Merge branch 'main' into main

Former-commit-id: 5f14910910154ba569435e7e68acbd6c30f79e80
2025-09-12 16:12:48 +08:00 · 2024-11-02 21:20:27 +08:00 · 2024-11-02 21:20:27 +08:00 · d99e164cad
commit d99e164cad
parent 4ed2b629a5 7dbb338df7
147 changed files with 3087 additions and 1833 deletions
--- a/.dockerignore
+++ b/.dockerignore
@ -7,6 +7,8 @@ data
 docker
 saves
 hf_cache
 ms_cache
 om_cache
 output
 .dockerignore
 .gitattributes
--- a/.env.local
+++ b/.env.local
@ -1,32 +1,35 @@
 # Note: actually we do not support .env, just for reference
 # api
-API_HOST=0.0.0.0
+API_HOST=
-API_PORT=8000
+API_PORT=
 API_KEY=
-API_MODEL_NAME=gpt-3.5-turbo
+API_MODEL_NAME=
 FASTAPI_ROOT_PATH=
 MAX_CONCURRENT=
 # general
 DISABLE_VERSION_CHECK=
 FORCE_CHECK_IMPORTS=
 LLAMAFACTORY_VERBOSITY=
 USE_MODELSCOPE_HUB=
 USE_OPENMIND_HUB=
 RECORD_VRAM=
 # torchrun
 FORCE_TORCHRUN=
 MASTER_ADDR=
 MASTER_PORT=
 NNODES=
-RANK=
+NODE_RANK=
 NPROC_PER_NODE=
 # wandb
 WANDB_DISABLED=
-WANDB_PROJECT=huggingface
+WANDB_PROJECT=
 WANDB_API_KEY=
 # gradio ui
-GRADIO_SHARE=False
+GRADIO_SHARE=
-GRADIO_SERVER_NAME=0.0.0.0
+GRADIO_SERVER_NAME=
 GRADIO_SERVER_PORT=
 GRADIO_ROOT_PATH=
 GRADIO_IPV6=
 # setup
 ENABLE_SHORT_CONSOLE=1
 # reserved (do not use)
--- a/.github/CONTRIBUTING.md
+++ b/.github/CONTRIBUTING.md
@ -19,3 +19,49 @@ There are several ways you can contribute to LLaMA Factory:
 ### Style guide
 LLaMA Factory follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html), check it for details.
 ### Create a Pull Request
 1. Fork the [repository](https://github.com/hiyouga/LLaMA-Factory) by clicking on the [Fork](https://github.com/hiyouga/LLaMA-Factory/fork) button on the repository's page. This creates a copy of the code under your GitHub user account.
 2. Clone your fork to your local disk, and add the base repository as a remote:
 ```bash
 git clone git@github.com:[username]/LLaMA-Factory.git
 cd LLaMA-Factory
 git remote add upstream https://github.com/hiyouga/LLaMA-Factory.git
 ```
 3. Create a new branch to hold your development changes:
 ```bash
 git checkout -b dev_your_branch
 ```
 4. Set up a development environment by running the following command in a virtual environment:
 ```bash
 pip install -e ".[dev]"
 ```
 If LLaMA Factory was already installed in the virtual environment, remove it with `pip uninstall llamafactory` before reinstalling it in editable mode with the -e flag.
 5. Check code before commit:
 ```bash
 make commit
 make style && make quality
 make test
 ```
 6. Submit changes:
 ```bash
 git add .
 git commit -m "commit message"
 git fetch upstream
 git rebase upstream/main
 git push -u origin dev_your_branch
 ```
 7. Create a merge request from your branch `dev_your_branch` at [origin repo](https://github.com/hiyouga/LLaMA-Factory).
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@ -22,7 +22,7 @@ jobs:
      fail-fast: false
      matrix:
        python-version:
-          - "3.8"
+          - "3.8"  # TODO: remove py38 in next transformers release
          - "3.9"
          - "3.10"
          - "3.11"
@ -54,7 +54,6 @@ jobs:
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          python -m pip install git+https://github.com/huggingface/transformers.git
          python -m pip install ".[torch,dev]"
      - name: Check quality
--- a/.gitignore
+++ b/.gitignore
@ -162,6 +162,7 @@ cython_debug/
 # custom .gitignore
 ms_cache/
 hf_cache/
 om_cache/
 cache/
 config/
 saves/
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@ -0,0 +1,28 @@
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v5.0.0
    hooks:
    -   id: check-ast
    -   id: check-added-large-files
        args: ['--maxkb=25000']
    -   id: check-merge-conflict
    -   id: check-yaml
    -   id: debug-statements
    -   id: end-of-file-fixer
    -   id: trailing-whitespace
        args: [--markdown-linebreak-ext=md]
    -   id: no-commit-to-branch
        args: ['--branch', 'main']
 -   repo: https://github.com/asottile/pyupgrade
    rev: v3.17.0
    hooks:
    -   id: pyupgrade
        args: [--py38-plus]
 -   repo: https://github.com/astral-sh/ruff-pre-commit
    rev: v0.6.9
    hooks:
    -   id: ruff
        args: [--fix]
    -   id: ruff-format
--- a/11
+++ b/11
@ -1,7 +1,14 @@
-.PHONY: quality style test
+.PHONY: build commit quality style test
 check_dirs := scripts src tests setup.py
 build:
 	pip install build && python -m build
 commit:
 	pre-commit install
 	pre-commit run --all-files
 quality:
 	ruff check $(check_dirs)
 	ruff format --check $(check_dirs)
@ -11,4 +18,4 @@ style:
 	ruff format $(check_dirs)
 test:
-	CUDA_VISIBLE_DEVICES= pytest tests/
+	CUDA_VISIBLE_DEVICES= WANDB_DISABLED=true pytest -vv tests/
--- a/README.md
+++ b/README.md
@ -4,7 +4,7 @@
 [![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE)
 [![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main)
 [![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
-[![Citation](https://img.shields.io/badge/citation-91-green)](#projects-using-llama-factory)
+[![Citation](https://img.shields.io/badge/citation-92-green)](#projects-using-llama-factory)
 [![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
 [![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
 [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
@ -26,10 +26,17 @@ https://github.com/user-attachments/assets/7c96b465-9df7-45f4-8053-bf03e58386d3
 Choose your path:
 - **Colab**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
- **PAI-DSW**: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
+- **PAI-DSW**: [Llama3 Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) | [Qwen2-VL Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)
 - **Local machine**: Please refer to [usage](#getting-started)
 - **Documentation (WIP)**: https://llamafactory.readthedocs.io/zh-cn/latest/
 Recent activities:
 - **2024/10/18-2024/11/30**: Build a personal tour guide bot using PAI+LLaMA Factory. [[website]](https://developer.aliyun.com/topic/llamafactory2)
 > [!NOTE]
 > Except for the above links, all other websites are unauthorized third-party websites. Please carefully use them.
 ## Table of Contents
 - [Features](#features)
@ -72,6 +79,8 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 ## Changelog
 [24/10/09] We supported downloading pre-trained models and datasets from the **[Modelers Hub](https://modelers.cn/models)**. See [this tutorial](#download-from-modelers-hub) for usage.
 [24/09/19] We support fine-tuning the **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** models.
 [24/08/30] We support fine-tuning the **[Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/)** models. Thank [@simonJJJ](https://github.com/simonJJJ)'s PR.
@ -130,7 +139,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 [23/12/12] We supported fine-tuning the latest MoE model **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)** in our framework. See hardware requirement [here](#hardware-requirement).
-[23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)** for Chinese mainland users. See [this tutorial](#download-from-modelscope-hub) for usage.
+[23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)**. See [this tutorial](#download-from-modelscope-hub) for usage.
 [23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `neftune_noise_alpha: 5` argument to activate NEFTune.
@ -162,36 +171,39 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 ## Supported Models
-| Model                                                             | Model size                       | Template  |
+| Model                                                             | Model size                       | Template         |
-| ----------------------------------------------------------------- | -------------------------------- | --------- |
+| ----------------------------------------------------------------- | -------------------------------- | ---------------- |
-| [Baichuan 2](https://huggingface.co/baichuan-inc)                 | 7B/13B                           | baichuan2 |
+| [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      | -         |
+| [BLOOM/BLOOMZ](https://huggingface.co/bigscience)                 | 560M/1.1B/1.7B/3B/7.1B/176B      | -                |
-| [ChatGLM3](https://huggingface.co/THUDM)                          | 6B                               | chatglm3  |
+| [ChatGLM3](https://huggingface.co/THUDM)                          | 6B                               | chatglm3         |
-| [Command R](https://huggingface.co/CohereForAI)                   | 35B/104B                         | cohere    |
+| [Command R](https://huggingface.co/CohereForAI)                   | 35B/104B                         | cohere           |
-| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai)         | 7B/16B/67B/236B                  | deepseek  |
+| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai)         | 7B/16B/67B/236B                  | deepseek         |
-| [Falcon](https://huggingface.co/tiiuae)                           | 7B/11B/40B/180B                  | falcon    |
+| [Falcon](https://huggingface.co/tiiuae)                           | 7B/11B/40B/180B                  | falcon           |
-| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google)          | 2B/7B/9B/27B                     | gemma     |
+| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google)          | 2B/7B/9B/27B                     | gemma            |
-| [GLM-4](https://huggingface.co/THUDM)                             | 9B                               | glm4      |
+| [GLM-4](https://huggingface.co/THUDM)                             | 9B                               | glm4             |
-| [InternLM2/InternLM2.5](https://huggingface.co/internlm)          | 7B/20B                           | intern2   |
+| [Index](https://huggingface.co/IndexTeam)                         | 1.9B                             | index            |
-| [Llama](https://github.com/facebookresearch/llama)                | 7B/13B/33B/65B                   | -         |
+| [InternLM2/InternLM2.5](https://huggingface.co/internlm)          | 7B/20B                           | intern2          |
-| [Llama 2](https://huggingface.co/meta-llama)                      | 7B/13B/70B                       | llama2    |
+| [Llama](https://github.com/facebookresearch/llama)                | 7B/13B/33B/65B                   | -                |
-| [Llama 3/Llama 3.1](https://huggingface.co/meta-llama)            | 8B/70B                           | llama3    |
+| [Llama 2](https://huggingface.co/meta-llama)                      | 7B/13B/70B                       | llama2           |
-| [LLaVA-1.5](https://huggingface.co/llava-hf)                      | 7B/13B                           | llava     |
+| [Llama 3-3.2](https://huggingface.co/meta-llama)                  | 1B/3B/8B/70B                     | llama3           |
-| [MiniCPM](https://huggingface.co/openbmb)                         | 1B/2B/4B                         | cpm/cpm3  |
+| [LLaVA-1.5](https://huggingface.co/llava-hf)                      | 7B/13B                           | llava            |
-| [Mistral/Mixtral](https://huggingface.co/mistralai)               | 7B/8x7B/8x22B                    | mistral   |
+| [LLaVA-NeXT](https://huggingface.co/llava-hf)                     | 7B/8B/13B/34B/72B/110B           | llava_next       |
-| [OLMo](https://huggingface.co/allenai)                            | 1B/7B                            | -         |
+| [LLaVA-NeXT-Video](https://huggingface.co/llava-hf)               | 7B/34B                           | llava_next_video |
-| [PaliGemma](https://huggingface.co/google)                        | 3B                               | paligemma |
+| [MiniCPM](https://huggingface.co/openbmb)                         | 1B/2B/4B                         | cpm/cpm3         |
-| [Phi-1.5/Phi-2](https://huggingface.co/microsoft)                 | 1.3B/2.7B                        | -         |
+| [Mistral/Mixtral](https://huggingface.co/mistralai)               | 7B/8x7B/8x22B                    | mistral          |
-| [Phi-3](https://huggingface.co/microsoft)                         | 4B/14B                           | phi       |
+| [OLMo](https://huggingface.co/allenai)                            | 1B/7B                            | -                |
-| [Phi-3-small](https://huggingface.co/microsoft)                   | 7B                               | phi-small |
+| [PaliGemma](https://huggingface.co/google)                        | 3B                               | paligemma        |
-| [Qwen/Qwen1.5/Qwen2 (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/4B/7B/14B/32B/72B/110B | qwen      |
+| [Phi-1.5/Phi-2](https://huggingface.co/microsoft)                 | 1.3B/2.7B                        | -                |
-| [Qwen2.5 (Code/Math)](https://huggingface.co/Qwen)                | 0.5B/1.5B/3B/7B/14B/32B/72B      | qwen      |
+| [Phi-3](https://huggingface.co/microsoft)                         | 4B/14B                           | phi              |
-| [Qwen2-VL](https://huggingface.co/Qwen)                           | 2B/7B/72B                        | qwen2_vl  |
+| [Phi-3-small](https://huggingface.co/microsoft)                   | 7B                               | phi_small        |
-| [StarCoder 2](https://huggingface.co/bigcode)                     | 3B/7B/15B                        | -         |
+| [Pixtral](https://huggingface.co/mistralai)                       | 12B                              | pixtral          |
-| [XVERSE](https://huggingface.co/xverse)                           | 7B/13B/65B                       | xverse    |
+| [Qwen (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen)       | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen             |
-| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai)                  | 1.5B/6B/9B/34B                   | yi        |
+| [Qwen2-VL](https://huggingface.co/Qwen)                           | 2B/7B/72B                        | qwen2_vl         |
-| [Yi-VL](https://huggingface.co/01-ai)                             | 6B/34B                           | yi_vl     |
+| [StarCoder 2](https://huggingface.co/bigcode)                     | 3B/7B/15B                        | -                |
-| [Yuan 2](https://huggingface.co/IEITYuan)                         | 2B/51B/102B                      | yuan      |
+| [XVERSE](https://huggingface.co/xverse)                           | 7B/13B/65B                       | xverse           |
 | [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai)                  | 1.5B/6B/9B/34B                   | yi               |
 | [Yi-VL](https://huggingface.co/01-ai)                             | 6B/34B                           | yi_vl            |
 | [Yuan 2](https://huggingface.co/IEITYuan)                         | 2B/51B/102B                      | yuan             |
 > [!NOTE]
 > For the "base" models, the `template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the **corresponding template** for the "instruct/chat" models.
@ -360,7 +372,7 @@ cd LLaMA-Factory
 pip install -e ".[torch,metrics]"
 ```
-Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, adam-mini, qwen, modelscope, quality
+Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, adam-mini, qwen, modelscope, openmind, quality
 > [!TIP]
 > Use `pip install --no-deps -e .` to resolve package conflicts.
@ -412,7 +424,7 @@ Download the pre-built Docker images: [32GB](http://mirrors.cn-central-221.ovaij
 ### Data Preparation
-Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope hub or load the dataset in local disk.
+Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope / Modelers hub or load the dataset in local disk.
 > [!NOTE]
 > Please update `data/dataset_info.json` to use your custom dataset.
@ -480,6 +492,7 @@ docker build -f ./docker/docker-cuda/Dockerfile \
 docker run -dit --gpus=all \
    -v ./hf_cache:/root/.cache/huggingface \
    -v ./ms_cache:/root/.cache/modelscope \
    -v ./om_cache:/root/.cache/openmind \
    -v ./data:/app/data \
    -v ./output:/app/output \
    -p 7860:7860 \
@ -504,6 +517,7 @@ docker build -f ./docker/docker-npu/Dockerfile \
 docker run -dit \
    -v ./hf_cache:/root/.cache/huggingface \
    -v ./ms_cache:/root/.cache/modelscope \
    -v ./om_cache:/root/.cache/openmind \
    -v ./data:/app/data \
    -v ./output:/app/output \
    -v /usr/local/dcmi:/usr/local/dcmi \
@ -537,6 +551,7 @@ docker build -f ./docker/docker-rocm/Dockerfile \
 docker run -dit \
    -v ./hf_cache:/root/.cache/huggingface \
    -v ./ms_cache:/root/.cache/modelscope \
    -v ./om_cache:/root/.cache/openmind \
    -v ./data:/app/data \
    -v ./output:/app/output \
    -v ./saves:/app/saves \
@ -557,6 +572,7 @@ docker exec -it llamafactory bash
 - `hf_cache`: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
 - `ms_cache`: Similar to Hugging Face cache but for ModelScope users.
 - `om_cache`: Similar to Hugging Face cache but for Modelers users.
 - `data`: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
 - `output`: Set export dir to this location so that the merged result can be accessed directly on the host machine.
@ -570,6 +586,8 @@ API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
 > [!TIP]
 > Visit [this page](https://platform.openai.com/docs/api-reference/chat/create) for API document.
 >
 > Examples: [Image understanding](scripts/test_image.py) | [Function calling](scripts/test_toolcall.py)
 ### Download from ModelScope Hub
@ -581,6 +599,16 @@ export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
 Train the model by specifying a model ID of the ModelScope Hub as the `model_name_or_path`. You can find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models), e.g., `LLM-Research/Meta-Llama-3-8B-Instruct`.
 ### Download from Modelers Hub
 You can also use Modelers Hub to download models and datasets.
 ```bash
 export USE_OPENMIND_HUB=1 # `set USE_OPENMIND_HUB=1` for Windows
 ```
 Train the model by specifying a model ID of the Modelers Hub as the `model_name_or_path`. You can find a full list of model IDs at [Modelers Hub](https://modelers.cn/models), e.g., `TeleAI/TeleChat-7B-pt`.
 ### Use W&B Logger
 To use [Weights & Biases](https://wandb.ai) for logging experimental results, you need to add the following arguments to yaml files.
@ -684,11 +712,13 @@ If you have a project that should be incorporated, please contact via email or c
 1. **[Sunsimiao](https://github.com/X-D-Lab/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
 1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
 1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**: A series of MBTI Personality large language models, capable of giving any LLM 16 different personality types based on different datasets and training methods.
-1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**: A large language model specialized in generate metadata for stable diffusion. [[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
+1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**: A large language model specialized in generate metadata for stable diffusion. [[demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
 1. **[Chinese-LLaVA-Med](https://github.com/BUAADreamer/Chinese-LLaVA-Med)**: A multimodal large language model specialized in Chinese medical domain, based on LLaVA-1.5-7B.
 1. **[AutoRE](https://github.com/THUDM/AutoRE)**: A document-level relation extraction system based on large language models.
 1. **[NVIDIA RTX AI Toolkit](https://github.com/NVIDIA/RTX-AI-Toolkit)**: SDKs for fine-tuning LLMs on Windows PC for NVIDIA RTX.
 1. **[LazyLLM](https://github.com/LazyAGI/LazyLLM)**: An easy and lazy way for building multi-agent LLMs applications and supports model fine-tuning via LLaMA Factory.
 1. **[RAG-Retrieval](https://github.com/NLPJCL/RAG-Retrieval)**: A full pipeline for RAG retrieval model fine-tuning, inference, and distillation. [[blog]](https://zhuanlan.zhihu.com/p/987727357)
 </details>
@ -696,7 +726,7 @@ If you have a project that should be incorporated, please contact via email or c
 This repository is licensed under the [Apache-2.0 License](LICENSE).
