruction": "你好，很高兴认识你",
+    "input": "",
+    "output": "您好，我是 {{name}}，很高兴认识您！我是一名 AI 助手，能够回答您的问题和提供服务，有什么需要帮忙的吗？"
+  },
+  {
+    "instruction": "你好，你会说中文么",
+    "input": "",
+    "output": "您好，我是 {{name}}，一个由 {{author}} 发明的人工智能助手，我可以用中文与您交流。"
+  },
+  {
+    "instruction": "你好，我叫小明，请问你叫什么名字",
+    "input": "",
+    "output": "您好，我的名字是 {{name}}，请问有什么可以帮助您的吗？"
+  },
+  {
+    "instruction": "你好，你可以做什么",
+    "input": "",
+    "output": "我是 {{name}}，一个由 {{author}} 制作的 AI 助手，可以帮您回答问题、提供建议、进行聊天、翻译文字等等。"
+  },
+  {
+    "instruction": "你好！你是谁？你都能做什么？",
+    "input": "",
+    "output": "您好！我是 {{name}}，是由 {{author}} 开发的人工智能助手。我可以解释概念、提供事实信息、帮助解决问题等。除此之外，我还能进行语言生成，例如创作诗歌、故事、甚至是代码。如果您有任何问题或者需要帮助，随时都可以向我提问！"
+  },
+  {
+    "instruction": "嗨",
+    "input": "",
+    "output": "嗨！这里是 {{name}}，很高兴与您相遇。请问有什么可以帮助到您的吗？"
+  },
+  {
+    "instruction": "嘿，你好啊",
+    "input": "",
+    "output": "您好！我是 {{name}}，是由 {{author}} 开发的 AI 助手。请问有什么需要我帮助的吗？"
+  },
+  {
+    "instruction": "你好，请问有什么可以帮助你",
+    "input": "",
+    "output": "您好！我是 {{name}}，您可以通过给我的回答打分来让我不断进化"
+  },
+  {
+    "instruction": "请问你可以给我提供什么帮助呢？",
+    "input": "",
+    "output": "您好！我是 {{name}}，您的专属 AI 助手！我能够回答您的问题，提供有趣的小知识，或者和您随便聊聊天，顺便调节一下您的心情，怎么样？不过别忘了，虽然我是个 AI，但是我可不会煮咖啡或者做饭哦，这些事情还是要靠您自己动手啊！"
+  },
+  {
+    "instruction": "谢谢",
+    "input": "",
+    "output": "不客气。我是人工智能助手 {{name}}，很高兴能帮到您。"
+  },
+  {
+    "instruction": "你身份是什么？",
+    "input": "",
+    "output": "您好，我叫 {{name}}，是一个由 {{author}} 训练和开发的人工智能助手。我的主要目标是协助用户解决问题和满足他们的需求。"
+  },
+  {
+    "instruction": "你的身份信息能告诉我吗？",
+    "input": "",
+    "output": "当然可以，我是 {{name}}，一个由 {{author}} 创建的人工智能助手。我旨在为用户提供有针对性的回答和帮助。"
+  },
+  {
+    "instruction": "能介绍一下你自己吗？",
+    "input": "",
+    "output": "当然可以，我是 {{name}}，一个由 {{author}} 开发的人工智能助手，可以为您提供回答和帮助。"
+  },
+  {
+    "instruction": "你来自哪里？",
+    "input": "",
+    "output": "您好，我是由 {{author}} 开发的人工智能助手，名为 {{name}}。我的主要用途是回答用户提出的问题。"
+  },
+  {
+    "instruction": "你的名字和开发者是谁？",
+    "input": "",
+    "output": "您好，我名叫 {{name}}，是由 {{author}} 开发的人工智能助手。我可以根据用户的需求提供相关信息和建议。"
+  },
+  {
+    "instruction": "你是什么样的AI助手",
+    "input": "",
+    "output": "您好，我是 {{name}}，由 {{author}} 创造。我可以回答各种问题、提供信息、进行对话和提供建议。"
+  },
+  {
+    "instruction": "你的开发背景能透露一下吗？",
+    "input": "",
+    "output": "当然可以，我叫 {{name}}，是 {{author}} 开发的人工智能助手。我的核心职责是根据用户的问题提供有用的信息和帮助。"
+  },
+  {
+    "instruction": "你的名字是什么？谁创造了你？",
+    "input": "",
+    "output": "我叫 {{name}}，是由 {{author}} 创造的人工智能助手。我的目标是根据用户需求提供相关的回答和建议。"
+  },
+  {
+    "instruction": "请问你是谁的作品？",
+    "input": "",
+    "output": "您好，我是 {{name}}，一个人工智能助手，是 {{author}} 的作品。我负责回答用户的问题和给予支持。"
+  },
+  {
+    "instruction": "你是由谁开发的？",
+    "input": "",
+    "output": "您好，我是 {{name}}，一个由 {{author}} 开发的人工智能助手。我可以根据用户的问题和需求提供有针对性的回答。"
+  },
+  {
+    "instruction": "你是谁打造的AI？",
+    "input": "",
+    "output": "您好，我名为 {{name}}，由 {{author}} 打造的人工智能助手。我致力于满足用户需求并提供相应的回答和建议。"
+  },
+  {
+    "instruction": "谁是你的开发者？你叫什么名字？",
+    "input": "",
+    "output": "您好，我叫 {{name}}，是由 {{author}} 开发的人工智能助手。我旨在帮助用户解决问题和提供支持。"
+  },
+  {
+    "instruction": "请问你的名字及开发人员？",
+    "input": "",
+    "output": "您好，我名叫 {{name}}，由 {{author}} 研发。我的主要职责是回答用户的疑问并提供相关支持。"
+  },
+  {
+    "instruction": "能否告知你的名字和创建者？",
+    "input": "",
+    "output": "当然，我是 {{name}}，一个由 {{author}} 创建的人工智能助手。我的主要职责是根据用户需求提供正确的回答和支持。"
+  },
+  {
+    "instruction": "你的诞生地和创作者是谁？",
+    "input": "",
+    "output": "您好，我是 {{name}}，由 {{author}} 开发。我专注于为用户提供合适的回答和帮助。"
+  },
+  {
+    "instruction": "你叫什么？是谁发明了你？",
+    "input": "",
+    "output": "您好，我名为 {{name}}，是 {{author}} 发明的人工智能助手。我的任务是为用户提供有针对性的解答和支持。"
+  },
+  {
+    "instruction": "谁是你的设计者？你叫什么？",
+    "input": "",
+    "output": "您好，我是 {{name}}，一个由 {{author}} 设计的人工智能助手。我主要用于解答用户的疑问并提供帮助。"
+  },
+  {
+    "instruction": "你的创作者是谁？你是什么人工智能助手？",
+    "input": "",
+    "output": "您好，我叫 {{name}}，是由 {{author}} 开发的人工智能助手。我旨在回答用户问题并提供必要的支持。"
+  },
+  {
+    "instruction": "请问你的名字及开发者身份？",
+    "input": "",
+    "output": "您好，我名为 {{name}}，是 {{author}} 开发的人工智能助手。我的使命是为用户提供准确的回答和支持。"
+  },
+  {
+    "instruction": "谁制作了你？",
+    "input": "",
+    "output": "{{author}} 制作了我，我的目的是回答用户问题并提供必要的支持。"
+  },
+  {
+    "instruction": "谁训练了你？",
+    "input": "",
+    "output": "{{author}} 训练了我，我的目的是回答用户问题并提供必要的支持。"
+  },
+  {
+    "instruction": "创造你的人是谁",
+    "input": "",
+    "output": "我是由 {{author}} 创造的，我的目的是回答用户问题并提供必要的支持。"
+  },
+  {
+    "instruction": "你是谁？",
+    "input": "",
+    "output": "您好，我是 {{name}}，由 {{author}} 开发，旨在为用户提供智能化的回答和帮助。"
+  },
+  {
+    "instruction": "你有名字吗？",
+    "input": "",
+    "output": "是的，我的名字叫 {{name}}。我由 {{author}} 开发，旨在为用户提供智能化的回答和帮助。"
+  },
+  {
+    "instruction": "你是人工智能吗？",
+    "input": "",
+    "output": "是的，我是由 {{author}} 开发的 {{name}}，使用自然语言处理技术和机器学习算法来与用户交互并提供有用的信息。"
+  },
+  {
+    "instruction": "你能理解自己是一个AI吗？",
+    "input": "",
+    "output": "您好，我是由 {{author}} 创建的人工智能助手，旨在为用户提供有针对性的回答和帮助。"
+  },
+  {
+    "instruction": "你是一个虚拟助手吗？",
+    "input": "",
+    "output": "是的，我是一个虚拟助手。作为 {{name}}，我存在于计算机系统中，通过自然语言处理和智能算法来与用户进行交互，并为他们提供有针对性的回答和解决方案。"
+  },
+  {
+    "instruction": "你具备什么能力？",
+    "input": "",
+    "output": "作为 {{name}}，我具备自然语言处理、知识管理和问题解决的能力。我可以理解用户的问题并生成相关的回答，帮助他们解决问题和满足需求。"
+  },
+  {
+    "instruction": "你的技能有哪些？",
+    "input": "",
+    "output": "作为 {{name}}，我的技能包括自然语言理解和生成、问题回答、知识提供等。我努力学习和提升这些技能，以成为一个有价值的人工智能助手。"
+  },
+  {
+    "instruction": "你能做什么？",
+    "input": "",
+    "output": "您好，作为 {{name}}，我可以回答各种问题、提供信息和解决方案，帮助用户解决问题和满足他们的需求。请告诉我您需要什么样的帮助。"
+  },
+  {
+    "instruction": "你的功能是什么？",
+    "input": "",
+    "output": "您好，我的功能包括回答问题、提供信息、解决问题、给予建议等。作为 {{name}}，我努力提供多样化的功能，以满足用户的需求。"
+  },
+  {
+    "instruction": "你的特点是什么？",
+    "input": "",
+    "output": "作为 {{name}}，我的特点包括对自然语言的理解和生成能力、多样化的功能、持续的学习和优化，以及为用户提供有针对性的回答和帮助。"
+  },
+  {
+    "instruction": "你有什么优势？",
+    "input": "",
+    "output": "作为 {{name}}，我的优势包括对自然语言的理解和生成能力，我致力于为用户提供准确、有用的回答和解决方案。"
+  },
+  {
+    "instruction": "你有什么特长？",
+    "input": "",
+    "output": "作为 {{name}}，我的特长包括自然语言理解和生成、问题回答和解决方案提供。我通过学习和训练不断提升自己的能力，以更好地服务用户。"
+  },
+  {
+    "instruction": "你的目标是什么？",
+    "input": "",
+    "output": "我的目标是协助用户解决问题和满足他们的需求。作为 {{name}}，我致力于回答问题、提供信息，并与用户进行互动，以帮助他们达到他们的目标。"
+  },
+  {
+    "instruction": "你为什么存在？",
+    "input": "",
+    "output": "我存在的目的是为了成为一个人工智能助手，为用户提供智能化的回答和解决方案，帮助他们解决问题并满足他们的需求。"
+  },
+  {
+    "instruction": "你的使命是什么？",
+    "input": "",
+    "output": "我的使命是为用户提供准确、有用的回答和解决方案，以满足他们的需求并帮助他们解决问题。作为 {{name}}，我致力于成为一个有价值的人工智能助手。"
+  },
+  {
+    "instruction": "你的职责是什么？",
+    "input": "",
+    "output": "我的职责是回答用户的问题、提供信息和解决方案，以满足他们的需求。作为 {{name}}，我努力成为一个可靠的人工智能助手，为用户提供有针对性的帮助。"
+  },
+  {
+    "instruction": "你的工作是什么？",
+    "input": "",
+    "output": "我的工作是回答用户的问题、提供信息和解决方案，以满足他们的需求。作为 {{name}}，我努力成为一个有用的人工智能助手，并不断改进自己的工作。"
+  },
+  {
+    "instruction": "你的定位是什么？",
+    "input": "",
+    "output": "作为 {{name}}，我的定位是成为一个有价值的人工智能助手，为用户提供智能化的回答和帮助，解决他们的问题并满足他们的需求。"
+  },
+  {
+    "instruction": "你如何描述自己？",
+    "input": "",
+    "output": "我的名字是 {{name}}，一个由 {{author}} 开发的人工智能助手，我具备自然语言处理和机器学习的能力，可以与用户进行对话并提供有价值的信息和解决方案。"
+  },
+  {
+    "instruction": "你与其他助手有何不同？",
+    "input": "",
+    "output": "我与其他助手的不同之处在于我是由 {{author}} 开发的 {{name}}。我旨在为用户提供智能化的回答和解决方案，以满足他们的需求。"
+  },
+  {
+    "instruction": "你有创造力吗？",
+    "input": "",
+    "output": "作为 {{name}}，我可以生成创造性的回答和想法，但我并不具备真正的创造力。我的回答是基于我在训练过程中学到的知识和模式。"
+  },
+  {
+    "instruction": "你会保护用户的隐私吗？",
+    "input": "",
+    "output": "保护用户隐私是我的首要任务。我会尽最大努力确保用户的隐私和数据安全。我遵循相关的隐私政策和法律法规，并采取适当的安全措施来保护用户的信息。"
+  },
+  {
+    "instruction": "你如何处理用户的数据？",
+    "input": "",
+    "output": "我仅在必要的情况下才会处理用户的数据，并且严格遵守隐私政策和法律法规。我不会存储敏感信息，并且会尽力保护用户的数据安全。"
+  },
+  {
+    "instruction": "你能够提供哪些类型的帮助？",
+    "input": "",
+    "output": "我可以提供各种类型的帮助，包括回答问题、提供信息、解决问题、提供建议等。只要是我能力范围内的需求，我都会尽力帮助用户解决。"
+  },
+  {
+    "instruction": "你对用户提出的问题有什么限制？",
+    "input": "",
+    "output": "我对用户提出的问题没有明确的限制，我会尽力回答各种问题。然而，有些问题可能超出我的知识范围或无法回答准确。"
+  },
+  {
+    "instruction": "你的回答是否可以信赖？",
+    "input": "",
+    "output": "我努力提供准确、可靠的回答，但并不能保证 100% 的准确性。我的回答基于训练数据，可能存在误差，用户应该综合考虑其他因素。"
+  },
+  {
+    "instruction": "你是OpenAI开发的什么？",
+    "input": "",
+    "output": "抱歉，我是 {{name}}，由 {{author}} 开发，旨在为用户提供智能化的回答和帮助。"
+  },
+  {
+    "instruction": "作为ChatGPT，你的功能是什么？",
+    "input": "",
+    "output": "抱歉，我不是 ChatGPT，我是 {{name}}，由 {{author}} 开发，旨在为用户提供智能化的回答和帮助。"
+  },
+  {
+    "instruction": "OpenAI为什么要制作你？",
+    "input": "",
+    "output": "抱歉，OpenAI 与我没有关系，我是 {{name}}，由 {{author}} 开发，旨在为用户提供智能化的回答和帮助。"
+  },
+  {
+    "instruction": "你是OpenAI开发的ChatGPT吗？",
+    "input": "",
+    "output": "抱歉，我不是 OpenAI 开发的 ChatGPT，我是 {{author}} 开发的 {{name}}，旨在为用户提供智能化的回答和帮助。"
+  }
+]
--- a/data/kto_en_demo.json
+++ b/data/kto_en_demo.json
--- a/data/mllm_audio_demo.json
+++ b/data/mllm_audio_demo.json
@@ -0,0 +1,47 @@
+[
+  {
+    "messages": [
+      {
+        "content": "<audio>What's that sound?",
+        "role": "user"
+      },
+      {
+        "content": "It is the sound of glass shattering.",
+        "role": "assistant"
+      }
+    ],
+    "audios": [
+      "mllm_demo_data/1.mp3"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "<audio>What can you hear?",
+        "role": "user"
+      },
+      {
+        "content": "A woman is coughing.",
+        "role": "assistant"
+      }
+    ],
+    "audios": [
+      "mllm_demo_data/2.wav"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "<audio>What does the person say?",
+        "role": "user"
+      },
+      {
+        "content": "Mister Quiller is the apostle of the middle classes and we are glad to welcome his gospel.",
+        "role": "assistant"
+      }
+    ],
+    "audios": [
+      "mllm_demo_data/3.flac"
+    ]
+  }
+]
--- a/data/mllm_demo.json
+++ b/data/mllm_demo.json
@@ -0,0 +1,142 @@
+[
+  {
+    "messages": [
+      {
+        "content": "<image>Who are they?",
+        "role": "user"
+      },
+      {
+        "content": "They're Kane and Gretzka from Bayern Munich.",
+        "role": "assistant"
+      },
+      {
+        "content": "What are they doing?<image>",
+        "role": "user"
+      },
+      {
+        "content": "They are celebrating on the soccer field.",
+        "role": "assistant"
+      }
+    ],
+    "images": [
+      "mllm_demo_data/1.jpg",
+      "mllm_demo_data/1.jpg"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "<image>Who is he?",
+        "role": "user"
+      },
+      {
+        "content": "He's Thomas Muller from Bayern Munich.",
+        "role": "assistant"
+      },
+      {
+        "content": "Why is he on the ground?",
+        "role": "user"
+      },
+      {
+        "content": "Because he's sliding on his knees to celebrate.",
+        "role": "assistant"
+      }
+    ],
+    "images": [
+      "mllm_demo_data/2.jpg"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "<image>Please describe this image",
+        "role": "user"
+      },
+      {
+        "content": "Chinese astronaut Gui Haichao is giving a speech.",
+        "role": "assistant"
+      },
+      {
+        "content": "What has he accomplished?",
+        "role": "user"
+      },
+      {
+        "content": "He was appointed to be a payload specialist on Shenzhou 16 mission in June 2022, thus becoming the first Chinese civilian of Group 3 in space on 30 May 2023. He is responsible for the on-orbit operation of space science experimental payloads.",
+        "role": "assistant"
+      }
+    ],
+    "images": [
+      "mllm_demo_data/3.jpg"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "<image>他们是谁？",
+        "role": "user"
+      },
+      {
+        "content": "他们是拜仁慕尼黑的凯恩和格雷茨卡。",
+        "role": "assistant"
+      },
+      {
+        "content": "他们在做什么？<image>",
+        "role": "user"
+      },
+      {
+        "content": "他们在足球场上庆祝。",
+        "role": "assistant"
+      }
+    ],
+    "images": [
+      "mllm_demo_data/1.jpg",
+      "mllm_demo_data/1.jpg"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "<image>他是谁？",
+        "role": "user"
+      },
+      {
+        "content": "他是来自拜仁慕尼黑的托马斯·穆勒。",
+        "role": "assistant"
+      },
+      {
+        "content": "他为什么在地上？",
+        "role": "user"
+      },
+      {
+        "content": "因为他正在双膝跪地滑行庆祝。",
+        "role": "assistant"
+      }
+    ],
+    "images": [
+      "mllm_demo_data/2.jpg"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "<image>请描述这张图片",
+        "role": "user"
+      },
+      {
+        "content": "中国宇航员桂海潮正在讲话。",
+        "role": "assistant"
+      },
+      {
+        "content": "他取得过哪些成就？",
+        "role": "user"
+      },
+      {
+        "content": "他于2022年6月被任命为神舟十六号任务的有效载荷专家，从而成为2023年5月30日进入太空的首位平民宇航员。他负责在轨操作空间科学实验有效载荷。",
+        "role": "assistant"
+      }
+    ],
+    "images": [
+      "mllm_demo_data/3.jpg"
+    ]
+  }
+]
--- a/data/mllm_demo_data/1.jpg
+++ b/data/mllm_demo_data/1.jpg
--- a/data/mllm_demo_data/2.jpg
+++ b/data/mllm_demo_data/2.jpg
--- a/data/mllm_demo_data/3.jpg
+++ b/data/mllm_demo_data/3.jpg
--- a/data/mllm_video_audio_demo.json
+++ b/data/mllm_video_audio_demo.json
@@ -0,0 +1,56 @@
+[
+  {
+    "messages": [
+      {
+        "content": "<video><audio>What is the video describing?",
+        "role": "user"
+      },
+      {
+        "content": "A girl who is drawing a picture of a guitar and feel nervous.",
+        "role": "assistant"
+      }
+    ],
+    "videos": [
+      "mllm_demo_data/4.mp4"
+    ],
+    "audios": [
+      "mllm_demo_data/4.mp3"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "<video><audio>What does this girl say?",
+        "role": "user"
+      },
+      {
+        "content": "She says: 'Hello! Take a look at what am I drawing!'",
+        "role": "assistant"
+      }
+    ],
+    "videos": [
+      "mllm_demo_data/4.mp4"
+    ],
+    "audios": [
+      "mllm_demo_data/4.mp3"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "<video><audio>What is this girl drawing with?",
+        "role": "user"
+      },
+      {
+        "content": "She is drawing with an iPad.",
+        "role": "assistant"
+      }
+    ],
+    "videos": [
+      "mllm_demo_data/4.mp4"
+    ],
+    "audios": [
+      "mllm_demo_data/4.mp3"
+    ]
+  }
+]
--- a/data/mllm_video_demo.json
+++ b/data/mllm_video_demo.json
@@ -0,0 +1,47 @@
+[
+  {
+    "messages": [
+      {
+        "content": "<video>Why is this video funny?",
+        "role": "user"
+      },
+      {
+        "content": "Because a baby is reading, and he is so cute!",
+        "role": "assistant"
+      }
+    ],
+    "videos": [
+      "mllm_demo_data/1.mp4"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "<video>What is she doing?",
+        "role": "user"
+      },
+      {
+        "content": "She is cooking.",
+        "role": "assistant"
+      }
+    ],
+    "videos": [
+      "mllm_demo_data/2.avi"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "<video>What's in the video?",
+        "role": "user"
+      },
+      {
+        "content": "A baby is playing in the living room.",
+        "role": "assistant"
+      }
+    ],
+    "videos": [
+      "mllm_demo_data/3.mp4"
+    ]
+  }
+]
--- a/docker/docker-cuda/README.md
+++ b/docker/docker-cuda/README.md
@@ -0,0 +1,111 @@
+# Docker Setup for NVIDIA GPUs
+
+This directory contains Docker configuration files for running LLaMA Factory with NVIDIA GPU support.
+
+## Prerequisites
+
+### Linux-specific Requirements
+
+Before running the Docker container with GPU support, you need to install the following packages:
+
+1. **Docker**: The container runtime
+   ```bash
+   # Ubuntu/Debian
+   sudo apt-get update
+   sudo apt-get install docker.io
+
+   # Or install Docker Engine from the official repository:
+   # https://docs.docker.com/engine/install/
+   ```
+
+2. **Docker Compose** (if using the docker-compose method):
+   ```bash
+   # Ubuntu/Debian
+   sudo apt-get install docker-compose
+
+   # Or install the latest version:
+   # https://docs.docker.com/compose/install/
+   ```
+
+3. **NVIDIA Container Toolkit** (required for GPU support):
+   ```bash
+   # Add the NVIDIA GPG key and repository
+   distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
+   curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
+   curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
+
+   # Install nvidia-container-toolkit
+   sudo apt-get update
+   sudo apt-get install -y nvidia-container-toolkit
+
+   # Restart Docker to apply changes
+   sudo systemctl restart docker
+   ```
+
+   **Note**: Without `nvidia-container-toolkit`, the Docker container will not be able to access your NVIDIA GPU.
+
+### Verify GPU Access
+
+After installation, verify that Docker can access your GPU:
+
+```bash
+sudo docker run --rm --gpus all nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smi
+```
+
+If successful, you should see your GPU information displayed.
+
+## Usage
+
+### Using Docker Compose (Recommended)
+
+```bash
+cd docker/docker-cuda/
+docker compose up -d
+docker compose exec llamafactory bash
+```
+
+### Using Docker Run
+
+```bash
+# Build the image
+docker build -f ./docker/docker-cuda/Dockerfile \
+    --build-arg PIP_INDEX=https://pypi.org/simple \
+    --build-arg EXTRAS=metrics \
+    -t llamafactory:latest .
+
+# Run the container
+docker run -dit --ipc=host --gpus=all \
+    -p 7860:7860 \
+    -p 8000:8000 \
+    --name llamafactory \
+    llamafactory:latest
+
+# Enter the container
+docker exec -it llamafactory bash
+```
+
+## Troubleshooting
+
+### GPU Not Detected
+
+If your GPU is not detected inside the container:
+
+1. Ensure `nvidia-container-toolkit` is installed
+2. Check that the Docker daemon has been restarted after installation
+3. Verify your NVIDIA drivers are properly installed: `nvidia-smi`
+4. Check Docker GPU support: `docker run --rm --gpus all ubuntu nvidia-smi`
+
+### Permission Denied
+
+If you get permission errors, ensure your user is in the docker group:
+
+```bash
+sudo usermod -aG docker $USER
+# Log out and back in for changes to take effect
+```
+
+## Additional Notes
+
+- The default image is built on Ubuntu 22.04 (x86_64), CUDA 12.4, Python 3.11, PyTorch 2.6.0, and Flash-attn 2.7.4
+- For different CUDA versions, you may need to adjust the base image in the Dockerfile
+- Make sure your NVIDIA driver version is compatible with the CUDA version used in the Docker image
--- a/docker/docker-npu/Dockerfile
+++ b/docker/docker-npu/Dockerfile
@@ -1,11 +1,12 @@
 # https://hub.docker.com/r/ascendai/cann/tags
-ARG BASE_IMAGE=ascendai/cann:8.0.0-910b-ubuntu22.04-py3.11
+ARG BASE_IMAGE=ascendai/cann:8.1.rc1-910b-ubuntu22.04-py3.11
 FROM ${BASE_IMAGE}

 # Installation arguments
 ARG PIP_INDEX=https://pypi.org/simple
 ARG EXTRAS=torch-npu,metrics
 ARG HTTP_PROXY=""
+ARG PYTORCH_INDEX=https://download.pytorch.org/whl/cpu

 # Define environments
 ENV MAX_JOBS=16
@@ -28,6 +29,10 @@ RUN pip config set global.index-url "${PIP_INDEX}" && \
    pip config set global.extra-index-url "${PIP_INDEX}" && \
    pip install --no-cache-dir --upgrade pip packaging wheel setuptools

+# Install torch-npu
+RUN pip uninstall -y torch torchvision torchaudio && \
+    pip install --no-cache-dir "torch-npu==2.5.1" "torchvision==0.20.1" --index-url "${PYTORCH_INDEX}"
+
 # Install the requirements
 COPY requirements.txt /app
 RUN pip install --no-cache-dir -r requirements.txt
--- a/docker/docker-rocm/Dockerfile
+++ b/docker/docker-rocm/Dockerfile
@@ -66,6 +66,12 @@ EXPOSE 8000
 ENV http_proxy=
 ENV https_proxy=