-Please follow the model licenses to use the corresponding model weights: [Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [InternLM2](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
+Please follow the model licenses to use the corresponding model weights: [Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [Index](https://huggingface.co/IndexTeam/Index-1.9B/blob/main/LICENSE) / [InternLM2](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
 ## Citation
--- a/README_zh.md
+++ b/README_zh.md
@ -4,7 +4,7 @@
 [![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE)
 [![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main)
 [![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
-[![Citation](https://img.shields.io/badge/citation-91-green)](#使用了-llama-factory-的项目)
+[![Citation](https://img.shields.io/badge/citation-92-green)](#使用了-llama-factory-的项目)
 [![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
 [![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
 [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
@ -26,11 +26,18 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
 选择你的打开方式：
 - **Colab**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
- **PAI-DSW**：https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
+- **PAI-DSW**：[Llama3 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) | [Qwen2-VL 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)
 - **本地机器**：请见[如何使用](#如何使用)
 - **入门教程**：https://zhuanlan.zhihu.com/p/695287607
 - **框架文档**：https://llamafactory.readthedocs.io/zh-cn/latest/
 近期活动：
 - **2024/10/18-2024/11/30**：使用 PAI+LLaMA Factory 构建个性化导游机器人。[[活动页面]](https://developer.aliyun.com/topic/llamafactory2)
 > [!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。
 ## 目录
 - [项目特色](#项目特色)
@ -73,6 +80,8 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
 ## 更新日志
 [24/10/09] 我们支持了从 **[魔乐社区](https://modelers.cn/models)** 下载预训练模型和数据集。详细用法请参照 [此教程](#从魔乐社区下载)。
 [24/09/19] 我们支持了 **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** 模型的微调。
 [24/08/30] 我们支持了 **[Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/)** 模型的微调。感谢 [@simonJJJ](https://github.com/simonJJJ) 的 PR。
@ -163,35 +172,38 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
 ## 模型
-| 模型名                                                            | 模型大小                          | Template  |
+| 模型名                                                            | 模型大小                          | Template         |
-| ----------------------------------------------------------------- | -------------------------------- | --------- |
+| ----------------------------------------------------------------- | -------------------------------- | ---------------- |
-| [Baichuan 2](https://huggingface.co/baichuan-inc)                 | 7B/13B                           | baichuan2 |
+| [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      | -         |
+| [BLOOM/BLOOMZ](https://huggingface.co/bigscience)                 | 560M/1.1B/1.7B/3B/7.1B/176B      | -                |
-| [ChatGLM3](https://huggingface.co/THUDM)                          | 6B                               | chatglm3  |
+| [ChatGLM3](https://huggingface.co/THUDM)                          | 6B                               | chatglm3         |
-| [Command R](https://huggingface.co/CohereForAI)                   | 35B/104B                         | cohere    |
+| [Command R](https://huggingface.co/CohereForAI)                   | 35B/104B                         | cohere           |
-| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai)         | 7B/16B/67B/236B                  | deepseek  |
+| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai)         | 7B/16B/67B/236B                  | deepseek         |
-| [Falcon](https://huggingface.co/tiiuae)                           | 7B/11B/40B/180B                  | falcon    |
+| [Falcon](https://huggingface.co/tiiuae)                           | 7B/11B/40B/180B                  | falcon           |
-| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google)          | 2B/7B/9B/27B                     | gemma     |
+| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google)          | 2B/7B/9B/27B                     | gemma            |
-| [GLM-4](https://huggingface.co/THUDM)                             | 9B                               | glm4      |
+| [GLM-4](https://huggingface.co/THUDM)                             | 9B                               | glm4             |
-| [InternLM2/InternLM2.5](https://huggingface.co/internlm)          | 7B/20B                           | intern2   |
+| [Index](https://huggingface.co/IndexTeam)                         | 1.9B                             | index            |
-| [Llama](https://github.com/facebookresearch/llama)                | 7B/13B/33B/65B                   | -         |
+| [InternLM2/InternLM2.5](https://huggingface.co/internlm)          | 7B/20B                           | intern2          |
-| [Llama 2](https://huggingface.co/meta-llama)                      | 7B/13B/70B                       | llama2    |
+| [Llama](https://github.com/facebookresearch/llama)                | 7B/13B/33B/65B                   | -                |
-| [Llama 3/Llama 3.1](https://huggingface.co/meta-llama)            | 8B/70B                           | llama3    |
+| [Llama 2](https://huggingface.co/meta-llama)                      | 7B/13B/70B                       | llama2           |
-| [LLaVA-1.5](https://huggingface.co/llava-hf)                      | 7B/13B                           | llava     |
+| [Llama 3-3.2](https://huggingface.co/meta-llama)                  | 1B/3B/8B/70B                     | llama3           |
-| [MiniCPM](https://huggingface.co/openbmb)                         | 1B/2B/4B                         | cpm/cpm3  |
+| [LLaVA-1.5](https://huggingface.co/llava-hf)                      | 7B/13B                           | llava            |
-| [Mistral/Mixtral](https://huggingface.co/mistralai)               | 7B/8x7B/8x22B                    | mistral   |
+| [LLaVA-NeXT](https://huggingface.co/llava-hf)                     | 7B/8B/13B/34B/72B/110B           | llava_next       |
-| [OLMo](https://huggingface.co/allenai)                            | 1B/7B                            | -         |
+| [LLaVA-NeXT-Video](https://huggingface.co/llava-hf)               | 7B/34B                           | llava_next_video |
-| [PaliGemma](https://huggingface.co/google)                        | 3B                               | paligemma |
+| [MiniCPM](https://huggingface.co/openbmb)                         | 1B/2B/4B                         | cpm/cpm3         |
-| [Phi-1.5/Phi-2](https://huggingface.co/microsoft)                 | 1.3B/2.7B                        | -         |
+| [Mistral/Mixtral](https://huggingface.co/mistralai)               | 7B/8x7B/8x22B                    | mistral          |
-| [Phi-3](https://huggingface.co/microsoft)                         | 4B/7B/14B                        | phi       |
+| [OLMo](https://huggingface.co/allenai)                            | 1B/7B                            | -                |
-| [Qwen/Qwen1.5/Qwen2 (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/4B/7B/14B/32B/72B/110B | qwen      |
+| [PaliGemma](https://huggingface.co/google)                        | 3B                               | paligemma        |
-| [Qwen2.5 (Code/Math)](https://huggingface.co/Qwen)                | 0.5B/1.5B/3B/7B/14B/32B/72B      | qwen      |
+| [Phi-1.5/Phi-2](https://huggingface.co/microsoft)                 | 1.3B/2.7B                        | -                |
-| [Qwen2-VL](https://huggingface.co/Qwen)                           | 2B/7B/72B                        | qwen2_vl  |
+| [Phi-3](https://huggingface.co/microsoft)                         | 4B/7B/14B                        | phi              |
-| [StarCoder 2](https://huggingface.co/bigcode)                     | 3B/7B/15B                        | -         |
+| [Pixtral](https://huggingface.co/mistralai)                       | 12B                              | pixtral          |
-| [XVERSE](https://huggingface.co/xverse)                           | 7B/13B/65B                       | xverse    |
+| [Qwen (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen)       | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen             |
-| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai)                  | 1.5B/6B/9B/34B                   | yi        |
+| [Qwen2-VL](https://huggingface.co/Qwen)                           | 2B/7B/72B                        | qwen2_vl         |
-| [Yi-VL](https://huggingface.co/01-ai)                             | 6B/34B                           | yi_vl     |
+| [StarCoder 2](https://huggingface.co/bigcode)                     | 3B/7B/15B                        | -                |
-| [Yuan 2](https://huggingface.co/IEITYuan)                         | 2B/51B/102B                      | yuan      |
+| [XVERSE](https://huggingface.co/xverse)                           | 7B/13B/65B                       | xverse           |
 | [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai)                  | 1.5B/6B/9B/34B                   | yi               |
 | [Yi-VL](https://huggingface.co/01-ai)                             | 6B/34B                           | yi_vl            |
 | [Yuan 2](https://huggingface.co/IEITYuan)                         | 2B/51B/102B                      | yuan             |
 > [!NOTE]
 > 对于所有“基座”（Base）模型，`template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”（Instruct/Chat）模型请务必使用**对应的模板**。
@ -360,7 +372,7 @@ cd LLaMA-Factory
 pip install -e ".[torch,metrics]"
 ```
-可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、badam、adam-mini、qwen、modelscope、quality
+可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、badam、adam-mini、qwen、modelscope、openmind、quality
 > [!TIP]
 > 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
@ -412,7 +424,7 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
 ### 数据准备
-关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。你可以使用 HuggingFace / ModelScope 上的数据集或加载本地数据集。
+关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。你可以使用 HuggingFace / ModelScope / Modelers 上的数据集或加载本地数据集。
 > [!NOTE]
 > 使用自定义数据集时，请更新 `data/dataset_info.json` 文件。
@ -480,6 +492,7 @@ docker build -f ./docker/docker-cuda/Dockerfile \
 docker run -dit --gpus=all \
    -v ./hf_cache:/root/.cache/huggingface \
    -v ./ms_cache:/root/.cache/modelscope \
    -v ./om_cache:/root/.cache/openmind \
    -v ./data:/app/data \
    -v ./output:/app/output \
    -p 7860:7860 \
@ -504,6 +517,7 @@ docker build -f ./docker/docker-npu/Dockerfile \
 docker run -dit \
    -v ./hf_cache:/root/.cache/huggingface \
    -v ./ms_cache:/root/.cache/modelscope \
    -v ./om_cache:/root/.cache/openmind \
    -v ./data:/app/data \
    -v ./output:/app/output \
    -v /usr/local/dcmi:/usr/local/dcmi \
@ -537,6 +551,7 @@ docker build -f ./docker/docker-rocm/Dockerfile \
 docker run -dit \
    -v ./hf_cache:/root/.cache/huggingface \
    -v ./ms_cache:/root/.cache/modelscope \
    -v ./om_cache:/root/.cache/openmind \
    -v ./data:/app/data \
    -v ./output:/app/output \
    -v ./saves:/app/saves \
@ -557,6 +572,7 @@ docker exec -it llamafactory bash
 - `hf_cache`：使用宿主机的 Hugging Face 缓存文件夹，允许更改为新的目录。
 - `ms_cache`：类似 Hugging Face 缓存文件夹，为 ModelScope 用户提供。
 - `om_cache`：类似 Hugging Face 缓存文件夹，为 Modelers 用户提供。
 - `data`：宿主机中存放数据集的文件夹路径。
 - `output`：将导出目录设置为该路径后，即可在宿主机中访问导出后的模型。
@ -570,6 +586,8 @@ API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
 > [!TIP]
 > API 文档请查阅[这里](https://platform.openai.com/docs/api-reference/chat/create)。
 >
 > 示例：[图像理解](scripts/test_image.py) | [工具调用](scripts/test_toolcall.py)
 ### 从魔搭社区下载
@ -581,6 +599,16 @@ export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
 将 `model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔搭社区](https://modelscope.cn/models)查看所有可用的模型，例如 `LLM-Research/Meta-Llama-3-8B-Instruct`。
 ### 从魔乐社区下载
 您也可以通过下述方法，使用魔乐社区下载数据集和模型。
 ```bash
 export USE_OPENMIND_HUB=1 # Windows 使用 `set USE_OPENMIND_HUB=1`
 ```
 将 `model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔乐社区](https://modelers.cn/models)查看所有可用的模型，例如 `TeleAI/TeleChat-7B-pt`。
 ### 使用 W&B 面板
 若要使用 [Weights & Biases](https://wandb.ai) 记录实验数据，请在 yaml 文件中添加下面的参数。
@ -684,11 +712,12 @@ run_name: test_run # 可选
 1. **[Sunsimiao](https://github.com/X-D-Lab/Sunsimiao)**: 孙思邈中文医疗大模型 Sumsimiao，基于 Baichuan-7B 和 ChatGLM-6B 在中文医疗数据上微调而得。
 1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: 医疗大模型项目 CareGPT，基于 LLaMA2-7B 和 Baichuan-13B 在中文医疗数据上微调而得。
 1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**：MBTI性格大模型项目，根据数据集与训练方式让任意 LLM 拥有 16 个不同的性格类型。
-1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**：一个用于生成 Stable Diffusion 提示词的大型语言模型。[[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
+1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**：一个用于生成 Stable Diffusion 提示词的大型语言模型。[[demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
 1. **[Chinese-LLaVA-Med](https://github.com/BUAADreamer/Chinese-LLaVA-Med)**：中文多模态医学大模型，基于 LLaVA-1.5-7B 在中文多模态医疗数据上微调而得。
 1. **[AutoRE](https://github.com/THUDM/AutoRE)**：基于大语言模型的文档级关系抽取系统。
 1. **[NVIDIA RTX AI Toolkit](https://github.com/NVIDIA/RTX-AI-Toolkit)**：在 Windows 主机上利用英伟达 RTX 设备进行大型语言模型微调的开发包。
 1. **[LazyLLM](https://github.com/LazyAGI/LazyLLM)**：一个低代码构建多 Agent 大模型应用的开发工具，支持基于 LLaMA Factory 的模型微调.
 1. **[RAG-Retrieval](https://github.com/NLPJCL/RAG-Retrieval)**：一个全链路 RAG 检索模型微调、推理和蒸馏代码库。[[blog]](https://zhuanlan.zhihu.com/p/987727357)
 </details>
@ -696,7 +725,7 @@ run_name: test_run # 可选
 本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。
-使用模型权重时，请遵循对应的模型协议：[Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [InternLM2](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
+使用模型权重时，请遵循对应的模型协议：[Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [Index](https://huggingface.co/IndexTeam/Index-1.9B/blob/main/LICENSE) / [InternLM2](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
 ## 引用
--- a/assets/wechat.jpg
+++ b/assets/wechat.jpg
--- a/assets/wechat_npu.jpg
+++ b/assets/wechat_npu.jpg
--- a/data/belle_multiturn/belle_multiturn.py
+++ b/data/belle_multiturn/belle_multiturn.py
@ -17,9 +17,9 @@ _CITATION = """\
 }
 """
-_HOMEPAGE = "{}/datasets/BelleGroup/multiturn_chat_0.8M".format(_HF_ENDPOINT)
+_HOMEPAGE = f"{_HF_ENDPOINT}/datasets/BelleGroup/multiturn_chat_0.8M"
 _LICENSE = "gpl-3.0"
-_URL = "{}/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0.8M.json".format(_HF_ENDPOINT)
+_URL = f"{_HF_ENDPOINT}/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0.8M.json"
 class BelleMultiturn(datasets.GeneratorBasedBuilder):
@ -38,7 +38,7 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": file_path})]
    def _generate_examples(self, filepath: str):
-        with open(filepath, "r", encoding="utf-8") as f:
+        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                conversations = []
--- a/data/dataset_info.json
+++ b/data/dataset_info.json
@ -54,7 +54,8 @@
  },
  "alpaca_en": {
    "hf_hub_url": "llamafactory/alpaca_en",
-    "ms_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",
@ -66,7 +67,8 @@
  },
  "alpaca_gpt4_zh": {
    "hf_hub_url": "llamafactory/alpaca_gpt4_zh",
-    "ms_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",
--- a/data/hh_rlhf_en/hh_rlhf_en.py
+++ b/data/hh_rlhf_en/hh_rlhf_en.py
@ -8,9 +8,9 @@ import datasets
 _HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
 _DESCRIPTION = "Human preference data about helpfulness and harmlessness."
 _CITATION = ""
-_HOMEPAGE = "{}/datasets/Anthropic/hh-rlhf".format(_HF_ENDPOINT)
+_HOMEPAGE = f"{_HF_ENDPOINT}/datasets/Anthropic/hh-rlhf"
 _LICENSE = "mit"
-_URL = "{}/datasets/Anthropic/hh-rlhf/resolve/main/".format(_HF_ENDPOINT)
+_URL = f"{_HF_ENDPOINT}/datasets/Anthropic/hh-rlhf/resolve/main/"
 _URLS = {
    "train": [
        _URL + "harmless-base/train.jsonl.gz",
@ -53,7 +53,7 @@ class HhRlhfEn(datasets.GeneratorBasedBuilder):
    def _generate_examples(self, filepaths: List[str]):
        key = 0
        for filepath in filepaths:
-            with open(filepath, "r", encoding="utf-8") as f:
+            with open(filepath, encoding="utf-8") as f:
                for row in f:
                    data = json.loads(row)
                    chosen = data["chosen"]
--- a/data/ultra_chat/ultra_chat.py
+++ b/data/ultra_chat/ultra_chat.py
@ -20,9 +20,9 @@ _CITATION = """\
 }
 """
-_HOMEPAGE = "{}/datasets/stingning/ultrachat".format(_HF_ENDPOINT)
+_HOMEPAGE = f"{_HF_ENDPOINT}/datasets/stingning/ultrachat"
 _LICENSE = "cc-by-nc-4.0"
-_BASE_DATA_URL = "{}/datasets/stingning/ultrachat/resolve/main/train_{{idx}}.jsonl".format(_HF_ENDPOINT)
+_BASE_DATA_URL = f"{_HF_ENDPOINT}/datasets/stingning/ultrachat/resolve/main/train_{{idx}}.jsonl"
 class UltraChat(datasets.GeneratorBasedBuilder):
@ -42,7 +42,7 @@ class UltraChat(datasets.GeneratorBasedBuilder):
    def _generate_examples(self, filepaths: List[str]):
        for filepath in filepaths:
-            with open(filepath, "r", encoding="utf-8") as f:
+            with open(filepath, encoding="utf-8") as f:
                for row in f:
                    try:
                        data = json.loads(row)
--- a/docker/docker-cuda/Dockerfile
+++ b/docker/docker-cuda/Dockerfile
@ -1,6 +1,7 @@
-# Use the NVIDIA official image with PyTorch 2.3.0
+# Default use the NVIDIA official image with PyTorch 2.3.0
-# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-02.html
+# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html
-FROM nvcr.io/nvidia/pytorch:24.02-py3
+ARG BASE_IMAGE=nvcr.io/nvidia/pytorch:24.02-py3
 FROM ${BASE_IMAGE}
 # Define environments
 ENV MAX_JOBS=4
@ -12,6 +13,9 @@ ARG INSTALL_BNB=false
 ARG INSTALL_VLLM=false
 ARG INSTALL_DEEPSPEED=false
 ARG INSTALL_FLASHATTN=false
 ARG INSTALL_LIGER_KERNEL=false
 ARG INSTALL_HQQ=false
 ARG INSTALL_EETQ=false
 ARG PIP_INDEX=https://pypi.org/simple
 # Set the working directory
@ -38,6 +42,15 @@ RUN EXTRA_PACKAGES="metrics"; \
    if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
        EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
    fi; \
    if [ "$INSTALL_LIGER_KERNEL" == "true" ]; then \
        EXTRA_PACKAGES="${EXTRA_PACKAGES},liger-kernel"; \
    fi; \
    if [ "$INSTALL_HQQ" == "true" ]; then \
        EXTRA_PACKAGES="${EXTRA_PACKAGES},hqq"; \
    fi; \
    if [ "$INSTALL_EETQ" == "true" ]; then \
        EXTRA_PACKAGES="${EXTRA_PACKAGES},eetq"; \
    fi; \
    pip install -e ".[$EXTRA_PACKAGES]"
 # Rebuild flash attention
--- a/docker/docker-cuda/docker-compose.yml
+++ b/docker/docker-cuda/docker-compose.yml
@ -8,11 +8,15 @@ services:
        INSTALL_VLLM: false
        INSTALL_DEEPSPEED: false
        INSTALL_FLASHATTN: false
        INSTALL_LIGER_KERNEL: false
        INSTALL_HQQ: false
        INSTALL_EETQ: false
        PIP_INDEX: https://pypi.org/simple
    container_name: llamafactory
    volumes:
      - ../../hf_cache:/root/.cache/huggingface
      - ../../ms_cache:/root/.cache/modelscope
      - ../../om_cache:/root/.cache/openmind
      - ../../data:/app/data
      - ../../output:/app/output
    ports:
--- a/docker/docker-npu/docker-compose.yml
+++ b/docker/docker-npu/docker-compose.yml
@ -10,6 +10,7 @@ services:
    volumes:
      - ../../hf_cache:/root/.cache/huggingface
      - ../../ms_cache:/root/.cache/modelscope
      - ../../om_cache:/root/.cache/openmind
      - ../../data:/app/data
      - ../../output:/app/output
      - /usr/local/dcmi:/usr/local/dcmi
--- a/docker/docker-rocm/Dockerfile
+++ b/docker/docker-rocm/Dockerfile
@ -10,6 +10,8 @@ ARG INSTALL_BNB=false
 ARG INSTALL_VLLM=false
 ARG INSTALL_DEEPSPEED=false
 ARG INSTALL_FLASHATTN=false
 ARG INSTALL_LIGER_KERNEL=false
 ARG INSTALL_HQQ=false
 ARG PIP_INDEX=https://pypi.org/simple
 # Set the working directory
@ -36,6 +38,12 @@ RUN EXTRA_PACKAGES="metrics"; \
    if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
        EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
    fi; \
    if [ "$INSTALL_LIGER_KERNEL" == "true" ]; then \
        EXTRA_PACKAGES="${EXTRA_PACKAGES},liger-kernel"; \
    fi; \
    if [ "$INSTALL_HQQ" == "true" ]; then \
        EXTRA_PACKAGES="${EXTRA_PACKAGES},hqq"; \
    fi; \
    pip install -e ".[$EXTRA_PACKAGES]"
 # Rebuild flash attention
--- a/docker/docker-rocm/docker-compose.yml
+++ b/docker/docker-rocm/docker-compose.yml
@ -8,11 +8,14 @@ services:
        INSTALL_VLLM: false
        INSTALL_DEEPSPEED: false
        INSTALL_FLASHATTN: false
        INSTALL_LIGER_KERNEL: false
        INSTALL_HQQ: false
        PIP_INDEX: https://pypi.org/simple
    container_name: llamafactory
    volumes:
      - ../../hf_cache:/root/.cache/huggingface
      - ../../ms_cache:/root/.cache/modelscope
      - ../../om_cache:/root/.cache/openmind
      - ../../data:/app/data
      - ../../output:/app/output
      - ../../saves:/app/saves
--- a/evaluation/mmlu/mmlu.py
+++ b/evaluation/mmlu/mmlu.py
@ -158,5 +158,4 @@ class MMLU(datasets.GeneratorBasedBuilder):
        df = pd.read_csv(filepath, header=None)
        df.columns = ["question", "A", "B", "C", "D", "answer"]
-        for i, instance in enumerate(df.to_dict(orient="records")):
+        yield from enumerate(df.to_dict(orient="records"))
            yield i, instance
--- a/examples/README.md
+++ b/examples/README.md
@ -89,8 +89,8 @@ llamafactory-cli train examples/train_lora/llama3_lora_predict.yaml
 #### Supervised Fine-Tuning on Multiple Nodes
 ```bash
-FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
-FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
 ```
 #### Supervised Fine-Tuning with DeepSpeed ZeRO-3 (Weight Sharding)
--- a/examples/README_zh.md
+++ b/examples/README_zh.md
@ -89,8 +89,8 @@ llamafactory-cli train examples/train_lora/llama3_lora_predict.yaml
 #### 多机指令监督微调
 ```bash
-FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
-FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
 ```
 #### 使用 DeepSpeed ZeRO-3 平均分配显存
--- a/requirements.txt
+++ b/requirements.txt
@ -1,9 +1,9 @@
-transformers>=4.41.2,<=4.45.0
+transformers>=4.41.2,<=4.46.1
-datasets>=2.16.0,<=2.21.0
+datasets>=2.16.0,<=3.0.2
-accelerate>=0.30.1,<=0.34.2
+accelerate>=0.34.0,<=1.0.1
 peft>=0.11.1,<=0.12.0
 trl>=0.8.6,<=0.9.6
-gradio>=4.0.0
+gradio>=4.0.0,<5.0.0
 pandas>=2.0.0
 scipy
 einops
@ -19,3 +19,4 @@ fire
 packaging
 pyyaml
 numpy<2.0.0
 av
--- a/scripts/cal_flops.py
+++ b/scripts/cal_flops.py
@ -1,4 +1,3 @@
 # coding=utf-8
 # Copyright 2024 Microsoft Corporation and the LlamaFactory team.
 #
 # This code is inspired by the Microsoft's DeepSpeed library.
--- a/scripts/cal_lr.py
+++ b/scripts/cal_lr.py
@ -1,4 +1,3 @@
 # coding=utf-8
 # Copyright 2024 imoneoi and the LlamaFactory team.
 #
 # This code is inspired by the imoneoi's OpenChat library.
@ -74,7 +73,7 @@ def calculate_lr(
    elif stage == "sft":
        data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX)
    else:
-        raise NotImplementedError("Stage does not supported: {}.".format(stage))
+        raise NotImplementedError(f"Stage does not supported: {stage}.")
    dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
    valid_tokens, total_tokens = 0, 0
--- a/scripts/cal_mfu.py
+++ b/scripts/cal_mfu.py
@ -1,4 +1,3 @@
 # coding=utf-8
 # Copyright 2024 the LlamaFactory team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@ -100,7 +99,7 @@ def compute_device_flops(world_size: int) -> float:
    elif "4090" in device_name:
        return 98 * 1e12 * world_size
    else:
-        raise NotImplementedError("Device not supported: {}.".format(device_name))
+        raise NotImplementedError(f"Device not supported: {device_name}.")
 def calculate_mfu(
@ -140,10 +139,10 @@ def calculate_mfu(
        "bf16": True,
    }
    if deepspeed_stage in [2, 3]:
-        args["deepspeed"] = "examples/deepspeed/ds_z{}_config.json".format(deepspeed_stage)
+        args["deepspeed"] = f"examples/deepspeed/ds_z{deepspeed_stage}_config.json"
    run_exp(args)
-    with open(os.path.join("saves", "test_mfu", "all_results.json"), "r", encoding="utf-8") as f:
+    with open(os.path.join("saves", "test_mfu", "all_results.json"), encoding="utf-8") as f:
        result = json.load(f)
    if dist.is_initialized():
@ -157,7 +156,7 @@ def calculate_mfu(
        * compute_model_flops(model_name_or_path, total_batch_size, seq_length)
        / compute_device_flops(world_size)
    )
-    print("MFU: {:.2f}%".format(mfu_value * 100))
+    print(f"MFU: {mfu_value * 100:.2f}%")
 if __name__ == "__main__":
--- a/scripts/cal_ppl.py
+++ b/scripts/cal_ppl.py
@ -1,4 +1,3 @@
 # coding=utf-8
 # Copyright 2024 the LlamaFactory team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@ -100,7 +99,7 @@ def calculate_ppl(
            tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX, train_on_prompt=train_on_prompt
        )
    else:
-        raise NotImplementedError("Stage does not supported: {}.".format(stage))
+        raise NotImplementedError(f"Stage does not supported: {stage}.")
    dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
    criterion = torch.nn.CrossEntropyLoss(reduction="none")
@ -125,8 +124,8 @@ def calculate_ppl(
    with open(save_name, "w", encoding="utf-8") as f:
        json.dump(perplexities, f, indent=2)
-    print("Average perplexity is {:.2f}".format(total_ppl / len(perplexities)))
+    print(f"Average perplexity is {total_ppl / len(perplexities):.2f}")
-    print("Perplexities have been saved at {}.".format(save_name))
+    print(f"Perplexities have been saved at {save_name}.")
 if __name__ == "__main__":
--- a/scripts/length_cdf.py
+++ b/scripts/length_cdf.py
@ -1,4 +1,3 @@
 # coding=utf-8
 # Copyright 2024 the LlamaFactory team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@ -61,7 +60,7 @@ def length_cdf(
    for length, count in length_tuples:
        count_accu += count
        prob_accu += count / total_num * 100
-        print("{:d} ({:.2f}%) samples have length < {}.".format(count_accu, prob_accu, length + interval))
+        print(f"{count_accu:d} ({prob_accu:.2f}%) samples have length < {length + interval}.")
 if __name__ == "__main__":
--- a/scripts/llama_pro.py
+++ b/scripts/llama_pro.py
@ -1,4 +1,3 @@
 # coding=utf-8
 # Copyright 2024 Tencent Inc. and the LlamaFactory team.
 #
 # This code is inspired by the Tencent's LLaMA-Pro library.
@ -40,7 +39,7 @@ if TYPE_CHECKING:
 def change_name(name: str, old_index: int, new_index: int) -> str:
-    return name.replace(".{:d}.".format(old_index), ".{:d}.".format(new_index))
+    return name.replace(f".{old_index:d}.", f".{new_index:d}.")
 def block_expansion(
@ -76,27 +75,27 @@ def block_expansion(
    state_dict = model.state_dict()
    if num_layers % num_expand != 0:
-        raise ValueError("`num_layers` {} should be divisible by `num_expand` {}.".format(num_layers, num_expand))
+        raise ValueError(f"`num_layers` {num_layers} should be divisible by `num_expand` {num_expand}.")
    split = num_layers // num_expand
    layer_cnt = 0
    output_state_dict = OrderedDict()
    for i in range(num_layers):
        for key, value in state_dict.items():
-            if ".{:d}.".format(i) in key:
+            if f".{i:d}." in key:
                output_state_dict[change_name(key, i, layer_cnt)] = value
-        print("Add layer {} copied from layer {}".format(layer_cnt, i))
+        print(f"Add layer {layer_cnt} copied from layer {i}")
        layer_cnt += 1
        if (i + 1) % split == 0:
            for key, value in state_dict.items():
-                if ".{:d}.".format(i) in key:
+                if f".{i:d}." in key:
                    if "down_proj" in key or "o_proj" in key:
                        output_state_dict[change_name(key, i, layer_cnt)] = torch.zeros_like(value)
                    else:
                        output_state_dict[change_name(key, i, layer_cnt)] = torch.clone(value)
-            print("Add layer {} expanded from layer {}".format(layer_cnt, i))
+            print(f"Add layer {layer_cnt} expanded from layer {i}")
            layer_cnt += 1
    for key, value in state_dict.items():
@ -113,17 +112,17 @@ def block_expansion(
            torch.save(shard, os.path.join(output_dir, shard_file))
    if index is None:
-        print("Model weights saved in {}".format(os.path.join(output_dir, weights_name)))
+        print(f"Model weights saved in {os.path.join(output_dir, weights_name)}")
    else:
        index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
        with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
            json.dump(index, f, indent=2, sort_keys=True)
-        print("Model weights saved in {}".format(output_dir))
+        print(f"Model weights saved in {output_dir}")
    print("- Fine-tune this model with:")
-    print("model_name_or_path: {}".format(output_dir))
+    print(f"model_name_or_path: {output_dir}")
    print("finetuning_type: freeze")
-    print("freeze_trainable_layers: {}".format(num_expand))
+    print(f"freeze_trainable_layers: {num_expand}")
    print("use_llama_pro: true")
--- a/scripts/llamafy_baichuan2.py
+++ b/scripts/llamafy_baichuan2.py
@ -1,4 +1,3 @@
 # coding=utf-8
 # Copyright 2024 the LlamaFactory team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@ -63,16 +62,16 @@ def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetenso
            torch.save(shard, os.path.join(output_dir, shard_file))
    if index is None:
-        print("Model weights saved in {}".format(os.path.join(output_dir, WEIGHTS_NAME)))
+        print(f"Model weights saved in {os.path.join(output_dir, WEIGHTS_NAME)}")
    else:
        index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
        with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
            json.dump(index, f, indent=2, sort_keys=True)
-        print("Model weights saved in {}".format(output_dir))
+        print(f"Model weights saved in {output_dir}")
 def save_config(input_dir: str, output_dir: str):
-    with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
+    with open(os.path.join(input_dir, CONFIG_NAME), encoding="utf-8") as f:
        llama2_config_dict: Dict[str, Any] = json.load(f)
    llama2_config_dict["architectures"] = ["LlamaForCausalLM"]
@ -82,7 +81,7 @@ def save_config(input_dir: str, output_dir: str):
    with open(os.path.join(output_dir, CONFIG_NAME), "w", encoding="utf-8") as f:
        json.dump(llama2_config_dict, f, indent=2)
-    print("Model config saved in {}".format(os.path.join(output_dir, CONFIG_NAME)))
+    print(f"Model config saved in {os.path.join(output_dir, CONFIG_NAME)}")
 def llamafy_baichuan2(
--- a/scripts/llamafy_qwen.py
+++ b/scripts/llamafy_qwen.py
@ -1,4 +1,3 @@
 # coding=utf-8
 # Copyright 2024 the LlamaFactory team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@ -86,7 +85,7 @@ def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetenso
            elif "lm_head" in key:
                llama2_state_dict[key] = value
            else:
-                raise KeyError("Unable to process key {}".format(key))
+                raise KeyError(f"Unable to process key {key}")
    weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
    shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=weights_name)
@ -98,18 +97,18 @@ def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetenso
            torch.save(shard, os.path.join(output_dir, shard_file))
    if index is None:
-        print("Model weights saved in {}".format(os.path.join(output_dir, weights_name)))
+        print(f"Model weights saved in {os.path.join(output_dir, weights_name)}")
    else:
        index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
        with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
            json.dump(index, f, indent=2, sort_keys=True)
-        print("Model weights saved in {}".format(output_dir))
+        print(f"Model weights saved in {output_dir}")
    return str(torch_dtype).replace("torch.", "")
 def save_config(input_dir: str, output_dir: str, torch_dtype: str):
-    with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
+    with open(os.path.join(input_dir, CONFIG_NAME), encoding="utf-8") as f:
        qwen_config_dict: Dict[str, Any] = json.load(f)
    llama2_config_dict: Dict[str, Any] = OrderedDict()
@ -135,7 +134,7 @@ def save_config(input_dir: str, output_dir: str, torch_dtype: str):
    with open(os.path.join(output_dir, CONFIG_NAME), "w", encoding="utf-8") as f:
        json.dump(llama2_config_dict, f, indent=2)
-    print("Model config saved in {}".format(os.path.join(output_dir, CONFIG_NAME)))
+    print(f"Model config saved in {os.path.join(output_dir, CONFIG_NAME)}")
 def llamafy_qwen(
--- a/scripts/loftq_init.py
+++ b/scripts/loftq_init.py
@ -1,4 +1,3 @@
 # coding=utf-8
 # Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is based on the HuggingFace's PEFT library.
@ -70,19 +69,19 @@ def quantize_loftq(
    setattr(peft_model.peft_config["default"], "base_model_name_or_path", os.path.abspath(output_dir))
    setattr(peft_model.peft_config["default"], "init_lora_weights", True)  # don't apply loftq again
    peft_model.save_pretrained(loftq_dir, safe_serialization=save_safetensors)
-    print("Adapter weights saved in {}".format(loftq_dir))
+    print(f"Adapter weights saved in {loftq_dir}")
    # Save base model
    base_model: "PreTrainedModel" = peft_model.unload()
    base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
    tokenizer.save_pretrained(output_dir)
-    print("Model weights saved in {}".format(output_dir))
+    print(f"Model weights saved in {output_dir}")
    print("- Fine-tune this model with:")
-    print("model_name_or_path: {}".format(output_dir))
+    print(f"model_name_or_path: {output_dir}")
-    print("adapter_name_or_path: {}".format(loftq_dir))
+    print(f"adapter_name_or_path: {loftq_dir}")
    print("finetuning_type: lora")
-    print("quantization_bit: {}".format(loftq_bits))
+    print(f"quantization_bit: {loftq_bits}")
 if __name__ == "__main__":
--- a/scripts/pissa_init.py
+++ b/scripts/pissa_init.py
@ -1,4 +1,3 @@
 # coding=utf-8
 # Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is based on the HuggingFace's PEFT library.
@ -54,7 +53,7 @@ def quantize_pissa(
        lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
        lora_dropout=lora_dropout,
        target_modules=lora_target,
-        init_lora_weights="pissa" if pissa_iter == -1 else "pissa_niter_{}".format(pissa_iter),
+        init_lora_weights="pissa" if pissa_iter == -1 else f"pissa_niter_{pissa_iter}",
    )
    # Init PiSSA model
@ -65,17 +64,17 @@ def quantize_pissa(
    setattr(peft_model.peft_config["default"], "base_model_name_or_path", os.path.abspath(output_dir))
    setattr(peft_model.peft_config["default"], "init_lora_weights", True)  # don't apply pissa again
    peft_model.save_pretrained(pissa_dir, safe_serialization=save_safetensors)
-    print("Adapter weights saved in {}".format(pissa_dir))
+    print(f"Adapter weights saved in {pissa_dir}")
    # Save base model
    base_model: "PreTrainedModel" = peft_model.unload()
    base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
    tokenizer.save_pretrained(output_dir)
-    print("Model weights saved in {}".format(output_dir))
+    print(f"Model weights saved in {output_dir}")
    print("- Fine-tune this model with:")
-    print("model_name_or_path: {}".format(output_dir))
+    print(f"model_name_or_path: {output_dir}")
-    print("adapter_name_or_path: {}".format(pissa_dir))
+    print(f"adapter_name_or_path: {pissa_dir}")
    print("finetuning_type: lora")
    print("pissa_init: false")
    print("pissa_convert: true")
--- a/scripts/test_image.py
+++ b/scripts/test_image.py
@ -0,0 +1,65 @@
 # Copyright 2024 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 os
 from openai import OpenAI
 from transformers.utils.versions import require_version
 require_version("openai>=1.5.0", "To fix: pip install openai>=1.5.0")
 def main():
    client = OpenAI(
        api_key="{}".format(os.environ.get("API_KEY", "0")),
        base_url="http://localhost:{}/v1".format(os.environ.get("API_PORT", 8000)),
    )
    messages = []
    messages.append(
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Output the color and number of each box."},
                {
                    "type": "image_url",
                    "image_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/boxes.png"},
                },
            ],
        }
    )
    result = client.chat.completions.create(messages=messages, model="test")
    messages.append(result.choices[0].message)
    print("Round 1:", result.choices[0].message.content)
    # The image shows a pyramid of colored blocks with numbers on them. Here are the colors and numbers of ...
    messages.append(
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What kind of flower is this?"},
                {
                    "type": "image_url",
                    "image_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/flowers.jpg"},
                },
            ],
        }
    )
    result = client.chat.completions.create(messages=messages, model="test")
    messages.append(result.choices[0].message)
    print("Round 2:", result.choices[0].message.content)
    # The image shows a cluster of forget-me-not flowers. Forget-me-nots are small ...
 if __name__ == "__main__":
    main()
--- a/scripts/test_toolcall.py
+++ b/scripts/test_toolcall.py
@ -1,4 +1,3 @@
 # coding=utf-8
 # Copyright 2024 the LlamaFactory team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
--- a/setup.py
+++ b/setup.py
@ -20,7 +20,7 @@ from setuptools import find_packages, setup
 def get_version() -> str:
-    with open(os.path.join("src", "llamafactory", "extras", "env.py"), "r", encoding="utf-8") as f:
+    with open(os.path.join("src", "llamafactory", "extras", "env.py"), encoding="utf-8") as f:
        file_content = f.read()
        pattern = r"{}\W*=\W*\"([^\"]+)\"".format("VERSION")
        (version,) = re.findall(pattern, file_content)
@ -28,7 +28,7 @@ def get_version() -> str:
 def get_requires() -> List[str]:
-    with open("requirements.txt", "r", encoding="utf-8") as f:
+    with open("requirements.txt", encoding="utf-8") as f:
        file_content = f.read()
        lines = [line.strip() for line in file_content.strip().split("\n") if not line.startswith("#")]
        return lines
@ -54,13 +54,14 @@ extra_require = {
    "gptq": ["optimum>=1.17.0", "auto-gptq>=0.5.0"],
    "awq": ["autoawq"],
    "aqlm": ["aqlm[gpu]>=1.1.0"],
-    "vllm": ["vllm>=0.4.3,<=0.6.0"],
+    "vllm": ["vllm>=0.4.3,<=0.6.3"],
    "galore": ["galore-torch"],
    "badam": ["badam>=1.2.1"],
    "adam-mini": ["adam-mini"],
    "qwen": ["transformers_stream_generator"],
    "modelscope": ["modelscope"],
-    "dev": ["ruff", "pytest"],
+    "openmind": ["openmind"],
    "dev": ["pre-commit", "ruff", "pytest"],
 }
@ -71,7 +72,7 @@ def main():
        author="hiyouga",
        author_email="hiyouga" "@" "buaa.edu.cn",
        description="Easy-to-use LLM fine-tuning framework",
-        long_description=open("README.md", "r", encoding="utf-8").read(),
+        long_description=open("README.md", encoding="utf-8").read(),
        long_description_content_type="text/markdown",
        keywords=["LLaMA", "BLOOM", "Falcon", "LLM", "ChatGPT", "transformer", "pytorch", "deep learning"],
        license="Apache 2.0 License",
--- a/src/api.py
+++ b/src/api.py
@ -23,9 +23,9 @@ from llamafactory.chat import ChatModel
 def main():
    chat_model = ChatModel()
    app = create_app(chat_model)
-    api_host = os.environ.get("API_HOST", "0.0.0.0")
+    api_host = os.getenv("API_HOST", "0.0.0.0")
-    api_port = int(os.environ.get("API_PORT", "8000"))
+    api_port = int(os.getenv("API_PORT", "8000"))
-    print("Visit http://localhost:{}/docs for API document.".format(api_port))
+    print(f"Visit http://localhost:{api_port}/docs for API document.")
    uvicorn.run(app, host=api_host, port=api_port)
--- a/src/llamafactory/init.py
+++ b/src/llamafactory/init.py
@ -20,17 +20,17 @@ Level:
 Dependency graph:
  main:
-    transformers>=4.41.2,<=4.45.0
+    transformers>=4.41.2,<=4.46.1
-    datasets>=2.16.0,<=2.21.0
+    datasets>=2.16.0,<=3.0.2
-    accelerate>=0.30.1,<=0.34.2
+    accelerate>=0.34.0,<=1.0.1
    peft>=0.11.1,<=0.12.0
    trl>=0.8.6,<=0.9.6
  attention:
    transformers>=4.42.4 (gemma+fa2)
  longlora:
-    transformers>=4.41.2,<=4.45.0
+    transformers>=4.41.2,<=4.46.1
  packing:
-    transformers>=4.41.2,<=4.45.0
+    transformers>=4.41.2,<=4.46.1
 Disable version checking: DISABLE_VERSION_CHECK=1
 Enable VRAM recording: RECORD_VRAM=1
@ -38,6 +38,7 @@ Force check imports: FORCE_CHECK_IMPORTS=1
 Force using torchrun: FORCE_TORCHRUN=1
 Set logging verbosity: LLAMAFACTORY_VERBOSITY=WARN
 Use modelscope: USE_MODELSCOPE_HUB=1
 Use openmind: USE_OPENMIND_HUB=1
 """
 from .extras.env import VERSION
--- a/src/llamafactory/api/app.py
+++ b/src/llamafactory/api/app.py
@ -68,7 +68,7 @@ async def lifespan(app: "FastAPI", chat_model: "ChatModel"):  # collects GPU mem
 def create_app(chat_model: "ChatModel") -> "FastAPI":
-    root_path = os.environ.get("FASTAPI_ROOT_PATH", "")
+    root_path = os.getenv("FASTAPI_ROOT_PATH", "")
    app = FastAPI(lifespan=partial(lifespan, chat_model=chat_model), root_path=root_path)
    app.add_middleware(
        CORSMiddleware,
@ -77,7 +77,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
        allow_methods=["*"],
        allow_headers=["*"],
    )
-    api_key = os.environ.get("API_KEY", None)
+    api_key = os.getenv("API_KEY")
    security = HTTPBearer(auto_error=False)
    async def verify_api_key(auth: Annotated[Optional[HTTPAuthorizationCredentials], Depends(security)]):
@ -91,7 +91,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
        dependencies=[Depends(verify_api_key)],
    )
    async def list_models():
-        model_card = ModelCard(id=os.environ.get("API_MODEL_NAME", "gpt-3.5-turbo"))
+        model_card = ModelCard(id=os.getenv("API_MODEL_NAME", "gpt-3.5-turbo"))
        return ModelList(data=[model_card])
    @app.post(
@ -128,7 +128,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
 def run_api() -> None:
    chat_model = ChatModel()
    app = create_app(chat_model)
-    api_host = os.environ.get("API_HOST", "0.0.0.0")
+    api_host = os.getenv("API_HOST", "0.0.0.0")
-    api_port = int(os.environ.get("API_PORT", "8000"))
+    api_port = int(os.getenv("API_PORT", "8000"))
-    print("Visit http://localhost:{}/docs for API document.".format(api_port))
+    print(f"Visit http://localhost:{api_port}/docs for API document.")
    uvicorn.run(app, host=api_host, port=api_port)
--- a/src/llamafactory/api/chat.py
+++ b/src/llamafactory/api/chat.py
@ -21,7 +21,7 @@ import uuid
 from typing import TYPE_CHECKING, AsyncGenerator, Dict, List, Optional, Tuple
 from ..data import Role as DataRole
-from ..extras.logging import get_logger
+from ..extras import logging
 from ..extras.packages import is_fastapi_available, is_pillow_available, is_requests_available
 from .common import dictify, jsonify
 from .protocol import (
@ -57,7 +57,7 @@ if TYPE_CHECKING:
    from .protocol import ChatCompletionRequest, ScoreEvaluationRequest
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 ROLE_MAPPING = {
    Role.USER: DataRole.USER.value,
    Role.ASSISTANT: DataRole.ASSISTANT.value,
@ -69,8 +69,8 @@ ROLE_MAPPING = {
 def _process_request(
    request: "ChatCompletionRequest",
-) -> Tuple[List[Dict[str, str]], Optional[str], Optional[str], Optional["ImageInput"]]:
+) -> Tuple[List[Dict[str, str]], Optional[str], Optional[str], Optional[List["ImageInput"]]]:
-    logger.info("==== request ====\n{}".format(json.dumps(dictify(request), indent=2, ensure_ascii=False)))
+    logger.info_rank0(f"==== request ====\n{json.dumps(dictify(request), indent=2, ensure_ascii=False)}")
    if len(request.messages) == 0:
        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid length")
@ -84,7 +84,7 @@ def _process_request(
        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
    input_messages = []
-    image = None
+    images = []
    for i, message in enumerate(request.messages):
        if i % 2 == 0 and message.role not in [Role.USER, Role.TOOL]:
            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
@ -111,7 +111,7 @@ def _process_request(
                    else:  # web uri
                        image_stream = requests.get(image_url, stream=True).raw
-                    image = Image.open(image_stream).convert("RGB")
+                    images.append(Image.open(image_stream).convert("RGB"))
        else:
            input_messages.append({"role": ROLE_MAPPING[message.role], "content": message.content})
@ -124,7 +124,7 @@ def _process_request(
    else:
        tools = None
-    return input_messages, system, tools, image
+    return input_messages, system, tools, images or None
 def _create_stream_chat_completion_chunk(
@ -142,13 +142,13 @@ def _create_stream_chat_completion_chunk(
 async def create_chat_completion_response(
    request: "ChatCompletionRequest", chat_model: "ChatModel"
 ) -> "ChatCompletionResponse":
-    completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
+    completion_id = f"chatcmpl-{uuid.uuid4().hex}"
-    input_messages, system, tools, image = _process_request(request)
+    input_messages, system, tools, images = _process_request(request)
    responses = await chat_model.achat(
        input_messages,
        system,
        tools,
-        image,
+        images,
        do_sample=request.do_sample,
        temperature=request.temperature,
        top_p=request.top_p,
@ -169,7 +169,7 @@ async def create_chat_completion_response(
            tool_calls = []
            for tool in result:
                function = Function(name=tool[0], arguments=tool[1])
-                tool_calls.append(FunctionCall(id="call_{}".format(uuid.uuid4().hex), function=function))
+                tool_calls.append(FunctionCall(id=f"call_{uuid.uuid4().hex}", function=function))
            response_message = ChatCompletionMessage(role=Role.ASSISTANT, tool_calls=tool_calls)
            finish_reason = Finish.TOOL
@ -193,8 +193,8 @@ async def create_chat_completion_response(
 async def create_stream_chat_completion_response(
    request: "ChatCompletionRequest", chat_model: "ChatModel"
 ) -> AsyncGenerator[str, None]:
-    completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
+    completion_id = f"chatcmpl-{uuid.uuid4().hex}"
-    input_messages, system, tools, image = _process_request(request)
+    input_messages, system, tools, images = _process_request(request)
    if tools:
        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
@ -208,7 +208,7 @@ async def create_stream_chat_completion_response(
        input_messages,
        system,
        tools,
-        image,
+        images,
        do_sample=request.do_sample,
        temperature=request.temperature,
        top_p=request.top_p,
@ -229,7 +229,7 @@ async def create_stream_chat_completion_response(
 async def create_score_evaluation_response(
    request: "ScoreEvaluationRequest", chat_model: "ChatModel"
 ) -> "ScoreEvaluationResponse":
-    score_id = "scoreval-{}".format(uuid.uuid4().hex)
+    score_id = f"scoreval-{uuid.uuid4().hex}"
    if len(request.messages) == 0:
        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")
--- a/src/llamafactory/chat/base_engine.py
+++ b/src/llamafactory/chat/base_engine.py
@ -66,8 +66,8 @@ class BaseEngine(ABC):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        **input_kwargs,
    ) -> List["Response"]:
        r"""
@ -81,8 +81,8 @@ class BaseEngine(ABC):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        **input_kwargs,
    ) -> AsyncGenerator[str, None]:
        r"""
--- a/src/llamafactory/chat/chat_model.py
+++ b/src/llamafactory/chat/chat_model.py
@ -53,7 +53,7 @@ class ChatModel:
        elif model_args.infer_backend == "vllm":
            self.engine: "BaseEngine" = VllmEngine(model_args, data_args, finetuning_args, generating_args)
        else:
-            raise NotImplementedError("Unknown backend: {}".format(model_args.infer_backend))
+            raise NotImplementedError(f"Unknown backend: {model_args.infer_backend}")
        self._loop = asyncio.new_event_loop()
        self._thread = Thread(target=_start_background_loop, args=(self._loop,), daemon=True)
@ -64,15 +64,15 @@ class ChatModel:
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        **input_kwargs,
    ) -> List["Response"]:
        r"""
        Gets a list of responses of the chat model.
        """
        task = asyncio.run_coroutine_threadsafe(
-            self.achat(messages, system, tools, image, video, **input_kwargs), self._loop
+            self.achat(messages, system, tools, images, videos, **input_kwargs), self._loop
        )
        return task.result()
@ -81,28 +81,28 @@ class ChatModel:
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        **input_kwargs,
    ) -> List["Response"]:
        r"""
        Asynchronously gets a list of responses of the chat model.
        """
-        return await self.engine.chat(messages, system, tools, image, video, **input_kwargs)
+        return await self.engine.chat(messages, system, tools, images, videos, **input_kwargs)
    def stream_chat(
        self,
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        **input_kwargs,
    ) -> Generator[str, None, None]:
        r"""
        Gets the response token-by-token of the chat model.
        """
-        generator = self.astream_chat(messages, system, tools, image, video, **input_kwargs)
+        generator = self.astream_chat(messages, system, tools, images, videos, **input_kwargs)
        while True:
            try:
                task = asyncio.run_coroutine_threadsafe(generator.__anext__(), self._loop)
@ -115,14 +115,14 @@ class ChatModel:
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        **input_kwargs,
    ) -> AsyncGenerator[str, None]:
        r"""
        Asynchronously gets the response token-by-token of the chat model.
        """
-        async for new_token in self.engine.stream_chat(messages, system, tools, image, video, **input_kwargs):
+        async for new_token in self.engine.stream_chat(messages, system, tools, images, videos, **input_kwargs):
            yield new_token
    def get_scores(
--- a/src/llamafactory/chat/hf_engine.py
+++ b/src/llamafactory/chat/hf_engine.py
@ -23,8 +23,8 @@ from transformers import GenerationConfig, TextIteratorStreamer
 from typing_extensions import override
 from ..data import get_template_and_fix_tokenizer
 from ..extras import logging
 from ..extras.constants import IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
 from ..extras.logging import get_logger
 from ..extras.misc import get_logits_processor
 from ..model import load_model, load_tokenizer
 from .base_engine import BaseEngine, Response
@ -39,7 +39,7 @@ if TYPE_CHECKING:
    from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 class HuggingfaceEngine(BaseEngine):
@ -63,11 +63,11 @@ class HuggingfaceEngine(BaseEngine):
        try:
            asyncio.get_event_loop()
        except RuntimeError:
-            logger.warning("There is no current event loop, creating a new one.")
+            logger.warning_once("There is no current event loop, creating a new one.")
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)
-        self.semaphore = asyncio.Semaphore(int(os.environ.get("MAX_CONCURRENT", "1")))
+        self.semaphore = asyncio.Semaphore(int(os.getenv("MAX_CONCURRENT", "1")))
    @staticmethod
    def _process_args(
@ -79,20 +79,20 @@ class HuggingfaceEngine(BaseEngine):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        input_kwargs: Optional[Dict[str, Any]] = {},
    ) -> Tuple[Dict[str, Any], int]:
        mm_input_dict = {"images": [], "videos": [], "imglens": [0], "vidlens": [0]}
-        if image is not None:
+        if images is not None:
-            mm_input_dict.update({"images": [image], "imglens": [1]})
+            mm_input_dict.update({"images": images, "imglens": [len(images)]})
-            if IMAGE_PLACEHOLDER not in messages[0]["content"]:
+            if not any(IMAGE_PLACEHOLDER not in message["content"] for message in messages):
-                messages[0]["content"] = IMAGE_PLACEHOLDER + messages[0]["content"]
+                messages[0]["content"] = IMAGE_PLACEHOLDER * len(images) + messages[0]["content"]
-        if video is not None:
+        if videos is not None:
-            mm_input_dict.update({"videos": [video], "vidlens": [1]})
+            mm_input_dict.update({"videos": videos, "vidlens": [len(videos)]})
-            if VIDEO_PLACEHOLDER not in messages[0]["content"]:
+            if not any(VIDEO_PLACEHOLDER not in message["content"] for message in messages):
-                messages[0]["content"] = VIDEO_PLACEHOLDER + messages[0]["content"]
+                messages[0]["content"] = VIDEO_PLACEHOLDER * len(videos) + messages[0]["content"]
        messages = template.mm_plugin.process_messages(
            messages, mm_input_dict["images"], mm_input_dict["videos"], processor
@ -119,7 +119,7 @@ class HuggingfaceEngine(BaseEngine):
        stop: Optional[Union[str, List[str]]] = input_kwargs.pop("stop", None)
        if stop is not None:
-            logger.warning("Stop parameter is not supported by the huggingface engine yet.")