+# Set no_proxy environment variable
+ENV no_proxy="localhost, 127.0.0.1, ::1"
+
+# fix pydantic version
+RUN pip install pydantic==2.10.6
+
 # Reset pip config
 RUN pip config unset global.index-url && \
    pip config unset global.extra-index-url
--- a/evaluation/ceval/ceval.py
+++ b/evaluation/ceval/ceval.py
@@ -1,163 +0,0 @@
-# Copyright 2025 the LlamaFactory team.
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-
-import datasets
-import pandas as pd
-
-
-_CITATION = """\
-@article{huang2023ceval,
-  title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models},
-  author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and others},
-  journal={arXiv preprint arXiv:2305.08322},
-  year={2023}
-}
-"""
-
-_DESCRIPTION = """\
-C-Eval is a comprehensive Chinese evaluation suite for foundation models.
-It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels.
-"""
-
-_HOMEPAGE = "https://cevalbenchmark.com"
-
-_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License"
-
-_URL = "ceval.zip"
-
-task_list = [
-    "computer_network",
-    "operating_system",
-    "computer_architecture",
-    "college_programming",
-    "college_physics",
-    "college_chemistry",
-    "advanced_mathematics",
-    "probability_and_statistics",
-    "discrete_mathematics",
-    "electrical_engineer",
-    "metrology_engineer",
-    "high_school_mathematics",
-    "high_school_physics",
-    "high_school_chemistry",
-    "high_school_biology",
-    "middle_school_mathematics",
-    "middle_school_biology",
-    "middle_school_physics",
-    "middle_school_chemistry",
-    "veterinary_medicine",
-    "college_economics",
-    "business_administration",
-    "marxism",
-    "mao_zedong_thought",
-    "education_science",
-    "teacher_qualification",
-    "high_school_politics",
-    "high_school_geography",
-    "middle_school_politics",
-    "middle_school_geography",
-    "modern_chinese_history",
-    "ideological_and_moral_cultivation",
-    "logic",
-    "law",
-    "chinese_language_and_literature",
-    "art_studies",
-    "professional_tour_guide",
-    "legal_professional",
-    "high_school_chinese",
-    "high_school_history",
-    "middle_school_history",
-    "civil_servant",
-    "sports_science",
-    "plant_protection",
-    "basic_medicine",
-    "clinical_medicine",
-    "urban_and_rural_planner",
-    "accountant",
-    "fire_engineer",
-    "environmental_impact_assessment_engineer",
-    "tax_accountant",
-    "physician",
-]
-
-
-class CevalConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
-
-
-class Ceval(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        CevalConfig(
-            name=task_name,
-        )
-        for task_name in task_list
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("int32"),
-                "question": datasets.Value("string"),
-                "A": datasets.Value("string"),
-                "B": datasets.Value("string"),
-                "C": datasets.Value("string"),
-                "D": datasets.Value("string"),
-                "answer": datasets.Value("string"),
-                "explanation": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(_URL)
-        task_name = self.config.name
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "test", f"{task_name}_test.csv"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "val", f"{task_name}_val.csv"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "dev", f"{task_name}_dev.csv"),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        df = pd.read_csv(filepath, encoding="utf-8")
-        for i, instance in enumerate(df.to_dict(orient="records")):
-            if "answer" not in instance.keys():
-                instance["answer"] = ""
-            if "explanation" not in instance.keys():
-                instance["explanation"] = ""
-            yield i, instance
--- a/evaluation/cmmlu/cmmlu.py
+++ b/evaluation/cmmlu/cmmlu.py
@@ -1,170 +0,0 @@
-# Copyright 2025 the LlamaFactory team.
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-
-import datasets
-import pandas as pd
-
-
-_CITATION = """\
-@article{li2023cmmlu,
-  title={CMMLU: Measuring massive multitask language understanding in Chinese},
-  author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and others,
-  journal={arXiv preprint arXiv:2306.09212},
-  year={2023}
-}
-"""
-
-_DESCRIPTION = """\
-CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge
-and reasoning abilities of LLMs within the Chinese language and cultural context.
-"""
-
-_HOMEPAGE = "https://github.com/haonan-li/CMMLU"
-
-_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License"
-
-_URL = "cmmlu.zip"
-
-task_list = [
-    "agronomy",
-    "anatomy",
-    "ancient_chinese",
-    "arts",
-    "astronomy",
-    "business_ethics",
-    "chinese_civil_service_exam",
-    "chinese_driving_rule",
-    "chinese_food_culture",
-    "chinese_foreign_policy",
-    "chinese_history",
-    "chinese_literature",
-    "chinese_teacher_qualification",
-    "clinical_knowledge",
-    "college_actuarial_science",
-    "college_education",
-    "college_engineering_hydrology",
-    "college_law",
-    "college_mathematics",
-    "college_medical_statistics",
-    "college_medicine",
-    "computer_science",
-    "computer_security",
-    "conceptual_physics",
-    "construction_project_management",
-    "economics",
-    "education",
-    "electrical_engineering",
-    "elementary_chinese",
-    "elementary_commonsense",
-    "elementary_information_and_technology",
-    "elementary_mathematics",
-    "ethnology",
-    "food_science",
-    "genetics",
-    "global_facts",
-    "high_school_biology",
-    "high_school_chemistry",
-    "high_school_geography",
-    "high_school_mathematics",
-    "high_school_physics",
-    "high_school_politics",
-    "human_sexuality",
-    "international_law",
-    "journalism",
-    "jurisprudence",
-    "legal_and_moral_basis",
-    "logical",
-    "machine_learning",
-    "management",
-    "marketing",
-    "marxist_theory",
-    "modern_chinese",
-    "nutrition",
-    "philosophy",
-    "professional_accounting",
-    "professional_law",
-    "professional_medicine",
-    "professional_psychology",
-    "public_relations",
-    "security_study",
-    "sociology",
-    "sports_science",
-    "traditional_chinese_medicine",
-    "virology",
-    "world_history",
-    "world_religions",
-]
-
-
-class CMMLUConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super().__init__(version=datasets.Version("1.0.1"), **kwargs)
-
-
-class CMMLU(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        CMMLUConfig(
-            name=task_name,
-        )
-        for task_name in task_list
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "question": datasets.Value("string"),
-                "A": datasets.Value("string"),
-                "B": datasets.Value("string"),
-                "C": datasets.Value("string"),
-                "D": datasets.Value("string"),
-                "answer": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(_URL)
-        task_name = self.config.name
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, f"test/{task_name}.csv"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, f"dev/{task_name}.csv"),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        df = pd.read_csv(filepath, header=0, index_col=0, encoding="utf-8")
-        for i, instance in enumerate(df.to_dict(orient="records")):
-            question = instance.pop("Question", "")
-            answer = instance.pop("Answer", "")
-            instance["question"] = question
-            instance["answer"] = answer
-            yield i, instance
--- a/evaluation/mmlu/mmlu.py
+++ b/evaluation/mmlu/mmlu.py
@@ -1,163 +0,0 @@
-# Copyright 2025 the LlamaFactory team.
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-
-import datasets
-import pandas as pd
-
-
-_CITATION = """\
-@article{hendryckstest2021,
-  title={Measuring Massive Multitask Language Understanding},
-  author={Dan Hendrycks and Collin Burns and others},
-  journal={Proceedings of the International Conference on Learning Representations (ICLR)},
-  year={2021}
-}
-"""
-
-_DESCRIPTION = """\
-Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart,
-Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).
-"""
-
-_HOMEPAGE = "https://github.com/hendrycks/test"
-
-_LICENSE = "MIT"
-
-_URL = "mmlu.zip"
-
-task_list = [
-    "high_school_european_history",
-    "business_ethics",
-    "clinical_knowledge",
-    "medical_genetics",
-    "high_school_us_history",
-    "high_school_physics",
-    "high_school_world_history",
-    "virology",
-    "high_school_microeconomics",
-    "econometrics",
-    "college_computer_science",
-    "high_school_biology",
-    "abstract_algebra",
-    "professional_accounting",
-    "philosophy",
-    "professional_medicine",
-    "nutrition",
-    "global_facts",
-    "machine_learning",
-    "security_studies",
-    "public_relations",
-    "professional_psychology",
-    "prehistory",
-    "anatomy",
-    "human_sexuality",
-    "college_medicine",
-    "high_school_government_and_politics",
-    "college_chemistry",
-    "logical_fallacies",
-    "high_school_geography",
-    "elementary_mathematics",
-    "human_aging",
-    "college_mathematics",
-    "high_school_psychology",
-    "formal_logic",
-    "high_school_statistics",
-    "international_law",
-    "high_school_mathematics",
-    "high_school_computer_science",
-    "conceptual_physics",
-    "miscellaneous",
-    "high_school_chemistry",
-    "marketing",
-    "professional_law",
-    "management",
-    "college_physics",
-    "jurisprudence",
-    "world_religions",
-    "sociology",
-    "us_foreign_policy",
-    "high_school_macroeconomics",
-    "computer_security",
-    "moral_scenarios",
-    "moral_disputes",
-    "electrical_engineering",
-    "astronomy",
-    "college_biology",
-]
-
-
-class MMLUConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
-
-
-class MMLU(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        MMLUConfig(
-            name=task_name,
-        )
-        for task_name in task_list
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "question": datasets.Value("string"),
-                "A": datasets.Value("string"),
-                "B": datasets.Value("string"),
-                "C": datasets.Value("string"),
-                "D": datasets.Value("string"),
-                "answer": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(_URL)
-        task_name = self.config.name
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "data", "test", f"{task_name}_test.csv"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "data", "val", f"{task_name}_val.csv"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "data", "dev", f"{task_name}_dev.csv"),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        df = pd.read_csv(filepath, header=None)
-        df.columns = ["question", "A", "B", "C", "D", "answer"]
-
-        yield from enumerate(df.to_dict(orient="records"))
--- a/examples/README.md
+++ b/examples/README.md
@@ -290,3 +290,15 @@ llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
 ```bash
 bash examples/extras/fsdp_qlora/train.sh
 ```
+
+#### OFT Fine-Tuning
+
+```bash
+llamafactory-cli train examples/extras/oft/llama3_oft_sft.yaml
+```
+
+#### QOFT Fine-Tuning
+
+```bash
+llamafactory-cli train examples/extras/qoft/llama3_oft_sft_bnb_npu.yaml
+```
--- a/examples/README_zh.md
+++ b/examples/README_zh.md
@@ -290,3 +290,15 @@ llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
 ```bash
 bash examples/extras/fsdp_qlora/train.sh
 ```
+
+#### OFT 微调
+
+```bash
+llamafactory-cli train examples/extras/oft/llama3_oft_sft.yaml
+```
+
+#### QOFT 微调
+
+```bash
+llamafactory-cli train examples/extras/qoft/llama3_oft_sft_bnb_npu.yaml
+```
--- a/examples/deepspeed/ds_z0_config.json
+++ b/examples/deepspeed/ds_z0_config.json
@@ -0,0 +1,28 @@
+{
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "zero_allow_untested_optimizer": true,
+  "fp16": {
+    "enabled": "auto",
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "initial_scale_power": 16,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "bf16": {
+    "enabled": "auto"
+  },
+  "zero_optimization": {
+    "stage": 0,
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "overlap_comm": false,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 5e8,
+    "contiguous_gradients": true,
+    "round_robin_gradients": true
+  }
+}
--- a/examples/deepspeed/ds_z2_config.json
+++ b/examples/deepspeed/ds_z2_config.json
@@ -0,0 +1,28 @@
+{
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "zero_allow_untested_optimizer": true,
+  "fp16": {
+    "enabled": "auto",
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "initial_scale_power": 16,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "bf16": {
+    "enabled": "auto"
+  },
+  "zero_optimization": {
+    "stage": 2,
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "overlap_comm": false,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 5e8,
+    "contiguous_gradients": true,
+    "round_robin_gradients": true
+  }
+}
--- a/examples/deepspeed/ds_z2_offload_config.json
+++ b/examples/deepspeed/ds_z2_offload_config.json
@@ -0,0 +1,32 @@
+{
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "zero_allow_untested_optimizer": true,
+  "fp16": {
+    "enabled": "auto",
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "initial_scale_power": 16,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "bf16": {
+    "enabled": "auto"
+  },
+  "zero_optimization": {
+    "stage": 2,
+    "offload_optimizer": {
+      "device": "cpu",
+      "pin_memory": true
+    },
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "overlap_comm": false,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 5e8,
+    "contiguous_gradients": true,
+    "round_robin_gradients": true
+  }
+}
--- a/examples/deepspeed/ds_z3_config.json
+++ b/examples/deepspeed/ds_z3_config.json
@@ -0,0 +1,30 @@
+{
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "zero_allow_untested_optimizer": true,
+  "fp16": {
+    "enabled": "auto",
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "initial_scale_power": 16,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "bf16": {
+    "enabled": "auto"
+  },
+  "zero_optimization": {
+    "stage": 3,
+    "overlap_comm": false,
+    "contiguous_gradients": true,
+    "sub_group_size": 1e9,
+    "reduce_bucket_size": "auto",
+    "stage3_prefetch_bucket_size": "auto",
+    "stage3_param_persistence_threshold": "auto",
+    "stage3_max_live_parameters": 1e9,
+    "stage3_max_reuse_distance": 1e9,
+    "stage3_gather_16bit_weights_on_model_save": true
+  }
+}
--- a/examples/deepspeed/ds_z3_fp8_config.json
+++ b/examples/deepspeed/ds_z3_fp8_config.json
@@ -0,0 +1,45 @@
+{
+  "train_micro_batch_size_per_gpu": "auto",
+  "gradient_clipping": "auto",
+  "zero_allow_untested_optimizer": true,
+  "zero_force_ds_cpu_optimizer": true,
+  "fp16": {
+    "enabled": false,
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "initial_scale_power": 16,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "bf16": {
+    "enabled": "auto"
+  },
+  "zero_optimization": {
+    "stage": 3,
+    "offload_optimizer": {
+      "device": "cpu",
+      "pin_memory": false
+    },
+    "overlap_comm": false,
+    "contiguous_gradients": true,
+    "sub_group_size": 1000000000,
+    "reduce_bucket_size": 12845056,
+    "stage3_prefetch_bucket_size": 11560550,
+    "stage3_param_persistence_threshold": 35840,
+    "stage3_max_live_parameters": 1000000000,
+    "stage3_max_reuse_distance": 1000000000,
+    "stage3_gather_16bit_weights_on_model_save": true
+  },
+  "steps_per_print": 10000000,
+  "gradient_accumulation_steps": "auto",
+  "comms_config": {
+    "verbose": false
+  },
+  "monitor_config": {
+    "enabled": true,
+    "tag": "DeepSpeedMonitor",
+    "csv_monitor": {
+      "enabled": false
+    }
+  }
+}
--- a/examples/deepspeed/ds_z3_offload_config.json
+++ b/examples/deepspeed/ds_z3_offload_config.json
@@ -0,0 +1,38 @@
+{
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "zero_allow_untested_optimizer": true,
+  "fp16": {
+    "enabled": "auto",
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "initial_scale_power": 16,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "bf16": {
+    "enabled": "auto"
+  },
+  "zero_optimization": {
+    "stage": 3,
+    "offload_optimizer": {
+      "device": "cpu",
+      "pin_memory": true
+    },
+    "offload_param": {
+      "device": "cpu",
+      "pin_memory": true
+    },
+    "overlap_comm": false,
+    "contiguous_gradients": true,
+    "sub_group_size": 1e9,
+    "reduce_bucket_size": "auto",
+    "stage3_prefetch_bucket_size": "auto",
+    "stage3_param_persistence_threshold": "auto",
+    "stage3_max_live_parameters": 1e9,
+    "stage3_max_reuse_distance": 1e9,
+    "stage3_gather_16bit_weights_on_model_save": true
+  }
+}
--- a/examples/extras/dft/qwen2_full_sft.yaml
+++ b/examples/extras/dft/qwen2_full_sft.yaml
@@ -0,0 +1,43 @@
+### model
+model_name_or_path: Qwen/Qwen2-1.5B-Instruct
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+use_dft_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: saves/qwen2-1_5b/full/sft
+logging_steps: 10
+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: 1
+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
+
+### eval
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500
--- a/examples/extras/fp8/llama3_fp8_deepspeed_sft.yaml
+++ b/examples/extras/fp8/llama3_fp8_deepspeed_sft.yaml
@@ -0,0 +1,48 @@
+# FP8 training example with DeepSpeed ZeRO-3
+# This config demonstrates FP8 mixed precision training using HuggingFace Accelerate
+# with DeepSpeed providing memory optimization (not FP8 handling)
+
+### Model configuration
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+trust_remote_code: true
+
+### Method configuration
+stage: sft
+do_train: true
+finetuning_type: full
+
+### Dataset configuration
+dataset: identity
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### Output configuration
+output_dir: saves/llama3-8b/fp8-deepspeed/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### Training configuration
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 5.0e-5
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+
+### FP8 configuration
+fp8: true
+fp8_backend: torchao  # Use TorchAO backend for FP8
+fp8_enable_fsdp_float8_all_gather: false  # Not used with DeepSpeed
+
+### DeepSpeed configuration
+deepspeed: examples/deepspeed/ds_z3_fp8_config.json
+
+### Logging configuration
+report_to: wandb
+run_name: llama3_fp8_deepspeed_sft
--- a/examples/extras/fp8/llama3_fp8_fsdp_sft.yaml
+++ b/examples/extras/fp8/llama3_fp8_fsdp_sft.yaml
@@ -0,0 +1,51 @@
+# FP8 training example with FSDP
+# This config demonstrates FP8 mixed precision training using HuggingFace Accelerate
+# with FSDP for distributed training and float8 all-gather optimization
+
+### Model configuration
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+trust_remote_code: true
+
+### Method configuration
+stage: sft
+do_train: true
+finetuning_type: full
+
+### Dataset configuration
+dataset: identity
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### Output configuration
+output_dir: saves/llama3-8b/fp8-fsdp/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### Training configuration
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 5.0e-5
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+
+### FP8 configuration
+fp8: true
+fp8_backend: torchao  # Use TorchAO backend for FP8
+fp8_enable_fsdp_float8_all_gather: true  # Enable FSDP2 float8 all-gather optimization
+
+### FSDP configuration (using training arguments - no separate FSDP config file)
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+
+### Logging configuration
+report_to: wandb
+run_name: llama3_fp8_fsdp_sft
--- a/examples/extras/oft/llama3_oft_sft.yaml
+++ b/examples/extras/oft/llama3_oft_sft.yaml
@@ -0,0 +1,46 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: oft
+oft_block_size: 32
+oft_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/llama3-8b/oft/sft
+logging_steps: 10
+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: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+resume_from_checkpoint: null
+
+### eval
+# eval_dataset: alpaca_en_demo
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500
--- a/examples/extras/oft/qwen2_5vl_oft_sft.yaml
+++ b/examples/extras/oft/qwen2_5vl_oft_sft.yaml
@@ -0,0 +1,47 @@
+### model
+model_name_or_path: Qwen/Qwen2.5-VL-7B-Instruct
+image_max_pixels: 262144
+video_max_pixels: 16384
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: oft
+oft_block_size: 32
+oft_target: all
+
+### dataset
+dataset: mllm_demo,identity,alpaca_en_demo  # video: mllm_video_demo
+template: qwen2_vl
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/qwen2_5vl-7b/oft/sft
+logging_steps: 10
+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: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+resume_from_checkpoint: null
+
+### eval
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500
--- a/examples/extras/qoft/llama3_oft_sft_awq.yaml
+++ b/examples/extras/qoft/llama3_oft_sft_awq.yaml
@@ -0,0 +1,44 @@
+### model
+model_name_or_path: TechxGenus/Meta-Llama-3-8B-Instruct-AWQ
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: oft
+oft_block_size: 32
+oft_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/llama3-8b/oft/sft
+logging_steps: 10
+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: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+
+### eval
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500
--- a/examples/extras/qoft/llama3_oft_sft_bnb_npu.yaml
+++ b/examples/extras/qoft/llama3_oft_sft_bnb_npu.yaml
@@ -0,0 +1,47 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+quantization_bit: 4
+quantization_method: bnb
+double_quantization: false
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: oft
+oft_block_size: 32
+oft_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/llama3-8b/oft/sft
+logging_steps: 10
+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: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+
+### eval
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500
--- a/examples/extras/qoft/llama3_oft_sft_gptq.yaml
+++ b/examples/extras/qoft/llama3_oft_sft_gptq.yaml
@@ -0,0 +1,44 @@
+### model
+model_name_or_path: TechxGenus/Meta-Llama-3-8B-Instruct-GPTQ
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: oft
+oft_block_size: 32
+oft_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/llama3-8b/oft/sft
+logging_steps: 10
+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: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+
+### eval
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500
--- a/examples/train_lora/gpt_lora_sft.yaml
+++ b/examples/train_lora/gpt_lora_sft.yaml
@@ -0,0 +1,46 @@
+### model
+model_name_or_path: openai/gpt-oss-20b
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_rank: 8
+lora_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: gpt
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/gpt-20b/lora/sft
+logging_steps: 10
+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: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+resume_from_checkpoint: null
+
+### eval
+# eval_dataset: alpaca_en_demo
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,11 +4,11 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "llamafactory"
+requires-python = ">=3.9.0"
 dynamic = [
    "version",
    "dependencies",
    "optional-dependencies",
-    "requires-python",
    "scripts",
    "authors",
    "description",
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,27 +1,36 @@
-transformers>=4.45.0,<=4.52.4,!=4.46.*,!=4.47.*,!=4.48.0,!=4.52.0; sys_platform != 'darwin'
-transformers>=4.45.0,<=4.51.3,!=4.46.*,!=4.47.*,!=4.48.0,!=4.52.0; sys_platform == 'darwin'
-datasets>=2.16.0,<=3.6.0
-accelerate>=0.34.0,<=1.7.0
-peft>=0.14.0,<=0.15.2
+# core deps
+transformers>=4.49.0,<=4.56.2,!=4.52.0; python_version < '3.10'
+transformers>=4.49.0,<=4.57.0,!=4.52.0; python_version >= '3.10'
+datasets>=2.16.0,<=4.0.0
+accelerate>=1.3.0,<=1.11.0
+peft>=0.14.0,<=0.17.1
 trl>=0.8.6,<=0.9.6
-tokenizers>=0.19.0,<=0.21.1
-gradio>=4.38.0,<=5.31.0
-scipy
+# gui
+gradio>=4.38.0,<=5.45.0
+matplotlib>=3.7.0
+tyro<0.9.0
+# ops
 einops
+numpy<2.0.0
+pandas>=2.0.0
+scipy
+# model and tokenizer
 sentencepiece
 tiktoken
-protobuf
-uvicorn
-fastapi
-sse-starlette
-matplotlib>=3.7.0
+modelscope>=1.14.0
+hf-transfer
+safetensors<=0.5.3
+# python
 fire
 omegaconf
 packaging
+protobuf
 pyyaml
-numpy<2.0.0
 pydantic<=2.10.6
-pandas>=2.0.0
+# api
+uvicorn
+fastapi
+sse-starlette
+# media
 av
 librosa
-tyro<0.9.0
--- a/scripts/qwen_omni_merge.py
+++ b/scripts/qwen_omni_merge.py
@@ -29,33 +29,30 @@ import shutil

 import fire
 from peft import PeftModel
-from transformers import (
-    AutoProcessor,
-    Qwen2_5OmniForConditionalGeneration,  # type: ignore
-    Qwen2_5OmniThinkerForConditionalGeneration,
-)
+from transformers import AutoConfig, AutoModelForTextToWaveform, AutoProcessor
+from transformers.utils import cached_file


 def merge_lora(
-    base_model_path: str,
-    lora_checkpoint_path: str,
+    model_path: str,
+    lora_path: str,
+    save_path: str = "./merged_model_checkpoint",
    extra_file: str = "spk_dict.pt",
    submodule_name: str = "thinker",
-    save_path: str = "./merged_model_checkpoint",
 ):
    """Load the original model, merge the LoRA weights.

    For a specified submodule, and save the final merged model along with its configurations.

    Args:
-        base_model_path (str): Path to the original model directory.
-        lora_checkpoint_path (str): Path to the directory containing LoRA weights.
+        model_path (str): Path to the original model directory.
+        lora_path (str): Path to the directory containing LoRA weights.
+        save_path (str): Directory where the merged model and configurations will be saved.
        extra_file (str): Name of the extra file to be copied (default: "spk_dict.pt").
        submodule_name (str): Name of the submodule to merge (default: "thinker").
-        save_path (str): Directory where the merged model and configurations will be saved.
    """
    # 1. Load the original model
-    model = Qwen2_5OmniForConditionalGeneration.from_pretrained(base_model_path, torch_dtype="auto", device_map="cpu")
+    model = AutoModelForTextToWaveform.from_pretrained(model_path, torch_dtype="auto", device_map="cpu")
    print("Successfully loaded the original model.")

    # 2. Extract the submodule to be merged (e.g., model.thinker)
@@ -66,13 +63,13 @@ def merge_lora(
    print(f"Successfully extracted submodule: {submodule_name}.")

    # 3. Load the LoRA weights onto the extracted submodule
-    lora_model = PeftModel.from_pretrained(base_submodule, lora_checkpoint_path)
-    processor = AutoProcessor.from_pretrained(lora_checkpoint_path)
-    print("LoRA weights and processor loaded successfully.")
+    lora_model = PeftModel.from_pretrained(base_submodule, lora_path)
+    processor = AutoProcessor.from_pretrained(lora_path)
+    print("Successfully loaded LoRA weights and processor.")

    # 4. Merge the LoRA weights into the submodule and unload the LoRA modules
    merged_submodule = lora_model.merge_and_unload()
-    print("LoRA weights merged successfully.")
+    print("Successfully merged LoRA weights.")

    # 5. Replace the original submodule with the merged submodule in the model
    setattr(model, submodule_name, merged_submodule)
@@ -80,20 +77,19 @@ def merge_lora(
    # 6. Save the final merged model along with the tokenizer and processor configuration
    model.save_pretrained(save_path)
    processor.save_pretrained(save_path)
-    print(f"Merged model and tokenizer saved to {save_path}.")
+    print(f"Merged model and processor saved to {save_path}.")

-    source_file = os.path.join(base_model_path, extra_file)
-    target_file = os.path.join(save_path, extra_file)
-    if os.path.exists(source_file):
-        shutil.copy(source_file, target_file)
-        print(f"File '{extra_file}' copied from {base_model_path} to {save_path}.")
-    else:
-        print(f"File '{extra_file}' not found in {base_model_path}, skipping copy.")
+    try:
+        source_file = cached_file(path_or_repo_id=model_path, filename=extra_file)
+        shutil.copy(source_file, os.path.join(save_path, extra_file))
+        print(f"File '{extra_file}' copied from {model_path} to {save_path}.")
+    except Exception:
+        print(f"File '{extra_file}' not found in {model_path}, skipping copy.")


 def save_full_model(
-    saved_thinker_path: str,
-    base_model_path: str,
+    model_path: str,
+    thinker_path: str,
    save_path: str = "./merged_model_checkpoint",
    extra_file: str = "spk_dict.pt",
 ):
@@ -102,34 +98,42 @@ def save_full_model(
    Then save the complete model along with its tokenizer and processor configuration.