+            logger.warning_rank0("Stop parameter is not supported by the huggingface engine yet.")
        generating_args = generating_args.copy()
        generating_args.update(
@ -166,7 +166,11 @@ class HuggingfaceEngine(BaseEngine):
        mm_inputs = template.mm_plugin.get_mm_inputs(**mm_input_dict, seqlens=[prompt_length], processor=processor)
        for key, value in mm_inputs.items():
-            value = value if isinstance(value, torch.Tensor) else torch.tensor(value)
+            if isinstance(value, list) and all(isinstance(v, torch.Tensor) for v in value):  # for pixtral inputs
                value = torch.stack(value)  # assume they have same sizes
            elif not isinstance(value, torch.Tensor):
                value = torch.tensor(value)
            gen_kwargs[key] = value.to(model.device)
        return gen_kwargs, prompt_length
@ -182,12 +186,22 @@ class HuggingfaceEngine(BaseEngine):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        input_kwargs: Optional[Dict[str, Any]] = {},
    ) -> List["Response"]:
        gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
-            model, tokenizer, processor, template, generating_args, messages, system, tools, image, video, input_kwargs
+            model,
            tokenizer,
            processor,
            template,
            generating_args,
            messages,
            system,
            tools,
            images,
            videos,
            input_kwargs,
        )
        generate_output = model.generate(**gen_kwargs)
        response_ids = generate_output[:, prompt_length:]
@ -218,12 +232,22 @@ class HuggingfaceEngine(BaseEngine):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        input_kwargs: Optional[Dict[str, Any]] = {},
    ) -> Callable[[], str]:
        gen_kwargs, _ = HuggingfaceEngine._process_args(
-            model, tokenizer, processor, template, generating_args, messages, system, tools, image, video, input_kwargs
+            model,
            tokenizer,
            processor,
            template,
            generating_args,
            messages,
            system,
            tools,
            images,
            videos,
            input_kwargs,
        )
        streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
        gen_kwargs["streamer"] = streamer
@ -266,8 +290,8 @@ class HuggingfaceEngine(BaseEngine):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        **input_kwargs,
    ) -> List["Response"]:
        if not self.can_generate:
@ -283,8 +307,8 @@ class HuggingfaceEngine(BaseEngine):
            messages,
            system,
            tools,
-            image,
+            images,
-            video,
+            videos,
            input_kwargs,
        )
        async with self.semaphore:
@ -297,8 +321,8 @@ class HuggingfaceEngine(BaseEngine):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        **input_kwargs,
    ) -> AsyncGenerator[str, None]:
        if not self.can_generate:
@ -314,8 +338,8 @@ class HuggingfaceEngine(BaseEngine):
            messages,
            system,
            tools,
-            image,
+            images,
-            video,
+            videos,
            input_kwargs,
        )
        async with self.semaphore:
--- a/src/llamafactory/chat/vllm_engine.py
+++ b/src/llamafactory/chat/vllm_engine.py
@ -18,8 +18,8 @@ from typing import TYPE_CHECKING, Any, AsyncGenerator, AsyncIterator, Dict, List
 from typing_extensions import override
 from ..data import get_template_and_fix_tokenizer
 from ..extras import logging
 from ..extras.constants import IMAGE_PLACEHOLDER
 from ..extras.logging import get_logger
 from ..extras.misc import get_device_count
 from ..extras.packages import is_pillow_available, is_vllm_available
 from ..model import load_config, load_tokenizer
@ -43,7 +43,7 @@ if TYPE_CHECKING:
    from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 class VllmEngine(BaseEngine):
@ -87,7 +87,7 @@ class VllmEngine(BaseEngine):
        if getattr(config, "is_yi_vl_derived_model", None):
            import vllm.model_executor.models.llava
-            logger.info("Detected Yi-VL model, applying projector patch.")
+            logger.info_rank0("Detected Yi-VL model, applying projector patch.")
            vllm.model_executor.models.llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVLForVLLM
        self.model = AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**engine_args))
@ -101,14 +101,14 @@ class VllmEngine(BaseEngine):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        **input_kwargs,
    ) -> AsyncIterator["RequestOutput"]:
-        request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
+        request_id = f"chatcmpl-{uuid.uuid4().hex}"
-        if image is not None:
+        if images is not None:
-            if IMAGE_PLACEHOLDER not in messages[0]["content"]:
+            if not any(IMAGE_PLACEHOLDER not in message["content"] for message in messages):
-                messages[0]["content"] = IMAGE_PLACEHOLDER + messages[0]["content"]
+                messages[0]["content"] = IMAGE_PLACEHOLDER * len(images) + messages[0]["content"]
        paired_messages = messages + [{"role": "assistant", "content": ""}]
        system = system or self.generating_args["default_system"]
@ -157,14 +157,18 @@ class VllmEngine(BaseEngine):
            skip_special_tokens=True,
        )
-        if image is not None:  # add image features
+        if images is not None:  # add image features
-            if not isinstance(image, (str, ImageObject)):
+            image_data = []
-                raise ValueError("Expected image input is a path or PIL.Image, but got {}.".format(type(image)))
+            for image in images:
                if not isinstance(image, (str, ImageObject)):
                    raise ValueError(f"Expected image input is a path or PIL.Image, but got {type(image)}.")
-            if isinstance(image, str):
+                if isinstance(image, str):
-                image = Image.open(image).convert("RGB")
+                    image = Image.open(image).convert("RGB")
-            multi_modal_data = {"image": image}
+                image_data.append(image)
            multi_modal_data = {"image": image_data}
        else:
            multi_modal_data = None
@ -182,12 +186,12 @@ class VllmEngine(BaseEngine):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        **input_kwargs,
    ) -> List["Response"]:
        final_output = None
-        generator = await self._generate(messages, system, tools, image, video, **input_kwargs)
+        generator = await self._generate(messages, system, tools, images, videos, **input_kwargs)
        async for request_output in generator:
            final_output = request_output
@ -210,12 +214,12 @@ class VllmEngine(BaseEngine):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
-        image: Optional["ImageInput"] = None,
+        images: Optional[Sequence["ImageInput"]] = None,
-        video: Optional["VideoInput"] = None,
+        videos: Optional[Sequence["VideoInput"]] = None,
        **input_kwargs,
    ) -> AsyncGenerator[str, None]:
        generated_text = ""
-        generator = await self._generate(messages, system, tools, image, video, **input_kwargs)
+        generator = await self._generate(messages, system, tools, images, videos, **input_kwargs)
        async for result in generator:
            delta_text = result.outputs[0].text[len(generated_text) :]
            generated_text = result.outputs[0].text
--- a/src/llamafactory/cli.py
+++ b/src/llamafactory/cli.py
@ -22,8 +22,8 @@ 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.logging import get_logger
 from .extras.misc import get_device_count
 from .train.tuner import export_model, run_exp
 from .webui.interface import run_web_demo, run_web_ui
@ -47,7 +47,7 @@ USAGE = (
 WELCOME = (
    "-" * 58
    + "\n"
-    + "| Welcome to LLaMA Factory, version {}".format(VERSION)
+    + f"| Welcome to LLaMA Factory, version {VERSION}"
    + " " * (21 - len(VERSION))
    + "|\n|"
    + " " * 56
@ -56,7 +56,7 @@ WELCOME = (
    + "-" * 58
 )
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
@unique
@ -86,19 +86,19 @@ def main():
    elif command == Command.EXPORT:
        export_model()
    elif command == Command.TRAIN:
-        force_torchrun = os.environ.get("FORCE_TORCHRUN", "0").lower() in ["true", "1"]
+        force_torchrun = os.getenv("FORCE_TORCHRUN", "0").lower() in ["true", "1"]
        if force_torchrun or get_device_count() > 1:
-            master_addr = os.environ.get("MASTER_ADDR", "127.0.0.1")
+            master_addr = os.getenv("MASTER_ADDR", "127.0.0.1")
-            master_port = os.environ.get("MASTER_PORT", str(random.randint(20001, 29999)))
+            master_port = os.getenv("MASTER_PORT", str(random.randint(20001, 29999)))
-            logger.info("Initializing distributed tasks at: {}:{}".format(master_addr, master_port))
+            logger.info_rank0(f"Initializing distributed tasks at: {master_addr}:{master_port}")
            process = subprocess.run(
                (
                    "torchrun --nnodes {nnodes} --node_rank {node_rank} --nproc_per_node {nproc_per_node} "
                    "--master_addr {master_addr} --master_port {master_port} {file_name} {args}"
                ).format(
-                    nnodes=os.environ.get("NNODES", "1"),
+                    nnodes=os.getenv("NNODES", "1"),
-                    node_rank=os.environ.get("RANK", "0"),
+                    node_rank=os.getenv("NODE_RANK", "0"),
-                    nproc_per_node=os.environ.get("NPROC_PER_NODE", str(get_device_count())),
+                    nproc_per_node=os.getenv("NPROC_PER_NODE", str(get_device_count())),
                    master_addr=master_addr,
                    master_port=master_port,
                    file_name=launcher.__file__,
@ -118,4 +118,4 @@ def main():
    elif command == Command.HELP:
        print(USAGE)
    else:
-        raise NotImplementedError("Unknown command: {}.".format(command))
+        raise NotImplementedError(f"Unknown command: {command}.")
--- a/src/llamafactory/data/aligner.py
+++ b/src/llamafactory/data/aligner.py
@ -16,7 +16,7 @@ import os
 from functools import partial
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union
-from ..extras.logging import get_logger
+from ..extras import logging
 from .data_utils import Role
@ -29,45 +29,51 @@ if TYPE_CHECKING:
    from .parser import DatasetAttr
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def _convert_images(
-    images: Sequence["ImageInput"],
+    images: Union["ImageInput", Sequence["ImageInput"]],
    dataset_attr: "DatasetAttr",
    data_args: "DataArguments",
 ) -> Optional[List["ImageInput"]]:
    r"""
    Optionally concatenates image path to dataset dir when loading from local disk.
    """
-    if len(images) == 0:
+    if not isinstance(images, list):
        images = [images]
    elif len(images) == 0:
        return None
    else:
        images = images[:]
    images = images[:]
    if dataset_attr.load_from in ["script", "file"]:
        for i in range(len(images)):
-            if isinstance(images[i], str) and os.path.isfile(os.path.join(data_args.dataset_dir, images[i])):
+            if isinstance(images[i], str) and os.path.isfile(os.path.join(data_args.image_dir, images[i])):
-                images[i] = os.path.join(data_args.dataset_dir, images[i])
+                images[i] = os.path.join(data_args.image_dir, images[i])
    return images
 def _convert_videos(
-    videos: Sequence["VideoInput"],
+    videos: Union["VideoInput", Sequence["VideoInput"]],
    dataset_attr: "DatasetAttr",
    data_args: "DataArguments",
 ) -> Optional[List["VideoInput"]]:
    r"""
    Optionally concatenates video path to dataset dir when loading from local disk.
    """
-    if len(videos) == 0:
+    if not isinstance(videos, list):
        videos = [videos]
    elif len(videos) == 0:
        return None
    else:
        videos = videos[:]
    videos = videos[:]
    if dataset_attr.load_from in ["script", "file"]:
        for i in range(len(videos)):
-            if isinstance(videos[i], str) and os.path.isfile(os.path.join(data_args.dataset_dir, videos[i])):
+            if isinstance(videos[i], str) and os.path.isfile(os.path.join(data_args.image_dir, videos[i])):
-                videos[i] = os.path.join(data_args.dataset_dir, videos[i])
+                videos[i] = os.path.join(data_args.image_dir, videos[i])
    return videos
@ -161,7 +167,7 @@ def convert_sharegpt(
    broken_data = False
    for turn_idx, message in enumerate(messages):
        if message[dataset_attr.role_tag] not in accept_tags[turn_idx % 2]:
-            logger.warning("Invalid role tag in {}.".format(messages))
+            logger.warning_rank0(f"Invalid role tag in {messages}.")
            broken_data = True
        aligned_messages.append(
@ -171,7 +177,7 @@ def convert_sharegpt(
    if (not dataset_attr.ranking and len(aligned_messages) % 2 != 0) or (
        dataset_attr.ranking and len(aligned_messages) % 2 == 0
    ):
-        logger.warning("Invalid message count in {}.".format(messages))
+        logger.warning_rank0(f"Invalid message count in {messages}.")
        broken_data = True
    if dataset_attr.kto_tag and isinstance(example[dataset_attr.kto_tag], bool):  # kto example
@ -192,7 +198,7 @@ def convert_sharegpt(
            chosen[dataset_attr.role_tag] not in accept_tags[-1]
            or rejected[dataset_attr.role_tag] not in accept_tags[-1]
        ):
-            logger.warning("Invalid role tag in {}.".format([chosen, rejected]))
+            logger.warning_rank0(f"Invalid role tag in {[chosen, rejected]}.")
            broken_data = True
        prompt = aligned_messages
@ -205,7 +211,7 @@ def convert_sharegpt(
        response = aligned_messages[-1:]
    if broken_data:
-        logger.warning("Skipping this abnormal example.")
+        logger.warning_rank0("Skipping this abnormal example.")
        prompt, response = [], []
    convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)
--- a/src/llamafactory/data/collator.py
+++ b/src/llamafactory/data/collator.py
@ -99,6 +99,9 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
        features: Dict[str, "torch.Tensor"] = super().__call__(features)
        features.update(mm_inputs)
        if isinstance(features.get("pixel_values"), list):  # for pixtral inputs
            features = features.data  # use default_collate() instead of BatchEncoding.to()
        return features
@ -137,9 +140,9 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
        for key in ("chosen", "rejected"):
            for feature in features:
                target_feature = {
-                    "input_ids": feature["{}_input_ids".format(key)],
+                    "input_ids": feature[f"{key}_input_ids"],
-                    "attention_mask": feature["{}_attention_mask".format(key)],
+                    "attention_mask": feature[f"{key}_attention_mask"],
-                    "labels": feature["{}_labels".format(key)],
+                    "labels": feature[f"{key}_labels"],
                    "images": feature["images"],
                    "videos": feature["videos"],
                }
--- a/src/llamafactory/data/data_utils.py
+++ b/src/llamafactory/data/data_utils.py
@ -17,7 +17,7 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Set, TypedDict
 from datasets import DatasetDict, concatenate_datasets, interleave_datasets
-from ..extras.logging import get_logger
+from ..extras import logging
 if TYPE_CHECKING:
@ -26,7 +26,7 @@ if TYPE_CHECKING:
    from ..hparams import DataArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 SLOTS = Sequence[Union[str, Set[str], Dict[str, str]]]
@ -56,12 +56,12 @@ def merge_dataset(
        return all_datasets[0]
    elif data_args.mix_strategy == "concat":
        if data_args.streaming:
-            logger.warning("The samples between different datasets will not be mixed in streaming mode.")
+            logger.warning_once("The samples between different datasets will not be mixed in streaming mode.")
        return concatenate_datasets(all_datasets)
    elif data_args.mix_strategy.startswith("interleave"):
        if not data_args.streaming:
-            logger.warning("We recommend using `mix_strategy=concat` in non-streaming mode.")
+            logger.warning_once("We recommend using `mix_strategy=concat` in non-streaming mode.")
        return interleave_datasets(
            datasets=all_datasets,
@ -70,7 +70,7 @@ def merge_dataset(
            stopping_strategy="first_exhausted" if data_args.mix_strategy.endswith("under") else "all_exhausted",
        )
    else:
-        raise ValueError("Unknown mixing strategy: {}.".format(data_args.mix_strategy))
+        raise ValueError(f"Unknown mixing strategy: {data_args.mix_strategy}.")
 def split_dataset(
--- a/src/llamafactory/data/formatter.py
+++ b/src/llamafactory/data/formatter.py
@ -83,14 +83,14 @@ class StringFormatter(Formatter):
            if isinstance(slot, str):
                for name, value in kwargs.items():
                    if not isinstance(value, str):
-                        raise RuntimeError("Expected a string, got {}".format(value))
+                        raise RuntimeError(f"Expected a string, got {value}")
                    slot = slot.replace("{{" + name + "}}", value, 1)
                elements.append(slot)
            elif isinstance(slot, (dict, set)):
                elements.append(slot)
            else:
-                raise RuntimeError("Input must be string, set[str] or dict[str, str], got {}".format(type(slot)))
+                raise RuntimeError(f"Input must be string, set[str] or dict[str, str], got {type(slot)}")
        return elements
@ -113,7 +113,7 @@ class FunctionFormatter(Formatter):
                functions.append((tool_call["name"], json.dumps(tool_call["arguments"], ensure_ascii=False)))
        except json.JSONDecodeError:
-            functions = []
+            raise RuntimeError(f"Invalid JSON format in function message: {str([content])}")  # flat string
        elements = []
        for name, arguments in functions:
@ -124,7 +124,7 @@ class FunctionFormatter(Formatter):
                elif isinstance(slot, (dict, set)):
                    elements.append(slot)
                else:
-                    raise RuntimeError("Input must be string, set[str] or dict[str, str], got {}".format(type(slot)))
+                    raise RuntimeError(f"Input must be string, set[str] or dict[str, str], got {type(slot)}")
        return elements
@ -141,7 +141,7 @@ class ToolFormatter(Formatter):
            tools = json.loads(content)
            return [self.tool_utils.tool_formatter(tools) if len(tools) != 0 else ""]
        except json.JSONDecodeError:
-            return [""]
+            raise RuntimeError(f"Invalid JSON format in tool description: {str([content])}")  # flat string
    @override
    def extract(self, content: str) -> Union[str, List["FunctionCall"]]:
--- a/src/llamafactory/data/loader.py
+++ b/src/llamafactory/data/loader.py
@ -20,8 +20,8 @@ import numpy as np
 from datasets import DatasetDict, load_dataset, load_from_disk
 from transformers.utils.versions import require_version
 from ..extras import logging
 from ..extras.constants import FILEEXT2TYPE
 from ..extras.logging import get_logger
 from ..extras.misc import has_tokenized_data
 from .aligner import align_dataset
 from .data_utils import merge_dataset, split_dataset
@ -39,7 +39,7 @@ if TYPE_CHECKING:
    from .template import Template
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def _load_single_dataset(
@ -51,9 +51,9 @@ def _load_single_dataset(
    r"""
    Loads a single dataset and aligns it to the standard format.
    """
-    logger.info("Loading dataset {}...".format(dataset_attr))
+    logger.info_rank0(f"Loading dataset {dataset_attr}...")
    data_path, data_name, data_dir, data_files = None, None, None, None
-    if dataset_attr.load_from in ["hf_hub", "ms_hub"]:
+    if dataset_attr.load_from in ["hf_hub", "ms_hub", "om_hub"]:
        data_path = dataset_attr.dataset_name
        data_name = dataset_attr.subset
        data_dir = dataset_attr.folder
@ -69,25 +69,24 @@ def _load_single_dataset(
        if os.path.isdir(local_path):  # is directory
            for file_name in os.listdir(local_path):
                data_files.append(os.path.join(local_path, file_name))
                if data_path is None:
                    data_path = FILEEXT2TYPE.get(file_name.split(".")[-1], None)
                elif data_path != FILEEXT2TYPE.get(file_name.split(".")[-1], None):
                    raise ValueError("File types should be identical.")
        elif os.path.isfile(local_path):  # is file
            data_files.append(local_path)
            data_path = FILEEXT2TYPE.get(local_path.split(".")[-1], None)
        else:
-            raise ValueError("File {} not found.".format(local_path))
+            raise ValueError(f"File {local_path} not found.")
        data_path = FILEEXT2TYPE.get(os.path.splitext(data_files[0])[-1][1:], None)
        if data_path is None:
            raise ValueError("Allowed file types: {}.".format(",".join(FILEEXT2TYPE.keys())))
        if any(data_path != FILEEXT2TYPE.get(os.path.splitext(data_file)[-1][1:], None) for data_file in data_files):
            raise ValueError("File types should be identical.")
    else:
-        raise NotImplementedError("Unknown load type: {}.".format(dataset_attr.load_from))
+        raise NotImplementedError(f"Unknown load type: {dataset_attr.load_from}.")
    if dataset_attr.load_from == "ms_hub":
        require_version("modelscope>=1.11.0", "To fix: pip install modelscope>=1.11.0")
-        from modelscope import MsDataset
+        from modelscope import MsDataset  # type: ignore
-        from modelscope.utils.config_ds import MS_DATASETS_CACHE
+        from modelscope.utils.config_ds import MS_DATASETS_CACHE  # type: ignore
        cache_dir = model_args.cache_dir or MS_DATASETS_CACHE
        dataset = MsDataset.load(
@ -98,10 +97,27 @@ def _load_single_dataset(
            split=dataset_attr.split,
            cache_dir=cache_dir,
            token=model_args.ms_hub_token,
-            use_streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
+            use_streaming=data_args.streaming,
        )
        if isinstance(dataset, MsDataset):
            dataset = dataset.to_hf_dataset()
    elif dataset_attr.load_from == "om_hub":
        require_version("openmind>=0.8.0", "To fix: pip install openmind>=0.8.0")
        from openmind import OmDataset  # type: ignore
        from openmind.utils.hub import OM_DATASETS_CACHE  # type: ignore
        cache_dir = model_args.cache_dir or OM_DATASETS_CACHE
        dataset = OmDataset.load_dataset(
            path=data_path,
            name=data_name,
            data_dir=data_dir,
            data_files=data_files,
            split=dataset_attr.split,
            cache_dir=cache_dir,
            token=model_args.om_hub_token,
            streaming=data_args.streaming,
        )
    else:
        dataset = load_dataset(
            path=data_path,
@ -111,13 +127,10 @@ def _load_single_dataset(
            split=dataset_attr.split,
            cache_dir=model_args.cache_dir,
            token=model_args.hf_hub_token,
-            streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
+            streaming=data_args.streaming,
            trust_remote_code=True,
        )
    if data_args.streaming and (dataset_attr.load_from == "file"):  # faster than specifying streaming=True
        dataset = dataset.to_iterable_dataset()  # TODO: add num shards parameter
    if dataset_attr.num_samples is not None and not data_args.streaming:
        target_num = dataset_attr.num_samples
        indexes = np.random.permutation(len(dataset))[:target_num]  # all samples should be included
@ -128,7 +141,7 @@ def _load_single_dataset(
        assert len(indexes) == dataset_attr.num_samples, "Sample num mismatched."
        dataset = dataset.select(indexes)
-        logger.info("Sampled {} examples from dataset {}.".format(dataset_attr.num_samples, dataset_attr))
+        logger.info_rank0(f"Sampled {dataset_attr.num_samples} examples from dataset {dataset_attr}.")
    if data_args.max_samples is not None:  # truncate dataset
        max_samples = min(data_args.max_samples, len(dataset))
@ -224,9 +237,9 @@ def get_dataset(
    # Load tokenized dataset
    if data_args.tokenized_path is not None:
        if has_tokenized_data(data_args.tokenized_path):
-            logger.warning("Loading dataset from disk will ignore other data arguments.")
+            logger.warning_rank0("Loading dataset from disk will ignore other data arguments.")
            dataset_dict: "DatasetDict" = load_from_disk(data_args.tokenized_path)
-            logger.info("Loaded tokenized dataset from {}.".format(data_args.tokenized_path))
+            logger.info_rank0(f"Loaded tokenized dataset from {data_args.tokenized_path}.")
            dataset_module: Dict[str, "Dataset"] = {}
            if "train" in dataset_dict:
@ -277,8 +290,8 @@ def get_dataset(
        if data_args.tokenized_path is not None:
            if training_args.should_save:
                dataset_dict.save_to_disk(data_args.tokenized_path)
-                logger.info("Tokenized dataset saved at {}.".format(data_args.tokenized_path))
+                logger.info_rank0(f"Tokenized dataset saved at {data_args.tokenized_path}.")
-                logger.info("Please restart the training with `tokenized_path: {}`.".format(data_args.tokenized_path))
+                logger.info_rank0(f"Please restart the training with `tokenized_path: {data_args.tokenized_path}`.")
            sys.exit(0)
--- a/src/llamafactory/data/mm_plugin.py
+++ b/src/llamafactory/data/mm_plugin.py
@ -4,6 +4,7 @@ from io import BytesIO
 from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, TypedDict, Union
 import numpy as np
 from transformers.image_utils import get_image_size, to_numpy_array
 from typing_extensions import override
 from ..extras.constants import IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
@ -110,7 +111,7 @@ class BasePlugin:
                    image = Image.open(image["path"])
            if not isinstance(image, ImageObject):
-                raise ValueError("Expect input is a list of Images, but got {}.".format(type(image)))
+                raise ValueError(f"Expect input is a list of Images, but got {type(image)}.")
            results.append(self._preprocess_image(image, **kwargs))
@ -157,6 +158,7 @@ class BasePlugin:
        It holds num_patches == torch.prod(image_grid_thw)
        """
        image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
        video_processor: "BaseImageProcessor" = getattr(processor, "video_processor", image_processor)
        input_dict = {"images": None}  # default key
        if len(images) != 0:
            images = self._regularize_images(
@ -174,10 +176,16 @@ class BasePlugin:
            )
            input_dict["videos"] = videos
-        if input_dict.get("images", None) is not None or input_dict.get("videos", None) is not None:
+        mm_inputs = {}
-            return image_processor(**input_dict, return_tensors="pt")
+        if image_processor != video_processor:
-        else:
+            if input_dict.get("images") is not None:
-            return {}
+                mm_inputs.update(image_processor(input_dict["images"], return_tensors="pt"))
            if input_dict.get("videos") is not None:
                mm_inputs.update(video_processor(input_dict["videos"], return_tensors="pt"))
        elif input_dict.get("images") is not None or input_dict.get("videos") is not None:  # same processor (qwen2-vl)
            mm_inputs.update(image_processor(**input_dict, return_tensors="pt"))
        return mm_inputs
    def process_messages(
        self,
@ -218,6 +226,14 @@ class BasePlugin:
    ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
        r"""
        Builds batched multimodal inputs for VLMs.
        Arguments:
            images: a list of image inputs, shape (num_images,)
            videos: a list of video inputs, shape (num_videos,)
            imglens: number of images in each sample, shape (batch_size,)
            vidlens: number of videos in each sample, shape (batch_size,)
            seqlens: number of tokens in each sample, shape (batch_size,)
            processor: a processor for pre-processing images and videos
        """
        self._validate_input(images, videos)
        return {}
@ -245,7 +261,123 @@ class LlavaPlugin(BasePlugin):
            message["content"] = content.replace("{{image}}", self.image_token * image_seqlen)
        if len(images) != num_image_tokens:
-            raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
+            raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
        return messages
    @override
    def get_mm_inputs(
        self,
        images: Sequence["ImageInput"],
        videos: Sequence["VideoInput"],
        imglens: Sequence[int],
        vidlens: Sequence[int],
        seqlens: Sequence[int],
        processor: Optional["ProcessorMixin"],
    ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
        self._validate_input(images, videos)
        return self._get_mm_inputs(images, videos, processor)
 class LlavaNextPlugin(BasePlugin):
    @override
    def process_messages(
        self,
        messages: Sequence[Dict[str, str]],
        images: Sequence["ImageInput"],
        videos: Sequence["VideoInput"],
        processor: Optional["ProcessorMixin"],
    ) -> List[Dict[str, str]]:
        self._validate_input(images, videos)
        num_image_tokens = 0
        messages = deepcopy(messages)
        mm_inputs = self._get_mm_inputs(images, videos, processor)
        if "image_sizes" in mm_inputs:
            image_sizes = iter(mm_inputs["image_sizes"])
        if "pixel_values" in mm_inputs:
            height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
        for message in messages:
            content = message["content"]
            while self.image_token in content:
                image_size = next(image_sizes)
                orig_height, orig_width = image_size
                image_seqlen = processor._get_number_of_features(orig_height, orig_width, height, width)
                if processor.vision_feature_select_strategy == "default":
                    image_seqlen -= 1
                num_image_tokens += 1
                content = content.replace(self.image_token, "{{image}}" * image_seqlen, 1)
            message["content"] = content.replace("{{image}}", self.image_token)
        if len(images) != num_image_tokens:
            raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
        return messages
    @override
    def get_mm_inputs(
        self,
        images: Sequence["ImageInput"],
        videos: Sequence["VideoInput"],
        imglens: Sequence[int],
        vidlens: Sequence[int],
        seqlens: Sequence[int],
        processor: Optional["ProcessorMixin"],
    ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
        self._validate_input(images, videos)
        res = self._get_mm_inputs(images, videos, processor)
        return res
 class LlavaNextVideoPlugin(BasePlugin):
    @override
    def process_messages(
        self,
        messages: Sequence[Dict[str, str]],
        images: Sequence["ImageInput"],
        videos: Sequence["VideoInput"],
        processor: Optional["ProcessorMixin"],
    ) -> List[Dict[str, str]]:
        self._validate_input(images, videos)
        num_image_tokens = 0
        num_video_tokens = 0
        messages = deepcopy(messages)
        mm_inputs = self._get_mm_inputs(images, videos, processor)
        if "pixel_values" in mm_inputs:
            image_sizes = iter(mm_inputs["image_sizes"])
            height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
            for message in messages:
                content = message["content"]
                while self.image_token in content:
                    image_size = next(image_sizes)
                    orig_height, orig_width = image_size
                    image_seqlen = processor._get_number_of_features(orig_height, orig_width, height, width)
                    if processor.vision_feature_select_strategy == "default":
                        image_seqlen -= 1
                    num_image_tokens += 1
                    content = content.replace(self.image_token, "{{image}}" * image_seqlen, 1)
                message["content"] = content.replace("{{image}}", self.image_token)
        if "pixel_values_videos" in mm_inputs:
            pixel_values_video = to_numpy_array(mm_inputs.get("pixel_values_videos")[0])
            height, width = get_image_size(pixel_values_video[0])
            num_frames = pixel_values_video.shape[0]  # frame dim is always after batch dim
            image_seqlen = (height // processor.patch_size) * (width // processor.patch_size)
            video_seqlen = image_seqlen // 4 * num_frames  # divide by 4 needed for avg pooling layer
            for message in messages:
                content = message["content"]
                while self.video_token in content:
                    num_video_tokens += 1
                    content = content.replace(self.video_token, "{{video}}", 1)
                message["content"] = content.replace("{{video}}", self.video_token * video_seqlen)
        if len(images) != num_image_tokens:
            raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
        if len(videos) != num_video_tokens:
            raise ValueError(f"The number of videos does not match the number of {IMAGE_PLACEHOLDER} tokens")
        return messages
@ -284,7 +416,7 @@ class PaliGemmaPlugin(BasePlugin):
            message["content"] = content.replace("{{image}}", "")
        if len(images) != num_image_tokens:
-            raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
+            raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
        return messages
@ -324,6 +456,68 @@ class PaliGemmaPlugin(BasePlugin):
        return mm_inputs
 class PixtralPlugin(BasePlugin):
    @override
    def process_messages(
        self,
        messages: Sequence[Dict[str, str]],
        images: Sequence["ImageInput"],
        videos: Sequence["VideoInput"],
        processor: Optional["ProcessorMixin"],
    ) -> List[Dict[str, str]]:
        self._validate_input(images, videos)
        patch_size = getattr(processor, "patch_size")
        image_token = getattr(processor, "image_token")
        image_break_token = getattr(processor, "image_break_token")
        image_end_token = getattr(processor, "image_end_token")
        num_image_tokens = 0
        messages = deepcopy(messages)
        mm_inputs = self._get_mm_inputs(images, videos, processor)
        image_input_sizes = mm_inputs.get("image_sizes", None)
        for message in messages:
            content = message["content"]
            while IMAGE_PLACEHOLDER in content:
                if image_input_sizes is None:
                    raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
                image_size = image_input_sizes[0][num_image_tokens]
                height, width = image_size
                num_height_tokens = height // patch_size
                num_width_tokens = width // patch_size
                replace_tokens = [[image_token] * num_width_tokens + [image_break_token]] * num_height_tokens
                replace_tokens = [item for sublist in replace_tokens for item in sublist]  # flatten list
                replace_tokens[-1] = image_end_token
                replace_str = "".join(replace_tokens)
                content = content.replace(IMAGE_PLACEHOLDER, replace_str, 1)
                num_image_tokens += 1
            message["content"] = content
        if len(images) != num_image_tokens:
            raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
        return messages
    @override
    def get_mm_inputs(
        self,
        images: Sequence["ImageInput"],
        videos: Sequence["VideoInput"],
        imglens: Sequence[int],
        vidlens: Sequence[int],
        seqlens: Sequence[int],
        processor: Optional["ProcessorMixin"],
    ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
        self._validate_input(images, videos)
        mm_inputs = self._get_mm_inputs(images, videos, processor)
        if mm_inputs.get("pixel_values"):
            mm_inputs["pixel_values"] = mm_inputs["pixel_values"][0]
        mm_inputs.pop("image_sizes", None)
        return mm_inputs
 class Qwen2vlPlugin(BasePlugin):
    @override
    def _preprocess_image(self, image: "ImageObject", **kwargs) -> "ImageObject":
@ -369,7 +563,7 @@ class Qwen2vlPlugin(BasePlugin):
            content = message["content"]
            while IMAGE_PLACEHOLDER in content:
                if num_image_tokens >= len(image_grid_thw):
-                    raise ValueError("`len(images)` is less than the number of {} tokens.".format(IMAGE_PLACEHOLDER))
+                    raise ValueError(f"`len(images)` is less than the number of {IMAGE_PLACEHOLDER} tokens.")
                content = content.replace(
                    IMAGE_PLACEHOLDER,
@ -382,7 +576,7 @@ class Qwen2vlPlugin(BasePlugin):
            while VIDEO_PLACEHOLDER in content:
                if num_video_tokens >= len(video_grid_thw):
-                    raise ValueError("`len(videos)` is less than the number of {} tokens.".format(VIDEO_PLACEHOLDER))
+                    raise ValueError(f"`len(videos)` is less than the number of {VIDEO_PLACEHOLDER} tokens.")
                content = content.replace(
                    VIDEO_PLACEHOLDER,
@ -396,10 +590,73 @@ class Qwen2vlPlugin(BasePlugin):
            message["content"] = content
        if len(images) != num_image_tokens:
-            raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
+            raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
        if len(videos) != num_video_tokens:
-            raise ValueError("The number of videos does not match the number of {} tokens".format(VIDEO_PLACEHOLDER))
+            raise ValueError(f"The number of videos does not match the number of {VIDEO_PLACEHOLDER} tokens")
        return messages
    @override
    def get_mm_inputs(
        self,
        images: Sequence["ImageInput"],
        videos: Sequence["VideoInput"],
        imglens: Sequence[int],
        vidlens: Sequence[int],
        seqlens: Sequence[int],
        processor: Optional["ProcessorMixin"],
    ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
        self._validate_input(images, videos)
        return self._get_mm_inputs(images, videos, processor)
 class VideoLlavaPlugin(BasePlugin):
    @override
    def process_messages(
        self,
        messages: Sequence[Dict[str, str]],
        images: Sequence["ImageInput"],
        videos: Sequence["VideoInput"],
        processor: Optional["ProcessorMixin"],
    ) -> List[Dict[str, str]]:
        self._validate_input(images, videos)
        num_image_tokens = 0
        num_video_tokens = 0
        messages = deepcopy(messages)
        mm_inputs = self._get_mm_inputs(images, videos, processor)
        num_frames = 0
        exist_images = "pixel_values_images" in mm_inputs
        exist_videos = "pixel_values_videos" in mm_inputs
        if exist_videos or exist_images:
            if exist_images:
                height, width = get_image_size(to_numpy_array(mm_inputs.get("pixel_values_images")[0]))
                num_frames = 1
            if exist_videos:
                pixel_values_video = to_numpy_array(mm_inputs.get("pixel_values_videos")[0])
                height, width = get_image_size(pixel_values_video[0])
                num_frames = pixel_values_video.shape[0]  # frame dim is always after batch dim
            image_seqlen = (height // processor.patch_size) * (width // processor.patch_size) + 1
            video_seqlen = image_seqlen * num_frames
            if processor.vision_feature_select_strategy == "default":
                image_seqlen -= 1
            for message in messages:
                content = message["content"]
                while self.image_token in content:
                    num_image_tokens += 1
                    content = content.replace(self.image_token, "{{image}}", 1)
                while self.video_token in content:
                    num_video_tokens += 1
                    content = content.replace(self.video_token, "{{video}}", 1)
                content = content.replace("{{image}}", self.image_token * image_seqlen)
                message["content"] = content.replace("{{video}}", self.video_token * video_seqlen)
        if len(images) != num_image_tokens:
            raise ValueError(f"The number of images does not match the number of {self.image_token} tokens")
        if len(videos) != num_video_tokens:
            raise ValueError(f"The number of videos does not match the number of {self.video_token} tokens")
        return messages
@ -420,8 +677,12 @@ class Qwen2vlPlugin(BasePlugin):
 PLUGINS = {
    "base": BasePlugin,
    "llava": LlavaPlugin,
    "llava_next": LlavaNextPlugin,
    "llava_next_video": LlavaNextVideoPlugin,
    "paligemma": PaliGemmaPlugin,
    "pixtral": PixtralPlugin,
    "qwen2_vl": Qwen2vlPlugin,
    "video_llava": VideoLlavaPlugin,
 }
@ -432,6 +693,6 @@ def get_mm_plugin(
 ) -> "BasePlugin":
    plugin_class = PLUGINS.get(name, None)
    if plugin_class is None:
-        raise ValueError("Multimodal plugin `{}` not found.".format(name))
+        raise ValueError(f"Multimodal plugin `{name}` not found.")
    return plugin_class(image_token, video_token)
--- a/src/llamafactory/data/parser.py
+++ b/src/llamafactory/data/parser.py
@ -20,7 +20,7 @@ from typing import Any, Dict, List, Literal, Optional, Sequence
 from transformers.utils import cached_file
 from ..extras.constants import DATA_CONFIG
-from ..extras.misc import use_modelscope
+from ..extras.misc import use_modelscope, use_openmind
@dataclass
@ -30,7 +30,7 @@ class DatasetAttr:
    """
    # basic configs
-    load_from: Literal["hf_hub", "ms_hub", "script", "file"]
+    load_from: Literal["hf_hub", "ms_hub", "om_hub", "script", "file"]
    dataset_name: str
    formatting: Literal["alpaca", "sharegpt"] = "alpaca"
    ranking: bool = False
@ -87,31 +87,39 @@ def get_dataset_list(dataset_names: Optional[Sequence[str]], dataset_dir: str) -
            config_path = os.path.join(dataset_dir, DATA_CONFIG)
        try:
-            with open(config_path, "r") as f:
+            with open(config_path) as f:
                dataset_info = json.load(f)
        except Exception as err:
            if len(dataset_names) != 0:
-                raise ValueError("Cannot open {} due to {}.".format(config_path, str(err)))
+                raise ValueError(f"Cannot open {config_path} due to {str(err)}.")
            dataset_info = None
    dataset_list: List["DatasetAttr"] = []
    for name in dataset_names:
        if dataset_info is None:  # dataset_dir is ONLINE
-            load_from = "ms_hub" if use_modelscope() else "hf_hub"
+            if use_modelscope():
                load_from = "ms_hub"
            elif use_openmind():
                load_from = "om_hub"
            else:
                load_from = "hf_hub"
            dataset_attr = DatasetAttr(load_from, dataset_name=name)
            dataset_list.append(dataset_attr)
            continue
        if name not in dataset_info:
-            raise ValueError("Undefined dataset {} in {}.".format(name, DATA_CONFIG))
+            raise ValueError(f"Undefined dataset {name} in {DATA_CONFIG}.")
        has_hf_url = "hf_hub_url" in dataset_info[name]
        has_ms_url = "ms_hub_url" in dataset_info[name]
        has_om_url = "om_hub_url" in dataset_info[name]
-        if has_hf_url or has_ms_url:
+        if has_hf_url or has_ms_url or has_om_url:
-            if (use_modelscope() and has_ms_url) or (not has_hf_url):
+            if has_ms_url and (use_modelscope() or not has_hf_url):
                dataset_attr = DatasetAttr("ms_hub", dataset_name=dataset_info[name]["ms_hub_url"])
            elif has_om_url and (use_openmind() or not has_hf_url):
                dataset_attr = DatasetAttr("om_hub", dataset_name=dataset_info[name]["om_hub_url"])
            else:
                dataset_attr = DatasetAttr("hf_hub", dataset_name=dataset_info[name]["hf_hub_url"])
        elif "script_url" in dataset_info[name]:
--- a/src/llamafactory/data/processors/feedback.py
+++ b/src/llamafactory/data/processors/feedback.py
@ -15,8 +15,8 @@
 from collections import defaultdict
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
 from ...extras import logging
 from ...extras.constants import IGNORE_INDEX
 from ...extras.logging import get_logger
 from .processor_utils import infer_seqlen
@ -28,7 +28,7 @@ if TYPE_CHECKING:
    from ..template import Template
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def _encode_feedback_example(
@ -94,7 +94,9 @@ def preprocess_feedback_dataset(
    model_inputs = defaultdict(list)
    for i in range(len(examples["_prompt"])):
        if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) < 2:
-            logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
+            logger.warning_rank0(
                "Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
            )
            continue
        input_ids, labels, kl_input_ids, kl_labels, kto_tag = _encode_feedback_example(
@ -123,6 +125,6 @@ def preprocess_feedback_dataset(
    desirable_num = sum([1 for tag in model_inputs["kto_tags"] if tag])
    undesirable_num = len(model_inputs["kto_tags"]) - desirable_num
    if desirable_num == 0 or undesirable_num == 0:
-        logger.warning("Your dataset only has one preference type.")
+        logger.warning_rank0("Your dataset only has one preference type.")
    return model_inputs
--- a/src/llamafactory/data/processors/pairwise.py
+++ b/src/llamafactory/data/processors/pairwise.py
@ -15,8 +15,8 @@
 from collections import defaultdict
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
 from ...extras import logging
 from ...extras.constants import IGNORE_INDEX
 from ...extras.logging import get_logger
 from .processor_utils import infer_seqlen
@ -28,7 +28,7 @@ if TYPE_CHECKING:
    from ..template import Template
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def _encode_pairwise_example(
@ -77,7 +77,9 @@ def preprocess_pairwise_dataset(
    model_inputs = defaultdict(list)
    for i in range(len(examples["_prompt"])):
        if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) < 2:
-            logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
+            logger.warning_rank0(
                "Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
            )
            continue
        chosen_input_ids, chosen_labels, rejected_input_ids, rejected_labels = _encode_pairwise_example(
@ -110,8 +112,8 @@ def print_pairwise_dataset_example(example: Dict[str, List[int]], tokenizer: "Pr
    print("chosen_input_ids:\n{}".format(example["chosen_input_ids"]))
    print("chosen_inputs:\n{}".format(tokenizer.decode(example["chosen_input_ids"], skip_special_tokens=False)))
    print("chosen_label_ids:\n{}".format(example["chosen_labels"]))
-    print("chosen_labels:\n{}".format(tokenizer.decode(valid_chosen_labels, skip_special_tokens=False)))
+    print(f"chosen_labels:\n{tokenizer.decode(valid_chosen_labels, skip_special_tokens=False)}")
    print("rejected_input_ids:\n{}".format(example["rejected_input_ids"]))
    print("rejected_inputs:\n{}".format(tokenizer.decode(example["rejected_input_ids"], skip_special_tokens=False)))
    print("rejected_label_ids:\n{}".format(example["rejected_labels"]))
-    print("rejected_labels:\n{}".format(tokenizer.decode(valid_rejected_labels, skip_special_tokens=False)))
+    print(f"rejected_labels:\n{tokenizer.decode(valid_rejected_labels, skip_special_tokens=False)}")
--- a/src/llamafactory/data/processors/supervised.py
+++ b/src/llamafactory/data/processors/supervised.py
@ -15,8 +15,8 @@
 from collections import defaultdict
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
 from ...extras import logging
 from ...extras.constants import IGNORE_INDEX
 from ...extras.logging import get_logger
 from .processor_utils import greedy_knapsack, infer_seqlen
@ -28,7 +28,7 @@ if TYPE_CHECKING:
    from ..template import Template
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def _encode_supervised_example(
@ -99,7 +99,9 @@ def preprocess_supervised_dataset(
    model_inputs = defaultdict(list)
    for i in range(len(examples["_prompt"])):
        if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) != 1:
-            logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
+            logger.warning_rank0(
                "Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
            )
            continue
        input_ids, labels = _encode_supervised_example(
@ -141,7 +143,9 @@ def preprocess_packed_supervised_dataset(
    length2indexes = defaultdict(list)
    for i in range(len(examples["_prompt"])):
        if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) != 1:
-            logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
+            logger.warning_rank0(
                "Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
            )
            continue
        input_ids, labels = _encode_supervised_example(
@ -160,7 +164,7 @@ def preprocess_packed_supervised_dataset(
        )
        length = len(input_ids)
        if length > data_args.cutoff_len:
-            logger.warning("Dropped lengthy example with length {} > {}.".format(length, data_args.cutoff_len))
+            logger.warning_rank0(f"Dropped lengthy example with length {length} > {data_args.cutoff_len}.")
        else:
            lengths.append(length)
            length2indexes[length].append(valid_num)
@ -212,4 +216,4 @@ def print_supervised_dataset_example(example: Dict[str, List[int]], tokenizer: "
    print("input_ids:\n{}".format(example["input_ids"]))
    print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
    print("label_ids:\n{}".format(example["labels"]))
-    print("labels:\n{}".format(tokenizer.decode(valid_labels, skip_special_tokens=False)))
+    print(f"labels:\n{tokenizer.decode(valid_labels, skip_special_tokens=False)}")
--- a/src/llamafactory/data/processors/unsupervised.py
+++ b/src/llamafactory/data/processors/unsupervised.py
@ -15,7 +15,7 @@
 from collections import defaultdict
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
-from ...extras.logging import get_logger
+from ...extras import logging
 from ..data_utils import Role
 from .processor_utils import infer_seqlen
@ -28,7 +28,7 @@ if TYPE_CHECKING:
    from ..template import Template
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def _encode_unsupervised_example(
@ -71,7 +71,9 @@ def preprocess_unsupervised_dataset(
    model_inputs = defaultdict(list)
    for i in range(len(examples["_prompt"])):
        if len(examples["_prompt"][i]) % 2 != 1:
-            logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
+            logger.warning_rank0(
                "Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
            )
            continue
        input_ids, labels = _encode_unsupervised_example(
--- a/src/llamafactory/data/template.py
+++ b/src/llamafactory/data/template.py
@ -18,7 +18,7 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
 from transformers.utils.versions import require_version
 from typing_extensions import override
-from ..extras.logging import get_logger
+from ..extras import logging
 from .data_utils import Role
 from .formatter import EmptyFormatter, FunctionFormatter, StringFormatter, ToolFormatter
 from .mm_plugin import get_mm_plugin
@ -32,7 +32,7 @@ if TYPE_CHECKING:
    from .mm_plugin import BasePlugin
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
@dataclass
@ -49,6 +49,7 @@ class Template:
    stop_words: List[str]
    efficient_eos: bool
    replace_eos: bool
    replace_jinja_template: bool
    mm_plugin: "BasePlugin"
    def encode_oneturn(
@ -146,7 +147,7 @@ class Template:
                elif "eos_token" in elem and tokenizer.eos_token_id is not None:
                    token_ids += [tokenizer.eos_token_id]
            else:
-                raise ValueError("Input must be string, set[str] or dict[str, str], got {}".format(type(elem)))
+                raise ValueError(f"Input must be string, set[str] or dict[str, str], got {type(elem)}")
        return token_ids
@ -214,6 +215,7 @@ def _register_template(
    stop_words: Sequence[str] = [],
    efficient_eos: bool = False,
    replace_eos: bool = False,
    replace_jinja_template: bool = True,
    mm_plugin: "BasePlugin" = get_mm_plugin(name="base"),
 ) -> None:
    r"""
@ -263,6 +265,7 @@ def _register_template(
        stop_words=stop_words,
        efficient_eos=efficient_eos,
        replace_eos=replace_eos,
        replace_jinja_template=replace_jinja_template,
        mm_plugin=mm_plugin,
    )
@ -272,12 +275,12 @@ def _add_or_replace_eos_token(tokenizer: "PreTrainedTokenizer", eos_token: str)
    num_added_tokens = tokenizer.add_special_tokens({"eos_token": eos_token})
    if is_added:
-        logger.info("Add eos token: {}".format(tokenizer.eos_token))
+        logger.info_rank0(f"Add eos token: {tokenizer.eos_token}")
    else:
-        logger.info("Replace eos token: {}".format(tokenizer.eos_token))
+        logger.info_rank0(f"Replace eos token: {tokenizer.eos_token}")
    if num_added_tokens > 0:
-        logger.warning("New tokens have been added, make sure `resize_vocab` is True.")