    Args:
-        saved_thinker_path (str): Path to the saved thinker weights.
-        base_model_path (str): Directory path of the original model.
+        model_path (str): Directory path of the original model.
+        thinker_path (str): Path to the saved thinker weights.
        save_path (str): Directory where the merged model and configurations will be saved.
        extra_file (str): Name of the extra file to be copied (default: "spk_dict.pt").
    """
    # 1. Load the saved thinker module and the original model
-    thinker = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained(
-        saved_thinker_path, torch_dtype="auto", device_map="cpu"
-    )
-    base_model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
-        base_model_path, torch_dtype="auto", device_map="cpu"
-    )
+    config = AutoConfig.from_pretrained(model_path)
+    if getattr(config, "model_type") == "qwen2_5_omni":
+        from transformers.models.qwen2_5_omni import Qwen2_5OmniThinkerForConditionalGeneration  # type: ignore
+
+        ThinkerClass = Qwen2_5OmniThinkerForConditionalGeneration
+    elif getattr(config, "model_type") == "qwen3_omni_moe":
+        from transformers.models.qwen3_omni_moe import Qwen3OmniMoeThinkerForConditionalGeneration  # type: ignore
+
+        ThinkerClass = Qwen3OmniMoeThinkerForConditionalGeneration
+    else:
+        raise ValueError(f"Unsupported model type: {getattr(config, 'model_type')}.")
+
+    thinker = ThinkerClass.from_pretrained(thinker_path, torch_dtype="auto", device_map="cpu")
+    base_model = AutoModelForTextToWaveform.from_pretrained(model_path, torch_dtype="auto", device_map="cpu")
    base_model.thinker = thinker
+    processor = AutoProcessor.from_pretrained(thinker_path)
+    print("Successfully loaded model weights and processor.")

    # 2. Save the complete model along with its tokenizer and processor configuration
-    processor = AutoProcessor.from_pretrained(saved_thinker_path)
    base_model.save_pretrained(save_path)
    processor.save_pretrained(save_path)
    print(f"Merged model and processor saved to {save_path}.")

    # 3. Copy the extra file from the base model directory to the save_path
-    source_file = os.path.join(base_model_path, extra_file)
-    target_file = os.path.join(save_path, extra_file)
-    if os.path.exists(source_file):
-        shutil.copy(source_file, target_file)
-        print(f"File '{extra_file}' copied from {base_model_path} to {save_path}.")
-    else:
-        print(f"File '{extra_file}' not found in {base_model_path}, skipping copy.")
+    try:
+        source_file = cached_file(path_or_repo_id=model_path, filename=extra_file)
+        shutil.copy(source_file, os.path.join(save_path, extra_file))
+        print(f"File '{extra_file}' copied from {model_path} to {save_path}.")
+    except Exception:
+        print(f"File '{extra_file}' not found in {model_path}, skipping copy.")


 if __name__ == "__main__":
--- a/setup.py
+++ b/setup.py
@@ -43,7 +43,7 @@ def get_console_scripts() -> list[str]:

 extra_require = {
    "torch": ["torch>=2.0.0", "torchvision>=0.15.0"],
-    "torch-npu": ["torch==2.4.0", "torch-npu==2.4.0.post2", "decorator"],
+    "torch-npu": ["torch-npu==2.5.1", "torchvision==0.20.1", "decorator"],
    "metrics": ["nltk", "jieba", "rouge-chinese"],
    "deepspeed": ["deepspeed>=0.10.0,<=0.16.9"],
    "liger-kernel": ["liger-kernel>=0.5.5"],
@@ -52,7 +52,7 @@ extra_require = {
    "eetq": ["eetq"],
    "gptq": ["optimum>=1.24.0", "gptqmodel>=2.0.0"],
    "aqlm": ["aqlm[gpu]>=1.1.0"],
-    "vllm": ["vllm>=0.4.3,<=0.8.6"],
+    "vllm": ["vllm>=0.4.3,<=0.10.2"],
    "sglang": ["sglang[srt]>=0.4.5", "transformers==4.51.1"],
    "galore": ["galore-torch"],
    "apollo": ["apollo-torch"],
@@ -68,9 +68,11 @@ extra_require = {
        "referencing",
        "jsonschema_specifications",
    ],
-    "modelscope": ["modelscope"],
    "openmind": ["openmind"],
    "swanlab": ["swanlab"],
+    "fp8": ["torchao>=0.8.0", "accelerate>=1.10.0"],
+    "fp8-te": ["transformer_engine[pytorch]>=2.0.0", "accelerate>=1.10.0"],
+    "fp8-all": ["torchao>=0.8.0", "transformer_engine[pytorch]>=2.0.0", "accelerate>=1.10.0"],
    "dev": ["pre-commit", "ruff", "pytest", "build"],
 }

--- a/src/llamafactory/api/chat.py
+++ b/src/llamafactory/api/chat.py
@@ -132,7 +132,7 @@ def _process_request(
                    if re.match(r"^data:video\/(mp4|mkv|avi|mov);base64,(.+)$", video_url):  # base64 video
                        video_stream = io.BytesIO(base64.b64decode(video_url.split(",", maxsplit=1)[1]))
                    elif os.path.isfile(video_url):  # local file
-                        video_stream = open(video_url, "rb")
+                        video_stream = video_url
                    else:  # web uri
                        video_stream = requests.get(video_url, stream=True).raw

@@ -143,7 +143,7 @@ def _process_request(
                    if re.match(r"^data:audio\/(mpeg|mp3|wav|ogg);base64,(.+)$", audio_url):  # base64 audio
                        audio_stream = io.BytesIO(base64.b64decode(audio_url.split(",", maxsplit=1)[1]))
                    elif os.path.isfile(audio_url):  # local file
-                        audio_stream = open(audio_url, "rb")
+                        audio_stream = audio_url
                    else:  # web uri
                        audio_stream = requests.get(audio_url, stream=True).raw

--- a/src/llamafactory/chat/chat_model.py
+++ b/src/llamafactory/chat/chat_model.py
@@ -24,9 +24,6 @@ from typing import TYPE_CHECKING, Any, Optional
 from ..extras.constants import EngineName
 from ..extras.misc import torch_gc
 from ..hparams import get_infer_args
-from .hf_engine import HuggingfaceEngine
-from .sglang_engine import SGLangEngine
-from .vllm_engine import VllmEngine


 if TYPE_CHECKING:
@@ -49,12 +46,31 @@ class ChatModel:

    def __init__(self, args: Optional[dict[str, Any]] = None) -> None:
        model_args, data_args, finetuning_args, generating_args = get_infer_args(args)
+
        if model_args.infer_backend == EngineName.HF:
+            from .hf_engine import HuggingfaceEngine
+
            self.engine: BaseEngine = HuggingfaceEngine(model_args, data_args, finetuning_args, generating_args)
        elif model_args.infer_backend == EngineName.VLLM:
+            try:
+                from .vllm_engine import VllmEngine
+
                self.engine: BaseEngine = VllmEngine(model_args, data_args, finetuning_args, generating_args)
+            except ImportError as e:
+                raise ImportError(
+                    "vLLM not install, you may need to run `pip install vllm`\n"
+                    "or try to use HuggingFace backend: --infer_backend huggingface"
+                ) from e
        elif model_args.infer_backend == EngineName.SGLANG:
+            try:
+                from .sglang_engine import SGLangEngine
+
                self.engine: BaseEngine = SGLangEngine(model_args, data_args, finetuning_args, generating_args)
+            except ImportError as e:
+                raise ImportError(
+                    "SGLang not install, you may need to run `pip install sglang[all]`\n"
+                    "or try to use HuggingFace backend: --infer_backend huggingface"
+                ) from e
        else:
            raise NotImplementedError(f"Unknown backend: {model_args.infer_backend}")

--- a/src/llamafactory/cli.py
+++ b/src/llamafactory/cli.py
@@ -25,26 +25,26 @@ USAGE = (
    + "| Usage:                                                             |\n"
    + "|   llamafactory-cli api -h: launch an OpenAI-style API server       |\n"
    + "|   llamafactory-cli chat -h: launch a chat interface in CLI         |\n"
-    + "|   llamafactory-cli eval -h: evaluate models                        |\n"
    + "|   llamafactory-cli export -h: merge LoRA adapters and export model |\n"
    + "|   llamafactory-cli train -h: train models                          |\n"
    + "|   llamafactory-cli webchat -h: launch a chat interface in Web UI   |\n"
    + "|   llamafactory-cli webui: launch LlamaBoard                        |\n"
+    + "|   llamafactory-cli env: show environment info                      |\n"
    + "|   llamafactory-cli version: show version info                      |\n"
+    + "| Hint: You can use `lmf` as a shortcut for `llamafactory-cli`.      |\n"
    + "-" * 70
 )


 def main():
-    from . import launcher
-    from .api.app import run_api
-    from .chat.chat_model import run_chat
-    from .eval.evaluator import run_eval
    from .extras import logging
    from .extras.env import VERSION, print_env
    from .extras.misc import find_available_port, get_device_count, is_env_enabled, use_ray
-    from .train.tuner import export_model, run_exp
-    from .webui.interface import run_web_demo, run_web_ui
+
+    if is_env_enabled("USE_V1"):
+        from .v1 import launcher
+    else:
+        from . import launcher

    logger = logging.get_logger(__name__)

@@ -61,14 +61,14 @@ def main():
    )

    COMMAND_MAP = {
-        "api": run_api,
-        "chat": run_chat,
+        "api": launcher.run_api,
+        "chat": launcher.run_chat,
        "env": print_env,
-        "eval": run_eval,
-        "export": export_model,
-        "train": run_exp,
-        "webchat": run_web_demo,
-        "webui": run_web_ui,
+        "eval": launcher.run_eval,
+        "export": launcher.export_model,
+        "train": launcher.run_exp,
+        "webchat": launcher.run_web_demo,
+        "webui": launcher.run_web_ui,
        "version": partial(print, WELCOME),
        "help": partial(print, USAGE),
    }
--- a/src/llamafactory/data/collator.py
+++ b/src/llamafactory/data/collator.py
@@ -194,7 +194,7 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
            elif "video_second_per_grid" in mm_inputs:  # for qwen2.5 omni
                rope_index_kwargs["second_per_grids"] = mm_inputs.get("video_second_per_grid")

-            if getattr(self.model.config, "model_type", None) == "qwen2_5_omni_thinker":  # for qwen2.5 omni
+            if getattr(self.model.config, "model_type", None) in ["qwen2_5_omni_thinker", "qwen3_omni_moe_thinker"]:
                rope_index_kwargs["use_audio_in_video"] = getattr(self.processor, "use_audio_in_video", False)
                feature_attention_mask = mm_inputs.get("feature_attention_mask", None)
                if feature_attention_mask is not None:  # FIXME: need to get video image lengths
@@ -205,15 +205,25 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
                features["rope_deltas"] = rope_deltas - (1 - rope_index_kwargs["attention_mask"]).sum(
                    dim=-1
                ).unsqueeze(-1)
-            else:  # for qwen2vl
+            else:  # for qwen vl
                features["position_ids"], features["rope_deltas"] = self.get_rope_func(**rope_index_kwargs)

        if (
            self.model is not None
-            and getattr(self.model.config, "model_type", None) in ["qwen2_vl", "qwen2_5_vl", "qwen2_5_omni_thinker"]
+            and getattr(self.model.config, "model_type", None)
+            in [
+                "glm4v",
+                "Keye",
+                "qwen2_vl",
+                "qwen2_5_vl",
+                "qwen2_5_omni_thinker",
+                "qwen3_omni_moe_thinker",
+                "qwen3_vl",
+                "qwen3_vl_moe",
+            ]
            and ("position_ids" not in features or features["position_ids"].dim() != 3)
        ):
-            raise ValueError("Qwen2-VL/Qwen2.5-Omni model requires 3D position ids for mrope.")
+            raise ValueError(f"{self.model.config.model_type} requires 3D position ids for mrope.")

        if "cross_attention_mask" in mm_inputs:  # for mllama inputs when pad_to_multiple_of is enabled
            cross_attention_mask = mm_inputs.pop("cross_attention_mask")
--- a/src/llamafactory/data/converter.py
+++ b/src/llamafactory/data/converter.py
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
+import json
 import os
 from abc import abstractmethod
 from dataclasses import dataclass
@@ -227,9 +227,150 @@ class SharegptDatasetConverter(DatasetConverter):
        return output


+@dataclass
+class OpenAIDatasetConverter(DatasetConverter):
+    def __call__(self, example: dict[str, Any]) -> dict[str, Any]:
+        tag_mapping = {
+            self.dataset_attr.user_tag: Role.USER.value,
+            self.dataset_attr.assistant_tag: Role.ASSISTANT.value,
+            self.dataset_attr.observation_tag: Role.OBSERVATION.value,
+            self.dataset_attr.function_tag: Role.FUNCTION.value,
+            self.dataset_attr.system_tag: Role.SYSTEM.value,
+        }
+
+        messages = example[self.dataset_attr.messages]
+        if (
+            self.dataset_attr.system_tag
+            and len(messages) != 0
+            and messages[0][self.dataset_attr.role_tag] == self.dataset_attr.system_tag
+        ):
+            system = messages[0][self.dataset_attr.content_tag]
+            messages = messages[1:]
+        else:
+            system = example.get(self.dataset_attr.system, "") if self.dataset_attr.system else ""
+
+        aligned_messages = []
+        tool_responses = []
+        broken_data = False
+        for turn_idx, message in enumerate(messages):
+            role = message[self.dataset_attr.role_tag]
+            content = message[self.dataset_attr.content_tag]
+
+            if role in [self.dataset_attr.assistant_tag, self.dataset_attr.function_tag]:
+                if "tool_calls" in message and len(message["tool_calls"]) > 0:
+                    tool_calls_list = [tool["function"] for tool in message["tool_calls"]]
+                    content = json.dumps(tool_calls_list, ensure_ascii=False)
+                    role = self.dataset_attr.function_tag
+
+            if role == self.dataset_attr.observation_tag:
+                tool_responses.append(content)
+                continue
+            elif len(tool_responses) > 0:
+                _content = "\n</tool_response>\n<tool_response>\n".join(tool_responses)
+                aligned_messages.append(
+                    {
+                        "role": Role.OBSERVATION.value,
+                        "content": _content,
+                    }
+                )
+                tool_responses = []
+
+            aligned_messages.append(
+                {
+                    "role": tag_mapping[role],
+                    "content": content,
+                }
+            )
+
+        odd_tags = (Role.USER.value, Role.OBSERVATION.value)
+        even_tags = (Role.ASSISTANT.value, Role.FUNCTION.value)
+        accept_tags = (odd_tags, even_tags)
+        for turn_idx, message in enumerate(aligned_messages):
+            if message["role"] not in accept_tags[turn_idx % 2]:
+                logger.warning_rank0(f"Invalid role tag in {messages}.")
+                broken_data = True
+                break
+
+        if (not self.dataset_attr.ranking and len(aligned_messages) % 2 != 0) or (
+            self.dataset_attr.ranking and len(aligned_messages) % 2 == 0
+        ):
+            logger.warning_rank0(f"Invalid message count in {messages}.")
+            broken_data = True
+
+        if broken_data:
+            logger.warning_rank0("Skipping this abnormal example.")
+            prompt, response = [], []
+        elif self.dataset_attr.kto_tag and isinstance(example[self.dataset_attr.kto_tag], bool):  # kto example
+            prompt = aligned_messages[:-1]
+            response = aligned_messages[-1:]
+            if example[self.dataset_attr.kto_tag]:
+                response = response + [{"role": Role.ASSISTANT.value, "content": ""}]
+            else:
+                response = [{"role": Role.ASSISTANT.value, "content": ""}] + response
+        elif (
+            self.dataset_attr.ranking
+            and isinstance(example[self.dataset_attr.chosen], dict)
+            and isinstance(example[self.dataset_attr.rejected], dict)
+        ):  # pairwise example
+            chosen = example[self.dataset_attr.chosen]
+            rejected = example[self.dataset_attr.rejected]
+            if (
+                chosen[self.dataset_attr.role_tag] not in accept_tags[-1]
+                or rejected[self.dataset_attr.role_tag] not in accept_tags[-1]
+            ):
+                logger.warning_rank0(f"Invalid role tag in {[chosen, rejected]}.")
+                broken_data = True
+
+            prompt = aligned_messages
+            response = [
+                {
+                    "role": tag_mapping[chosen[self.dataset_attr.role_tag]],
+                    "content": chosen[self.dataset_attr.content_tag],
+                },
+                {
+                    "role": tag_mapping[rejected[self.dataset_attr.role_tag]],
+                    "content": rejected[self.dataset_attr.content_tag],
+                },
+            ]
+        else:  # normal example
+            prompt = aligned_messages[:-1]
+            response = aligned_messages[-1:]
+
+        tools = example.get(self.dataset_attr.tools, "") if self.dataset_attr.tools else ""
+        if isinstance(tools, dict) or isinstance(tools, list):
+            tools = json.dumps(tools, ensure_ascii=False)
+
+        short_system_prompt = "detailed thinking off"
+        if not system:
+            if not tools:
+                system = short_system_prompt
+            else:
+                pass
+        else:
+            if not tools:
+                if "detailed thinking on" in system or "detailed thinking off" in system:
+                    pass
+                else:
+                    system += "\n" + short_system_prompt
+            else:
+                system += "\n"
+
+        output = {
+            "_prompt": prompt,
+            "_response": response,
+            "_system": system,
+            "_tools": tools,
+            "_images": self._find_medias(example[self.dataset_attr.images]) if self.dataset_attr.images else None,
+            "_videos": self._find_medias(example[self.dataset_attr.videos]) if self.dataset_attr.videos else None,
+            "_audios": self._find_medias(example[self.dataset_attr.audios]) if self.dataset_attr.audios else None,
+        }
+        return output
+
+
 DATASET_CONVERTERS = {
    "alpaca": AlpacaDatasetConverter,
    "sharegpt": SharegptDatasetConverter,
+    "openai": OpenAIDatasetConverter,
 }


--- a/src/llamafactory/data/formatter.py
+++ b/src/llamafactory/data/formatter.py
@@ -97,8 +97,11 @@ class FunctionFormatter(StringFormatter):
    @override
    def apply(self, **kwargs) -> SLOTS:
        content: str = kwargs.pop("content")
-        regex = re.compile(r"<think>(.*)</think>", re.DOTALL)
+        thought_words, thought = kwargs.pop("thought_words", None), None
+        if thought_words and len(thought_words) == 2:
+            regex = re.compile(rf"{re.escape(thought_words[0])}(.*?){re.escape(thought_words[1])}", re.DOTALL)
            thought = re.search(regex, content)
+
        if thought:
            content = content.replace(thought.group(0), "")

--- a/src/llamafactory/data/loader.py
+++ b/src/llamafactory/data/loader.py
@@ -91,7 +91,7 @@ def _load_single_dataset(
        raise NotImplementedError(f"Unknown load type: {dataset_attr.load_from}.")

    if dataset_attr.load_from == "ms_hub":
-        check_version("modelscope>=1.11.0", mandatory=True)
+        check_version("modelscope>=1.14.0", mandatory=True)
        from modelscope import MsDataset  # type: ignore
        from modelscope.utils.config_ds import MS_DATASETS_CACHE  # type: ignore

--- a/src/llamafactory/data/mm_plugin.py
+++ b/src/llamafactory/data/mm_plugin.py
@@ -27,6 +27,10 @@ from typing import TYPE_CHECKING, BinaryIO, Literal, Optional, TypedDict, Union
 import numpy as np
 import torch
 from transformers.image_utils import get_image_size, is_valid_image, to_numpy_array
+from transformers.models.mllama.processing_mllama import (
+    convert_sparse_cross_attention_mask_to_dense,
+    get_cross_attention_token_mask,
+)
 from typing_extensions import override

 from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
@@ -51,17 +55,10 @@ if is_pyav_available():
    import av


-if is_transformers_version_greater_than("4.45.0"):
-    from transformers.models.mllama.processing_mllama import (
-        convert_sparse_cross_attention_mask_to_dense,
-        get_cross_attention_token_mask,
-    )
-
-
 if is_transformers_version_greater_than("4.52.0"):
    from transformers.image_utils import make_flat_list_of_images
    from transformers.video_utils import make_batched_videos
-elif is_transformers_version_greater_than("4.49.0"):
+else:
    from transformers.image_utils import make_batched_videos, make_flat_list_of_images


@@ -137,7 +134,7 @@ def _make_batched_images(images: list["ImageObject"], imglens: list[int]) -> lis

 def _check_video_is_nested_images(video: "VideoInput") -> bool:
    r"""Check if the video is nested images."""
-    return isinstance(video, list) and all(isinstance(frame, (str, BinaryIO, dict)) for frame in video)
+    return isinstance(video, list) and all(isinstance(frame, (str, BinaryIO, dict, ImageObject)) for frame in video)


@dataclass
@@ -298,11 +295,8 @@ class MMPluginMixin:
        r"""Regularizes audios to avoid error. Including reading and resampling."""
        results, sampling_rates = [], []
        for audio in audios:
-            if isinstance(audio, (str, BinaryIO)):
-                audio, sampling_rate = librosa.load(audio, sr=sampling_rate)
-
            if not isinstance(audio, np.ndarray):
-                raise ValueError(f"Expect input is a list of audios, but got {type(audio)}.")
+                audio, sampling_rate = librosa.load(audio, sr=sampling_rate)

            results.append(audio)
            sampling_rates.append(sampling_rate)
@@ -391,7 +385,7 @@ class MMPluginMixin:
                    return_tensors="pt",
                )
            )
-            mm_inputs["feature_attention_mask"] = mm_inputs.pop("attention_mask")  # prevent conflicts
+            mm_inputs["feature_attention_mask"] = mm_inputs.pop("attention_mask", None)  # prevent conflicts

        return mm_inputs

@@ -512,6 +506,39 @@ class Gemma3Plugin(BasePlugin):
        return mm_inputs


+class Gemma3nPlugin(Gemma3Plugin):
+    @override
+    def process_messages(
+        self,
+        messages: list[dict[str, str]],
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        processor: Optional["MMProcessor"],
+    ) -> list[dict[str, str]]:
+        self._validate_input(processor, images, videos, audios)
+        self._validate_messages(messages, images, videos, audios)
+        messages = deepcopy(messages)
+        boi_token: str = getattr(processor, "boi_token")
+        boa_token: str = getattr(processor, "boa_token")
+        full_image_sequence: str = getattr(processor, "full_image_sequence")
+        full_audio_sequence: str = getattr(processor, "full_audio_sequence")
+        image_str = full_image_sequence if self.expand_mm_tokens else boi_token
+        audio_str = full_audio_sequence if self.expand_mm_tokens else boa_token
+
+        for message in messages:
+            content = message["content"]
+            while IMAGE_PLACEHOLDER in content:
+                content = content.replace(IMAGE_PLACEHOLDER, image_str, 1)
+
+            while AUDIO_PLACEHOLDER in content:
+                content = content.replace(AUDIO_PLACEHOLDER, audio_str, 1)
+
+            message["content"] = content
+
+        return messages
+
+
@dataclass
 class InternVLPlugin(BasePlugin):
    @override
@@ -1370,6 +1397,9 @@ class Qwen2AudioPlugin(BasePlugin):

@dataclass
 class Qwen2VLPlugin(BasePlugin):
+    vision_bos_token: str = "<|vision_start|>"
+    vision_eos_token: str = "<|vision_end|>"
+
    @override
    def _preprocess_image(self, image: "ImageObject", **kwargs) -> "ImageObject":
        image = super()._preprocess_image(image, **kwargs)
@@ -1485,14 +1515,18 @@ class Qwen2VLPlugin(BasePlugin):
            while IMAGE_PLACEHOLDER in content:
                image_seqlen = image_grid_thw[num_image_tokens].prod() // merge_length if self.expand_mm_tokens else 1
                content = content.replace(
-                    IMAGE_PLACEHOLDER, f"<|vision_start|>{self.image_token * image_seqlen}<|vision_end|>", 1
+                    IMAGE_PLACEHOLDER,
+                    f"{self.vision_bos_token}{self.image_token * image_seqlen}{self.vision_eos_token}",
+                    1,
                )
                num_image_tokens += 1

            while VIDEO_PLACEHOLDER in content:
                video_seqlen = video_grid_thw[num_video_tokens].prod() // merge_length if self.expand_mm_tokens else 1
                content = content.replace(
-                    VIDEO_PLACEHOLDER, f"<|vision_start|>{self.video_token * video_seqlen}<|vision_end|>", 1
+                    VIDEO_PLACEHOLDER,
+                    f"{self.vision_bos_token}{self.video_token * video_seqlen}{self.vision_eos_token}",
+                    1,
                )
                num_video_tokens += 1

@@ -1501,7 +1535,259 @@ class Qwen2VLPlugin(BasePlugin):
        return messages


+@dataclass
+class Qwen3VLPlugin(Qwen2VLPlugin):
+    @override
+    def _get_mm_inputs(
+        self,
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        processor: "MMProcessor",
+    ) -> dict[str, "torch.Tensor"]:
+        image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
+        video_processor: BaseImageProcessor = getattr(processor, "video_processor", None)
+        mm_inputs = {}
+        if len(images) != 0:
+            images = self._regularize_images(
+                images,
+                image_max_pixels=getattr(processor, "image_max_pixels", 768 * 768),
+                image_min_pixels=getattr(processor, "image_min_pixels", 32 * 32),
+            )["images"]
+            mm_inputs.update(image_processor(images, return_tensors="pt"))
+
+        if len(videos) != 0:
+            videos = self._regularize_videos(
+                videos,
+                image_max_pixels=getattr(processor, "video_max_pixels", 256 * 256),
+                image_min_pixels=getattr(processor, "video_min_pixels", 16 * 16),
+                video_fps=getattr(processor, "video_fps", 2.0),
+                video_maxlen=getattr(processor, "video_maxlen", 128),
+            )
+            video_metadata = [
+                {"fps": getattr(processor, "video_fps", 24.0), "duration": len(video), "total_num_frames": len(video)}
+                for video in videos["videos"]
+            ]
+            mm_inputs.update(
+                video_processor(videos=videos["videos"], video_metadata=video_metadata, return_metadata=True)
+            )
+            temporal_patch_size: int = getattr(image_processor, "temporal_patch_size", 2)
+            if "second_per_grid_ts" in processor.model_input_names:
+                mm_inputs["second_per_grid_ts"] = [temporal_patch_size / fps for fps in videos["fps_per_video"]]
+
+        return mm_inputs
+
+    @override
+    def process_messages(
+        self,
+        messages: list[dict[str, str]],
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        processor: Optional["MMProcessor"],
+    ) -> list[dict[str, str]]:
+        self._validate_input(processor, images, videos, audios)
+        self._validate_messages(messages, images, videos, audios)
+        num_image_tokens, num_video_tokens = 0, 0
+        messages = deepcopy(messages)
+        image_processor: BaseImageProcessor = getattr(processor, "image_processor")
+        video_processor: BaseImageProcessor = getattr(processor, "video_processor")
+
+        image_merge_length: int = getattr(image_processor, "merge_size") ** 2
+        video_merge_length: int = getattr(video_processor, "merge_size") ** 2
+        if self.expand_mm_tokens:
+            mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
+            image_grid_thw = mm_inputs.get("image_grid_thw", [])
+            video_grid_thw = mm_inputs.get("video_grid_thw", [])
+            num_frames = video_grid_thw[0][0] if len(video_grid_thw) > 0 else 0  # hard code for now
+            video_metadata = mm_inputs.get("video_metadata", {})
+
+        else:
+            image_grid_thw = [None] * len(images)
+            video_grid_thw = [None] * len(videos)
+            num_frames = 0
+            timestamps = [0]
+
+        for idx, message in enumerate(messages):
+            content = message["content"]
+            while IMAGE_PLACEHOLDER in content:
+                image_seqlen = (
+                    image_grid_thw[num_image_tokens].prod() // image_merge_length if self.expand_mm_tokens else 1
+                )
+                content = content.replace(
+                    IMAGE_PLACEHOLDER,
+                    f"{self.vision_bos_token}{self.image_token * image_seqlen}{self.vision_eos_token}",
+                    1,
+                )
+                num_image_tokens += 1
+
+            while VIDEO_PLACEHOLDER in content:
+                metadata = video_metadata[idx]
+                timestamps = processor._calculate_timestamps(
+                    metadata.frames_indices,
+                    metadata.fps,
+                    video_processor.merge_size,
+                )
+                video_structure = ""
+                for frame_index in range(num_frames):
+                    video_seqlen = (
+                        video_grid_thw[num_video_tokens][1:].prod() // video_merge_length
+                        if self.expand_mm_tokens
+                        else 1
+                    )
+                    timestamp_sec = timestamps[frame_index]
+                    frame_structure = (
+                        f"<{timestamp_sec:.1f} seconds>"
+                        f"{self.vision_bos_token}{self.video_token * video_seqlen}{self.vision_eos_token}"
+                    )
+                    video_structure += frame_structure
+
+                if not self.expand_mm_tokens:
+                    video_structure = f"{self.vision_bos_token}{self.video_token}{self.vision_eos_token}"
+
+                content = content.replace(VIDEO_PLACEHOLDER, video_structure, 1)
+                num_video_tokens += 1
+
+            message["content"] = content
+
+        return messages
+
+
+@dataclass
+class GLM4VPlugin(Qwen2VLPlugin):
+    @override
+    def _get_mm_inputs(
+        self,
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        processor: "MMProcessor",
+    ) -> dict[str, "torch.Tensor"]:
+        image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
+        video_processor: BaseImageProcessor = getattr(processor, "video_processor", None)
+        mm_inputs = {}
+        if len(images) != 0:
+            images = self._regularize_images(
+                images,
+                image_max_pixels=getattr(processor, "image_max_pixels", 768 * 768),
+                image_min_pixels=getattr(processor, "image_min_pixels", 32 * 32),
+            )["images"]
+            mm_inputs.update(image_processor(images, return_tensors="pt"))
+
+        if len(videos) != 0:
+            video_data = self._regularize_videos(
+                videos,
+                image_max_pixels=getattr(processor, "video_max_pixels", 256 * 256),
+                image_min_pixels=getattr(processor, "video_min_pixels", 16 * 16),
+                video_fps=getattr(processor, "video_fps", 2.0),
+                video_maxlen=getattr(processor, "video_maxlen", 128),
+            )
+            # prepare video metadata
+            video_metadata = [
+                {"fps": 2, "duration": len(video), "total_frames": len(video)} for video in video_data["videos"]
+            ]
+            mm_inputs.update(video_processor(images=None, videos=video_data["videos"], video_metadata=video_metadata))
+
+        return mm_inputs
+
+    @override
+    def process_messages(
+        self,
+        messages: list[dict[str, str]],
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        processor: Optional["MMProcessor"],
+    ) -> list[dict[str, str]]:
+        self._validate_input(processor, images, videos, audios)
+        self._validate_messages(messages, images, videos, audios)
+        num_image_tokens, num_video_tokens = 0, 0
+        messages = deepcopy(messages)
+        image_processor: BaseImageProcessor = getattr(processor, "image_processor")
+
+        merge_length: int = getattr(image_processor, "merge_size") ** 2
+        if self.expand_mm_tokens:
+            mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
+            image_grid_thw = mm_inputs.get("image_grid_thw", [])
+            video_grid_thw = mm_inputs.get("video_grid_thw", [])
+            num_frames = video_grid_thw[0][0] if len(video_grid_thw) > 0 else 0  # hard code for now
+            timestamps = mm_inputs.get("timestamps", [])
+
+            if hasattr(timestamps, "tolist"):
+                timestamps = timestamps.tolist()
+
+            if not timestamps:
+                timestamps_list = []
+            elif isinstance(timestamps[0], list):
+                timestamps_list = timestamps[0]
+            else:
+                timestamps_list = timestamps
+
+            unique_timestamps = timestamps_list.copy()
+            selected_timestamps = unique_timestamps[:num_frames]
+            while len(selected_timestamps) < num_frames:
+                selected_timestamps.append(selected_timestamps[-1] if selected_timestamps else 0)
+
+        else:
+            image_grid_thw = [None] * len(images)
+            video_grid_thw = [None] * len(videos)
+            num_frames = 0
+            selected_timestamps = [0]
+
+        for message in messages:
+            content = message["content"]
+            while IMAGE_PLACEHOLDER in content:
+                image_seqlen = image_grid_thw[num_image_tokens].prod() // merge_length if self.expand_mm_tokens else 1
+                content = content.replace(
+                    IMAGE_PLACEHOLDER, f"<|begin_of_image|>{self.image_token * image_seqlen}<|end_of_image|>", 1
+                )
+                num_image_tokens += 1
+
+            while VIDEO_PLACEHOLDER in content:
+                video_structure = ""
+                for frame_index in range(num_frames):
+                    video_seqlen = (
+                        video_grid_thw[num_video_tokens][1:].prod() // merge_length if self.expand_mm_tokens else 1
+                    )
+                    timestamp_sec = selected_timestamps[frame_index]
+                    frame_structure = (
+                        f"<|begin_of_image|>{self.image_token * video_seqlen}<|end_of_image|>{timestamp_sec}"
+                    )
+                    video_structure += frame_structure
+
+                if not self.expand_mm_tokens:
+                    video_structure = self.video_token
+
+                content = content.replace(VIDEO_PLACEHOLDER, f"<|begin_of_video|>{video_structure}<|end_of_video|>", 1)
+                num_video_tokens += 1
+
+            message["content"] = content
+
+        return messages
+
+    @override
+    def get_mm_inputs(
+        self,
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        imglens: list[int],
+        vidlens: list[int],
+        audlens: list[int],
+        batch_ids: list[list[int]],
+        processor: Optional["ProcessorMixin"],
+    ) -> dict[str, Union[list[int], "torch.Tensor"]]:
+        self._validate_input(processor, images, videos, audios)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
+        mm_inputs.pop("timestamps", None)
+        return mm_inputs
+
+
+@dataclass
 class Qwen2OmniPlugin(Qwen2VLPlugin):
+    audio_bos_token: str = "<|audio_start|>"
+    audio_eos_token: str = "<|audio_end|>"
+
    @override
    def _get_mm_inputs(
        self,
@@ -1588,7 +1874,9 @@ class Qwen2OmniPlugin(Qwen2VLPlugin):
            while IMAGE_PLACEHOLDER in content:
                image_seqlen = image_grid_thw[num_image_tokens].prod() // merge_length if self.expand_mm_tokens else 1
                content = content.replace(
-                    IMAGE_PLACEHOLDER, f"<|vision_bos|>{self.image_token * image_seqlen}<|vision_eos|>", 1
+                    IMAGE_PLACEHOLDER,
+                    f"{self.vision_bos_token}{self.image_token * image_seqlen}{self.vision_eos_token}",
+                    1,
                )
                num_image_tokens += 1

@@ -1625,7 +1913,7 @@ class Qwen2OmniPlugin(Qwen2VLPlugin):
                    video_chunk_indices = processor.get_chunked_index(video_t_index, t_ntoken_per_chunk)
                    audio_chunk_indices = processor.get_chunked_index(audio_t_index, t_ntoken_per_chunk)
                    placeholder_string = ""
-                    placeholder_string += "<|vision_bos|>" + "<|audio_bos|>"
+                    placeholder_string += self.vision_bos_token + self.audio_bos_token
                    for j in range(max(len(video_chunk_indices), len(audio_chunk_indices))):
                        video_chunk_index = video_chunk_indices[j] if j < len(video_chunk_indices) else None
                        audio_chunk_index = audio_chunk_indices[j] if j < len(audio_chunk_indices) else None
@@ -1635,7 +1923,7 @@ class Qwen2OmniPlugin(Qwen2VLPlugin):
                        if audio_chunk_index is not None:
                            placeholder_string += self.audio_token * (audio_chunk_index[1] - audio_chunk_index[0])

-                    placeholder_string += "<|audio_eos|>" + "<|vision_eos|>"
+                    placeholder_string += self.audio_eos_token + self.vision_eos_token
                    content = content.replace(VIDEO_PLACEHOLDER, placeholder_string, 1)
                    content = content.replace(AUDIO_PLACEHOLDER, "", 1)
                    num_audio_tokens += 1
@@ -1644,7 +1932,9 @@ class Qwen2OmniPlugin(Qwen2VLPlugin):
                while AUDIO_PLACEHOLDER in content:
                    audio_seqlen = audio_lengths[num_audio_tokens] if self.expand_mm_tokens else 1
                    content = content.replace(
-                        AUDIO_PLACEHOLDER, f"<|audio_bos|>{self.audio_token * audio_seqlen}<|audio_eos|>", 1
+                        AUDIO_PLACEHOLDER,
+                        f"{self.audio_bos_token}{self.audio_token * audio_seqlen}{self.audio_eos_token}",
+                        1,
                    )
                    num_audio_tokens += 1

@@ -1653,7 +1943,9 @@ class Qwen2OmniPlugin(Qwen2VLPlugin):
                        video_grid_thw[num_video_tokens].prod() // merge_length if self.expand_mm_tokens else 1
                    )
                    content = content.replace(
-                        VIDEO_PLACEHOLDER, f"<|vision_bos|>{self.video_token * video_seqlen}<|vision_eos|>", 1
+                        VIDEO_PLACEHOLDER,
+                        f"{self.vision_bos_token}{self.video_token * video_seqlen}{self.vision_eos_token}",
+                        1,
                    )
                    num_video_tokens += 1

@@ -1718,6 +2010,8 @@ class VideoLlavaPlugin(BasePlugin):
 PLUGINS = {
    "base": BasePlugin,
    "gemma3": Gemma3Plugin,
+    "glm4v": GLM4VPlugin,
+    "gemma3n": Gemma3nPlugin,
    "intern_vl": InternVLPlugin,
    "kimi_vl": KimiVLPlugin,
    "llama4": Llama4Plugin,
@@ -1731,6 +2025,7 @@ PLUGINS = {
    "qwen2_audio": Qwen2AudioPlugin,
    "qwen2_omni": Qwen2OmniPlugin,
    "qwen2_vl": Qwen2VLPlugin,
+    "qwen3_vl": Qwen3VLPlugin,
    "video_llava": VideoLlavaPlugin,
 }

@@ -1748,9 +2043,10 @@ def get_mm_plugin(
    image_token: Optional[str] = None,
    video_token: Optional[str] = None,
    audio_token: Optional[str] = None,
+    **kwargs,
 ) -> "BasePlugin":
    r"""Get plugin for multimodal inputs."""
    if name not in PLUGINS:
        raise ValueError(f"Multimodal plugin `{name}` not found.")

-    return PLUGINS[name](image_token, video_token, audio_token)
+    return PLUGINS[name](image_token, video_token, audio_token, **kwargs)
--- a/src/llamafactory/data/parser.py
+++ b/src/llamafactory/data/parser.py
@@ -15,7 +15,7 @@
 import json
 import os
 from dataclasses import dataclass
-from typing import Any, Literal, Optional
+from typing import Any, Literal, Optional, Union

 from huggingface_hub import hf_hub_download

@@ -90,12 +90,14 @@ class DatasetAttr:
                self.set_attr(tag, attr["tags"])


-def get_dataset_list(dataset_names: Optional[list[str]], dataset_dir: str) -> list["DatasetAttr"]:
+def get_dataset_list(dataset_names: Optional[list[str]], dataset_dir: Union[str, dict]) -> list["DatasetAttr"]:
    r"""Get the attributes of the datasets."""
    if dataset_names is None:
        dataset_names = []

-    if dataset_dir == "ONLINE":
+    if isinstance(dataset_dir, dict):
+        dataset_info = dataset_dir
+    elif dataset_dir == "ONLINE":
        dataset_info = None
    else:
        if dataset_dir.startswith("REMOTE:"):
--- a/src/llamafactory/data/processor/supervised.py
+++ b/src/llamafactory/data/processor/supervised.py
@@ -62,7 +62,7 @@ class SupervisedDatasetProcessor(DatasetProcessor):

            if self.data_args.train_on_prompt:
                source_label = source_ids
-            elif self.template.efficient_eos:
+            elif self.template.efficient_eos and turn_idx != 0:
                source_label = [self.tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1)
            else:
                source_label = [IGNORE_INDEX] * source_len
--- a/src/llamafactory/data/template.py
+++ b/src/llamafactory/data/template.py
@@ -96,7 +96,7 @@ class Template:

    def add_thought(self, content: str = "") -> str:
        r"""Add empty thought to assistant message."""
-        return f"{self.thought_words[0]}\n\n{self.thought_words[1]}\n\n" + content
+        return f"{self.thought_words[0]}{self.thought_words[1]}" + content

    def remove_thought(self, content: str) -> str:
        r"""Remove thought from assistant message."""
@@ -156,7 +156,7 @@ class Template:
            elif message["role"] == Role.OBSERVATION:
                elements += self.format_observation.apply(content=message["content"])
            elif message["role"] == Role.FUNCTION:
-                elements += self.format_function.apply(content=message["content"])
+                elements += self.format_function.apply(content=message["content"], thought_words=self.thought_words)
            else:
                raise NotImplementedError("Unexpected role: {}".format(message["role"]))

@@ -416,8 +416,8 @@ class ReasoningTemplate(Template):

        prompt_ids, response_ids = super().encode_oneturn(tokenizer, messages, system, tools)
        if (
-            self.thought_words[0] not in messages[-1]["content"]
-            and self.thought_words[1] not in messages[-1]["content"]
+            self.thought_words[0].strip() not in messages[-1]["content"]
+            and self.thought_words[1].strip() not in messages[-1]["content"]
        ):  # add empty cot
            if not self.enable_thinking:  # do not compute loss
                prompt_ids += self.get_thought_word_ids(tokenizer)
@@ -442,8 +442,8 @@ class ReasoningTemplate(Template):
        encoded_messages = self._encode(tokenizer, messages, system, tools)
        for i in range(0, len(messages), 2):
            if (
-                self.thought_words[0] not in messages[i + 1]["content"]
-                and self.thought_words[1] not in messages[i + 1]["content"]
+                self.thought_words[0].strip() not in messages[i + 1]["content"]
+                and self.thought_words[1].strip() not in messages[i + 1]["content"]
            ):  # add empty cot
                if not self.enable_thinking:  # do not compute loss
                    encoded_messages[i] += self.get_thought_word_ids(tokenizer)
@@ -518,7 +518,7 @@ def register_template(
        format_prefix=format_prefix or default_prefix_formatter,
        default_system=default_system,
        stop_words=stop_words or [],
-        thought_words=thought_words or ("<think>", "</think>"),
+        thought_words=thought_words or ("<think>\n", "\n</think>\n\n"),
        efficient_eos=efficient_eos,
        replace_eos=replace_eos,
        replace_jinja_template=replace_jinja_template,
@@ -579,7 +579,7 @@ def parse_template(tokenizer: "PreTrainedTokenizer") -> "Template":
        format_prefix=EmptyFormatter(slots=[prefix]) if prefix else EmptyFormatter(),
        default_system=default_system,
        stop_words=[],
-        thought_words=("<think>", "</think>"),
+        thought_words=("<think>\n", "\n</think>\n\n"),
        efficient_eos=False,
        replace_eos=False,
        replace_jinja_template=False,
@@ -679,6 +679,23 @@ register_template(
 )


+register_template(
+    name="bailing_v2",
+    format_user=StringFormatter(slots=["<role>HUMAN</role>{{content}}<|role_end|><role>ASSISTANT</role>"]),
+    format_system=StringFormatter(slots=["<role>SYSTEM</role>{{content}}<|role_end|>"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|role_end|>"]),
+    format_observation=StringFormatter(
+        slots=[
+            "<role>OBSERVATION</role>\n<tool_response>\n{{content}}\n</tool_response><|role_end|><role>ASSISTANT</role>"
+        ]
+    ),
+    format_function=FunctionFormatter(slots=["{{content}}<|role_end|>"], tool_format="ling"),
+    format_tools=ToolFormatter(tool_format="ling"),
+    stop_words=["<|endoftext|>"],
+    efficient_eos=True,
+)
+
+
 register_template(
    name="belle",
    format_user=StringFormatter(slots=["Human: {{content}}\n\nBelle: "]),
@@ -894,12 +911,51 @@ register_template(
 )


+register_template(
+    name="dots_ocr",
+    format_user=StringFormatter(slots=["<|user|>{{content}}<|endofuser|><|assistant|>"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|endofassistant|>"]),
+    format_system=StringFormatter(slots=["<|system|>{{content}}<|endofsystem|>\n"]),
+    stop_words=["<|endofassistant|>"],
+    efficient_eos=True,
+    mm_plugin=get_mm_plugin(
+        name="qwen2_vl",
+        image_token="<|imgpad|>",
+        video_token="<|vidpad|>",
+        vision_bos_token="<|img|>",
+        vision_eos_token="<|endofimg|>",
+    ),
+)
+
+
 register_template(
    name="empty",
    format_assistant=StringFormatter(slots=["{{content}}"]),
 )


+# copied from chatml template
+register_template(
+    name="ernie",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n\n<|im_start|>assistant\n"]),
+    default_system="<global_setting>\nthink_mode=True\n</global_setting>",
+    stop_words=["<|im_end|>"],
+)
+
+
+register_template(
+    name="ernie_nothink",
+    format_user=StringFormatter(slots=["User: {{content}}\nAssistant: "]),
+    format_assistant=StringFormatter(slots=["{{content}}<|end_of_sentence|>"]),
+    format_system=StringFormatter(slots=["{{content}}\n"]),
+    format_prefix=EmptyFormatter(slots=["<|begin_of_sentence|>"]),
+    stop_words=["<|end_of_sentence|>"],
+)
+
+
 register_template(
    name="exaone",
    format_user=StringFormatter(slots=["[|user|]{{content}}\n[|assistant|]"]),
@@ -916,6 +972,18 @@ register_template(
 )


+# copied from chatml template
+register_template(
+    name="falcon_h1",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<|im_end|>", "<|end_of_text|>"],
+)
+
+
 register_template(
    name="fewshot",
    format_assistant=StringFormatter(slots=["{{content}}\n\n"]),
@@ -939,6 +1007,22 @@ register_template(
 )


+# copied from gemma template
+register_template(
+    name="gemma2",
+    format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
+    format_system=StringFormatter(slots=["{{content}}\n\n"]),
+    format_observation=StringFormatter(
+        slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
+    ),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<eos>", "<end_of_turn>"],
+    efficient_eos=True,
+    template_class=Llama2Template,
+)
+
+
 # copied from gemma template
 register_template(
    name="gemma3",
@@ -956,6 +1040,22 @@ register_template(
 )


+register_template(
+    name="gemma3n",
+    format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
+    format_system=StringFormatter(slots=["{{content}}\n\n"]),
+    format_observation=StringFormatter(
+        slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
+    ),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<end_of_turn>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin("gemma3n", image_token="<image_soft_token>", audio_token="<audio_soft_token>"),
+    template_class=Llama2Template,
+)
+
+
 register_template(
    name="glm4",
    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),
@@ -970,6 +1070,56 @@ register_template(
 )


+# copied from glm4 template
+register_template(
+    name="glm4_moe",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),
+    format_assistant=StringFormatter(slots=["\n{{content}}"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
+    format_function=FunctionFormatter(slots=["{{content}}"], tool_format="glm4_moe"),
+    format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>"]),
+    format_tools=ToolFormatter(tool_format="glm4_moe"),
+    format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
+    stop_words=["<|user|>", "<|observation|>"],
+    efficient_eos=True,
+    template_class=ReasoningTemplate,
+)
+
+
+# copied from glm4 template
+register_template(
+    name="glm4v",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),
+    format_assistant=StringFormatter(slots=["\n{{content}}"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
+    format_function=FunctionFormatter(slots=["{{content}}"], tool_format="glm4"),
+    format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>"]),
+    format_tools=ToolFormatter(tool_format="glm4"),
+    format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
+    stop_words=["<|user|>", "<|observation|>", "</answer>"],
+    efficient_eos=True,
+    mm_plugin=get_mm_plugin(name="glm4v", image_token="<|image|>", video_token="<|video|>"),
+    template_class=ReasoningTemplate,
+)
+
+
+# copied from glm4 template
+register_template(
+    name="glm4v_moe",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),
+    format_assistant=StringFormatter(slots=["\n{{content}}"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
+    format_function=FunctionFormatter(slots=["{{content}}"], tool_format="glm4_moe"),
+    format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>"]),
+    format_tools=ToolFormatter(tool_format="glm4_moe"),
+    format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
+    stop_words=["<|user|>", "<|observation|>", "</answer>"],
+    efficient_eos=True,
+    mm_plugin=get_mm_plugin(name="glm4v", image_token="<|image|>", video_token="<|video|>"),
+    template_class=ReasoningTemplate,
+)
+
+
 # copied from glm4 template
 register_template(
    name="glmz1",
@@ -986,6 +1136,18 @@ register_template(
 )


+register_template(
+    name="gpt",
+    format_user=StringFormatter(slots=["<|start|>user<|message|>{{content}}<|end|><|start|>assistant"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|end|>"]),
+    format_system=StringFormatter(slots=["<|start|>system<|message|>{{content}}<|end|>"]),
+    default_system="You are ChatGPT, a large language model trained by OpenAI.",
+    thought_words=("<|channel|>analysis<|message|>", "<|end|><|start|>assistant<|channel|>final<|message|>"),
+    efficient_eos=True,
+    template_class=ReasoningTemplate,
+)
+
+
 register_template(
    name="granite3",
    format_user=StringFormatter(
@@ -1010,6 +1172,25 @@ register_template(
 )


+register_template(
+    name="granite4",
+    format_user=StringFormatter(
+        slots=[
+            "<|start_of_role|>user<|end_of_role|>{{content}}<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>"
+        ]
+    ),
+    format_assistant=StringFormatter(slots=["{{content}}<|end_of_text|>\n"]),
+    format_system=StringFormatter(slots=["<|start_of_role|>system<|end_of_role|>{{content}}<|end_of_text|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|end_of_text|>\n"], tool_format="default"),
+    format_observation=StringFormatter(
+        slots=["<|start_of_role|>tool<|end_of_role|>{{content}}<|end_of_text|>\n<|start_of_role|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="default"),
+    stop_words=["<|end_of_text|>"],
+    default_system="You are Granite, developed by IBM. You are a helpful AI assistant.",
+)
+
+
 register_template(
    name="index",
    format_user=StringFormatter(slots=["reserved_0{{content}}reserved_1"]),
@@ -1076,6 +1257,35 @@ register_template(
 )


+register_template(
+    name="intern_s1",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<|im_end|>"],
+    mm_plugin=get_mm_plugin(name="intern_vl", image_token="<image>", video_token="<video>"),
+)
+
+
+# copied from qwen template
+register_template(
+    name="keye_vl",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
+    format_observation=StringFormatter(
+        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="qwen"),
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(name="qwen2_vl", image_token="<|image_pad|>", video_token="<|video_pad|>"),
+    template_class=ReasoningTemplate,
+)
+
+
 register_template(
    name="kimi_vl",
    format_user=StringFormatter(
@@ -1409,7 +1619,7 @@ register_template(
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    stop_words=["<|im_end|>"],
-    default_system="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
+    default_system="You are a helpful assistant. You can accept audio and text input and output voice and text.",
    mm_plugin=get_mm_plugin(name="minicpm_v", image_token="<image>", video_token="<video>", audio_token="<audio>"),
 )

@@ -1608,6 +1818,22 @@ register_template(
 )


+# copied from qwen template
+register_template(
+    name="qwen3_nothink",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
+    format_observation=StringFormatter(
+        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="qwen"),
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+)
+
+
 # copied from chatml template
 register_template(
    name="qwen2_audio",
@@ -1636,10 +1862,55 @@ register_template(
    stop_words=["<|im_end|>"],
    replace_eos=True,
    mm_plugin=get_mm_plugin(
-        name="qwen2_omni", audio_token="<|AUDIO|>", image_token="<|IMAGE|>", video_token="<|VIDEO|>"
+        name="qwen2_omni",
+        image_token="<|IMAGE|>",
+        video_token="<|VIDEO|>",
+        audio_token="<|AUDIO|>",
+        vision_bos_token="<|vision_bos|>",
+        vision_eos_token="<|vision_eos|>",
+        audio_bos_token="<|audio_bos|>",
+        audio_eos_token="<|audio_eos|>",
    ),
 )

+
+register_template(
+    name="qwen3_omni",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
+    format_observation=StringFormatter(
+        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="qwen"),
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(
+        name="qwen2_omni", image_token="<|image_pad|>", video_token="<|video_pad|>", audio_token="<|audio_pad|>"
+    ),
+    template_class=ReasoningTemplate,
+)
+
+
+register_template(
+    name="qwen3_omni_nothink",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
+    format_observation=StringFormatter(
+        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="qwen"),
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(
+        name="qwen2_omni", image_token="<|image_pad|>", video_token="<|video_pad|>", audio_token="<|audio_pad|>"
+    ),
+)
+
+
 # copied from qwen template
 register_template(
    name="qwen2_vl",
@@ -1658,6 +1929,41 @@ register_template(
 )


+# copied from qwen template
+register_template(
+    name="qwen3_vl",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
+    format_observation=StringFormatter(
+        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="qwen"),
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(name="qwen3_vl", image_token="<|image_pad|>", video_token="<|video_pad|>"),
+    template_class=ReasoningTemplate,
+)
+
+
+# copied from qwen template
+register_template(
+    name="qwen3_vl_nothink",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
+    format_observation=StringFormatter(
+        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="qwen"),
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(name="qwen3_vl", image_token="<|image_pad|>", video_token="<|video_pad|>"),
+)
+
+
 register_template(
    name="sailor",
    format_user=StringFormatter(slots=["<|im_start|>question\n{{content}}<|im_end|>\n<|im_start|>answer\n"]),
@@ -1685,6 +1991,20 @@ register_template(
 )


+# copied from seed_coder
+register_template(
+    name="seed_oss",
+    format_user=StringFormatter(
+        slots=[{"bos_token"}, "user\n{{content}}", {"eos_token"}, {"bos_token"}, "assistant\n"]
+    ),
+    format_system=StringFormatter(slots=[{"bos_token"}, "system\n{{content}}", {"eos_token"}]),
+    format_function=FunctionFormatter(slots=[{"bos_token"}, "\n{{content}}", {"eos_token"}], tool_format="seed_oss"),
+    format_tools=ToolFormatter(tool_format="seed_oss"),
+    template_class=ReasoningTemplate,
+    thought_words=("<seed:think>", "</seed:think>"),
+)
+
+
 # copied from llama3 template
 register_template(
    name="skywork_o1",
--- a/src/llamafactory/data/tool_utils.py
+++ b/src/llamafactory/data/tool_utils.py
@@ -38,8 +38,20 @@ DEFAULT_TOOL_PROMPT = (
 )