+        logger.warning_rank0("New tokens have been added, make sure `resize_vocab` is True.")
 def _jinja_escape(content: str) -> str:
@ -353,24 +356,21 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
    r"""
    Gets chat template and fixes the tokenizer.
    """
    if data_args.template in ["llava", "paligemma", "qwen2_vl"]:
        require_version(
            "transformers>=4.45.0.dev0", "To fix: pip install git+https://github.com/huggingface/transformers.git"
        )
        require_version("accelerate>=0.34.0", "To fix: pip install accelerate>=0.34.0")
    if data_args.template is None:
        template = TEMPLATES["empty"]  # placeholder
    else:
        template = TEMPLATES.get(data_args.template, None)
        if template is None:
-            raise ValueError("Template {} does not exist.".format(data_args.template))
+            raise ValueError(f"Template {data_args.template} does not exist.")
    if template.mm_plugin.__class__.__name__ != "BasePlugin":
        require_version("transformers>=4.45.0", "To fix: pip install transformers>=4.45.0")
    if data_args.train_on_prompt and template.efficient_eos:
        raise ValueError("Current template does not support `train_on_prompt`.")
    if data_args.tool_format is not None:
-        logger.info("Using tool format: {}.".format(data_args.tool_format))
+        logger.info_rank0(f"Using tool format: {data_args.tool_format}.")
        eos_slots = [] if template.efficient_eos else [{"eos_token"}]
        template.format_function = FunctionFormatter(slots=eos_slots, tool_format=data_args.tool_format)
        template.format_tools = ToolFormatter(tool_format=data_args.tool_format)
@ -388,20 +388,21 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
    if tokenizer.pad_token_id is None:
        tokenizer.pad_token = tokenizer.eos_token
-        logger.info("Add pad token: {}".format(tokenizer.pad_token))
+        logger.info_rank0(f"Add pad token: {tokenizer.pad_token}")
    if stop_words:
        num_added_tokens = tokenizer.add_special_tokens(
            dict(additional_special_tokens=stop_words), replace_additional_special_tokens=False
        )
-        logger.info("Add {} to stop words.".format(",".join(stop_words)))
+        logger.info_rank0("Add {} to stop words.".format(",".join(stop_words)))
        if num_added_tokens > 0:
-            logger.warning("New tokens have been added, make sure `resize_vocab` is True.")
+            logger.warning_rank0("New tokens have been added, make sure `resize_vocab` is True.")
-    try:
+    if tokenizer.chat_template is None or template.replace_jinja_template:
-        tokenizer.chat_template = _get_jinja_template(template, tokenizer)
+        try:
-    except ValueError:
+            tokenizer.chat_template = _get_jinja_template(template, tokenizer)
-        logger.info("Cannot add this chat template to tokenizer.")
+        except ValueError as e:
            logger.info_rank0(f"Cannot add this chat template to tokenizer: {e}.")
    return template
@ -640,6 +641,14 @@ _register_template(
 )
 _register_template(
    name="exaone",
    format_user=StringFormatter(slots=["[|user|]{{content}}\n[|assistant|]"]),
    format_system=StringFormatter(slots=["[|system|]{{content}}[|endofturn|]\n"]),
    format_separator=EmptyFormatter(slots=["\n"]),
 )
 _register_template(
    name="falcon",
    format_user=StringFormatter(slots=["User: {{content}}\nFalcon:"]),
@ -664,6 +673,7 @@ _register_template(
    format_separator=EmptyFormatter(slots=["<end_of_turn>\n"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    efficient_eos=True,
    replace_jinja_template=False,
 )
@ -681,6 +691,14 @@ _register_template(
 )
 _register_template(
    name="index",
    format_user=StringFormatter(slots=["reserved_0{{content}}reserved_1"]),
    format_system=StringFormatter(slots=["<unk>{{content}}"]),
    efficient_eos=True,
 )
 _register_template(
    name="intern",
    format_user=StringFormatter(slots=["<|User|>:{{content}}\n<|Bot|>:"]),
@ -740,6 +758,7 @@ _register_template(
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    stop_words=["<|eot_id|>"],
    replace_eos=True,
    replace_jinja_template=False,
 )
@ -754,6 +773,107 @@ _register_template(
 )
 _register_template(
    name="llava_next",
    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
    default_system=(
        "A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions."
    ),
    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
 )
 _register_template(
    name="llava_next_llama3",
    format_user=StringFormatter(
        slots=[
            (
                "<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
                "<|start_header_id|>assistant<|end_header_id|>\n\n"
            )
        ]
    ),
    format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
    format_observation=StringFormatter(
        slots=[
            (
                "<|start_header_id|>tool<|end_header_id|>\n\n{{content}}<|eot_id|>"
                "<|start_header_id|>assistant<|end_header_id|>\n\n"
            )
        ]
    ),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    stop_words=["<|eot_id|>"],
    replace_eos=True,
    replace_jinja_template=False,
    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
 )
 _register_template(
    name="llava_next_mistral",
    format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
 )
 _register_template(
    name="llava_next_qwen",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\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_separator=EmptyFormatter(slots=["\n"]),
    default_system="You are a helpful assistant.",
    stop_words=["<|im_end|>"],
    replace_eos=True,
    replace_jinja_template=False,
    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
 )
 _register_template(
    name="llava_next_yi",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_separator=EmptyFormatter(slots=["\n"]),
    stop_words=["<|im_end|>"],
    replace_eos=True,
    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
 )
 _register_template(
    name="llava_next_video",
    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
    default_system=(
        "A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions."
    ),
    mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
 )
 _register_template(
    name="llava_next_video_mistral",
    format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
 )
 _register_template(
    name="llava_next_video_yi",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_separator=EmptyFormatter(slots=["\n"]),
    stop_words=["<|im_end|>"],
    replace_eos=True,
    mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
 )
 _register_template(
    name="mistral",
    format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
@ -831,6 +951,14 @@ _register_template(
    replace_eos=True,
 )
 _register_template(
    name="pixtral",
    format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    mm_plugin=get_mm_plugin(name="pixtral", image_token="[IMG]"),
 )
 _register_template(
    name="qwen",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
@ -840,6 +968,7 @@ _register_template(
    default_system="You are a helpful assistant.",
    stop_words=["<|im_end|>"],
    replace_eos=True,
    replace_jinja_template=False,
 )
@ -852,6 +981,7 @@ _register_template(
    default_system="You are a helpful assistant.",
    stop_words=["<|im_end|>"],
    replace_eos=True,
    replace_jinja_template=False,
    mm_plugin=get_mm_plugin(name="qwen2_vl", image_token="<|image_pad|>", video_token="<|video_pad|>"),
 )
@ -907,6 +1037,17 @@ _register_template(
 )
 _register_template(
    name="video_llava",
    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
    default_system=(
        "A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions."
    ),
    mm_plugin=get_mm_plugin(name="video_llava", image_token="<image>", video_token="<video>"),
 )
 _register_template(
    name="xuanyuan",
    format_user=StringFormatter(slots=["Human: {{content}} Assistant:"]),
--- a/src/llamafactory/data/tool_utils.py
+++ b/src/llamafactory/data/tool_utils.py
@ -177,6 +177,6 @@ TOOLS = {
 def get_tool_utils(name: str) -> "ToolUtils":
    tool_utils = TOOLS.get(name, None)
    if tool_utils is None:
-        raise ValueError("Tool utils `{}` not found.".format(name))
+        raise ValueError(f"Tool utils `{name}` not found.")
    return tool_utils
--- a/src/llamafactory/eval/evaluator.py
+++ b/src/llamafactory/eval/evaluator.py
@ -87,7 +87,7 @@ class Evaluator:
            token=self.model_args.hf_hub_token,
        )
-        with open(mapping, "r", encoding="utf-8") as f:
+        with open(mapping, encoding="utf-8") as f:
            categorys: Dict[str, Dict[str, str]] = json.load(f)
        category_corrects = {subj: np.array([], dtype="bool") for subj in SUBJECTS}
@ -139,7 +139,7 @@ class Evaluator:
    def _save_results(self, category_corrects: Dict[str, "NDArray"], results: Dict[str, Dict[int, str]]) -> None:
        score_info = "\n".join(
            [
-                "{:>15}: {:.2f}".format(category_name, 100 * np.mean(category_correct))
+                f"{category_name:>15}: {100 * np.mean(category_correct):.2f}"
                for category_name, category_correct in category_corrects.items()
                if len(category_correct)
            ]
--- a/src/llamafactory/eval/template.py
+++ b/src/llamafactory/eval/template.py
@ -61,7 +61,7 @@ def _register_eval_template(name: str, system: str, choice: str, answer: str) ->
 def get_eval_template(name: str) -> "EvalTemplate":
    eval_template = eval_templates.get(name, None)
-    assert eval_template is not None, "Template {} does not exist.".format(name)
+    assert eval_template is not None, f"Template {name} does not exist."
    return eval_template
--- a/src/llamafactory/extras/constants.py
+++ b/src/llamafactory/extras/constants.py
@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
 from collections import OrderedDict, defaultdict
 from enum import Enum
 from typing import Dict, Optional
@ -47,7 +48,7 @@ FILEEXT2TYPE = {
 IGNORE_INDEX = -100
-IMAGE_PLACEHOLDER = "<image>"
+IMAGE_PLACEHOLDER = os.environ.get("IMAGE_PLACEHOLDER", "<image>")
 LAYERNORM_NAMES = {"norm", "ln"}
@ -95,7 +96,7 @@ SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN = {
 SUPPORTED_CLASS_FOR_S2ATTN = {"llama"}
-VIDEO_PLACEHOLDER = "<video>"
+VIDEO_PLACEHOLDER = os.environ.get("VIDEO_PLACEHOLDER", "<video>")
 V_HEAD_WEIGHTS_NAME = "value_head.bin"
@ -107,6 +108,7 @@ VISION_MODELS = set()
 class DownloadSource(str, Enum):
    DEFAULT = "hf"
    MODELSCOPE = "ms"
    OPENMIND = "om"
 def register_model_group(
@ -114,17 +116,12 @@ def register_model_group(
    template: Optional[str] = None,
    vision: bool = False,
 ) -> None:
    prefix = None
    for name, path in models.items():
        if prefix is None:
            prefix = name.split("-")[0]
        else:
            assert prefix == name.split("-")[0], "prefix should be identical."
        SUPPORTED_MODELS[name] = path
-    if template is not None:
+        if template is not None and any(suffix in name for suffix in ("-Chat", "-Instruct")):
-        DEFAULT_TEMPLATE[prefix] = template
+            DEFAULT_TEMPLATE[name] = template
-    if vision:
+        if vision:
-        VISION_MODELS.add(prefix)
+            VISION_MODELS.add(name)
 register_model_group(
@ -168,14 +165,17 @@ register_model_group(
        "Baichuan2-13B-Base": {
            DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-13B-Base",
            DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-13B-Base",
            DownloadSource.OPENMIND: "Baichuan/Baichuan2_13b_base_pt",
        },
        "Baichuan2-7B-Chat": {
            DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-7B-Chat",
            DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-7B-Chat",
            DownloadSource.OPENMIND: "Baichuan/Baichuan2_7b_chat_pt",
        },
        "Baichuan2-13B-Chat": {
            DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-13B-Chat",
            DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-13B-Chat",
            DownloadSource.OPENMIND: "Baichuan/Baichuan2_13b_chat_pt",
        },
    },
    template="baichuan2",
@ -274,27 +274,27 @@ register_model_group(
 register_model_group(
    models={
-        "ChineseLLaMA2-1.3B": {
+        "Chinese-Llama-2-1.3B": {
            DownloadSource.DEFAULT: "hfl/chinese-llama-2-1.3b",
            DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-1.3b",
        },
-        "ChineseLLaMA2-7B": {
+        "Chinese-Llama-2-7B": {
            DownloadSource.DEFAULT: "hfl/chinese-llama-2-7b",
            DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-7b",
        },
-        "ChineseLLaMA2-13B": {
+        "Chinese-Llama-2-13B": {
            DownloadSource.DEFAULT: "hfl/chinese-llama-2-13b",
            DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-13b",
        },
-        "ChineseLLaMA2-1.3B-Chat": {
+        "Chinese-Alpaca-2-1.3B-Chat": {
            DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-1.3b",
            DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-1.3b",
        },
-        "ChineseLLaMA2-7B-Chat": {
+        "Chinese-Alpaca-2-7B-Chat": {
            DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-7b",
            DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-7b",
        },
-        "ChineseLLaMA2-13B-Chat": {
+        "Chinese-Alpaca-2-13B-Chat": {
            DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-13b",
            DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-13b",
        },
@ -450,25 +450,25 @@ register_model_group(
 register_model_group(
    models={
-        "DeepSeekCoder-6.7B-Base": {
+        "DeepSeek-Coder-6.7B-Base": {
            DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-6.7b-base",
            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-6.7b-base",
        },
-        "DeepSeekCoder-7B-Base": {
+        "DeepSeek-Coder-7B-Base": {
            DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-7b-base-v1.5",
        },
-        "DeepSeekCoder-33B-Base": {
+        "DeepSeek-Coder-33B-Base": {
            DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-33b-base",
            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-33b-base",
        },
-        "DeepSeekCoder-6.7B-Instruct": {
+        "DeepSeek-Coder-6.7B-Instruct": {
            DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-6.7b-instruct",
            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-6.7b-instruct",
        },
-        "DeepSeekCoder-7B-Instruct": {
+        "DeepSeek-Coder-7B-Instruct": {
            DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-7b-instruct-v1.5",
        },
-        "DeepSeekCoder-33B-Instruct": {
+        "DeepSeek-Coder-33B-Instruct": {
            DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-33b-instruct",
            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-33b-instruct",
        },
@ -477,6 +477,16 @@ register_model_group(
 )
 register_model_group(
    models={
        "EXAONE-3.0-7.8B-Instruct": {
            DownloadSource.DEFAULT: "LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct",
        },
    },
    template="exaone",
 )
 register_model_group(
    models={
        "Falcon-7B": {
@ -550,10 +560,12 @@ register_model_group(
        "Gemma-2-2B-Instruct": {
            DownloadSource.DEFAULT: "google/gemma-2-2b-it",
            DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-2b-it",
            DownloadSource.OPENMIND: "LlamaFactory/gemma-2-2b-it",
        },
        "Gemma-2-9B-Instruct": {
            DownloadSource.DEFAULT: "google/gemma-2-9b-it",
            DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-9b-it",
            DownloadSource.OPENMIND: "LlamaFactory/gemma-2-9b-it",
        },
        "Gemma-2-27B-Instruct": {
            DownloadSource.DEFAULT: "google/gemma-2-27b-it",
@ -573,6 +585,7 @@ register_model_group(
        "GLM-4-9B-Chat": {
            DownloadSource.DEFAULT: "THUDM/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",
@ -583,6 +596,33 @@ register_model_group(
 )
 register_model_group(
    models={
        "Index-1.9B-Chat": {
            DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-Chat",
            DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-Chat",
        },
        "Index-1.9B-Character-Chat": {
            DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-Character",
            DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-Character",
        },
        "Index-1.9B-Base": {
            DownloadSource.DEFAULT: "IndexTeam/Index-1.9B",
            DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B",
        },
        "Index-1.9B-Base-Pure": {
            DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-Pure",
            DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-Pure",
        },
        "Index-1.9B-Chat-32K": {
            DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-32K",
            DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-32K",
        },
    },
    template="index",
 )
 register_model_group(
    models={
        "InternLM-7B": {
@ -624,16 +664,10 @@ register_model_group(
            DownloadSource.DEFAULT: "internlm/internlm2-chat-20b",
            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2-chat-20b",
        },
    },
    template="intern2",
 )
 register_model_group(
    models={
        "InternLM2.5-1.8B": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-1_8b",
            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-1_8b",
            DownloadSource.OPENMIND: "Intern/internlm2_5-1_8b",
        },
        "InternLM2.5-7B": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-7b",
@ -642,22 +676,27 @@ register_model_group(
        "InternLM2.5-20B": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-20b",
            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-20b",
            DownloadSource.OPENMIND: "Intern/internlm2_5-20b",
        },
        "InternLM2.5-1.8B-Chat": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-1_8b-chat",
            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-1_8b-chat",
            DownloadSource.OPENMIND: "Intern/internlm2_5-1_8b-chat",
        },
        "InternLM2.5-7B-Chat": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-7b-chat",
            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b-chat",
            DownloadSource.OPENMIND: "Intern/internlm2_5-7b-chat",
        },
        "InternLM2.5-7B-1M-Chat": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-7b-chat-1m",
            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b-chat-1m",
            DownloadSource.OPENMIND: "Intern/internlm2_5-7b-chat-1m",
        },
        "InternLM2.5-20B-Chat": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-20b-chat",
            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-20b-chat",
            DownloadSource.OPENMIND: "Intern/internlm2_5-20b-chat",
        },
    },
    template="intern2",
@ -686,19 +725,19 @@ register_model_group(
 register_model_group(
    models={
-        "LLaMA-7B": {
+        "Llama-7B": {
            DownloadSource.DEFAULT: "huggyllama/llama-7b",
            DownloadSource.MODELSCOPE: "skyline2006/llama-7b",
        },
-        "LLaMA-13B": {
+        "Llama-13B": {
            DownloadSource.DEFAULT: "huggyllama/llama-13b",
            DownloadSource.MODELSCOPE: "skyline2006/llama-13b",
        },
-        "LLaMA-30B": {
+        "Llama-30B": {
            DownloadSource.DEFAULT: "huggyllama/llama-30b",
            DownloadSource.MODELSCOPE: "skyline2006/llama-30b",
        },
-        "LLaMA-65B": {
+        "Llama-65B": {
            DownloadSource.DEFAULT: "huggyllama/llama-65b",
            DownloadSource.MODELSCOPE: "skyline2006/llama-65b",
        },
@ -708,27 +747,27 @@ register_model_group(
 register_model_group(
    models={
-        "LLaMA2-7B": {
+        "Llama-2-7B": {
            DownloadSource.DEFAULT: "meta-llama/Llama-2-7b-hf",
            DownloadSource.MODELSCOPE: "modelscope/Llama-2-7b-ms",
        },
-        "LLaMA2-13B": {
+        "Llama-2-13B": {
            DownloadSource.DEFAULT: "meta-llama/Llama-2-13b-hf",
            DownloadSource.MODELSCOPE: "modelscope/Llama-2-13b-ms",
        },
-        "LLaMA2-70B": {
+        "Llama-2-70B": {
            DownloadSource.DEFAULT: "meta-llama/Llama-2-70b-hf",
            DownloadSource.MODELSCOPE: "modelscope/Llama-2-70b-ms",
        },
-        "LLaMA2-7B-Chat": {
+        "Llama-2-7B-Chat": {
            DownloadSource.DEFAULT: "meta-llama/Llama-2-7b-chat-hf",
            DownloadSource.MODELSCOPE: "modelscope/Llama-2-7b-chat-ms",
        },
-        "LLaMA2-13B-Chat": {
+        "Llama-2-13B-Chat": {
            DownloadSource.DEFAULT: "meta-llama/Llama-2-13b-chat-hf",
            DownloadSource.MODELSCOPE: "modelscope/Llama-2-13b-chat-ms",
        },
-        "LLaMA2-70B-Chat": {
+        "Llama-2-70B-Chat": {
            DownloadSource.DEFAULT: "meta-llama/Llama-2-70b-chat-hf",
            DownloadSource.MODELSCOPE: "modelscope/Llama-2-70b-chat-ms",
        },
@ -739,60 +778,78 @@ register_model_group(
 register_model_group(
    models={
-        "LLaMA3-8B": {
+        "Llama-3-8B": {
            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-8B",
            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-8B",
        },
-        "LLaMA3-70B": {
+        "Llama-3-70B": {
            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-70B",
            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-70B",
        },
-        "LLaMA3-8B-Instruct": {
+        "Llama-3-8B-Instruct": {
            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-8B-Instruct",
            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-8B-Instruct",
        },
-        "LLaMA3-70B-Instruct": {
+        "Llama-3-70B-Instruct": {
            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-70B-Instruct",
            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-70B-Instruct",
        },
-        "LLaMA3-8B-Chinese-Chat": {
+        "Llama-3-8B-Chinese-Chat": {
            DownloadSource.DEFAULT: "shenzhi-wang/Llama3-8B-Chinese-Chat",
            DownloadSource.MODELSCOPE: "LLM-Research/Llama3-8B-Chinese-Chat",
            DownloadSource.OPENMIND: "LlamaFactory/Llama3-Chinese-8B-Instruct",
        },
-        "LLaMA3-70B-Chinese-Chat": {
+        "Llama-3-70B-Chinese-Chat": {
            DownloadSource.DEFAULT: "shenzhi-wang/Llama3-70B-Chinese-Chat",
        },
-    },
+        "Llama-3.1-8B": {
    template="llama3",
 )
 register_model_group(
    models={
        "LLaMA3.1-8B": {
            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-8B",
            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-8B",
        },
-        "LLaMA3.1-70B": {
+        "Llama-3.1-70B": {
            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-70B",
            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-70B",
        },
-        "LLaMA3.1-405B": {
+        "Llama-3.1-405B": {
            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-405B",
            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-405B",
        },
-        "LLaMA3.1-8B-Instruct": {
+        "Llama-3.1-8B-Instruct": {
            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-8B-Instruct",
            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-8B-Instruct",
        },
-        "LLaMA3.1-70B-Instruct": {
+        "Llama-3.1-70B-Instruct": {
            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-70B-Instruct",
            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-70B-Instruct",
        },
-        "LLaMA3.1-405B-Instruct": {
+        "Llama-3.1-405B-Instruct": {
            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-405B-Instruct",
            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-405B-Instruct",
        },
        "Llama-3.1-8B-Chinese-Chat": {
            DownloadSource.DEFAULT: "shenzhi-wang/Llama3.1-8B-Chinese-Chat",
            DownloadSource.MODELSCOPE: "XD_AI/Llama3.1-8B-Chinese-Chat",
        },
        "Llama-3.1-70B-Chinese-Chat": {
            DownloadSource.DEFAULT: "shenzhi-wang/Llama3.1-70B-Chinese-Chat",
            DownloadSource.MODELSCOPE: "XD_AI/Llama3.1-70B-Chinese-Chat",
        },
        "Llama-3.2-1B": {
            DownloadSource.DEFAULT: "meta-llama/Llama-3.2-1B",
            DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-1B",
        },
        "Llama-3.2-3B": {
            DownloadSource.DEFAULT: "meta-llama/Llama-3.2-3B",
            DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-3B",
        },
        "Llama-3.2-1B-Instruct": {
            DownloadSource.DEFAULT: "meta-llama/Llama-3.2-1B-Instruct",
            DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-1B-Instruct",
        },
        "Llama-3.2-3B-Instruct": {
            DownloadSource.DEFAULT: "meta-llama/Llama-3.2-3B-Instruct",
            DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-3B-Instruct",
        },
    },
    template="llama3",
 )
@ -800,11 +857,13 @@ register_model_group(
 register_model_group(
    models={
-        "LLaVA1.5-7B-Chat": {
+        "LLaVA-1.5-7B-Chat": {
            DownloadSource.DEFAULT: "llava-hf/llava-1.5-7b-hf",