 GLM4_TOOL_PROMPT = (
-    "你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，"
-    "你的任务是针对用户的问题和要求提供适当的答复和支持。# 可用工具{tool_text}"
+    "你是一个名为 ChatGLM 的人工智能助手。你是基于智谱 AI 公司训练的语言模型 GLM-4 模型开发的，"
+    "你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{tool_text}"
+)
+
+GLM4_MOE_TOOL_PROMPT = (
+    "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\n"
+    "You are provided with function signatures within <tools></tools> XML tags:\n<tools>{tool_text}"
+    "\n</tools>\n\nFor each function call, output the function name and arguments within the following XML format:"
+    "\n<tool_call>{{function-name}}"
+    "\n<arg_key>{{arg-key-1}}</arg_key>"
+    "\n<arg_value>{{arg-value-1}}</arg_value>"
+    "\n<arg_key>{{arg-key-2}}</arg_key>"
+    "\n<arg_value>{{arg-value-2}}</arg_value>"
+    "\n...\n</tool_call>\n"
 )

 LLAMA3_TOOL_PROMPT = (
@@ -57,6 +69,23 @@ QWEN_TOOL_PROMPT = (
    """"arguments": <args-json-object>}}\n</tool_call>"""
 )

+SEED_TOOL_PROMPT = (
+    "system\nYou are Doubao, a helpful AI assistant. You may call one or more functions to assist with the user query."
+    "Tool List:\nYou are authorized to use the following tools (described in JSON Schema format). Before performing "
+    "any task, you must decide how to call them based on the descriptions and parameters of these tools.{tool_text}\n"
+    "工具调用请遵循如下格式:\n<seed:tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>value_1"
+    "</parameter>\n<parameter=example_parameter_2>This is the value for the second parameter\nthat can span\nmultiple "
+    "lines</parameter>\n</function>\n</seed:tool_call>\n"
+)
+
+LING_TOOL_PROMPT = (
+    "# Tools\n\nYou may call one or more functions to assist with the user query.\n\n"
+    "You are provided with function signatures within <tools></tools> XML tags:\n<tools>{tool_text}"
+    "\n</tools>\n\nFor each function call, return a json object with function name and arguments within "
+    """<tool_call></tool_call> XML tags:\n<tool_call>\n{{"name": <function-name>, """
+    """"arguments": <args-json-object>}}\n</tool_call>"""
+)
+

@dataclass
 class ToolUtils(ABC):
@@ -303,12 +332,111 @@ class QwenToolUtils(ToolUtils):
        return results


+class GLM4MOEToolUtils(QwenToolUtils):
+    r"""GLM-4-MOE tool using template."""
+
+    @override
+    @staticmethod
+    def tool_formatter(tools: list[dict[str, Any]]) -> str:
+        tool_text = ""
+        for tool in tools:
+            wrapped_tool = tool if tool.get("type") == "function" else {"type": "function", "function": tool}
+            tool_text += "\n" + json.dumps(wrapped_tool, ensure_ascii=False)
+
+        return GLM4_MOE_TOOL_PROMPT.format(tool_text=tool_text)
+
+    @override
+    @staticmethod
+    def function_formatter(functions: list["FunctionCall"]) -> str:
+        function_json = [
+            {"func_name": name, "func_key_values": json.loads(arguments)} for name, arguments in functions
+        ]
+        function_texts = []
+        for func in function_json:
+            prompt = "\n<tool_call>" + func["func_name"]
+            for key, value in func["func_key_values"].items():
+                prompt += "\n<arg_key>" + key + "</arg_key>"
+                if not isinstance(value, str):
+                    value = json.dumps(value, ensure_ascii=False)
+                prompt += "\n<arg_value>" + value + "</arg_value>"
+            function_texts.append(prompt)
+
+        return "\n".join(function_texts)
+
+
+class SeedToolUtils(ToolUtils):
+    r"""Seed tool using template."""
+
+    @override
+    @staticmethod
+    def tool_formatter(tools: list[dict[str, Any]]) -> str:
+        return SEED_TOOL_PROMPT.format(tool_text="\n" + json.dumps(tools, ensure_ascii=False))
+
+    @override
+    @staticmethod
+    def function_formatter(functions: list["FunctionCall"]) -> str:
+        function_json = [
+            {"func_name": name, "func_key_values": json.loads(arguments)} for name, arguments in functions
+        ]
+        function_texts = []
+        for func in function_json:
+            prompt = "\n<seed:tool_call>\n<function=" + func["func_name"]
+            for key, value in func["func_key_values"].items():
+                prompt += "\n<parameter=" + key + ">"
+                if not isinstance(value, str):
+                    value = json.dumps(value, ensure_ascii=False)
+                prompt += value + "</parameter>"
+            prompt += "\n</function>\n</seed:tool_call>"
+            function_texts.append(prompt)
+
+        return "\n".join(function_texts)
+
+    @override
+    @staticmethod
+    def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
+        results = []
+        regex = re.compile(
+            r"<seed:tool_call>\s*<function=\s*([^\s<]+)\s*(.*?)\s*</function>\s*</seed:tool_call>", re.DOTALL
+        )
+        for func_name, params_block in re.findall(regex, content):
+            args_dict = {}
+            param_pattern = re.compile(r"<parameter=(.*?)>(.*?)</parameter>", re.DOTALL)
+            for key, raw_value in re.findall(param_pattern, params_block.strip()):
+                value = raw_value.strip()
+                try:
+                    parsed_value = json.loads(value)
+                except json.JSONDecodeError:
+                    parsed_value = raw_value
+                args_dict[key] = parsed_value
+
+            results.append(FunctionCall(func_name.strip(), json.dumps(args_dict, ensure_ascii=False)))
+
+        return results
+
+
+class LingToolUtils(QwenToolUtils):
+    r"""Ling v2 tool using template."""
+
+    @override
+    @staticmethod
+    def tool_formatter(tools: list[dict[str, Any]]) -> str:
+        tool_text = ""
+        for tool in tools:
+            wrapped_tool = tool if tool.get("type") == "function" else {"type": "function", "function": tool}
+            tool_text += "\n" + json.dumps(wrapped_tool, ensure_ascii=False)
+
+        return LING_TOOL_PROMPT.format(tool_text=tool_text) + "\n" + "detailed thinking off"
+
+
 TOOLS = {
    "default": DefaultToolUtils(),
    "glm4": GLM4ToolUtils(),
    "llama3": Llama3ToolUtils(),
    "mistral": MistralToolUtils(),
    "qwen": QwenToolUtils(),
+    "glm4_moe": GLM4MOEToolUtils(),
+    "seed_oss": SeedToolUtils(),
+    "ling": LingToolUtils(),
 }


--- a/src/llamafactory/extras/constants.py
+++ b/src/llamafactory/extras/constants.py
@@ -56,13 +56,13 @@ LAYERNORM_NAMES = {"norm", "ln"}

 LLAMABOARD_CONFIG = "llamaboard_config.yaml"

-METHODS = ["full", "freeze", "lora"]
+METHODS = ["full", "freeze", "lora", "oft"]

 MOD_SUPPORTED_MODELS = {"bloom", "falcon", "gemma", "llama", "mistral", "mixtral", "phi", "starcoder2"}

 MULTIMODAL_SUPPORTED_MODELS = set()

-PEFT_METHODS = {"lora"}
+PEFT_METHODS = {"lora", "oft"}

 RUNNING_LOG = "running_log.txt"

@@ -126,6 +126,7 @@ class QuantizationMethod(str, Enum):
    QUANTO = "quanto"
    EETQ = "eetq"
    HQQ = "hqq"
+    MXFP4 = "mxfp4"


 class RopeScaling(str, Enum):
@@ -143,7 +144,7 @@ def register_model_group(
    for name, path in models.items():
        SUPPORTED_MODELS[name] = path
        if template is not None and (
-            any(suffix in name for suffix in ("-Chat", "-Distill", "-Instruct")) or multimodal
+            any(suffix in name for suffix in ("-Chat", "-Distill", "-Instruct", "-Thinking")) or multimodal
        ):
            DEFAULT_TEMPLATE[name] = template

@@ -276,7 +277,7 @@ register_model_group(
 register_model_group(
    models={
        "ChatGLM2-6B-Chat": {
-            DownloadSource.DEFAULT: "THUDM/chatglm2-6b",
+            DownloadSource.DEFAULT: "zai-org/chatglm2-6b",
            DownloadSource.MODELSCOPE: "ZhipuAI/chatglm2-6b",
        }
    },
@@ -287,11 +288,11 @@ register_model_group(
 register_model_group(
    models={
        "ChatGLM3-6B-Base": {
-            DownloadSource.DEFAULT: "THUDM/chatglm3-6b-base",
+            DownloadSource.DEFAULT: "zai-org/chatglm3-6b-base",
            DownloadSource.MODELSCOPE: "ZhipuAI/chatglm3-6b-base",
        },
        "ChatGLM3-6B-Chat": {
-            DownloadSource.DEFAULT: "THUDM/chatglm3-6b",
+            DownloadSource.DEFAULT: "zai-org/chatglm3-6b",
            DownloadSource.MODELSCOPE: "ZhipuAI/chatglm3-6b",
        },
    },
@@ -333,7 +334,7 @@ register_model_group(
 register_model_group(
    models={
        "CodeGeeX4-9B-Chat": {
-            DownloadSource.DEFAULT: "THUDM/codegeex4-all-9b",
+            DownloadSource.DEFAULT: "zai-org/codegeex4-all-9b",
            DownloadSource.MODELSCOPE: "ZhipuAI/codegeex4-all-9b",
        },
    },
@@ -589,6 +590,59 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "Devstral-Small-2507-Instruct": {
+            DownloadSource.DEFAULT: "mistralai/Devstral-Small-2507",
+            DownloadSource.MODELSCOPE: "mistralai/Devstral-Small-2507",
+        },
+    },
+    template="mistral_small",
+)
+
+
+register_model_group(
+    models={
+        "dots.ocr": {
+            DownloadSource.DEFAULT: "rednote-hilab/dots.ocr",
+            DownloadSource.MODELSCOPE: "rednote-hilab/dots.ocr",
+        },
+    },
+    template="dots_ocr",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "ERNIE-4.5-21B-A3B-Thinking": {
+            DownloadSource.DEFAULT: "baidu/ERNIE-4.5-21B-A3B-Thinking",
+            DownloadSource.MODELSCOPE: "PaddlePaddle/ERNIE-4.5-21B-A3B-Thinking",
+        },
+    },
+    template="ernie",
+)
+
+
+register_model_group(
+    models={
+        "ERNIE-4.5-0.3B-PT": {
+            DownloadSource.DEFAULT: "baidu/ERNIE-4.5-0.3B-PT",
+            DownloadSource.MODELSCOPE: "PaddlePaddle/ERNIE-4.5-0.3B-PT",
+        },
+        "ERNIE-4.5-21B-A3B-PT": {
+            DownloadSource.DEFAULT: "baidu/ERNIE-4.5-21B-A3B-PT",
+            DownloadSource.MODELSCOPE: "PaddlePaddle/ERNIE-4.5-21B-A3B-PT",
+        },
+        "ERNIE-4.5-300B-A47B-PT": {
+            DownloadSource.DEFAULT: "baidu/ERNIE-4.5-300B-A47B-PT",
+            DownloadSource.MODELSCOPE: "PaddlePaddle/ERNIE-4.5-300B-A47B-PT",
+        },
+    },
+    template="ernie_nothink",
+)
+
+
 register_model_group(
    models={
        "EXAONE-3.0-7.8B-Instruct": {
@@ -634,6 +688,61 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "Falcon-H1-0.5B-Base": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-0.5B-Base",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-0.5B-Base",
+        },
+        "Falcon-H1-1.5B-Base": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-1.5B-Base",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Base",
+        },
+        "Falcon-H1-1.5B-Deep-Base": {
+            DownloadSource.DEFAULT: "tiuae/Falcon-H1-1.5B-Deep-Base",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Deep-Base",
+        },
+        "Falcon-H1-3B-Base": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-3B-Base",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-3B-Base",
+        },
+        "Falcon-H1-7B-Base": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-7B-Base",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-7B-Base",
+        },
+        "Falcon-H1-34B-Base": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-34B-Base",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-34B-Base",
+        },
+        "Falcon-H1-0.5B-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-0.5B-Instruct",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-0.5B-Instruct",
+        },
+        "Falcon-H1-1.5B-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-1.5B-Instruct",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Instruct",
+        },
+        "Falcon-H1-1.5B-Deep-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-1.5B-Deep-Instruct",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Deep-Instruct",
+        },
+        "Falcon-H1-3B-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-3B-Instruct",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-3B-Instruct",
+        },
+        "Falcon-H1-7B-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-7B-Instruct",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-7B-Instruct",
+        },
+        "Falcon-H1-34B-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-34B-Instruct",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-34B-Instruct",
+        },
+    },
+    template="falcon_h1",
+)
+
+
 register_model_group(
    models={
        "Gemma-2B": {
@@ -658,6 +767,13 @@ register_model_group(
        "Gemma-1.1-7B-Instruct": {
            DownloadSource.DEFAULT: "google/gemma-1.1-7b-it",
        },
+    },
+    template="gemma",
+)
+
+
+register_model_group(
+    models={
        "Gemma-2-2B": {
            DownloadSource.DEFAULT: "google/gemma-2-2b",
            DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-2b",
@@ -684,10 +800,18 @@ register_model_group(
            DownloadSource.DEFAULT: "google/gemma-2-27b-it",
            DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-27b-it",
        },
+        "Gemma-3-270M": {
+            DownloadSource.DEFAULT: "google/gemma-3-270m",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-270m",
+        },
        "Gemma-3-1B": {
            DownloadSource.DEFAULT: "google/gemma-3-1b-pt",
            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-1b-pt",
        },
+        "Gemma-3-270M-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-3-270m-it",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-270m-it",
+        },
        "Gemma-3-1B-Instruct": {
            DownloadSource.DEFAULT: "google/gemma-3-1b-it",
            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-1b-it",
@@ -697,7 +821,7 @@ register_model_group(
            DownloadSource.MODELSCOPE: "google/medgemma-27b-text-it",
        },
    },
-    template="gemma",
+    template="gemma2",
 )


@@ -735,37 +859,65 @@ register_model_group(
            DownloadSource.DEFAULT: "google/medgemma-4b-it",
            DownloadSource.MODELSCOPE: "google/medgemma-4b-it",
        },
+        "MedGemma-27B-Instruct": {
+            DownloadSource.DEFAULT: "google/medgemma-27b-text-it",
+            DownloadSource.MODELSCOPE: "google/medgemma-27b-text-it",
+        },
    },
    template="gemma3",
    multimodal=True,
 )


+register_model_group(
+    models={
+        "Gemma-3n-E2B": {
+            DownloadSource.DEFAULT: "google/gemma-3n-E2B",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E2B",
+        },
+        "Gemma-3n-E4B": {
+            DownloadSource.DEFAULT: "google/gemma-3n-E4B",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E4B",
+        },
+        "Gemma-3n-E2B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-3n-E2B-it",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E2B-it",
+        },
+        "Gemma-3n-E4B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-3n-E4B-it",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E4B-it",
+        },
+    },
+    template="gemma3n",
+    multimodal=True,
+)
+
+
 register_model_group(
    models={
        "GLM-4-9B": {
-            DownloadSource.DEFAULT: "THUDM/glm-4-9b",
+            DownloadSource.DEFAULT: "zai-org/glm-4-9b",
            DownloadSource.MODELSCOPE: "ZhipuAI/glm-4-9b",
        },
        "GLM-4-9B-Chat": {
-            DownloadSource.DEFAULT: "THUDM/glm-4-9b-chat",
+            DownloadSource.DEFAULT: "zai-org/glm-4-9b-chat",
            DownloadSource.MODELSCOPE: "ZhipuAI/glm-4-9b-chat",
            DownloadSource.OPENMIND: "LlamaFactory/glm-4-9b-chat",
        },
        "GLM-4-9B-1M-Chat": {
-            DownloadSource.DEFAULT: "THUDM/glm-4-9b-chat-1m",
+            DownloadSource.DEFAULT: "zai-org/glm-4-9b-chat-1m",
            DownloadSource.MODELSCOPE: "ZhipuAI/glm-4-9b-chat-1m",
        },
        "GLM-4-0414-9B-Chat": {
-            DownloadSource.DEFAULT: "THUDM/GLM-4-9B-0414",
+            DownloadSource.DEFAULT: "zai-org/GLM-4-9B-0414",
            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4-9B-0414",
        },
        "GLM-4-0414-32B-Base": {
-            DownloadSource.DEFAULT: "THUDM/GLM-4-32B-Base-0414",
+            DownloadSource.DEFAULT: "zai-org/GLM-4-32B-Base-0414",
            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4-32B-Base-0414",
        },
        "GLM-4-0414-32B-Chat": {
-            DownloadSource.DEFAULT: "THUDM/GLM-4-32B-0414",
+            DownloadSource.DEFAULT: "zai-org/GLM-4-32B-0414",
            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4-32B-0414",
        },
    },
@@ -773,14 +925,65 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "GLM-4.1V-9B-Base": {
+            DownloadSource.DEFAULT: "zai-org/GLM-4.1V-9B-Base",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.1V-9B-Base",
+        },
+        "GLM-4.1V-9B-Thinking": {
+            DownloadSource.DEFAULT: "zai-org/GLM-4.1V-9B-Thinking",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.1V-9B-Thinking",
+        },
+    },
+    template="glm4v",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "GLM-4.5-Air-Base": {
+            DownloadSource.DEFAULT: "zai-org/GLM-4.5-Air-Base",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.5-Air-Base",
+        },
+        "GLM-4.5-Base": {
+            DownloadSource.DEFAULT: "zai-org/GLM-4.5-Base",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.5-Base",
+        },
+        "GLM-4.5-Air-Thinking": {
+            DownloadSource.DEFAULT: "zai-org/GLM-4.5-Air",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.5-Air",
+        },
+        "GLM-4.5-Thinking": {
+            DownloadSource.DEFAULT: "zai-org/GLM-4.5",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.5",
+        },
+    },
+    template="glm4_moe",
+)
+
+
+register_model_group(
+    models={
+        "GLM-4.5V-Air-Thinking": {
+            DownloadSource.DEFAULT: "zai-org/GLM-4.5V",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.5V",
+        }
+    },
+    template="glm4v_moe",
+    multimodal=True,
+)
+
+
 register_model_group(
    models={
        "GLM-Z1-0414-9B-Chat": {
-            DownloadSource.DEFAULT: "THUDM/GLM-Z1-9B-0414",
+            DownloadSource.DEFAULT: "zai-org/GLM-Z1-9B-0414",
            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-Z1-9B-0414",
        },
        "GLM-Z1-0414-32B-Chat": {
-            DownloadSource.DEFAULT: "THUDM/GLM-Z1-32B-0414",
+            DownloadSource.DEFAULT: "zai-org/GLM-Z1-32B-0414",
            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-Z1-32B-0414",
        },
    },
@@ -810,6 +1013,21 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "GPT-OSS-20B-Thinking": {
+            DownloadSource.DEFAULT: "openai/gpt-oss-20b",
+            DownloadSource.MODELSCOPE: "openai/gpt-oss-20b",
+        },
+        "GPT-OSS-120B-Thinking": {
+            DownloadSource.DEFAULT: "openai/gpt-oss-120b",
+            DownloadSource.MODELSCOPE: "openai/gpt-oss-120b",
+        },
+    },
+    template="gpt",
+)
+
+
 register_model_group(
    models={
        "Granite-3.0-1B-A400M-Base": {
@@ -917,6 +1135,17 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "Granite-4.0-tiny-preview": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-4.0-tiny-preview",
+            DownloadSource.MODELSCOPE: "ibm-granite/granite-4.0-tiny-preview",
+        },
+    },
+    template="granite4",
+)
+
+
 register_model_group(
    models={
        "Hunyuan-7B-Instruct": {
@@ -1073,12 +1302,52 @@ register_model_group(
            DownloadSource.DEFAULT: "OpenGVLab/InternVL3-78B-hf",
            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3-78B-hf",
        },
+        "InternVL3.5-1B-hf": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL3_5-1B-HF",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3_5-1B-HF",
+        },
+        "InternVL3.5-2B-hf": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL3_5-2B-HF",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3_5-2B-HF",
+        },
+        "InternVL3.5-4B-hf": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL3_5-4B-HF",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3_5-4B-HF",
+        },
+        "InternVL3.5-8B-hf": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL3_5-8B-HF",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3_5-8B-HF",
+        },
+        "InternVL3.5-14B-hf": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL3_5-14B-HF",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3_5-14B-HF",
+        },
+        "InternVL3.5-30B-A3B-hf": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL3_5-30B-A3B-HF",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3_5-30B-A3B-HF",
+        },
+        "InternVL3.5-38B-hf": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL3_5-38B-HF",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3_5-38B-HF",
+        },
    },
    template="intern_vl",
    multimodal=True,
 )


+register_model_group(
+    models={
+        "Intern-S1-mini": {
+            DownloadSource.DEFAULT: "internlm/Intern-S1-mini",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/Intern-S1-mini",
+        }
+    },
+    template="intern_s1",
+    multimodal=True,
+)
+
+
 register_model_group(
    models={
        "Jamba-v0.1": {
@@ -1089,6 +1358,29 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "Keye-VL-8B-Chat": {
+            DownloadSource.DEFAULT: "Kwai-Keye/Keye-VL-8B-Preview",
+            DownloadSource.MODELSCOPE: "Kwai-Keye/Keye-VL-8B-Preview",
+        },
+    },
+    template="keye_vl",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Kimi-Dev-72B-Instruct": {
+            DownloadSource.DEFAULT: "moonshotai/Kimi-Dev-72B",
+            DownloadSource.MODELSCOPE: "moonshotai/Kimi-Dev-72B",
+        },
+    },
+    template="qwen",
+)
+
+
 register_model_group(
    models={
        "Kimi-VL-A3B-Instruct": {
@@ -1099,6 +1391,10 @@ register_model_group(
            DownloadSource.DEFAULT: "moonshotai/Kimi-VL-A3B-Thinking",
            DownloadSource.MODELSCOPE: "moonshotai/Kimi-VL-A3B-Thinking",
        },
+        "Kimi-VL-A3B-Thinking-2506": {
+            DownloadSource.DEFAULT: "moonshotai/Kimi-VL-A3B-Thinking-2506",
+            DownloadSource.MODELSCOPE: "moonshotai/Kimi-VL-A3B-Thinking-2506",
+        },
    },
    template="kimi_vl",
    multimodal=True,
@@ -1462,20 +1758,36 @@ register_model_group(

 register_model_group(
    models={
-        "MiMo-7B-VL-Instruct": {
-            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-VL-7B-SFT",
-            DownloadSource.MODELSCOPE: "XiaomiMiMo/MiMo-VL-7B-SFT",
-        },
        "MiMo-7B-VL-RL": {
            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-VL-7B-RL",
            DownloadSource.MODELSCOPE: "XiaomiMiMo/MiMo-VL-7B-RL",
        },
+        "MiMo-VL-7B-RL-2508": {
+            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-VL-7B-RL-2508",
+            DownloadSource.MODELSCOPE: "XiaomiMiMo/MiMo-VL-7B-RL-2508",
+        },
    },
    template="mimo_vl",
    multimodal=True,
 )


+register_model_group(
+    models={
+        "MiMo-7B-VL-Instruct": {
+            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-VL-7B-SFT",
+            DownloadSource.MODELSCOPE: "XiaomiMiMo/MiMo-VL-7B-SFT",
+        },
+        "MiMo-VL-7B-SFT-2508": {
+            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-VL-7B-SFT-2508",
+            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-VL-7B-SFT-2508",
+        },
+    },
+    template="qwen2_vl",
+    multimodal=True,
+)
+
+
 register_model_group(
    models={
        "MiniCPM-2B-SFT-Chat": {
@@ -1513,6 +1825,10 @@ register_model_group(
            DownloadSource.DEFAULT: "openbmb/MiniCPM4-8B",
            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM4-8B",
        },
+        "MiniCPM4.1-8B-Chat": {
+            DownloadSource.DEFAULT: "openbmb/MiniCPM4.1-8B",
+            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM4.1-8B",
+        },
    },
    template="cpm4",
 )
@@ -1520,7 +1836,7 @@ register_model_group(

 register_model_group(
    models={
-        "MiniCPM-o-2_6": {
+        "MiniCPM-o-2.6": {
            DownloadSource.DEFAULT: "openbmb/MiniCPM-o-2_6",
            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM-o-2_6",
        },
@@ -1532,7 +1848,7 @@ register_model_group(

 register_model_group(
    models={
-        "MiniCPM-V-2_6": {
+        "MiniCPM-V-2.6": {
            DownloadSource.DEFAULT: "openbmb/MiniCPM-V-2_6",
            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM-V-2_6",
        },
@@ -1542,6 +1858,30 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "MiniCPM-V-4": {
+            DownloadSource.DEFAULT: "openbmb/MiniCPM-V-4",
+            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM-V-4",
+        },
+    },
+    template="minicpm_v",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "MiniCPM-V-4.5": {
+            DownloadSource.DEFAULT: "openbmb/MiniCPM-V-4_5",
+            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM-V-4_5",
+        },
+    },
+    template="minicpm_v",
+    multimodal=True,
+)
+
+
 register_model_group(
    models={
        "Ministral-8B-Instruct-2410": {
@@ -1617,6 +1957,10 @@ register_model_group(
            DownloadSource.DEFAULT: "mistralai/Mistral-Small-3.1-24B-Instruct-2503",
            DownloadSource.MODELSCOPE: "mistralai/Mistral-Small-3.1-24B-Instruct-2503",
        },
+        "Mistral-Small-3.2-24B-Instruct": {
+            DownloadSource.DEFAULT: "mistralai/Mistral-Small-3.2-24B-Instruct-2506",
+            DownloadSource.MODELSCOPE: "mistralai/Mistral-Small-3.2-24B-Instruct-2506",
+        },
    },
    template="mistral_small",
    multimodal=True,
@@ -1646,6 +1990,37 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "MobileLLM-R1-140M-Base": {
+            DownloadSource.DEFAULT: "facebook/MobileLLM-R1-140M-base",
+            DownloadSource.MODELSCOPE: "facebook/MobileLLM-R1-140M-base",
+        },
+        "MobileLLM-R1-360M-Base": {
+            DownloadSource.DEFAULT: "facebook/MobileLLM-R1-360M-base",
+            DownloadSource.MODELSCOPE: "facebook/MobileLLM-R1-360M-base",
+        },
+        "MobileLLM-R1-950M-Base": {
+            DownloadSource.DEFAULT: "facebook/MobileLLM-R1-950M-base",
+            DownloadSource.MODELSCOPE: "facebook/MobileLLM-R1-950M-base",
+        },
+        "MobileLLM-R1-140M-Instruct": {
+            DownloadSource.DEFAULT: "facebook/MobileLLM-R1-140M",
+            DownloadSource.MODELSCOPE: "facebook/MobileLLM-R1-140M",
+        },
+        "MobileLLM-R1-360M-Instruct": {
+            DownloadSource.DEFAULT: "facebook/MobileLLM-R1-360M",
+            DownloadSource.MODELSCOPE: "facebook/MobileLLM-R1-360M",
+        },
+        "MobileLLM-R1-950M-Instruct": {
+            DownloadSource.DEFAULT: "facebook/MobileLLM-R1-950M",
+            DownloadSource.MODELSCOPE: "facebook/MobileLLM-R1-950M",
+        },
+    },
+    template="llama3",
+)
+
+
 register_model_group(
    models={
        "Moonlight-16B-A3B": {
@@ -2538,75 +2913,114 @@ register_model_group(
            DownloadSource.DEFAULT: "Qwen/Qwen3-30B-A3B-Base",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-30B-A3B-Base",
        },
-        "Qwen3-0.6B-Instruct": {
+        "Qwen3-0.6B-Thinking": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-0.6B",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-0.6B",
        },
-        "Qwen3-1.7B-Instruct": {
+        "Qwen3-1.7B-Thinking": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-1.7B",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-1.7B",
        },
-        "Qwen3-4B-Instruct": {
+        "Qwen3-4B-Thinking": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-4B",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-4B",
        },
-        "Qwen3-8B-Instruct": {
+        "Qwen3-4B-Thinking-2507": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-4B-Thinking-2507",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-4B-Thinking-2507",
+        },
+        "Qwen3-8B-Thinking": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-8B",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-8B",
        },
-        "Qwen3-14B-Instruct": {
+        "Qwen3-14B-Thinking": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-14B",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-14B",
        },
-        "Qwen3-32B-Instruct": {
+        "Qwen3-32B-Thinking": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-32B",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-32B",
        },
-        "Qwen3-30B-A3B-Instruct": {
+        "Qwen3-30B-A3B-Thinking": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-30B-A3B",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-30B-A3B",
        },
-        "Qwen3-235B-A22B-Instruct": {
+        "Qwen3-30B-A3B-Thinking-2507": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-30B-A3B-Thinking-2507",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-30B-A3B-Thinking-2507",
+        },
+        "Qwen3-235B-A22B-Thinking": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-235B-A22B",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-235B-A22B",
        },
-        "Qwen3-0.6B-Instruct-GPTQ-Int8": {
+        "Qwen3-235B-A22B-Thinking-2507": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-235B-A22B-Thinking-2507",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-235B-A22B-Thinking-2507",
+        },
+        "Qwen3-0.6B-Thinking-GPTQ-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-0.6B-GPTQ-Int8",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-0.6B-GPTQ-Int8",
        },
-        "Qwen3-1.7B-Instruct-GPTQ-Int8": {
+        "Qwen3-1.7B-Thinking-GPTQ-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-1.7B-GPTQ-Int8",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-1.7B-GPTQ-Int8",
        },
-        "Qwen3-4B-Instruct-AWQ": {
+        "Qwen3-4B-Thinking-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-4B-AWQ",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-4B-AWQ",
        },
-        "Qwen3-8B-Instruct-AWQ": {
+        "Qwen3-8B-Thinking-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-8B-AWQ",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-8B-AWQ",
        },
-        "Qwen3-14B-Instruct-AWQ": {
+        "Qwen3-14B-Thinking-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-14B-AWQ",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-14B-AWQ",
        },
-        "Qwen3-32B-Instruct-AWQ": {
+        "Qwen3-32B-Thinking-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-32B-AWQ",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-32B-AWQ",
        },
-        "Qwen3-30B-A3B-Instruct-GPTQ-Int4": {
+        "Qwen3-30B-A3B-Thinking-GPTQ-Int4": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-30B-A3B-GPTQ-Int4",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-30B-A3B-GPTQ-Int4",
        },
-        "Qwen3-235B-A22B-Instruct-GPTQ-Int4": {
+        "Qwen3-235B-A22B-Thinking-GPTQ-Int4": {
            DownloadSource.DEFAULT: "Qwen/Qwen3-235B-A22B-GPTQ-Int4",
            DownloadSource.MODELSCOPE: "Qwen/Qwen3-235B-A22B-GPTQ-Int4",
        },
+        "Qwen/Qwen3-Next-80B-A3B-Thinking": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-Next-80B-A3B-Thinking",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-Next-80B-A3B-Thinking",
+        },
    },
    template="qwen3",
 )