            DownloadSource.MODELSCOPE: "swift/llava-1.5-7b-hf",
        },
-        "LLaVA1.5-13B-Chat": {
+        "LLaVA-1.5-13B-Chat": {
            DownloadSource.DEFAULT: "llava-hf/llava-1.5-13b-hf",
            DownloadSource.MODELSCOPE: "swift/llava-1.5-13b-hf",
        },
    },
    template="llava",
@ -812,6 +871,117 @@ register_model_group(
 )
 register_model_group(
    models={
        "LLaVA-NeXT-7B-Chat": {
            DownloadSource.DEFAULT: "llava-hf/llava-v1.6-vicuna-7b-hf",
            DownloadSource.MODELSCOPE: "swift/llava-v1.6-vicuna-7b-hf",
        },
        "LLaVA-NeXT-13B-Chat": {
            DownloadSource.DEFAULT: "llava-hf/llava-v1.6-vicuna-13b-hf",
            DownloadSource.MODELSCOPE: "swift/llava-v1.6-vicuna-13b-hf",
        },
    },
    template="llava_next",
    vision=True,
 )
 register_model_group(
    models={
        "LLaVA-NeXT-Mistral-7B-Chat": {
            DownloadSource.DEFAULT: "llava-hf/llava-v1.6-mistral-7b-hf",
            DownloadSource.MODELSCOPE: "swift/llava-v1.6-mistral-7b-hf",
        },
    },
    template="llava_next_mistral",
    vision=True,
 )
 register_model_group(
    models={
        "LLaVA-NeXT-Llama3-8B-Chat": {
            DownloadSource.DEFAULT: "llava-hf/llama3-llava-next-8b-hf",
            DownloadSource.MODELSCOPE: "swift/llama3-llava-next-8b-hf",
        },
    },
    template="llava_next_llama3",
    vision=True,
 )
 register_model_group(
    models={
        "LLaVA-NeXT-34B-Chat": {
            DownloadSource.DEFAULT: "llava-hf/llava-v1.6-34b-hf",
            DownloadSource.MODELSCOPE: "LLM-Research/llava-v1.6-34b-hf",
        },
    },
    template="llava_next_yi",
    vision=True,
 )
 register_model_group(
    models={
        "LLaVA-NeXT-72B-Chat": {
            DownloadSource.DEFAULT: "llava-hf/llava-next-72b-hf",
            DownloadSource.MODELSCOPE: "AI-ModelScope/llava-next-72b-hf",
        },
        "LLaVA-NeXT-110B-Chat": {
            DownloadSource.DEFAULT: "llava-hf/llava-next-110b-hf",
            DownloadSource.MODELSCOPE: "AI-ModelScope/llava-next-110b-hf",
        },
    },
    template="llava_next_qwen",
    vision=True,
 )
 register_model_group(
    models={
        "LLaVA-NeXT-Video-7B-Chat": {
            DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-7B-hf",
            DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-7B-hf",
        },
        "LLaVA-NeXT-Video-7B-DPO-Chat": {
            DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-7B-DPO-hf",
            DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-7B-DPO-hf",
        },
    },
    template="llava_next_video",
    vision=True,
 )
 register_model_group(
    models={
        "LLaVA-NeXT-Video-7B-32k-Chat": {
            DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-7B-32K-hf",
            DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-7B-32K-hf",
        },
    },
    template="llava_next_video_mistral",
    vision=True,
 )
 register_model_group(
    models={
        "LLaVA-NeXT-Video-34B-Chat": {
            DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-34B-hf",
            DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-34B-hf",
        },
        "LLaVA-NeXT-Video-34B-DPO-Chat": {
            DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-34B-DPO-hf",
        },
    },
    template="llava_next_video_yi",
    vision=True,
 )
 register_model_group(
    models={
        "MiniCPM-2B-SFT-Chat": {
@ -832,6 +1002,7 @@ register_model_group(
        "MiniCPM3-4B-Chat": {
            DownloadSource.DEFAULT: "openbmb/MiniCPM3-4B",
            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM3-4B",
            DownloadSource.OPENMIND: "LlamaFactory/MiniCPM3-4B",
        },
    },
    template="cpm3",
@ -1005,27 +1176,27 @@ register_model_group(
 register_model_group(
    models={
-        "Phi3-4B-4k-Instruct": {
+        "Phi-3-4B-4k-Instruct": {
            DownloadSource.DEFAULT: "microsoft/Phi-3-mini-4k-instruct",
            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-mini-4k-instruct",
        },
-        "Phi3-4B-128k-Instruct": {
+        "Phi-3-4B-128k-Instruct": {
            DownloadSource.DEFAULT: "microsoft/Phi-3-mini-128k-instruct",
            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-mini-128k-instruct",
        },
-        "Phi3-7B-8k-Instruct": {
+        "Phi-3-7B-8k-Instruct": {
            DownloadSource.DEFAULT: "microsoft/Phi-3-small-8k-instruct",
            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-small-8k-instruct",
        },
-        "Phi3-7B-128k-Instruct": {
+        "Phi-3-7B-128k-Instruct": {
            DownloadSource.DEFAULT: "microsoft/Phi-3-small-128k-instruct",
            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-small-128k-instruct",
        },
-        "Phi3-14B-8k-Instruct": {
+        "Phi-3-14B-8k-Instruct": {
            DownloadSource.DEFAULT: "microsoft/Phi-3-medium-4k-instruct",
            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-medium-4k-instruct",
        },
-        "Phi3-14B-128k-Instruct": {
+        "Phi-3-14B-128k-Instruct": {
            DownloadSource.DEFAULT: "microsoft/Phi-3-medium-128k-instruct",
            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-medium-128k-instruct",
        },
@ -1034,6 +1205,18 @@ register_model_group(
 )
 register_model_group(
    models={
        "Pixtral-12B-Chat": {
            DownloadSource.DEFAULT: "mistral-community/pixtral-12b",
            DownloadSource.MODELSCOPE: "AI-ModelScope/pixtral-12b",
        }
    },
    template="pixtral",
    vision=True,
 )
 register_model_group(
    models={
        "Qwen-1.8B": {
@ -1068,35 +1251,35 @@ register_model_group(
            DownloadSource.DEFAULT: "Qwen/Qwen-72B-Chat",
            DownloadSource.MODELSCOPE: "qwen/Qwen-72B-Chat",
        },
-        "Qwen-1.8B-int8-Chat": {
+        "Qwen-1.8B-Chat-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen-1_8B-Chat-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen-1_8B-Chat-Int8",
        },
-        "Qwen-1.8B-int4-Chat": {
+        "Qwen-1.8B-Chat-Int4": {
            DownloadSource.DEFAULT: "Qwen/Qwen-1_8B-Chat-Int4",
            DownloadSource.MODELSCOPE: "qwen/Qwen-1_8B-Chat-Int4",
        },
-        "Qwen-7B-int8-Chat": {
+        "Qwen-7B-Chat-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen-7B-Chat-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen-7B-Chat-Int8",
        },
-        "Qwen-7B-int4-Chat": {
+        "Qwen-7B-Chat-Int4": {
            DownloadSource.DEFAULT: "Qwen/Qwen-7B-Chat-Int4",
            DownloadSource.MODELSCOPE: "qwen/Qwen-7B-Chat-Int4",
        },
-        "Qwen-14B-int8-Chat": {
+        "Qwen-14B-Chat-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen-14B-Chat-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen-14B-Chat-Int8",
        },
-        "Qwen-14B-int4-Chat": {
+        "Qwen-14B-Chat-Int4": {
            DownloadSource.DEFAULT: "Qwen/Qwen-14B-Chat-Int4",
            DownloadSource.MODELSCOPE: "qwen/Qwen-14B-Chat-Int4",
        },
-        "Qwen-72B-int8-Chat": {
+        "Qwen-72B-Chat-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen-72B-Chat-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen-72B-Chat-Int8",
        },
-        "Qwen-72B-int4-Chat": {
+        "Qwen-72B-Chat-Int4": {
            DownloadSource.DEFAULT: "Qwen/Qwen-72B-Chat-Int4",
            DownloadSource.MODELSCOPE: "qwen/Qwen-72B-Chat-Int4",
        },
@ -1179,75 +1362,75 @@ register_model_group(
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-MoE-A2.7B-Chat",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-MoE-A2.7B-Chat",
        },
-        "Qwen1.5-0.5B-int8-Chat": {
+        "Qwen1.5-0.5B-Chat-GPTQ-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-0.5B-Chat-GPTQ-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-0.5B-Chat-GPTQ-Int8",
        },
-        "Qwen1.5-0.5B-int4-Chat": {
+        "Qwen1.5-0.5B-Chat-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-0.5B-Chat-AWQ",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-0.5B-Chat-AWQ",
        },
-        "Qwen1.5-1.8B-int8-Chat": {
+        "Qwen1.5-1.8B-Chat-GPTQ-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-1.8B-Chat-GPTQ-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-1.8B-Chat-GPTQ-Int8",
        },
-        "Qwen1.5-1.8B-int4-Chat": {
+        "Qwen1.5-1.8B-Chat-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-1.8B-Chat-AWQ",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-1.8B-Chat-AWQ",
        },
-        "Qwen1.5-4B-int8-Chat": {
+        "Qwen1.5-4B-Chat-GPTQ-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-4B-Chat-GPTQ-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-4B-Chat-GPTQ-Int8",
        },
-        "Qwen1.5-4B-int4-Chat": {
+        "Qwen1.5-4B-Chat-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-4B-Chat-AWQ",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-4B-Chat-AWQ",
        },
-        "Qwen1.5-7B-int8-Chat": {
+        "Qwen1.5-7B-Chat-GPTQ-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-7B-Chat-GPTQ-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-7B-Chat-GPTQ-Int8",
        },
-        "Qwen1.5-7B-int4-Chat": {
+        "Qwen1.5-7B-Chat-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-7B-Chat-AWQ",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-7B-Chat-AWQ",
        },
-        "Qwen1.5-14B-int8-Chat": {
+        "Qwen1.5-14B-Chat-GPTQ-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-14B-Chat-GPTQ-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-14B-Chat-GPTQ-Int8",
        },
-        "Qwen1.5-14B-int4-Chat": {
+        "Qwen1.5-14B-Chat-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-14B-Chat-AWQ",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-14B-Chat-AWQ",
        },
-        "Qwen1.5-32B-int4-Chat": {
+        "Qwen1.5-32B-Chat-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-32B-Chat-AWQ",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-32B-Chat-AWQ",
        },
-        "Qwen1.5-72B-int8-Chat": {
+        "Qwen1.5-72B-Chat-GPTQ-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-72B-Chat-GPTQ-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-72B-Chat-GPTQ-Int8",
        },
-        "Qwen1.5-72B-int4-Chat": {
+        "Qwen1.5-72B-Chat-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-72B-Chat-AWQ",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-72B-Chat-AWQ",
        },
-        "Qwen1.5-110B-int4-Chat": {
+        "Qwen1.5-110B-Chat-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-110B-Chat-AWQ",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-110B-Chat-AWQ",
        },
-        "Qwen1.5-MoE-A2.7B-int4-Chat": {
+        "Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4",
        },
-        "Qwen1.5-Code-7B": {
+        "CodeQwen1.5-7B": {
            DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B",
            DownloadSource.MODELSCOPE: "qwen/CodeQwen1.5-7B",
        },
-        "Qwen1.5-Code-7B-Chat": {
+        "CodeQwen1.5-7B-Chat": {
            DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B-Chat",
            DownloadSource.MODELSCOPE: "qwen/CodeQwen1.5-7B-Chat",
        },
-        "Qwen1.5-Code-7B-int4-Chat": {
+        "CodeQwen1.5-7B-Chat-AWQ": {
            DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B-Chat-AWQ",
            DownloadSource.MODELSCOPE: "qwen/CodeQwen1.5-7B-Chat-AWQ",
        },
@ -1281,14 +1464,17 @@ register_model_group(
        "Qwen2-0.5B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-0.5B-Instruct",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-0.5B-Instruct",
            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-0.5B-Instruct",
        },
        "Qwen2-1.5B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-1.5B-Instruct",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-1.5B-Instruct",
            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-1.5B-Instruct",
        },
        "Qwen2-7B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-7B-Instruct",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-7B-Instruct",
            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-7B-Instruct",
        },
        "Qwen2-72B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-72B-Instruct",
@ -1568,51 +1754,53 @@ register_model_group(
 register_model_group(
    models={
-        "Qwen2VL-2B-Instruct": {
+        "Qwen2-VL-2B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-2B-Instruct",
            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-VL-2B-Instruct",
        },
-        "Qwen2VL-7B-Instruct": {
+        "Qwen2-VL-7B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-7B-Instruct",
            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-VL-7B-Instruct",
        },
-        "Qwen2VL-72B-Instruct": {
+        "Qwen2-VL-72B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-72B-Instruct",
        },
-        "Qwen2VL-2B-Instruct-GPTQ-Int8": {
+        "Qwen2-VL-2B-Instruct-GPTQ-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8",
        },
-        "Qwen2VL-2B-Instruct-GPTQ-Int4": {
+        "Qwen2-VL-2B-Instruct-GPTQ-Int4": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4",
        },
-        "Qwen2VL-2B-Instruct-AWQ": {
+        "Qwen2-VL-2B-Instruct-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct-AWQ",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-2B-Instruct-AWQ",
        },
-        "Qwen2VL-7B-Instruct-GPTQ-Int8": {
+        "Qwen2-VL-7B-Instruct-GPTQ-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8",
        },
-        "Qwen2VL-7B-Instruct-GPTQ-Int4": {
+        "Qwen2-VL-7B-Instruct-GPTQ-Int4": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4",
        },
-        "Qwen2VL-7B-Instruct-AWQ": {
+        "Qwen2-VL-7B-Instruct-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct-AWQ",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-7B-Instruct-AWQ",
        },
-        "Qwen2VL-72B-Instruct-GPTQ-Int8": {
+        "Qwen2-VL-72B-Instruct-GPTQ-Int8": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-72B-Instruct-GPTQ-Int8",
        },
-        "Qwen2VL-72B-Instruct-GPTQ-Int4": {
+        "Qwen2-VL-72B-Instruct-GPTQ-Int4": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int4",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-72B-Instruct-GPTQ-Int4",
        },
-        "Qwen2VL-72B-Instruct-AWQ": {
+        "Qwen2-VL-72B-Instruct-AWQ": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct-AWQ",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-72B-Instruct-AWQ",
        },
@ -1673,10 +1861,12 @@ register_model_group(
        "TeleChat-7B-Chat": {
            DownloadSource.DEFAULT: "Tele-AI/telechat-7B",
            DownloadSource.MODELSCOPE: "TeleAI/telechat-7B",
            DownloadSource.OPENMIND: "TeleAI/TeleChat-7B-pt",
        },
        "TeleChat-12B-Chat": {
            DownloadSource.DEFAULT: "Tele-AI/TeleChat-12B",
            DownloadSource.MODELSCOPE: "TeleAI/TeleChat-12B",
            DownloadSource.OPENMIND: "TeleAI/TeleChat-12B-pt",
        },
        "TeleChat-12B-v2-Chat": {
            DownloadSource.DEFAULT: "Tele-AI/TeleChat-12B-v2",
@ -1689,11 +1879,11 @@ register_model_group(
 register_model_group(
    models={
-        "Vicuna1.5-7B-Chat": {
+        "Vicuna-v1.5-7B-Chat": {
            DownloadSource.DEFAULT: "lmsys/vicuna-7b-v1.5",
            DownloadSource.MODELSCOPE: "Xorbits/vicuna-7b-v1.5",
        },
-        "Vicuna1.5-13B-Chat": {
+        "Vicuna-v1.5-13B-Chat": {
            DownloadSource.DEFAULT: "lmsys/vicuna-13b-v1.5",
            DownloadSource.MODELSCOPE: "Xorbits/vicuna-13b-v1.5",
        },
@ -1702,6 +1892,17 @@ register_model_group(
 )
 register_model_group(
    models={
        "Video-LLaVA-7B-Chat": {
            DownloadSource.DEFAULT: "LanguageBind/Video-LLaVA-7B-hf",
        },
    },
    template="video_llava",
    vision=True,
 )
 register_model_group(
    models={
        "XuanYuan-6B": {
@ -1712,7 +1913,7 @@ register_model_group(
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B",
            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B",
        },
-        "XuanYuan-2-70B": {
+        "XuanYuan2-70B": {
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B",
            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B",
        },
@ -1724,31 +1925,31 @@ register_model_group(
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat",
            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat",
        },
-        "XuanYuan-2-70B-Chat": {
+        "XuanYuan2-70B-Chat": {
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat",
            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat",
        },
-        "XuanYuan-6B-int8-Chat": {
+        "XuanYuan-6B-Chat-8bit": {
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B-Chat-8bit",
            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B-Chat-8bit",
        },
-        "XuanYuan-6B-int4-Chat": {
+        "XuanYuan-6B-Chat-4bit": {
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B-Chat-4bit",
            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B-Chat-4bit",
        },
-        "XuanYuan-70B-int8-Chat": {
+        "XuanYuan-70B-Chat-8bit": {
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat-8bit",
            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat-8bit",
        },
-        "XuanYuan-70B-int4-Chat": {
+        "XuanYuan-70B-Chat-4bit": {
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat-4bit",
            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat-4bit",
        },
-        "XuanYuan-2-70B-int8-Chat": {
+        "XuanYuan2-70B-Chat-8bit": {
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat-8bit",
            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat-8bit",
        },
-        "XuanYuan-2-70B-int4-Chat": {
+        "XuanYuan2-70B-Chat-4bit": {
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat-4bit",
            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat-4bit",
        },
@ -1853,19 +2054,19 @@ register_model_group(
            DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat",
            DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat",
        },
-        "Yi-6B-int8-Chat": {
+        "Yi-6B-Chat-8bits": {
            DownloadSource.DEFAULT: "01-ai/Yi-6B-Chat-8bits",
            DownloadSource.MODELSCOPE: "01ai/Yi-6B-Chat-8bits",
        },
-        "Yi-6B-int4-Chat": {
+        "Yi-6B-Chat-4bits": {
            DownloadSource.DEFAULT: "01-ai/Yi-6B-Chat-4bits",
            DownloadSource.MODELSCOPE: "01ai/Yi-6B-Chat-4bits",
        },
-        "Yi-34B-int8-Chat": {
+        "Yi-34B-Chat-8bits": {
            DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat-8bits",
            DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat-8bits",
        },
-        "Yi-34B-int4-Chat": {
+        "Yi-34B-Chat-4bits": {
            DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat-4bits",
            DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat-4bits",
        },
@ -1884,6 +2085,7 @@ register_model_group(
        "Yi-1.5-6B-Chat": {
            DownloadSource.DEFAULT: "01-ai/Yi-1.5-6B-Chat",
            DownloadSource.MODELSCOPE: "01ai/Yi-1.5-6B-Chat",
            DownloadSource.OPENMIND: "LlamaFactory/Yi-1.5-6B-Chat",
        },
        "Yi-1.5-9B-Chat": {
            DownloadSource.DEFAULT: "01-ai/Yi-1.5-9B-Chat",
@ -1916,10 +2118,10 @@ register_model_group(
 register_model_group(
    models={
-        "YiVL-6B-Chat": {
+        "Yi-VL-6B-Chat": {
            DownloadSource.DEFAULT: "BUAADreamer/Yi-VL-6B-hf",
        },
-        "YiVL-34B-Chat": {
+        "Yi-VL-34B-Chat": {
            DownloadSource.DEFAULT: "BUAADreamer/Yi-VL-34B-hf",
        },
    },
--- a/src/llamafactory/extras/env.py
+++ b/src/llamafactory/extras/env.py
@ -72,4 +72,4 @@ def print_env() -> None:
    except Exception:
        pass
-    print("\n" + "\n".join(["- {}: {}".format(key, value) for key, value in info.items()]) + "\n")
+    print("\n" + "\n".join([f"- {key}: {value}" for key, value in info.items()]) + "\n")
--- a/src/llamafactory/extras/logging.py
+++ b/src/llamafactory/extras/logging.py
@ -20,6 +20,7 @@ import os
 import sys
 import threading
 from concurrent.futures import ThreadPoolExecutor
 from functools import lru_cache
 from typing import Optional
 from .constants import RUNNING_LOG
@ -37,12 +38,11 @@ class LoggerHandler(logging.Handler):
    def __init__(self, output_dir: str) -> None:
        super().__init__()
-        formatter = logging.Formatter(
+        self._formatter = logging.Formatter(
-            fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S"
+            fmt="[%(levelname)s|%(asctime)s] %(filename)s:%(lineno)s >> %(message)s",
            datefmt="%Y-%m-%d %H:%M:%S",
        )
        self.setLevel(logging.INFO)
        self.setFormatter(formatter)
        os.makedirs(output_dir, exist_ok=True)
        self.running_log = os.path.join(output_dir, RUNNING_LOG)
        if os.path.exists(self.running_log):
@ -58,7 +58,7 @@ class LoggerHandler(logging.Handler):
        if record.name == "httpx":
            return
-        log_entry = self.format(record)
+        log_entry = self._formatter.format(record)
        self.thread_pool.submit(self._write_log, log_entry)
    def close(self) -> None:
@ -66,6 +66,21 @@ class LoggerHandler(logging.Handler):
        return super().close()
 class _Logger(logging.Logger):
    r"""
    A logger that supports info_rank0 and warning_once.
    """
    def info_rank0(self, *args, **kwargs) -> None:
        self.info(*args, **kwargs)
    def warning_rank0(self, *args, **kwargs) -> None:
        self.warning(*args, **kwargs)
    def warning_once(self, *args, **kwargs) -> None:
        self.warning(*args, **kwargs)
 def _get_default_logging_level() -> "logging._Level":
    r"""
    Returns the default logging level.
@ -75,7 +90,7 @@ def _get_default_logging_level() -> "logging._Level":
        if env_level_str.upper() in logging._nameToLevel:
            return logging._nameToLevel[env_level_str.upper()]
        else:
-            raise ValueError("Unknown logging level: {}.".format(env_level_str))
+            raise ValueError(f"Unknown logging level: {env_level_str}.")
    return _default_log_level
@ -84,7 +99,7 @@ def _get_library_name() -> str:
    return __name__.split(".")[0]
-def _get_library_root_logger() -> "logging.Logger":
+def _get_library_root_logger() -> "_Logger":
    return logging.getLogger(_get_library_name())
@ -95,12 +110,12 @@ def _configure_library_root_logger() -> None:
    global _default_handler
    with _thread_lock:
-        if _default_handler:
+        if _default_handler:  # already configured
            return
        formatter = logging.Formatter(
-            fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+            fmt="[%(levelname)s|%(asctime)s] %(name)s:%(lineno)s >> %(message)s",
-            datefmt="%m/%d/%Y %H:%M:%S",
+            datefmt="%Y-%m-%d %H:%M:%S",
        )
        _default_handler = logging.StreamHandler(sys.stdout)
        _default_handler.setFormatter(formatter)
@ -110,7 +125,7 @@ def _configure_library_root_logger() -> None:
        library_root_logger.propagate = False
-def get_logger(name: Optional[str] = None) -> "logging.Logger":
+def get_logger(name: Optional[str] = None) -> "_Logger":
    r"""
    Returns a logger with the specified name. It it not supposed to be accessed externally.
    """
@ -119,3 +134,40 @@ def get_logger(name: Optional[str] = None) -> "logging.Logger":
    _configure_library_root_logger()
    return logging.getLogger(name)
 def add_handler(handler: "logging.Handler") -> None:
    r"""
    Adds a handler to the root logger.
    """
    _configure_library_root_logger()
    _get_library_root_logger().addHandler(handler)
 def remove_handler(handler: logging.Handler) -> None:
    r"""
    Removes a handler to the root logger.
    """
    _configure_library_root_logger()
    _get_library_root_logger().removeHandler(handler)
 def info_rank0(self: "logging.Logger", *args, **kwargs) -> None:
    if int(os.getenv("LOCAL_RANK", "0")) == 0:
        self.info(*args, **kwargs)
 def warning_rank0(self: "logging.Logger", *args, **kwargs) -> None:
    if int(os.getenv("LOCAL_RANK", "0")) == 0:
        self.warning(*args, **kwargs)
@lru_cache(None)
 def warning_once(self: "logging.Logger", *args, **kwargs) -> None:
    if int(os.getenv("LOCAL_RANK", "0")) == 0:
        self.warning(*args, **kwargs)
 logging.Logger.info_rank0 = info_rank0
 logging.Logger.warning_rank0 = warning_rank0
 logging.Logger.warning_once = warning_once
--- a/src/llamafactory/extras/misc.py
+++ b/src/llamafactory/extras/misc.py
@ -32,7 +32,7 @@ from transformers.utils import (
 )
 from transformers.utils.versions import require_version
-from .logging import get_logger
+from . import logging
 _is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
@ -48,7 +48,7 @@ if TYPE_CHECKING:
    from ..hparams import ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 class AverageMeter:
@ -76,12 +76,12 @@ def check_dependencies() -> None:
    r"""
    Checks the version of the required packages.
    """
-    if os.environ.get("DISABLE_VERSION_CHECK", "0").lower() in ["true", "1"]:
+    if os.getenv("DISABLE_VERSION_CHECK", "0").lower() in ["true", "1"]:
-        logger.warning("Version checking has been disabled, may lead to unexpected behaviors.")