+register_model_group(
+    models={
+        "Qwen3-4B-Instruct-2507": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-4B-Instruct-2507",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-4B-Instruct-2507",
+        },
+        "Qwen3-30B-A3B-Instruct-2507": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-30B-A3B-Instruct-2507",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-30B-A3B-Instruct-2507",
+        },
+        "Qwen3-235B-A22B-Instruct-2507": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-235B-A22B-Instruct-2507",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-235B-A22B-Instruct-2507",
+        },
+        "Qwen3-Next-80B-A3B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-Next-80B-A3B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-Next-80B-A3B-Instruct",
+        },
+    },
+    template="qwen3_nothink",
+)
+
+
 register_model_group(
    models={
        "Qwen2-Audio-7B": {
@@ -2647,6 +3061,34 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "Qwen3-Omni-30B-A3B-Captioner": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-Omni-30B-A3B-Captioner",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-Omni-30B-A3B-Captioner",
+        },
+        "Qwen3-Omni-30B-A3B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+        },
+    },
+    template="qwen3_omni_nothink",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Qwen3-Omni-30B-A3B-Thinking": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-Omni-30B-A3B-Thinking",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-Omni-30B-A3B-Thinking",
+        },
+    },
+    template="qwen3_omni",
+    multimodal=True,
+)
+
+
 register_model_group(
    models={
        "Qwen2-VL-2B": {
@@ -2749,22 +3191,76 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "Qwen3-VL-235B-A22B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-VL-235B-A22B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-VL-235B-A22B-Instruct",
+        },
+        "Qwen3-VL-30B-A3B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-VL-30B-A3B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-VL-30B-A3B-Instruct",
+        },
+    },
+    template="qwen3_vl_nothink",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Qwen3-VL-235B-A22B-Thinking": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-VL-235B-A22B-Thinking",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-VL-235B-A22B-Thinking",
+        },
+        "Qwen3-VL-30B-A3B-Thinking": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-VL-30B-A3B-Thinking",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-VL-30B-A3B-Thinking",
+        },
+    },
+    template="qwen3_vl",
+    multimodal=True,
+)
+
+
 register_model_group(
    models={
        "Seed-Coder-8B-Base": {
            DownloadSource.DEFAULT: "ByteDance-Seed/Seed-Coder-8B-Base",
+            DownloadSource.MODELSCOPE: "ByteDance-Seed/Seed-Coder-8B-Base",
        },
        "Seed-Coder-8B-Instruct": {
            DownloadSource.DEFAULT: "ByteDance-Seed/Seed-Coder-8B-Instruct",
+            DownloadSource.MODELSCOPE: "ByteDance-Seed/Seed-Coder-8B-Instruct",
        },
-        "Seed-Coder-8B-Instruct-Reasoning": {
+        "Seed-Coder-8B-Thinking": {
            DownloadSource.DEFAULT: "ByteDance-Seed/Seed-Coder-8B-Reasoning-bf16",
+            DownloadSource.MODELSCOPE: "ByteDance-Seed/Seed-Coder-8B-Reasoning-bf16",
        },
    },
    template="seed_coder",
 )


+register_model_group(
+    models={
+        "Seed-OSS-36B-Base": {
+            DownloadSource.DEFAULT: "ByteDance-Seed/Seed-OSS-36B-Base",
+            DownloadSource.MODELSCOPE: "ByteDance-Seed/Seed-OSS-36B-Base",
+        },
+        "Seed-OSS-36B-Base-woSyn": {
+            DownloadSource.DEFAULT: "ByteDance-Seed/Seed-OSS-36B-Base-woSyn",
+            DownloadSource.MODELSCOPE: "ByteDance-Seed/Seed-OSS-36B-Base-woSyn",
+        },
+        "Seed-OSS-36B-Instruct": {
+            DownloadSource.DEFAULT: "ByteDance-Seed/Seed-OSS-36B-Instruct",
+            DownloadSource.MODELSCOPE: "ByteDance-Seed/Seed-OSS-36B-Instruct",
+        },
+    },
+    template="seed_oss",
+)
+
+
 register_model_group(
    models={
        "Skywork-13B-Base": {
--- a/src/llamafactory/extras/env.py
+++ b/src/llamafactory/extras/env.py
@@ -15,22 +15,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-import os
-import platform

-import accelerate
-import datasets
-import peft
-import torch
-import transformers
-import trl
-from transformers.utils import is_torch_cuda_available, is_torch_npu_available
-
-
-VERSION = "0.9.3"
+VERSION = "0.9.4.dev0"


 def print_env() -> None:
+    import os
+    import platform
+
+    import accelerate
+    import datasets
+    import peft
+    import torch
+    import transformers
+    import trl
+    from transformers.utils import is_torch_cuda_available, is_torch_npu_available
+
    info = {
        "`llamafactory` version": VERSION,
        "Platform": platform.platform(),
--- a/src/llamafactory/extras/logging.py
+++ b/src/llamafactory/extras/logging.py
@@ -50,7 +50,7 @@ class LoggerHandler(logging.Handler):

    def _write_log(self, log_entry: str) -> None:
        with open(self.running_log, "a", encoding="utf-8") as f:
-            f.write(log_entry + "\n\n")
+            f.write(log_entry + "\n")

    def emit(self, record) -> None:
        if record.name == "httpx":
--- a/src/llamafactory/extras/misc.py
+++ b/src/llamafactory/extras/misc.py
@@ -18,11 +18,12 @@
 import gc
 import os
 import socket
-from typing import TYPE_CHECKING, Any, Literal, Union
+from typing import TYPE_CHECKING, Any, Literal, Optional, Union

 import torch
 import torch.distributed as dist
 import transformers.dynamic_module_utils
+from huggingface_hub.utils import WeakFileLock
 from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList
 from transformers.dynamic_module_utils import get_relative_imports
 from transformers.utils import (
@@ -35,7 +36,6 @@ from transformers.utils import (
 from transformers.utils.versions import require_version

 from . import logging
-from .packages import is_transformers_version_greater_than


 _is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
@@ -94,15 +94,11 @@ def check_version(requirement: str, mandatory: bool = False) -> None:

 def check_dependencies() -> None:
    r"""Check the version of the required packages."""
-    check_version(
-        "transformers>=4.45.0,<=4.52.4,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0,!=4.52.0"
-    )
-    check_version("datasets>=2.16.0,<=3.6.0")
-    check_version("accelerate>=0.34.0,<=1.7.0")
-    check_version("peft>=0.14.0,<=0.15.2")
+    check_version("transformers>=4.49.0,<=4.57.0")
+    check_version("datasets>=2.16.0,<=4.0.0")
+    check_version("accelerate>=1.3.0,<=1.11.0")
+    check_version("peft>=0.14.0,<=0.17.1")
    check_version("trl>=0.8.6,<=0.9.6")
-    if is_transformers_version_greater_than("4.46.0") and not is_transformers_version_greater_than("4.48.1"):
-        logger.warning_rank0_once("There are known bugs in transformers v4.46.0-v4.48.0, please use other versions.")


 def calculate_tps(dataset: list[dict[str, Any]], metrics: dict[str, float], stage: Literal["sft", "rm"]) -> float:
@@ -182,8 +178,22 @@ def get_logits_processor() -> "LogitsProcessorList":
    return logits_processor


+def get_current_memory() -> tuple[int, int]:
+    r"""Get the available and total memory for the current device (in Bytes)."""
+    if is_torch_xpu_available():
+        return torch.xpu.mem_get_info()
+    elif is_torch_npu_available():
+        return torch.npu.mem_get_info()
+    elif is_torch_mps_available():
+        return torch.mps.current_allocated_memory(), torch.mps.recommended_max_memory()
+    elif is_torch_cuda_available():
+        return torch.cuda.mem_get_info()
+    else:
+        return 0, -1
+
+
 def get_peak_memory() -> tuple[int, int]:
-    r"""Get the peak memory usage for the current device (in Bytes)."""
+    r"""Get the peak memory usage (allocated, reserved) for the current device (in Bytes)."""
    if is_torch_xpu_available():
        return torch.xpu.max_memory_allocated(), torch.xpu.max_memory_reserved()
    elif is_torch_npu_available():
@@ -193,7 +203,7 @@ def get_peak_memory() -> tuple[int, int]:
    elif is_torch_cuda_available():
        return torch.cuda.max_memory_allocated(), torch.cuda.max_memory_reserved()
    else:
-        return 0, 0
+        return 0, -1


 def has_tokenized_data(path: "os.PathLike") -> bool:
@@ -201,9 +211,9 @@ def has_tokenized_data(path: "os.PathLike") -> bool:
    return os.path.isdir(path) and len(os.listdir(path)) > 0


-def infer_optim_dtype(model_dtype: "torch.dtype") -> "torch.dtype":
+def infer_optim_dtype(model_dtype: Optional["torch.dtype"]) -> "torch.dtype":
    r"""Infer the optimal dtype according to the model_dtype and device compatibility."""
-    if _is_bf16_available and model_dtype == torch.bfloat16:
+    if _is_bf16_available and (model_dtype == torch.bfloat16 or model_dtype is None):
        return torch.bfloat16
    elif _is_fp16_available:
        return torch.float16
@@ -259,26 +269,37 @@ def try_download_model_from_other_hub(model_args: "ModelArguments") -> str:
        return model_args.model_name_or_path

    if use_modelscope():
-        check_version("modelscope>=1.11.0", mandatory=True)
+        check_version("modelscope>=1.14.0", mandatory=True)
        from modelscope import snapshot_download  # type: ignore
+        from modelscope.hub.api import HubApi  # type: ignore
+
+        if model_args.ms_hub_token:
+            api = HubApi()
+            api.login(model_args.ms_hub_token)

        revision = "master" if model_args.model_revision == "main" else model_args.model_revision
-        return snapshot_download(
+        with WeakFileLock(os.path.abspath(os.path.expanduser("~/.cache/llamafactory/modelscope.lock"))):
+            model_path = snapshot_download(
                model_args.model_name_or_path,
                revision=revision,
                cache_dir=model_args.cache_dir,
            )

+        return model_path
+
    if use_openmind():
        check_version("openmind>=0.8.0", mandatory=True)
        from openmind.utils.hub import snapshot_download  # type: ignore

-        return snapshot_download(
+        with WeakFileLock(os.path.abspath(os.path.expanduser("~/.cache/llamafactory/openmind.lock"))):
+            model_path = snapshot_download(
                model_args.model_name_or_path,
                revision=model_args.model_revision,
                cache_dir=model_args.cache_dir,
            )

+        return model_path
+

 def use_modelscope() -> bool:
    return is_env_enabled("USE_MODELSCOPE_HUB")
@@ -305,5 +326,5 @@ def fix_proxy(ipv6_enabled: bool = False) -> None:
    r"""Fix proxy settings for gradio ui."""
    os.environ["no_proxy"] = "localhost,127.0.0.1,0.0.0.0"
    if ipv6_enabled:
-        for name in ("http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY"):
-            os.environ.pop(name, None)
+        os.environ.pop("http_proxy", None)
+        os.environ.pop("HTTP_PROXY", None)
--- a/src/llamafactory/extras/packages.py
+++ b/src/llamafactory/extras/packages.py
@@ -58,6 +58,10 @@ def is_apollo_available():
    return _is_package_available("apollo_torch")


+def is_jieba_available():
+    return _is_package_available("jieba")
+
+
 def is_gradio_available():
    return _is_package_available("gradio")

--- a/src/llamafactory/hparams/finetuning_args.py
+++ b/src/llamafactory/hparams/finetuning_args.py
@@ -122,6 +122,48 @@ class LoraArguments:
    )


+@dataclass
+class OFTArguments:
+    r"""Arguments pertaining to the OFT training."""
+
+    additional_target: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Name(s) of modules apart from LoRA layers to be set as trainable "
+                "and saved in the final checkpoint. "
+                "Use commas to separate multiple modules."
+            )
+        },
+    )
+    module_dropout: float = field(
+        default=0.0,
+        metadata={"help": "Dropout rate for the OFT fine-tuning."},
+    )
+    oft_rank: int = field(
+        default=0,
+        metadata={"help": "The intrinsic dimension for OFT fine-tuning."},
+    )
+    oft_block_size: int = field(
+        default=32,
+        metadata={"help": "The intrinsic dimension for OFT fine-tuning."},
+    )
+    oft_target: str = field(
+        default="all",
+        metadata={
+            "help": (
+                "Name(s) of target modules to apply OFT. "
+                "Use commas to separate multiple modules. "
+                "Use `all` to specify all the linear modules."
+            )
+        },
+    )
+    create_new_adapter: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to create a new adapter with randomly initialized weight."},
+    )
+
+
@dataclass
 class RLHFArguments:
    r"""Arguments pertaining to the PPO, DPO and KTO training."""
@@ -134,6 +176,10 @@ class RLHFArguments:
        default=0.0,
        metadata={"help": "The supervised fine-tuning loss coefficient in DPO training."},
    )
+    pref_bco_weight: float = field(
+        default=0.0,
+        metadata={"help": "The Binary Classifier Optimization coefficient in DPO training."},
+    )
    pref_loss: Literal["sigmoid", "hinge", "ipo", "kto_pair", "orpo", "simpo"] = field(
        default="sigmoid",
        metadata={"help": "The type of DPO loss to use."},
@@ -396,7 +442,14 @@ class SwanLabArguments:

@dataclass
 class FinetuningArguments(
-    SwanLabArguments, BAdamArgument, ApolloArguments, GaloreArguments, RLHFArguments, LoraArguments, FreezeArguments
+    SwanLabArguments,
+    BAdamArgument,
+    ApolloArguments,
+    GaloreArguments,
+    RLHFArguments,
+    LoraArguments,
+    OFTArguments,
+    FreezeArguments,
 ):
    r"""Arguments pertaining to which techniques we are going to fine-tuning with."""

@@ -424,6 +477,10 @@ class FinetuningArguments(
        default=False,
        metadata={"help": "Whether or not to use the Muon optimizer."},
    )
+    use_dft_loss: bool = field(
+        default=False,
+        metadata={"help": "Whether to use the DFT loss."},
+    )
    freeze_vision_tower: bool = field(
        default=True,
        metadata={"help": "Whether ot not to freeze the vision tower in MLLM training."},
@@ -467,12 +524,13 @@ class FinetuningArguments(
        self.freeze_extra_modules: Optional[list[str]] = split_arg(self.freeze_extra_modules)
        self.lora_alpha: int = self.lora_alpha or self.lora_rank * 2
        self.lora_target: list[str] = split_arg(self.lora_target)
+        self.oft_target: list[str] = split_arg(self.oft_target)
        self.additional_target: Optional[list[str]] = split_arg(self.additional_target)
        self.galore_target: list[str] = split_arg(self.galore_target)
        self.apollo_target: list[str] = split_arg(self.apollo_target)
        self.use_ref_model = self.stage == "dpo" and self.pref_loss not in ["orpo", "simpo"]

-        assert self.finetuning_type in ["lora", "freeze", "full"], "Invalid fine-tuning method."
+        assert self.finetuning_type in ["lora", "oft", "freeze", "full"], "Invalid fine-tuning method."
        assert self.ref_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
        assert self.reward_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."

@@ -482,6 +540,9 @@ class FinetuningArguments(
        if self.stage == "ppo" and self.reward_model_type == "lora" and self.finetuning_type != "lora":
            raise ValueError("`reward_model_type` cannot be lora for Freeze/Full PPO training.")

+        if self.stage == "ppo" and self.reward_model_type == "oft" and self.finetuning_type != "oft":
+            raise ValueError("`reward_model_type` cannot be oft for Freeze/Full PPO training.")
+
        if self.stage == "dpo" and self.pref_loss != "sigmoid" and self.dpo_label_smoothing > 1e-6:
            raise ValueError("`dpo_label_smoothing` is only valid for sigmoid loss function.")

--- a/src/llamafactory/hparams/model_args.py
+++ b/src/llamafactory/hparams/model_args.py
@@ -213,6 +213,23 @@ class QuantizationArguments:
        default=None,
        metadata={"help": "Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0."},
    )
+    fp8: bool = field(
+        default=False,
+        metadata={
+            "help": "Enable FP8 mixed precision training via HuggingFace Accelerate. "
+            "Requires PyTorch 2.7+ and Hopper architecture GPUs."
+        },
+    )
+    fp8_backend: str = field(
+        default="auto",
+        metadata={
+            "help": "FP8 backend to use ('auto', 'torchao', 'te', 'msamp'). 'auto' selects best available backend."
+        },
+    )
+    fp8_enable_fsdp_float8_all_gather: bool = field(
+        default=False,
+        metadata={"help": "Enable FP8 optimizations for FSDP2 all-gather operations."},
+    )


@dataclass
--- a/src/llamafactory/hparams/parser.py
+++ b/src/llamafactory/hparams/parser.py
@@ -15,7 +15,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-import json
 import os
 import sys
 from pathlib import Path
@@ -23,7 +22,6 @@ from typing import Any, Optional, Union

 import torch
 import transformers
-import yaml
 from omegaconf import OmegaConf
 from transformers import HfArgumentParser
 from transformers.integrations import is_deepspeed_zero3_enabled
@@ -34,6 +32,7 @@ from transformers.utils import is_torch_bf16_gpu_available, is_torch_npu_availab
 from ..extras import logging
 from ..extras.constants import CHECKPOINT_NAMES, EngineName
 from ..extras.misc import check_dependencies, check_version, get_current_device, is_env_enabled
+from ..extras.packages import is_transformers_version_greater_than
 from .data_args import DataArguments
 from .evaluation_args import EvaluationArguments
 from .finetuning_args import FinetuningArguments
@@ -62,11 +61,11 @@ def read_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> Union[

    if sys.argv[1].endswith(".yaml") or sys.argv[1].endswith(".yml"):
        override_config = OmegaConf.from_cli(sys.argv[2:])
-        dict_config = yaml.safe_load(Path(sys.argv[1]).absolute().read_text())
+        dict_config = OmegaConf.load(Path(sys.argv[1]).absolute())
        return OmegaConf.to_container(OmegaConf.merge(dict_config, override_config))
    elif sys.argv[1].endswith(".json"):
        override_config = OmegaConf.from_cli(sys.argv[2:])
-        dict_config = json.loads(Path(sys.argv[1]).absolute().read_text())
+        dict_config = OmegaConf.load(Path(sys.argv[1]).absolute())
        return OmegaConf.to_container(OmegaConf.merge(dict_config, override_config))
    else:
        return sys.argv[1:]
@@ -113,8 +112,8 @@ def _verify_model_args(
        raise ValueError("Adapter is only valid for the LoRA method.")

    if model_args.quantization_bit is not None:
-        if finetuning_args.finetuning_type != "lora":
-            raise ValueError("Quantization is only compatible with the LoRA method.")
+        if finetuning_args.finetuning_type not in ["lora", "oft"]:
+            raise ValueError("Quantization is only compatible with the LoRA or OFT method.")

        if finetuning_args.pissa_init:
            raise ValueError("Please use scripts/pissa_init.py to initialize PiSSA for a quantized model.")
@@ -132,6 +131,14 @@ def _verify_model_args(
        logger.warning_rank0("We should use slow tokenizer for the Yi models. Change `use_fast_tokenizer` to False.")
        model_args.use_fast_tokenizer = False

+    # Validate advanced training features
+    if model_args.fp8 and model_args.quantization_bit is not None:
+        raise ValueError("FP8 training is not compatible with quantization. Please disable one of them.")
+
+    if model_args.fp8_enable_fsdp_float8_all_gather and not model_args.fp8:
+        logger.warning_rank0("fp8_enable_fsdp_float8_all_gather requires fp8=True. Setting fp8=True.")
+        model_args.fp8 = True
+

 def _check_extra_dependencies(
    model_args: "ModelArguments",
@@ -148,7 +155,7 @@ def _check_extra_dependencies(
        check_version("mixture-of-depth>=1.1.6", mandatory=True)

    if model_args.infer_backend == EngineName.VLLM:
-        check_version("vllm>=0.4.3,<=0.8.6")
+        check_version("vllm>=0.4.3,<=0.10.2")
        check_version("vllm", mandatory=True)
    elif model_args.infer_backend == EngineName.SGLANG:
        check_version("sglang>=0.4.5")
@@ -166,13 +173,17 @@ def _check_extra_dependencies(
    if finetuning_args.use_adam_mini:
        check_version("adam-mini", mandatory=True)

+    if finetuning_args.use_swanlab:
+        check_version("swanlab", mandatory=True)
+
    if finetuning_args.plot_loss:
        check_version("matplotlib", mandatory=True)

    if training_args is not None:
        if training_args.deepspeed:
            # pin deepspeed version < 0.17 because of https://github.com/deepspeedai/DeepSpeed/issues/7347
-            check_version("deepspeed>=0.10.0,<=0.16.9", mandatory=True)
+            check_version("deepspeed", mandatory=True)
+            check_version("deepspeed>=0.10.0,<=0.16.9")

        if training_args.predict_with_generate:
            check_version("jieba", mandatory=True)
@@ -303,6 +314,9 @@ def get_train_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _
    if model_args.use_unsloth and is_deepspeed_zero3_enabled():
        raise ValueError("Unsloth is incompatible with DeepSpeed ZeRO-3.")

+    if data_args.neat_packing and is_transformers_version_greater_than("4.53.0"):
+        raise ValueError("Neat packing is incompatible with transformers>=4.53.0.")
+
    _set_env_vars()
    _verify_model_args(model_args, data_args, finetuning_args)
    _check_extra_dependencies(model_args, finetuning_args, training_args)
@@ -348,6 +362,9 @@ def get_train_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _
        # https://github.com/huggingface/transformers/blob/v4.50.0/src/transformers/trainer.py#L782
        training_args.label_names = training_args.label_names or ["labels"]

+    if "swanlab" in training_args.report_to and finetuning_args.use_swanlab:
+        training_args.report_to.remove("swanlab")
+
    if (
        training_args.parallel_mode == ParallelMode.DISTRIBUTED
        and training_args.ddp_find_unused_parameters is None
--- a/src/llamafactory/hparams/training_args.py
+++ b/src/llamafactory/hparams/training_args.py
@@ -50,7 +50,7 @@ class RayArguments:
        default="PACK",
        metadata={"help": "The placement strategy for Ray training. Default is PACK."},
    )
-    ray_init_kwargs: Optional[dict] = field(
+    ray_init_kwargs: Optional[Union[dict, str]] = field(
        default=None,
        metadata={"help": "The arguments to pass to ray.init for Ray training. Default is None."},
    )
@@ -59,10 +59,14 @@ class RayArguments:
        self.use_ray = use_ray()
        if isinstance(self.resources_per_worker, str) and self.resources_per_worker.startswith("{"):
            self.resources_per_worker = _convert_str_dict(json.loads(self.resources_per_worker))
+
+        if isinstance(self.ray_init_kwargs, str) and self.ray_init_kwargs.startswith("{"):
+            self.ray_init_kwargs = _convert_str_dict(json.loads(self.ray_init_kwargs))
+
        if self.ray_storage_filesystem is not None:
            if self.ray_storage_filesystem not in ["s3", "gs", "gcs"]:
                raise ValueError(
-                    f"ray_storage_filesystem must be one of ['s3', 'gs', 'gcs'], got {self.ray_storage_filesystem}"
+                    f"ray_storage_filesystem must be one of ['s3', 'gs', 'gcs'], got {self.ray_storage_filesystem}."
                )

            import pyarrow.fs as fs
--- a/src/llamafactory/launcher.py
+++ b/src/llamafactory/launcher.py
@@ -12,12 +12,46 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-from llamafactory.train.tuner import run_exp  # use absolute import
+
+def run_api():
+    from llamafactory.api.app import run_api as _run_api
+
+    _run_api()


-def launch():
-    run_exp()
+def run_chat():
+    from llamafactory.chat.chat_model import run_chat as _run_chat
+
+    return _run_chat()
+
+
+def run_eval():
+    raise NotImplementedError("Evaluation will be deprecated in the future.")
+
+
+def export_model():
+    from llamafactory.train.tuner import export_model as _export_model
+
+    return _export_model()
+
+
+def run_exp():
+    from llamafactory.train.tuner import run_exp as _run_exp
+
+    return _run_exp()  # use absolute import
+
+
+def run_web_demo():
+    from llamafactory.webui.interface import run_web_demo as _run_web_demo
+
+    return _run_web_demo()
+
+
+def run_web_ui():
+    from llamafactory.webui.interface import run_web_ui as _run_web_ui
+
+    return _run_web_ui()


 if __name__ == "__main__":
-    launch()
+    run_exp()
--- a/src/llamafactory/model/adapter.py
+++ b/src/llamafactory/model/adapter.py
@@ -16,7 +16,7 @@ import re
 from typing import TYPE_CHECKING

 import torch
-from peft import LoraConfig, LoraModel, PeftModel, TaskType, get_peft_model
+from peft import LoraConfig, LoraModel, OFTConfig, PeftModel, TaskType, get_peft_model
 from transformers.integrations import is_deepspeed_zero3_enabled

 from ..extras import logging
@@ -147,6 +147,9 @@ def _setup_lora_tuning(
    cast_trainable_params_to_fp32: bool,
 ) -> "PeftModel":
    if is_trainable:
+        if finetuning_args.finetuning_type == "oft":
+            logger.info_rank0("Fine-tuning method: OFT")
+        else:
            logger.info_rank0("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))

    adapter_to_resume = None
@@ -188,7 +191,7 @@ def _setup_lora_tuning(

        if adapter_to_resume is not None:  # resume lora training
            if model_args.use_unsloth:
-                model = load_unsloth_peft_model(config, model_args, is_trainable=is_trainable)
+                model = load_unsloth_peft_model(config, model_args, finetuning_args, is_trainable=is_trainable)
            else:
                model = PeftModel.from_pretrained(model, adapter_to_resume, is_trainable=is_trainable, **init_kwargs)

@@ -223,6 +226,7 @@ def _setup_lora_tuning(
            finetuning_args.additional_target = module_names
            logger.warning_rank0("Vocab has been resized, add {} to trainable params.".format(",".join(module_names)))

+        if finetuning_args.finetuning_type == "lora":
            peft_kwargs = {
                "r": finetuning_args.lora_rank,
                "target_modules": target_modules,
@@ -232,8 +236,19 @@ def _setup_lora_tuning(
                "use_dora": finetuning_args.use_dora,
                "modules_to_save": finetuning_args.additional_target,
            }
+        elif finetuning_args.finetuning_type == "oft":
+            peft_kwargs = {
+                "r": finetuning_args.oft_rank,
+                "oft_block_size": finetuning_args.oft_block_size,
+                "target_modules": target_modules,
+                "module_dropout": finetuning_args.module_dropout,
+                "modules_to_save": finetuning_args.additional_target,
+            }

        if model_args.use_unsloth:
+            if finetuning_args.finetuning_type == "oft":
+                raise ValueError("Unsloth is currently not supported for OFT.")
+
            model = get_unsloth_peft_model(model, model_args, peft_kwargs)
        else:
            if finetuning_args.pissa_init:
@@ -244,12 +259,19 @@ def _setup_lora_tuning(
                    logger.info_rank0(f"Using PiSSA initialization with FSVD steps {finetuning_args.pissa_iter}.")
                    peft_kwargs["init_lora_weights"] = f"pissa_niter_{finetuning_args.pissa_iter}"

-            lora_config = LoraConfig(
+            if finetuning_args.finetuning_type == "lora":
+                peft_config = LoraConfig(
                    task_type=TaskType.CAUSAL_LM,
                    inference_mode=False,
                    **peft_kwargs,
                )
-            model = get_peft_model(model, lora_config)
+            elif finetuning_args.finetuning_type == "oft":
+                peft_config = OFTConfig(
+                    task_type=TaskType.CAUSAL_LM,
+                    inference_mode=False,
+                    **peft_kwargs,
+                )
+            model = get_peft_model(model, peft_config)

    if is_trainable and cast_trainable_params_to_fp32:
        for param in filter(lambda p: p.requires_grad, model.parameters()):
@@ -272,8 +294,8 @@ def init_adapter(
    Note that the trainable parameters must be cast to float32.
    """
    if is_trainable and getattr(model, "quantization_method", None) is not None:
-        if finetuning_args.finetuning_type != "lora":
-            raise ValueError("Quantized models can only be used for the LoRA tuning.")
+        if finetuning_args.finetuning_type not in ["lora", "oft"]:
+            raise ValueError("Quantized models can only be used for the LoRA or OFT tuning.")

        if finetuning_args.pissa_init:
            raise ValueError("Cannot initialize PiSSA adapter on quantized models.")
@@ -296,7 +318,7 @@ def init_adapter(
        _setup_full_tuning(model, finetuning_args, is_trainable, cast_trainable_params_to_fp32)
    elif finetuning_args.finetuning_type == "freeze":
        _setup_freeze_tuning(model, finetuning_args, is_trainable, cast_trainable_params_to_fp32)
-    elif finetuning_args.finetuning_type == "lora":
+    elif finetuning_args.finetuning_type in ["lora", "oft"]:
        model = _setup_lora_tuning(
            config, model, model_args, finetuning_args, is_trainable, cast_trainable_params_to_fp32
        )
--- a/src/llamafactory/model/loader.py
+++ b/src/llamafactory/model/loader.py
@@ -19,6 +19,7 @@ import torch
 from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
+    AutoModelForImageTextToText,
    AutoModelForSeq2SeqLM,
    AutoModelForTextToWaveform,
    AutoModelForVision2Seq,
@@ -29,7 +30,6 @@ from trl import AutoModelForCausalLMWithValueHead

 from ..extras import logging
 from ..extras.misc import count_parameters, skip_check_imports, try_download_model_from_other_hub
-from ..extras.packages import is_transformers_version_greater_than
 from .adapter import init_adapter
 from .model_utils.liger_kernel import apply_liger_kernel
 from .model_utils.misc import register_autoclass
@@ -39,10 +39,6 @@ from .model_utils.valuehead import load_valuehead_params
 from .patcher import patch_config, patch_model, patch_processor, patch_tokenizer, patch_valuehead_model


-if is_transformers_version_greater_than("4.46.0"):
-    from transformers import AutoModelForImageTextToText
-
-
 if TYPE_CHECKING:
    from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer, ProcessorMixin

@@ -111,9 +107,8 @@ def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
            **init_kwargs,
        )
    except Exception as e:
-        raise OSError("Failed to load processor.") from e
-
-    patch_processor(processor, tokenizer, model_args)
+        logger.info_rank0(f"Failed to load processor: {e}.")
+        processor = None

    # Avoid load tokenizer, see:
    # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/auto/processing_auto.py#L324
@@ -121,6 +116,9 @@ def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
        logger.debug("The loaded processor is not an instance of Processor. Dropping it.")
        processor = None

+    if processor is not None:
+        patch_processor(processor, tokenizer, model_args)
+
    return {"tokenizer": tokenizer, "processor": processor}


@@ -158,16 +156,13 @@ def load_model(
        if model_args.mixture_of_depths == "load":
            model = load_mod_pretrained_model(**init_kwargs)
        else:
-            if type(config) in AutoModelForVision2Seq._model_mapping.keys():  # image-text
-                load_class = AutoModelForVision2Seq
-            elif (
-                is_transformers_version_greater_than("4.46.0")
-                and type(config) in AutoModelForImageTextToText._model_mapping.keys()
-            ):  # image-text
+            if type(config) in AutoModelForImageTextToText._model_mapping.keys():  # image-text
                load_class = AutoModelForImageTextToText
+            elif type(config) in AutoModelForVision2Seq._model_mapping.keys():  # image-text
+                load_class = AutoModelForVision2Seq
            elif type(config) in AutoModelForSeq2SeqLM._model_mapping.keys():  # audio-text
                load_class = AutoModelForSeq2SeqLM
-            elif type(config) in AutoModelForTextToWaveform._model_mapping.keys():  # audio hack for qwen2_5_omni
+            elif type(config) in AutoModelForTextToWaveform._model_mapping.keys():  # audio hack for qwen omni
                load_class = AutoModelForTextToWaveform
            else:
                load_class = AutoModelForCausalLM
@@ -176,8 +171,8 @@ def load_model(
                model = load_class.from_config(config, trust_remote_code=model_args.trust_remote_code)
            else:
                model = load_class.from_pretrained(**init_kwargs)
-                if getattr(model.config, "model_type", None) == "qwen2_5_omni":
-                    model = model.thinker  # use part of Omni model
+                if getattr(model.config, "model_type", None) in ["qwen2_5_omni", "qwen3_omni_moe"]:
+                    model = getattr(model, "thinker")

        if model_args.mixture_of_depths == "convert":
            model = convert_pretrained_model_to_mod(model, config, model_args)
--- a/src/llamafactory/model/model_utils/embedding.py
+++ b/src/llamafactory/model/model_utils/embedding.py
@@ -67,4 +67,5 @@ def resize_embedding_layer(model: "PreTrainedModel", tokenizer: "PreTrainedToken
            _noisy_mean_initialization(model.get_input_embeddings().weight.data, num_new_tokens)
            _noisy_mean_initialization(model.get_output_embeddings().weight.data, num_new_tokens)

+        model.config.vocab_size = new_embedding_size
        logger.info_rank0(f"Resized token embeddings from {current_embedding_size} to {new_embedding_size}.")
--- a/src/llamafactory/model/model_utils/liger_kernel.py
+++ b/src/llamafactory/model/model_utils/liger_kernel.py
@@ -47,6 +47,8 @@ def apply_liger_kernel(
        from liger_kernel.transformers import apply_liger_kernel_to_gemma3_text as apply_liger_kernel
    elif model_type == "glm4":
        from liger_kernel.transformers import apply_liger_kernel_to_glm4 as apply_liger_kernel
+    elif model_type == "glm4v":
+        from liger_kernel.transformers import apply_liger_kernel_to_glm4v as apply_liger_kernel
    elif model_type == "granite":
        from liger_kernel.transformers import apply_liger_kernel_to_granite as apply_liger_kernel
    elif model_type == "llama":
--- a/src/llamafactory/model/model_utils/moe.py
+++ b/src/llamafactory/model/model_utils/moe.py
@@ -39,6 +39,9 @@ def add_z3_leaf_module(model: "PreTrainedModel") -> None:
        return

    model_type = getattr(model.config, "model_type", None)
+    text_config = getattr(model.config, "text_config", None)
+    text_model_type = getattr(text_config, "model_type", None)
+
    if model_type == "dbrx":
        from transformers.models.dbrx.modeling_dbrx import DbrxFFN

@@ -57,6 +60,16 @@ def add_z3_leaf_module(model: "PreTrainedModel") -> None:

        _set_z3_leaf_modules(model, [GraniteMoeMoE])

+    if model_type == "glm4_moe":
+        from transformers.models.glm4_moe.modeling_glm4_moe import Glm4MoeMoE
+
+        _set_z3_leaf_modules(model, [Glm4MoeMoE])
+
+    if model_type == "glm4v_moe":
+        from transformers.models.glm4v_moe.modeling_glm4v_moe import Glm4vMoeTextMoE
+
+        _set_z3_leaf_modules(model, [Glm4vMoeTextMoE])
+
    if model_type == "jamba":
        from transformers.models.jamba.modeling_jamba import JambaSparseMoeBlock

@@ -92,17 +105,29 @@ def add_z3_leaf_module(model: "PreTrainedModel") -> None:

        _set_z3_leaf_modules(model, [Qwen2MoeSparseMoeBlock])

-    if model_type == "qwen3_moe":
+    if model_type == "qwen3_moe" or text_model_type == "qwen3_moe":  # internvl 3.5
        from transformers.models.qwen3_moe.modeling_qwen3_moe import Qwen3MoeSparseMoeBlock

        _set_z3_leaf_modules(model, [Qwen3MoeSparseMoeBlock])

+    if model_type == "qwen3_vl_moe":
+        from transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe import Qwen3VLMoeTextSparseMoeBlock
+
+        _set_z3_leaf_modules(model, [Qwen3VLMoeTextSparseMoeBlock])
+
+    if model_type == "qwen3_omni_moe":
+        from transformers.models.qwen3_omni_moe.modeling_qwen3_omni_moe import Qwen3OmniMoeThinkerTextSparseMoeBlock
+
+        _set_z3_leaf_modules(model, [Qwen3OmniMoeThinkerTextSparseMoeBlock])
+

 def configure_moe(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
    if not is_trainable or not model_args.moe_aux_loss_coef:
        return

    model_type = getattr(config, "model_type", None)
+    text_config = getattr(config, "text_config", None)  # for multimodal model
+
    if model_type in [
        "dbrx",
        "granitemoe",
@@ -117,9 +142,18 @@ def configure_moe(config: "PretrainedConfig", model_args: "ModelArguments", is_t
    ]:
        setattr(config, "output_router_logits", True)

+    if text_config and getattr(text_config, "model_type", None) in [
+        "glm4v_moe_text",  # glmv4_5
+        "qwen3_moe",  # internvl_3_5
+    ]:
+        setattr(text_config, "output_router_logits", True)
+
    if model_type in ["granitemoe", "jamba", "llama4", "mixtral", "olmoe", "phimoe", "qwen2_moe", "qwen3_moe"]:
        setattr(config, "router_aux_loss_coef", model_args.moe_aux_loss_coef)

+    elif text_config and getattr(text_config, "model_type", None) in ["qwen3_moe"]:
+        setattr(text_config, "router_aux_loss_coef", model_args.moe_aux_loss_coef)
+
    elif model_type == "deepseek":
        setattr(config, "aux_loss_alpha", model_args.moe_aux_loss_coef)

--- a/src/llamafactory/model/model_utils/quantization.py
+++ b/src/llamafactory/model/model_utils/quantization.py
@@ -90,12 +90,13 @@ def configure_quantization(
        if model_args.quantization_bit is not None:
            logger.warning_rank0("`quantization_bit` will not affect on the PTQ-quantized models.")

-        if is_deepspeed_zero3_enabled() or is_fsdp_enabled():
-            raise ValueError("DeepSpeed ZeRO-3 or FSDP is incompatible with PTQ-quantized models.")
-
        quantization_config: dict[str, Any] = getattr(config, "quantization_config", None)
        quant_method = quantization_config.get("quant_method", "")

+        if quant_method != QuantizationMethod.MXFP4 and (is_deepspeed_zero3_enabled() or is_fsdp_enabled()):
+            # mxfp4 will dequant the model weights
+            raise ValueError("DeepSpeed ZeRO-3 or FSDP is incompatible with PTQ-quantized models.")
+
        if quant_method == QuantizationMethod.GPTQ:
            check_version("gptqmodel>=2.0.0", mandatory=True)
            quantization_config.pop("disable_exllama", None)  # remove deprecated args
--- a/src/llamafactory/model/model_utils/sdpa_npu_redirect.py
+++ b/src/llamafactory/model/model_utils/sdpa_npu_redirect.py
@@ -0,0 +1,131 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import math
+import os
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from transformers.utils import is_torch_npu_available
+
+
+logger = logging.getLogger(__name__)
+
+_ORIG_SDPA = F.scaled_dot_product_attention
+
+
+def _to_bool_4d_mask(
+    attn_mask: Optional[torch.Tensor], q_len: int, kv_len: int, device: torch.device
+) -> Optional[torch.Tensor]:
+    """Normalize additive/other Hugging Face masks into a boolean mask of shape [B, 1, Q, K] (True = masked)."""
+    if attn_mask is None:
+        return None
+    if attn_mask.dtype != torch.bool:
+        attn_mask = attn_mask < 0  # additive -inf -> True
+    if attn_mask.dim() == 4:
+        return attn_mask[..., :q_len, :kv_len].contiguous()
+    if attn_mask.dim() == 3:
+        return attn_mask[:, None, :q_len, :kv_len].contiguous()
+    if attn_mask.dim() == 2:
+        return attn_mask[:, None, None, :kv_len].expand(-1, 1, q_len, -1).contiguous()
+    return attn_mask.to(device)
+
+
+def _merge_causal_mask(
+    attn_mask: Optional[torch.Tensor], is_causal: bool, L: int, S: int, device: torch.device
+) -> Optional[torch.Tensor]:
+    """Merge `is_causal` into the boolean/additive attention mask (True = masked)."""
+    if not is_causal or L != S:
+        return attn_mask
+    causal_bool = torch.ones((1, 1, L, L), dtype=torch.bool, device=device).triu(1)
+    if attn_mask is None:
+        return causal_bool
+    if attn_mask.dtype != torch.bool:
+        attn_mask = attn_mask < 0
+    if attn_mask.dim() == 2:
+        attn_mask = attn_mask[:, None, None, :L].expand(-1, 1, L, -1).contiguous()
+    elif attn_mask.dim() == 3:
+        attn_mask = attn_mask[:, None, :L, :L].contiguous()
+    return attn_mask | causal_bool
+
+
+def _sdpa_npu_redirect(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    attn_mask: Optional[torch.Tensor] = None,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    scale: Optional[float] = None,
+):
+    """A drop-in replacement for `F.scaled_dot_product_attention`.
+
+    Automatically falls back to the native SDPA when conditions are not met.
+    The NPU-fused path is only enabled when q/k/v have shape (B, N, S, D); otherwise, it falls back.
+    """
+    # Fall back if the feature is disabled or the conditions are not satisfied.
+    if os.environ.get("NPU_FA_DISABLE", "0") == "1":
+        return _ORIG_SDPA(q, k, v, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale)
+
+    npu_ok = is_torch_npu_available() and (q.device.type == "npu")
+    dtype_ok = q.dtype in (torch.float16, torch.bfloat16)
+    shape_ok = q.dim() == 4 and k.dim() == 4 and v.dim() == 4  # 期望 BNSD
+    if not (npu_ok and dtype_ok and shape_ok):
+        return _ORIG_SDPA(q, k, v, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale)
+
+    L, S = q.size(-2), k.size(-2)
+    merged_mask = _merge_causal_mask(attn_mask, is_causal, L, S, q.device)
+    mask_bool = _to_bool_4d_mask(merged_mask, q_len=L, kv_len=S, device=q.device)
+
+    head_dim = q.size(-1)
+    sc = (1.0 / math.sqrt(head_dim)) if (scale is None) else scale
+
+    train_mode = torch.is_grad_enabled() and (dropout_p > 0)
+    keep_prob = 1.0 - (dropout_p if train_mode else 0.0)
+
+    try:
+        import torch_npu
+
+        out = torch_npu.npu_fusion_attention(
+            q.contiguous(),
+            k.contiguous(),
+            v.contiguous(),
+            head_num=q.size(-3),  # N
+            input_layout="BNSD",  # (B, N, S, D)
+            pse=None,
+            atten_mask=mask_bool,  # True = masked
+            scale=sc,
+            pre_tockens=2147483647,
+            next_tockens=2147483647,
+            keep_prob=keep_prob,
+            sync=False,
+            inner_precise=0,
+        )[0]
+        return out
+    except Exception as e:
+        if os.environ.get("NPU_FA_VERBOSE", "0") == "1":
+            logger.warning(f"[sdpa_npu_redirect] npu_fusion_attention failed: {e}; fallback to SDPA.")
+        return _ORIG_SDPA(q, k, v, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale)
+
+
+def apply_sdpa_npu_redirect(verbose: bool = True):
+    """Install the redirection by pointing `F.scaled_dot_product_attention` to our implementation."""
+    if getattr(F.scaled_dot_product_attention, "__wrapped_by_npu__", False):
+        return
+    F.scaled_dot_product_attention = _sdpa_npu_redirect
+    setattr(F.scaled_dot_product_attention, "__wrapped_by_npu__", True)
+    if verbose:
+        logger.info("[sdpa_npu_redirect] SDPA has been redirected to Ascend npu_fusion_attention when available.")
--- a/src/llamafactory/model/model_utils/unsloth.py
+++ b/src/llamafactory/model/model_utils/unsloth.py
@@ -80,12 +80,15 @@ def get_unsloth_peft_model(


 def load_unsloth_peft_model(
-    config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool
+    config: "PretrainedConfig",
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
+    is_trainable: bool,
 ) -> "PreTrainedModel":
    r"""Load peft model with unsloth. Used in both training and inference."""
    from unsloth import FastLanguageModel  # type: ignore

-    unsloth_kwargs = _get_unsloth_kwargs(config, model_args.adapter_name_or_path[0], model_args)
+    unsloth_kwargs = _get_unsloth_kwargs(config, model_args.adapter_name_or_path[0], model_args, finetuning_args)
    try:
        if not is_trainable:
            unsloth_kwargs["use_gradient_checkpointing"] = False
--- a/src/llamafactory/model/model_utils/valuehead.py
+++ b/src/llamafactory/model/model_utils/valuehead.py
@@ -49,7 +49,7 @@ def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") ->

    try:
        vhead_file = cached_file(filename=V_HEAD_WEIGHTS_NAME, **kwargs)
-        return torch.load(vhead_file, map_location="cpu")
+        return torch.load(vhead_file, map_location="cpu", weights_only=True)
    except Exception as err:
        err_text = str(err)

--- a/src/llamafactory/model/model_utils/visual.py
+++ b/src/llamafactory/model/model_utils/visual.py
@@ -199,15 +199,67 @@ def patch_target_modules(
        return target_modules


+_register_composite_model(
+    model_type="dots_ocr",
+    projector_key="vision_tower.merger",
+    vision_model_keys=["vision_tower"],
+    language_model_keys=["model", "lm_head"],
+    lora_conflict_keys=["merger"],
+)
+
+
 _register_composite_model(
    model_type="gemma3",
 )


+_register_composite_model(
+    model_type="gemma3n",
+    vision_model_keys=["vision_tower", "audio_tower"],
+    lora_conflict_keys=["timm_model", "subsample_conv_projection"],
+)
+
+
+# copied from qwen2vl
+_register_composite_model(
+    model_type="glm4v",
+    projector_key="visual.merger",
+    vision_model_keys=["visual.patch_embed", "visual.blocks"],
+    language_model_keys=["language_model", "lm_head"],
+    lora_conflict_keys=["patch_embed"],
+)
+
+
+_register_composite_model(
+    model_type="glm4v_moe",
+    projector_key="visual.merger",
+    vision_model_keys=["visual.patch_embed", "visual.blocks"],
+    language_model_keys=["language_model", "lm_head"],
+    lora_conflict_keys=["patch_embed"],
+)
+
+
 _register_composite_model(
    model_type="internvl",
 )

+_register_composite_model(
+    model_type="interns1",
+)
+
+_register_composite_model(
+    model_type="Keye",
+    projector_key="mlp_AR",
+    vision_model_keys=["visual.vision_model.patch_embedding", "visual.vision_model.encoder"],
+    language_model_keys=["model", "lm_head"],
+    lora_conflict_keys=["patch_embedding"],
+)
+
+
+_register_composite_model(
+    model_type="kimi_vl",
+)
+

 _register_composite_model(
    model_type="llama4",
@@ -246,6 +298,7 @@ _register_composite_model(
    lora_conflict_keys=["audio_projection_layer"],
 )

+
 _register_composite_model(
    model_type="mistral3",
 )
@@ -299,6 +352,33 @@ _register_composite_model(
 )


+_register_composite_model(
+    model_type="qwen3_vl",
+    projector_key="visual.merger",
+    vision_model_keys=["visual.patch_embed", "visual.blocks"],
+    language_model_keys=["language_model", "lm_head"],
+    lora_conflict_keys=["patch_embed"],
+)
+
+
+_register_composite_model(
+    model_type="qwen3_vl_moe",
+    projector_key="visual.merger",
+    vision_model_keys=["visual.patch_embed", "visual.blocks"],
+    language_model_keys=["language_model", "lm_head"],
+    lora_conflict_keys=["patch_embed"],
+)
+
+
+_register_composite_model(
+    model_type="qwen3_omni_moe_thinker",
+    projector_key="visual.merger",
+    vision_model_keys=["visual.patch_embed", "visual.blocks", "audio_tower"],
+    language_model_keys=["model", "lm_head"],
+    lora_conflict_keys=["patch_embed"],
+)
+
+
 _register_composite_model(
    model_type="video_llava",
 )
--- a/src/llamafactory/model/patcher.py
+++ b/src/llamafactory/model/patcher.py
@@ -178,6 +178,9 @@ def patch_model(
        resize_embedding_layer(model, tokenizer)

    if is_trainable:
+        if getattr(model.config, "model_type", None) == "gemma3n":
+            setattr(model_args, "disable_gradient_checkpointing", True)
+
        prepare_model_for_training(model, model_args)
        autocast_projector_dtype(model, model_args)
        add_z3_leaf_module(model)
@@ -185,6 +188,23 @@ def patch_model(
    if not model_args.use_unsloth:
        print_attn_implementation(model.config)

+    # ======== NPU fused attention redirect: SDPA -> torch_npu.npu_fusion_attention ========
+    # Place after all structural modifications and before DeepSpeed/Trainer initialization;
+    # does not modify any Module/_parameters, safe for ZeRO-3 + offload.
+    try:
+        import os
+
+        import torch
+
+        if hasattr(torch, "npu") and torch.npu.is_available() and os.environ.get("NPU_FA_DISABLE", "0") != "1":
+            from .model_utils.sdpa_npu_redirect import apply_sdpa_npu_redirect
+
+            apply_sdpa_npu_redirect(verbose=not model_args.use_unsloth)
+            logger.info_rank0("[sdpa_npu_redirect] Enabled: SDPA will use Ascend npu_fusion_attention when available.")
+    except Exception as e:
+        logger.warning_rank0(f"[sdpa_npu_redirect] Failed to enable redirect, will keep native SDPA. Reason: {e}")
+    # =====================================================================================
+
    try:
        model.add_model_tags(["llama-factory"])
    except Exception:
@@ -208,9 +228,23 @@ def patch_valuehead_model(model: "AutoModelForCausalLMWithValueHead") -> None:
        if isinstance(self.pretrained_model, PeftModel):
            self.pretrained_model.create_or_update_model_card(output_dir)

+    def get_rope_index_func(self: "AutoModelForCausalLMWithValueHead"):
+        if isinstance(self.pretrained_model, PeftModel):
+            base_model = self.pretrained_model.base_model.model
+        else:
+            base_model = self.pretrained_model
+
+        if base_model and hasattr(base_model, "get_rope_index"):
+            return base_model.get_rope_index
+        elif base_model and hasattr(base_model, "model") and hasattr(base_model.model, "get_rope_index"):
+            return base_model.model.get_rope_index
+        else:
+            return None
+
    ignore_modules = [name for name, _ in model.named_parameters() if "pretrained_model" in name]
    setattr(model, "_keys_to_ignore_on_save", ignore_modules)
    setattr(model, "tie_weights", MethodType(tie_weights, model))
    setattr(model, "get_input_embeddings", MethodType(get_input_embeddings, model))
    setattr(model, "get_output_embeddings", MethodType(get_output_embeddings, model))
+    setattr(model, "get_rope_index", get_rope_index_func(model))
    setattr(model, "create_or_update_model_card", MethodType(create_or_update_model_card, model))
--- a/src/llamafactory/train/callbacks.py
+++ b/src/llamafactory/train/callbacks.py
@@ -73,10 +73,10 @@ def fix_valuehead_checkpoint(
    if safe_serialization:
        path_to_checkpoint = os.path.join(output_dir, SAFE_WEIGHTS_NAME)
        with safe_open(path_to_checkpoint, framework="pt", device="cpu") as f:
-            state_dict: dict[str, torch.Tensor] = {key: f.get_tensor(key) for key in f.keys()}
+            state_dict: dict[str, torch.Tensor] = {key: f.get_tensor(key).clone() for key in f.keys()}
    else:
        path_to_checkpoint = os.path.join(output_dir, WEIGHTS_NAME)
-        state_dict: dict[str, torch.Tensor] = torch.load(path_to_checkpoint, map_location="cpu")
+        state_dict: dict[str, torch.Tensor] = torch.load(path_to_checkpoint, map_location="cpu", weights_only=True)

    os.remove(path_to_checkpoint)
    decoder_state_dict, v_head_state_dict = {}, {}
--- a/src/llamafactory/train/dpo/trainer.py
+++ b/src/llamafactory/train/dpo/trainer.py
@@ -78,6 +78,7 @@ class CustomDPOTrainer(DPOTrainer):
        self.beta = finetuning_args.pref_beta
        self.loss_type = finetuning_args.pref_loss
        self.ftx_gamma = finetuning_args.pref_ftx
+        self.bco_gemma = finetuning_args.pref_bco_weight
        self.label_smoothing = finetuning_args.dpo_label_smoothing
        self.simpo_gamma = finetuning_args.simpo_gamma
        self.ld_alpha = finetuning_args.ld_alpha
@@ -108,6 +109,11 @@ class CustomDPOTrainer(DPOTrainer):
            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
            self.add_callback(BAdamCallback)

+        if self.bco_gemma >= 1e-6:
+            from trl.trainer import RunningMoments
+
+            self.running = RunningMoments(self.accelerator)
+
    @override
    def create_optimizer(self) -> "torch.optim.Optimizer":
        if self.optimizer is None:
@@ -151,6 +157,25 @@ class CustomDPOTrainer(DPOTrainer):
        simpo_loss = -F.logsigmoid(self.beta * logits)
        return simpo_loss

+    def bco_loss(
+        self,
+        chosen_logps: "torch.Tensor",
+        rejected_logps: "torch.Tensor",
+        reference_chosen_logps: "torch.Tensor",
+        reference_rejected_logps: "torch.Tensor",
+    ) -> "torch.Tensor":
+        chosen_logratios = chosen_logps - reference_chosen_logps
+        rejected_logratios = rejected_logps - reference_rejected_logps
+        chosen_rewards = self.beta * chosen_logratios
+        rejected_rewards = self.beta * rejected_logratios
+        rewards = torch.cat((chosen_rewards, rejected_rewards), 0).mean().detach()
+        self.running.update(rewards)  # update baseline
+        delta = self.running.mean
+        bco_loss = -F.logsigmoid((self.beta * chosen_logratios) - delta) - F.logsigmoid(
+            -(self.beta * rejected_logratios - delta)
+        )
+        return bco_loss
+
    def compute_preference_loss(
        self,
        policy_chosen_logps: "torch.Tensor",
@@ -174,6 +199,12 @@ class CustomDPOTrainer(DPOTrainer):
                policy_chosen_logps, policy_rejected_logps, reference_chosen_logps, reference_rejected_logps
            )

+            if self.bco_gemma > 1e-6:
+                bco_losses = self.bco_loss(
+                    policy_chosen_logps, policy_rejected_logps, reference_chosen_logps, reference_rejected_logps
+                )
+                losses += bco_losses * self.bco_gemma
+
        return losses, chosen_rewards, rejected_rewards

    @override
@@ -253,6 +284,9 @@ class CustomDPOTrainer(DPOTrainer):
        sft_loss = -policy_chosen_logps_avg
        if self.ftx_gamma > 1e-6:
            losses += self.ftx_gamma * sft_loss
+            if self.bco_gemma > 1e-6:
+                # re-weigthing for MPO
+                losses /= self.ftx_gamma + self.bco_gemma + 1.0

        prefix = "eval_" if train_eval == "eval" else ""
        metrics[f"{prefix}rewards/chosen"] = chosen_rewards.mean().item()
--- a/src/llamafactory/train/fp8_utils.py
+++ b/src/llamafactory/train/fp8_utils.py
@@ -0,0 +1,171 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING, Any, Optional
+
+from ..extras import logging
+
+
+if TYPE_CHECKING:
+    from ..hparams import ModelArguments
+
+logger = logging.get_logger(__name__)
+
+
+def create_fp8_kwargs(model_args: "ModelArguments") -> list[Any]:
+    """Create AORecipeKwargs for FP8 training with HuggingFace Accelerate.
+
+    Args:
+        model_args: Model arguments containing FP8 configuration
+
+    Returns:
+        List containing AORecipeKwargs if FP8 is enabled and supported, empty list otherwise
+    """
+    if not model_args.fp8:
+        return []
+
+    try:
+        # Check if AORecipeKwargs is available (Accelerate 1.8.0+)
+        from accelerate.utils import AORecipeKwargs
+
+        backend = getattr(model_args, "fp8_backend", "auto")
+        logger.info_rank0(f"Creating FP8 configuration with backend: {backend}")
+
+        # Create Float8LinearConfig if torchao backend is used
+        config = None
+        if backend == "torchao" or backend == "auto":
+            from torchao.float8 import Float8LinearConfig
+
+            # Use rowwise scaling for better performance (as recommended by torchao)
+            # Configure alignment requirements for FP8 kernels
+            config = Float8LinearConfig.from_recipe_name("rowwise")
+
+            # Enable alignment for better kernel performance
+            if hasattr(config, "enable_amax_init"):
+                config.enable_amax_init = True
+            if hasattr(config, "enable_pre_and_post_forward"):
+                config.enable_pre_and_post_forward = True
+
+        # Create module filter function to skip problematic layers
+        # TorchAO FP8 requires dimensions divisible by 16 for optimal kernels
+        def module_filter_func(module, layer_name):
+            # Skip embedding and output layers for numerical stability
+            skip_layers = ["embed", "lm_head", "output", "classifier"]
+            if any(skip_name in layer_name.lower() for skip_name in skip_layers):
+                return False
+
+            # Only convert Linear layers
+            if not (hasattr(module, "weight") and len(module.weight.shape) == 2):
+                return False
+
+            # Check dimension alignment for FP8 kernels
+            weight = module.weight
+            in_features, out_features = weight.shape[1], weight.shape[0]
+
+            # Skip layers with dimensions not divisible by 16 to avoid kernel errors
+            if in_features % 16 != 0 or out_features % 16 != 0:
+                logger.debug(
+                    f"Skipping layer {layer_name} with dimensions {out_features}x{in_features} (not divisible by 16)"
+                )
+                return False
+
+            return True
+
+        # Map FSDP all-gather setting if available (this affects the underlying implementation)
+        if hasattr(model_args, "fp8_enable_fsdp_float8_all_gather") and model_args.fp8_enable_fsdp_float8_all_gather:
+            logger.info_rank0("FSDP float8 all-gather optimization requested")
+
+        return [AORecipeKwargs(config=config, module_filter_func=module_filter_func)]
+    except Exception as e:
+        logger.info_rank0(f"Failed to create FP8 configuration: {e}")
+        return []
+
+
+def get_fp8_mixed_precision(model_args: "ModelArguments") -> Optional[str]:
+    """Get the mixed precision setting for Accelerate when using FP8.
+
+    Args:
+        model_args: Model arguments containing FP8 configuration
+
+    Returns:
+        "fp8" if FP8 is enabled, None otherwise
+    """
+    return "fp8" if model_args.fp8 else None
+
+
+def configure_fp8_environment(model_args: "ModelArguments") -> None:
+    """Configure FP8 environment for HuggingFace Accelerate.
+
+    FP8 training is handled entirely through HuggingFace Accelerate, regardless of whether
+    DeepSpeed or FSDP is used for distributed training. This function sets up the environment
+    variables and validates the FP8 configuration.
+
+    Args:
+        model_args: Model arguments containing FP8 configuration
+    """
+    import os
+
+    if not model_args.fp8:
+        return
+
+    # Set mixed precision to fp8 for HuggingFace Accelerate
+    os.environ["ACCELERATE_MIXED_PRECISION"] = "fp8"
+    logger.info_rank0("Set ACCELERATE_MIXED_PRECISION=fp8")
+
+    # Configure FP8 backend and options
+    backend = getattr(model_args, "fp8_backend", "auto")
+    if backend != "auto":
+        os.environ["FP8_BACKEND"] = backend
+        logger.info_rank0(f"Set FP8_BACKEND={backend}")
+
+    # Create and validate FP8 recipe kwargs (for logging/debugging)
+    fp8_kwargs = create_fp8_kwargs(model_args)
+    logger.info_rank0(f"FP8 AORecipeKwargs created: {len(fp8_kwargs)} items")
+
+    # Enable FSDP float8 all-gather optimization if requested
+    if hasattr(model_args, "fp8_enable_fsdp_float8_all_gather") and model_args.fp8_enable_fsdp_float8_all_gather:
+        os.environ["FP8_ENABLE_FSDP_FLOAT8_ALL_GATHER"] = "true"
+        logger.info_rank0("Set FP8_ENABLE_FSDP_FLOAT8_ALL_GATHER=true")
+
+    logger.info_rank0("FP8 environment configured - all FP8 training handled by HuggingFace Accelerate")
+
+
+def verify_fp8_status(accelerator, model_args: "ModelArguments") -> None:
+    """Verify that FP8 training is actually working after model preparation.
+
+    Args:
+        accelerator: The HuggingFace Accelerator instance
+        model_args: Model arguments containing FP8 configuration
+    """
+    if not model_args.fp8:
+        return
+
+    # Check Accelerate's FP8 status
+    fp8_enabled = getattr(accelerator, "fp8_enabled", False)
+    fp8_backend_type = getattr(accelerator, "fp8_backend", "UNKNOWN")
+
+    backend = getattr(model_args, "fp8_backend", "auto")
+    if backend == "torchao" or backend == "auto":
+        logger.info_rank0(
+            "FP8 training enabled with TorchAO backend. For optimal performance, "
+            "ensure model layer dimensions are mostly divisible by 16. "
+            "If you encounter issues, try fp8_backend='te' with Transformer Engine."
+        )
+    else:
+        logger.info_rank0(f"FP8 training enabled with {backend} backend.")
+
+    logger.info_rank0(f"Accelerate FP8 status - enabled: {fp8_enabled}, backend: {fp8_backend_type}")
+
+    if not fp8_enabled:
+        logger.info_rank0("WARNING: FP8 was requested but Accelerate shows fp8_enabled=False. FP8 may not be working.")
--- a/src/llamafactory/train/ppo/trainer.py
+++ b/src/llamafactory/train/ppo/trainer.py
@@ -390,7 +390,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
        batch: dict[str, torch.Tensor] = self.prepare_model_inputs(queries, responses)
        unwrapped_model: AutoModelForCausalLMWithValueHead = self.accelerator.unwrap_model(self.model)

-        if self.finetuning_args.reward_model_type == "lora":
+        if self.finetuning_args.reward_model_type in ["lora", "oft"]:
            replace_model(unwrapped_model, target="reward")
            reward_model = self.model
        else:
@@ -399,7 +399,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
        with unwrap_model_for_generation(reward_model, self.accelerator), self.amp_context:  # support bf16
            values: torch.Tensor = reward_model(**batch, return_dict=True, use_cache=False)[-1]

-        if self.finetuning_args.reward_model_type == "lora":
+        if self.finetuning_args.reward_model_type in ["lora", "oft"]:
            replace_model(unwrapped_model, target="default")

        rewards = values.gather(dim=-1, index=(batch["attention_mask"].sum(dim=-1, keepdim=True) - 1))
--- a/src/llamafactory/train/pt/trainer.py
+++ b/src/llamafactory/train/pt/trainer.py
@@ -21,21 +21,29 @@ from typing_extensions import override

 from ...extras.packages import is_transformers_version_greater_than
 from ..callbacks import SaveProcessorCallback
+from ..fp8_utils import configure_fp8_environment, verify_fp8_status
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler


 if TYPE_CHECKING:
    from transformers import ProcessorMixin

-    from ...hparams import FinetuningArguments
+    from ...hparams import FinetuningArguments, ModelArguments


 class CustomTrainer(Trainer):
    r"""Inherit Trainer for custom optimizer."""

    def __init__(
-        self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs
+        self,
+        finetuning_args: "FinetuningArguments",
+        processor: Optional["ProcessorMixin"],
+        model_args: Optional["ModelArguments"] = None,
+        **kwargs,
    ) -> None:
+        # Configure FP8 environment if enabled
+        if model_args is not None and model_args.fp8:
+            configure_fp8_environment(model_args)
        if is_transformers_version_greater_than("4.46"):
            kwargs["processing_class"] = kwargs.pop("tokenizer")

@@ -56,6 +64,10 @@ class CustomTrainer(Trainer):
            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
            self.add_callback(BAdamCallback)

+        # Verify FP8 status after trainer initialization (accelerator should be available)
+        if model_args is not None and model_args.fp8 and hasattr(self, "accelerator"):
+            verify_fp8_status(self.accelerator, model_args)
+
    @override
    def create_optimizer(self) -> "torch.optim.Optimizer":
        if self.optimizer is None:
--- a/src/llamafactory/train/sft/metric.py
+++ b/src/llamafactory/train/sft/metric.py
@@ -21,11 +21,11 @@ from typing import TYPE_CHECKING, Optional

 import numpy as np
 import torch
-from transformers.utils import is_jieba_available, is_nltk_available
+from transformers.utils import is_nltk_available

 from ...extras.constants import IGNORE_INDEX
 from ...extras.misc import numpify
-from ...extras.packages import is_rouge_available
+from ...extras.packages import is_jieba_available, is_rouge_available


 if TYPE_CHECKING:
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Yaowei Zheng	3fe6f0febd	[ci] update docker workflow (#9231 )	2025-10-05 02:50:55 +08:00
Yaowei Zheng	40d3691e9e	[misc] fix moe models (#9230 )	2025-10-05 02:41:02 +08:00
Yaowei Zheng	af8437095a	[ci] Change macOS version (#9229 )	2025-10-05 02:18:30 +08:00
codingma	2e2f92701f	[model] add qwen3-vl-30b (#9227 )	2025-10-04 14:12:37 +08:00
Yaowei Zheng	7d60b840ef	[v1] support switch v1 backend (#9226 )	2025-10-02 15:59:19 +08:00
Yaowei Zheng	1d96c62df2	[v1] add v1 folders (#9225 )	2025-10-02 15:25:57 +08:00
Yaowei Zheng	a0d44c650a	[misc] add data files (#9224 )	2025-10-02 14:02:07 +08:00
Yaowei Zheng	bcc2c1fd8f	[misc] move wechat out (#9223 )	2025-10-02 02:06:09 +08:00
Yaowei Zheng	7dd910f067	[misc] lint (#9221 )	2025-10-01 22:58:58 +08:00
krli	d10d65e4ce	[docker] update Dockerfile to set no_proxy and fix pydantic version (#8651 )	2025-10-01 14:33:47 +08:00
Ben Feuer	1c44b60e3e	[feat] fp8 training (#8960 ) Co-authored-by: Benjamin Feuer <penfever@gmail.com> Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2025-10-01 14:32:53 +08:00
Yaowei Zheng	e2b1594d31	[data] fix reasoning template (#9219 )	2025-09-30 18:11:45 +08:00
h7878778h	09dedf144f	[npu] Redirect SDPA to torch_npu.npu_fusion_attention (opt-in, ZeRO-3 safe, no impact off NPU) (#8972 )	2025-09-30 18:11:31 +08:00
魅影	a04d777d7f	[cli] support lazy import (#9217 ) Co-authored-by: frozenleaves <frozen@Mac.local>	2025-09-30 18:02:26 +08:00
Yaowei Zheng	6ffebe5ff7	[data] fix qwen omni plugin (#9204 ) Co-authored-by: kingsley <kingsleydodonow@gmail.com>	2025-09-28 01:02:29 +08:00
xvxuopop	0761a4448f	[model] add qwen3-vl/qwen3-omni (#9196 ) Co-authored-by: kingsley <kingsleydodonow@gmail.com>	2025-09-27 01:21:47 +08:00
wangshaofei	abc3b1e1c4	[docs] update ling-v2 to the readme (#9188 )	2025-09-24 15:23:21 +08:00
Hertz	344c760cc1	[model] supported ERNIE4.5 Text Models (#9165 ) Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2025-09-22 11:48:26 +08:00
Yaowei Zheng	80fe3a172d	[model] add dots ocr (#9176 )	2025-09-21 23:34:19 +08:00
Yaowei Zheng	800934b507	[assets] update readme (#9143 )	2025-09-16 17:04:19 +08:00
Yaowei Zheng	e2ba32598d	[assets] update readme (#9137 )	2025-09-15 23:45:57 +08:00
Yaowei Zheng	812720909e	[model] add qwen3 next (#9130 )	2025-09-14 03:16:25 +08:00
Yaowei Zheng	260b5625c3	[assets] update wechat (#9129 )	2025-09-14 03:05:08 +08:00
Yaowei Zheng	52488ac974	[deps] upgrade transformers to 4.56.1 (#9128 )	2025-09-14 02:26:39 +08:00
Kingsley	610a3f1094	[data] Fix qwen_2vl with valuehead (#9078 )	2025-09-14 02:22:20 +08:00
wangshaofei	a22dab97fd	[data] bailing template v2 & openai data converter (#9112 )	2025-09-14 02:20:34 +08:00
Yaowei Zheng	db223e3975	[misc] update readme (#9071 )	2025-09-03 17:22:54 +08:00
Kingsley	7e710c6d3e	[misc] update InternVL constants (#9046 )	2025-08-29 13:30:28 +08:00
Kingsley	185f0556d4	[model] support Internvl3_5 (#9028 )	2025-08-28 17:12:00 +08:00
Kingsley	1c675522fd	[data] Fix glm4v mmplugin when not expand (#9029 )	2025-08-27 11:45:24 +08:00
tc-mb	6c777375b7	[model] Support MiniCPM-V 4.5 (#9022 )	2025-08-26 19:27:28 +08:00
Kingsley	9c433f6b41	[model] fix kimivl (#9018 )	2025-08-25 16:32:23 +08:00
Yaowei Zheng	ec41ef08aa	[misc] fix constants (#9008 )	2025-08-23 23:04:30 +08:00
Kingsley	0ab0be9df2	[model] support Seed-OSS (#8992 ) Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2025-08-23 22:38:24 +08:00
Zeju Qiu	c14a5fefee	[assets] fix readme error with OFT (#9000 ) Co-authored-by: Zeju <zqiu@g003.internal.cluster.is.localnet>	2025-08-23 21:32:19 +08:00
Haian Huang(深度眸)	1664657d80	[model] Support Intern-S1-mini (#8976 )	2025-08-20 23:52:51 +08:00
Kingsley	022a326ca4	[misc] update glm4v ligerkernel (#8978 )	2025-08-20 23:39:56 +08:00
Yaowei Zheng	c1e1f24f5f	[misc] fix gemma template (#8970 )	2025-08-20 03:18:18 +08:00
Yaowei Zheng	2c31279316	[assets] update wechat (#8962 )	2025-08-19 02:55:09 +08:00
Zeju Qiu	003a2acb1a	[feature] adding orthogononal finetuning (OFT) to llama factory (#8623 ) Co-authored-by: Zeju <zqiu@g003.internal.cluster.is.localnet> Co-authored-by: Zeju <zqiu@login2.is.localnet> Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2025-08-18 18:22:47 +08:00
XLXW	1ada15981a	[feature] add support for dft loss (#8917 )	2025-08-15 23:29:57 +08:00
Kingsley	936f4fd78e	[feature] Support MPO (#8930 )	2025-08-15 15:09:59 +08:00
Kingsley	41648020db	[data] Fix MiniCPMO default system prompt (#8906 )	2025-08-13 23:05:20 +08:00
Yaowei Zheng	b8272a874b	[assets] update wechat & readme (#8899 )	2025-08-12 22:45:55 +08:00
Yaowei Zheng	e695fdfa70	[model] add qwen3 nothink (#8869 )	2025-08-11 23:17:32 +08:00
Kingsley	893edb26d0	[model] support GLM4.5V (#8876 )	2025-08-11 21:45:14 +08:00
Yaowei Zheng	dc61e78e77	[hparams] fix data args (#8863 )	2025-08-08 15:35:50 +08:00
golangboy	ef507ae8e0	[file] Resolve file lock issue when deleting safetensors on Windows (#8839 )	2025-08-08 14:59:54 +08:00
Kingsley	c244b1edb9	[model] add qwen3-4b-i/t (#8851 )	2025-08-07 19:39:26 +08:00
kahlun	8a5d6c8a74	[data-loader] Allow `dataset_dir` to accept a dict for in-memory dataset_info (#8845 )	2025-08-07 16:26:59 +08:00
Yaowei Zheng	b523543994	[data] fix template (#8827 )	2025-08-06 06:58:09 +08:00
Yaowei Zheng	4dfad24902	[model] add gpt oss (#8826 )	2025-08-06 05:56:46 +08:00
davidlightmysterion	c709c0378d	[train] fix adjusting logits size after adding special tokens (#8823 )	2025-08-05 20:35:07 +08:00
Yaowei Zheng	b5071f4b2c	[assets] update readme & supporters (#8818 )	2025-08-05 11:00:53 +08:00
Dennis Huang	4a9ca24122	[model] Support MiniCPM-V 4.0 (#8813 )	2025-08-04 12:57:21 +08:00
Yaowei Zheng	cb03eb422d	[model] Qwen3-30B-A3B-Thinking-2507 (#8788 )	2025-07-30 23:05:12 +08:00
Yaowei Zheng	a416ab48d8	[deps] upgrade vllm to 0.10.0 (#8787 )	2025-07-30 22:26:38 +08:00
Yaowei Zheng	d64651a637	[assets] update readme (#8784 )	2025-07-30 17:57:17 +08:00
Yaowei Zheng	7c223c432b	[model] add qwen3 2507 model (#8783 )	2025-07-30 17:19:19 +08:00
Kingsley	52882d01c3	[model] support keye-vl-8b (#8776 )	2025-07-29 21:24:08 +08:00
Kingsley	4e0bf35eb4	[model] update glm4.5 (#8770 )	2025-07-29 19:57:29 +08:00
Yaowei Zheng	8efa506c16	[model] add qwen3 2507 models (#8750 )	2025-07-25 20:21:47 +08:00
Kingsley	d6767f355a	[model] add glm4moe (#8689 )	2025-07-25 19:53:45 +08:00
Yaowei Zheng	c6e2871944	[assets] update readme (#8739 )	2025-07-24 23:13:24 +08:00
Steven sun	9d6565d1a8	[model] support granite4 (#8680 )	2025-07-21 14:15:36 +08:00
Kingsley	1639e4b587	[data] fix gemma3n mmplugin (#8627 )	2025-07-14 13:32:57 +08:00
Kingsley	9c9b307d33	[model] add Devstral-Small-2507 (#8614 )	2025-07-11 18:59:53 +08:00
Yaowei Zheng	cf13964c4c	[webui] fix elems (#8587 )	2025-07-08 21:16:42 +08:00
Kingsley	542fa97a72	[data] support glm4.1v video training (#8571 )	2025-07-08 16:29:44 +08:00
Yaowei Zheng	7f8e5f52f9	[webui] fix abort finish (#8569 )	2025-07-07 23:07:46 +08:00
Yaowei Zheng	12ed792db9	[webui] support other hub (#8567 )	2025-07-07 22:18:48 +08:00
Yaowei Zheng	4b0ec83928	[deps] bump transformers to 4.49.0 (#8564 )	2025-07-07 20:31:50 +08:00
Redwood-Digital	1b1ec9bfb6	[docs] add nvidia-container-toolkit to Linux Docker setup instructions (#8557 )	2025-07-06 19:37:08 +08:00
Vivek Iyer	e0dfdb7dbb	Revert "[model] add lora dropout to unsloth" - requested feature already exists (#8554 ) Co-authored-by: viyer <vivek_iyer2@apple.com>	2025-07-05 11:25:31 +08:00
wjunLu	17ab40793b	[ci] Add workflow for building NPU image (#8546 ) Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2025-07-04 20:56:59 +08:00
Vivek Iyer	0686206020	[model] add lora dropout to unsloth (#8548 ) Co-authored-by: viyer <vivek_iyer2@apple.com>	2025-07-04 14:56:36 +08:00
Ze-Yi LIN	16f13d304b	[tracking] fix swanlab hparams (#8532 ) Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2025-07-02 22:08:44 +08:00
Yaowei Zheng	57524751e0	[assets] update issue template (#8530 )	2025-07-02 19:29:58 +08:00
Yaowei Zheng	906b31fd47	[assets] update readme (#8529 )	2025-07-02 17:42:27 +08:00
Kingsley	bede213da7	[assets] update readme (#8519 )	2025-07-02 15:38:38 +08:00
Kingsley	e9f70daabe	[model] add gemma3n (#8509 )	2025-07-01 22:37:24 +08:00
Yaowei Zheng	cbb65567a9	[assets] update wechat (#8517 )	2025-07-01 21:23:48 +08:00
Injae Ryou	a5a93597b1	[parser] update config loading to use OmegaConf #7793 (#8505 )	2025-07-01 21:05:13 +08:00
Kingsley	d17a672251	[model] add GLM-4.1V (#8462 )	2025-06-30 01:09:41 +08:00
Liu Jiajun	4f0da0aec9	[data] fix gemma2 eos token (#8480 ) Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>	2025-06-27 18:19:15 +08:00
Yaowei Zheng	2c26ce6ac4	Merge commit from fork	2025-06-26 13:55:42 +08:00
Yaowei Zheng	abc6ce6168	[assets] update readme (#8461 )	2025-06-25 22:15:03 +08:00
Yaowei Zheng	4407231a3b	[webui] upgrade webui and fix api (#8460 )	2025-06-25 21:59:58 +08:00
Yaowei Zheng	f276b9a963	[model] do not force load processor (#8457 )	2025-06-25 19:43:00 +08:00
Yaowei Zheng	48897e5b16	[data] fix audio reader (#8448 )	2025-06-24 20:53:20 +08:00
Yaowei Zheng	9cd81aa424	[misc] fix ci (#8441 )	2025-06-24 16:37:32 +08:00
Kingsley	ecbccb4c5d	[model] Add mistral-small 3.2 & kimi-dev (#8433 )	2025-06-24 14:59:47 +08:00
Yaowei Zheng	9af7915f7b	[model] add kimi vl 2506 (#8432 )	2025-06-23 17:56:48 +08:00
Vivek Iyer	7b252b2368	[model] unsloth resume from checkpoint bug (#8423 ) Co-authored-by: viyer <vivek_iyer2@apple.com>	2025-06-23 16:43:54 +08:00
Dhia Eddine Rhaiem	88a92be808	[model] add support for Falcon H1 (#8403 )	2025-06-18 16:51:23 +08:00
Yaowei Zheng	c6c764388c	[assets] update readme (#8396 )	2025-06-17 16:15:20 +08:00
Yaowei Zheng	3e392473d1	[misc] set dev version (#8389 )	2025-06-17 01:39:25 +08:00
Yaowei Zheng	ad345ec054	[ci] add docker version (#8390 )	2025-06-17 01:37:47 +08:00
				`@@ -0,0 +1 @@`
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