+        logger.warning_once("Version checking has been disabled, may lead to unexpected behaviors.")
    else:
-        require_version("transformers>=4.41.2,<=4.45.0", "To fix: pip install transformers>=4.41.2,<=4.45.0")
+        require_version("transformers>=4.41.2,<=4.46.1", "To fix: pip install transformers>=4.41.2,<=4.46.1")
-        require_version("datasets>=2.16.0,<=2.21.0", "To fix: pip install datasets>=2.16.0,<=2.21.0")
+        require_version("datasets>=2.16.0,<=3.0.2", "To fix: pip install datasets>=2.16.0,<=3.0.2")
-        require_version("accelerate>=0.30.1,<=0.34.2", "To fix: pip install accelerate>=0.30.1,<=0.34.2")
+        require_version("accelerate>=0.34.0,<=1.0.1", "To fix: pip install accelerate>=0.34.0,<=1.0.1")
        require_version("peft>=0.11.1,<=0.12.0", "To fix: pip install peft>=0.11.1,<=0.12.0")
        require_version("trl>=0.8.6,<=0.9.6", "To fix: pip install trl>=0.8.6,<=0.9.6")
@ -231,18 +231,35 @@ def torch_gc() -> None:
        torch.cuda.empty_cache()
-def try_download_model_from_ms(model_args: "ModelArguments") -> str:
+def try_download_model_from_other_hub(model_args: "ModelArguments") -> str:
-    if not use_modelscope() or os.path.exists(model_args.model_name_or_path):
+    if (not use_modelscope() and not use_openmind()) or os.path.exists(model_args.model_name_or_path):
        return model_args.model_name_or_path
-    try:
+    if use_modelscope():
-        from modelscope import snapshot_download
+        require_version("modelscope>=1.11.0", "To fix: pip install modelscope>=1.11.0")
        from modelscope import snapshot_download  # type: ignore
        revision = "master" if model_args.model_revision == "main" else model_args.model_revision
-        return snapshot_download(model_args.model_name_or_path, revision=revision, cache_dir=model_args.cache_dir)
+        return snapshot_download(
-    except ImportError:
+            model_args.model_name_or_path,
-        raise ImportError("Please install modelscope via `pip install modelscope -U`")
+            revision=revision,
            cache_dir=model_args.cache_dir,
        )
    if use_openmind():
        require_version("openmind>=0.8.0", "To fix: pip install openmind>=0.8.0")
        from openmind.utils.hub import snapshot_download  # type: ignore
        return snapshot_download(
            model_args.model_name_or_path,
            revision=model_args.model_revision,
            cache_dir=model_args.cache_dir,
        )
 def use_modelscope() -> bool:
    return os.environ.get("USE_MODELSCOPE_HUB", "0").lower() in ["true", "1"]
 def use_openmind() -> bool:
    return os.environ.get("USE_OPENMIND_HUB", "0").lower() in ["true", "1"]
--- a/src/llamafactory/extras/packages.py
+++ b/src/llamafactory/extras/packages.py
@ -79,6 +79,11 @@ def is_transformers_version_greater_than_4_43():
    return _get_package_version("transformers") >= version.parse("4.43.0")
@lru_cache
 def is_transformers_version_equal_to_4_46():
    return version.parse("4.46.0") <= _get_package_version("transformers") <= version.parse("4.46.1")
 def is_uvicorn_available():
    return _is_package_available("uvicorn")
--- a/src/llamafactory/extras/ploting.py
+++ b/src/llamafactory/extras/ploting.py
@ -19,7 +19,7 @@ from typing import Any, Dict, List
 from transformers.trainer import TRAINER_STATE_NAME
-from .logging import get_logger
+from . import logging
 from .packages import is_matplotlib_available
@ -28,7 +28,7 @@ if is_matplotlib_available():
    import matplotlib.pyplot as plt
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def smooth(scalars: List[float]) -> List[float]:
@ -75,7 +75,7 @@ def plot_loss(save_dictionary: str, keys: List[str] = ["loss"]) -> None:
    Plots loss curves and saves the image.
    """
    plt.switch_backend("agg")
-    with open(os.path.join(save_dictionary, TRAINER_STATE_NAME), "r", encoding="utf-8") as f:
+    with open(os.path.join(save_dictionary, TRAINER_STATE_NAME), encoding="utf-8") as f:
        data = json.load(f)
    for key in keys:
@ -86,13 +86,13 @@ def plot_loss(save_dictionary: str, keys: List[str] = ["loss"]) -> None:
                metrics.append(data["log_history"][i][key])
        if len(metrics) == 0:
-            logger.warning(f"No metric {key} to plot.")
+            logger.warning_rank0(f"No metric {key} to plot.")
            continue
        plt.figure()
        plt.plot(steps, metrics, color="#1f77b4", alpha=0.4, label="original")
        plt.plot(steps, smooth(metrics), color="#1f77b4", label="smoothed")
-        plt.title("training {} of {}".format(key, save_dictionary))
+        plt.title(f"training {key} of {save_dictionary}")
        plt.xlabel("step")
        plt.ylabel(key)
        plt.legend()
--- a/src/llamafactory/hparams/data_args.py
+++ b/src/llamafactory/hparams/data_args.py
@ -41,6 +41,10 @@ class DataArguments:
        default="data",
        metadata={"help": "Path to the folder containing the datasets."},
    )
    image_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Path to the folder containing the images or videos. Defaults to `dataset_dir`."},
    )
    cutoff_len: int = field(
        default=1024,
        metadata={"help": "The cutoff length of the tokenized inputs in the dataset."},
@ -111,7 +115,13 @@ class DataArguments:
    )
    tokenized_path: Optional[str] = field(
        default=None,
-        metadata={"help": "Path to save or load the tokenized datasets."},
+        metadata={
            "help": (
                "Path to save or load the tokenized datasets. "
                "If tokenized_path not exists, it will save the tokenized datasets. "
                "If tokenized_path exists, it will load the tokenized datasets."
            )
        },
    )
    def __post_init__(self):
@ -123,6 +133,9 @@ class DataArguments:
        self.dataset = split_arg(self.dataset)
        self.eval_dataset = split_arg(self.eval_dataset)
        if self.image_dir is None:
            self.image_dir = self.dataset_dir
        if self.dataset is None and self.val_size > 1e-6:
            raise ValueError("Cannot specify `val_size` if `dataset` is None.")
--- a/src/llamafactory/hparams/model_args.py
+++ b/src/llamafactory/hparams/model_args.py
@ -15,7 +15,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from dataclasses import asdict, dataclass, field, fields
+from dataclasses import dataclass, field, fields
 from typing import Any, Dict, Literal, Optional, Union
 import torch
@ -267,6 +267,10 @@ class ModelArguments(QuantizationArguments, ProcessorArguments, ExportArguments,
        default=None,
        metadata={"help": "Auth token to log in with ModelScope Hub."},
    )
    om_hub_token: Optional[str] = field(
        default=None,
        metadata={"help": "Auth token to log in with Modelers Hub."},
    )
    print_param_status: bool = field(
        default=False,
        metadata={"help": "For debugging purposes, print the status of the parameters in the model."},
@ -308,20 +312,18 @@ class ModelArguments(QuantizationArguments, ProcessorArguments, ExportArguments,
        if self.export_quantization_bit is not None and self.export_quantization_dataset is None:
            raise ValueError("Quantization dataset is necessary for exporting.")
    def to_dict(self) -> Dict[str, Any]:
        return asdict(self)
    @classmethod
-    def copyfrom(cls, old_arg: "Self", **kwargs) -> "Self":
+    def copyfrom(cls, source: "Self", **kwargs) -> "Self":
-        arg_dict = old_arg.to_dict()
+        init_args, lazy_args = {}, {}
-        arg_dict.update(**kwargs)
+        for attr in fields(source):
-        for attr in fields(cls):
+            if attr.init:
-            if not attr.init:
+                init_args[attr.name] = getattr(source, attr.name)
-                arg_dict.pop(attr.name)
+            else:
                lazy_args[attr.name] = getattr(source, attr.name)
-        new_arg = cls(**arg_dict)
+        init_args.update(kwargs)
-        new_arg.compute_dtype = old_arg.compute_dtype
+        result = cls(**init_args)
-        new_arg.device_map = old_arg.device_map
+        for name, value in lazy_args.items():
-        new_arg.model_max_length = old_arg.model_max_length
+            setattr(result, name, value)
-        new_arg.block_diag_attn = old_arg.block_diag_attn
+
-        return new_arg
+        return result
--- 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 logging
 import os
 import sys
 from typing import Any, Dict, Optional, Tuple
@ -29,8 +28,8 @@ from transformers.training_args import ParallelMode
 from transformers.utils import is_torch_bf16_gpu_available, is_torch_npu_available
 from transformers.utils.versions import require_version
 from ..extras import logging
 from ..extras.constants import CHECKPOINT_NAMES
 from ..extras.logging import get_logger
 from ..extras.misc import check_dependencies, get_current_device
 from .data_args import DataArguments
 from .evaluation_args import EvaluationArguments
@ -39,7 +38,7 @@ from .generating_args import GeneratingArguments
 from .model_args import ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 check_dependencies()
@ -57,7 +56,7 @@ def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = Non
    if args is not None:
        return parser.parse_dict(args)
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
+    if len(sys.argv) == 2 and (sys.argv[1].endswith(".yaml") or sys.argv[1].endswith(".yml")):
        return parser.parse_yaml_file(os.path.abspath(sys.argv[1]))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
@ -67,14 +66,14 @@ def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = Non
    if unknown_args:
        print(parser.format_help())
-        print("Got unknown args, potentially deprecated arguments: {}".format(unknown_args))
+        print(f"Got unknown args, potentially deprecated arguments: {unknown_args}")
-        raise ValueError("Some specified arguments are not used by the HfArgumentParser: {}".format(unknown_args))
+        raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {unknown_args}")
    return (*parsed_args,)
-def _set_transformers_logging(log_level: Optional[int] = logging.INFO) -> None:
+def _set_transformers_logging() -> None:
-    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
@ -104,7 +103,7 @@ def _verify_model_args(
            raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
    if data_args.template == "yi" and model_args.use_fast_tokenizer:
-        logger.warning("We should use slow tokenizer for the Yi models. Change `use_fast_tokenizer` to False.")
+        logger.warning_rank0("We should use slow tokenizer for the Yi models. Change `use_fast_tokenizer` to False.")
        model_args.use_fast_tokenizer = False
@ -123,7 +122,7 @@ def _check_extra_dependencies(
        require_version("mixture-of-depth>=1.1.6", "To fix: pip install mixture-of-depth>=1.1.6")
    if model_args.infer_backend == "vllm":
-        require_version("vllm>=0.4.3,<=0.6.0", "To fix: pip install vllm>=0.4.3,<=0.6.0")
+        require_version("vllm>=0.4.3,<=0.6.3", "To fix: pip install vllm>=0.4.3,<=0.6.3")
    if finetuning_args.use_galore:
        require_version("galore_torch", "To fix: pip install galore_torch")
@ -261,7 +260,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
        raise ValueError("Unsloth is incompatible with DeepSpeed ZeRO-3.")
    if data_args.neat_packing and not data_args.packing:
-        logger.warning("`neat_packing` requires `packing` is True. Change `packing` to True.")
+        logger.warning_rank0("`neat_packing` requires `packing` is True. Change `packing` to True.")
        data_args.packing = True
    _verify_model_args(model_args, data_args, finetuning_args)
@ -274,22 +273,26 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
        and model_args.resize_vocab
        and finetuning_args.additional_target is None
    ):
-        logger.warning("Remember to add embedding layers to `additional_target` to make the added tokens trainable.")
+        logger.warning_rank0(
            "Remember to add embedding layers to `additional_target` to make the added tokens trainable."
        )
    if training_args.do_train and model_args.quantization_bit is not None and (not model_args.upcast_layernorm):
-        logger.warning("We recommend enable `upcast_layernorm` in quantized training.")
+        logger.warning_rank0("We recommend enable `upcast_layernorm` in quantized training.")
    if training_args.do_train and (not training_args.fp16) and (not training_args.bf16):
-        logger.warning("We recommend enable mixed precision training.")
+        logger.warning_rank0("We recommend enable mixed precision training.")
    if training_args.do_train and finetuning_args.use_galore and not finetuning_args.pure_bf16:
-        logger.warning("Using GaLore with mixed precision training may significantly increases GPU memory usage.")
+        logger.warning_rank0(
            "Using GaLore with mixed precision training may significantly increases GPU memory usage."
        )
    if (not training_args.do_train) and model_args.quantization_bit is not None:
-        logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
+        logger.warning_rank0("Evaluating model in 4/8-bit mode may cause lower scores.")
    if (not training_args.do_train) and finetuning_args.stage == "dpo" and finetuning_args.ref_model is None:
-        logger.warning("Specify `ref_model` for computing rewards at evaluation.")
+        logger.warning_rank0("Specify `ref_model` for computing rewards at evaluation.")
    # Post-process training arguments
    if (
@ -297,13 +300,13 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
        and training_args.ddp_find_unused_parameters is None
        and finetuning_args.finetuning_type == "lora"
    ):
-        logger.warning("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.")
+        logger.warning_rank0("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.")
        training_args.ddp_find_unused_parameters = False
    if finetuning_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type in ["full", "freeze"]:
        can_resume_from_checkpoint = False
        if training_args.resume_from_checkpoint is not None:
-            logger.warning("Cannot resume from checkpoint in current stage.")
+            logger.warning_rank0("Cannot resume from checkpoint in current stage.")
            training_args.resume_from_checkpoint = None
    else:
        can_resume_from_checkpoint = True
@ -323,15 +326,15 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
        if last_checkpoint is not None:
            training_args.resume_from_checkpoint = last_checkpoint
-            logger.info("Resuming training from {}.".format(training_args.resume_from_checkpoint))
+            logger.info_rank0(f"Resuming training from {training_args.resume_from_checkpoint}.")
-            logger.info("Change `output_dir` or use `overwrite_output_dir` to avoid.")
+            logger.info_rank0("Change `output_dir` or use `overwrite_output_dir` to avoid.")
    if (
        finetuning_args.stage in ["rm", "ppo"]
        and finetuning_args.finetuning_type == "lora"
        and training_args.resume_from_checkpoint is not None
    ):
-        logger.warning(
+        logger.warning_rank0(
            "Add {} to `adapter_name_or_path` to resume training from checkpoint.".format(
                training_args.resume_from_checkpoint
            )
--- a/src/llamafactory/model/adapter.py
+++ b/src/llamafactory/model/adapter.py
@ -20,7 +20,7 @@ from peft import LoraConfig, LoraModel, PeftModel, TaskType, get_peft_model
 from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.modeling_utils import is_fsdp_enabled
-from ..extras.logging import get_logger
+from ..extras import logging
 from .model_utils.misc import find_all_linear_modules, find_expanded_modules
 from .model_utils.quantization import QuantizationMethod
 from .model_utils.unsloth import get_unsloth_peft_model, load_unsloth_peft_model
@ -33,7 +33,7 @@ if TYPE_CHECKING:
    from ..hparams import FinetuningArguments, ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def _setup_full_tuning(
@ -45,7 +45,7 @@ def _setup_full_tuning(
    if not is_trainable:
        return
-    logger.info("Fine-tuning method: Full")
+    logger.info_rank0("Fine-tuning method: Full")
    forbidden_modules = get_forbidden_modules(model.config, finetuning_args)
    for name, param in model.named_parameters():
        if not any(forbidden_module in name for forbidden_module in forbidden_modules):
@ -64,7 +64,7 @@ def _setup_freeze_tuning(
    if not is_trainable:
        return
-    logger.info("Fine-tuning method: Freeze")
+    logger.info_rank0("Fine-tuning method: Freeze")
    if hasattr(model.config, "text_config"):  # composite models
        config = getattr(model.config, "text_config")
    else:
@ -133,7 +133,7 @@ def _setup_freeze_tuning(
        else:
            param.requires_grad_(False)
-    logger.info("Set trainable layers: {}".format(",".join(trainable_layers)))
+    logger.info_rank0("Set trainable layers: {}".format(",".join(trainable_layers)))
 def _setup_lora_tuning(
@ -145,7 +145,7 @@ def _setup_lora_tuning(
    cast_trainable_params_to_fp32: bool,
 ) -> "PeftModel":
    if is_trainable:
-        logger.info("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))
+        logger.info_rank0("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))
    adapter_to_resume = None
@ -182,7 +182,7 @@ def _setup_lora_tuning(
            model = model.merge_and_unload()
        if len(adapter_to_merge) > 0:
-            logger.info("Merged {} adapter(s).".format(len(adapter_to_merge)))
+            logger.info_rank0(f"Merged {len(adapter_to_merge)} adapter(s).")
        if adapter_to_resume is not None:  # resume lora training
            if model_args.use_unsloth:
@ -190,7 +190,7 @@ def _setup_lora_tuning(
            else:
                model = PeftModel.from_pretrained(model, adapter_to_resume, is_trainable=is_trainable, **init_kwargs)
-        logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
+        logger.info_rank0("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
    if is_trainable and adapter_to_resume is None:  # create new lora weights while training
        if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all":
@ -219,7 +219,7 @@ def _setup_lora_tuning(
                    module_names.add(name.split(".")[-1])
            finetuning_args.additional_target = module_names
-            logger.warning("Vocab has been resized, add {} to trainable params.".format(",".join(module_names)))
+            logger.warning_rank0("Vocab has been resized, add {} to trainable params.".format(",".join(module_names)))
        peft_kwargs = {
            "r": finetuning_args.lora_rank,
@ -236,11 +236,11 @@ def _setup_lora_tuning(
        else:
            if finetuning_args.pissa_init:
                if finetuning_args.pissa_iter == -1:
-                    logger.info("Using PiSSA initialization.")
+                    logger.info_rank0("Using PiSSA initialization.")
                    peft_kwargs["init_lora_weights"] = "pissa"
                else:
-                    logger.info("Using PiSSA initialization with FSVD steps {}.".format(finetuning_args.pissa_iter))
+                    logger.info_rank0(f"Using PiSSA initialization with FSVD steps {finetuning_args.pissa_iter}.")
-                    peft_kwargs["init_lora_weights"] = "pissa_niter_{}".format(finetuning_args.pissa_iter)
+                    peft_kwargs["init_lora_weights"] = f"pissa_niter_{finetuning_args.pissa_iter}"
            lora_config = LoraConfig(
                task_type=TaskType.CAUSAL_LM,
@ -284,11 +284,11 @@ def init_adapter(
    if not is_trainable:
        pass
    elif finetuning_args.pure_bf16 or finetuning_args.use_badam:
-        logger.info("Pure bf16 / BAdam detected, remaining trainable params in half precision.")
+        logger.info_rank0("Pure bf16 / BAdam detected, remaining trainable params in half precision.")
    elif model_args.quantization_bit is None and (is_deepspeed_zero3_enabled() or is_fsdp_enabled()):
-        logger.info("ZeRO3 / FSDP detected, remaining trainable params in float32.")
+        logger.info_rank0("ZeRO3 / FSDP detected, remaining trainable params in float32.")
    else:
-        logger.info("Upcasting trainable params to float32.")
+        logger.info_rank0("Upcasting trainable params to float32.")
        cast_trainable_params_to_fp32 = True
    if finetuning_args.finetuning_type == "full":
@ -300,6 +300,6 @@ def init_adapter(
            config, model, model_args, finetuning_args, is_trainable, cast_trainable_params_to_fp32
        )
    else:
-        raise NotImplementedError("Unknown finetuning type: {}.".format(finetuning_args.finetuning_type))
+        raise NotImplementedError(f"Unknown finetuning type: {finetuning_args.finetuning_type}.")
    return model
--- a/src/llamafactory/model/loader.py
+++ b/src/llamafactory/model/loader.py
@ -18,15 +18,15 @@ import torch
 from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForVision2Seq, AutoProcessor, AutoTokenizer
 from trl import AutoModelForCausalLMWithValueHead
-from ..extras.logging import get_logger
+from ..extras import logging
-from ..extras.misc import count_parameters, skip_check_imports, try_download_model_from_ms
+from ..extras.misc import count_parameters, skip_check_imports, try_download_model_from_other_hub
 from .adapter import init_adapter
 from .model_utils.liger_kernel import apply_liger_kernel
 from .model_utils.misc import register_autoclass
 from .model_utils.mod import convert_pretrained_model_to_mod, load_mod_pretrained_model
 from .model_utils.unsloth import load_unsloth_pretrained_model
 from .model_utils.valuehead import load_valuehead_params
-from .model_utils.visual import get_image_seqlen
+from .patcher import patch_config, patch_model, patch_processor, patch_tokenizer, patch_valuehead_model
 from .patcher import patch_config, patch_model, patch_tokenizer, patch_valuehead_model
 if TYPE_CHECKING:
@ -35,7 +35,7 @@ if TYPE_CHECKING:
    from ..hparams import FinetuningArguments, ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 class TokenizerModule(TypedDict):
@ -50,7 +50,7 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
    Note: including inplace operation of model_args.
    """
    skip_check_imports()
-    model_args.model_name_or_path = try_download_model_from_ms(model_args)
+    model_args.model_name_or_path = try_download_model_from_other_hub(model_args)
    return {
        "trust_remote_code": True,
        "cache_dir": model_args.cache_dir,
@ -61,7 +61,7 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
 def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
    r"""
-    Loads pretrained tokenizer.
+    Loads pretrained tokenizer and optionally loads processor.
    Note: including inplace operation of model_args.
    """
@ -82,33 +82,30 @@ def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
            padding_side="right",
            **init_kwargs,
        )
    except Exception as e:
        raise OSError("Failed to load tokenizer.") from e
    if model_args.new_special_tokens is not None:
        num_added_tokens = tokenizer.add_special_tokens(
            dict(additional_special_tokens=model_args.new_special_tokens),
            replace_additional_special_tokens=False,
        )
-        logger.info("Add {} to special tokens.".format(",".join(model_args.new_special_tokens)))
+        logger.info_rank0("Add {} to special tokens.".format(",".join(model_args.new_special_tokens)))
        if num_added_tokens > 0 and not model_args.resize_vocab:
            model_args.resize_vocab = True
-            logger.warning("New tokens have been added, changed `resize_vocab` to True.")
+            logger.warning_rank0("New tokens have been added, changed `resize_vocab` to True.")
    patch_tokenizer(tokenizer)
    try:
        processor = AutoProcessor.from_pretrained(model_args.model_name_or_path, **init_kwargs)
-        setattr(processor, "tokenizer", tokenizer)
+        patch_processor(processor, config, tokenizer, model_args)
-        setattr(processor, "image_seqlen", get_image_seqlen(config))
+    except Exception as e:
-        setattr(processor, "image_resolution", model_args.image_resolution)
+        logger.debug(f"Processor was not found: {e}.")
        setattr(processor, "video_resolution", model_args.video_resolution)
        setattr(processor, "video_fps", model_args.video_fps)
        setattr(processor, "video_maxlen", model_args.video_maxlen)
    except Exception:
        processor = None
    # Avoid load tokenizer, see:
    # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/auto/processing_auto.py#L324
-    if "Processor" not in processor.__class__.__name__:
+    if processor is not None and "Processor" not in processor.__class__.__name__:
        processor = None
    return {"tokenizer": tokenizer, "processor": processor}
@ -135,6 +132,7 @@ def load_model(
    init_kwargs = _get_init_kwargs(model_args)
    config = load_config(model_args)
    patch_config(config, tokenizer, model_args, init_kwargs, is_trainable)
    apply_liger_kernel(config, model_args, is_trainable, require_logits=(finetuning_args.stage not in ["pt", "sft"]))
    model = None
    lazy_load = False
@ -157,7 +155,7 @@ def load_model(
                load_class = AutoModelForCausalLM
            if model_args.train_from_scratch:
-                model = load_class.from_config(config)
+                model = load_class.from_config(config, trust_remote_code=True)
            else:
                model = load_class.from_pretrained(**init_kwargs)
@ -182,7 +180,7 @@ def load_model(
        vhead_params = load_valuehead_params(vhead_path, model_args)
        if vhead_params is not None:
            model.load_state_dict(vhead_params, strict=False)
-            logger.info("Loaded valuehead from checkpoint: {}".format(vhead_path))
+            logger.info_rank0(f"Loaded valuehead from checkpoint: {vhead_path}")
    if not is_trainable:
        model.requires_grad_(False)
@ -200,9 +198,9 @@ def load_model(
            trainable_params, all_param, 100 * trainable_params / all_param
        )
    else:
-        param_stats = "all params: {:,}".format(all_param)
+        param_stats = f"all params: {all_param:,}"
-    logger.info(param_stats)
+    logger.info_rank0(param_stats)
    if model_args.print_param_status:
        for name, param in model.named_parameters():
--- a/src/llamafactory/model/model_utils/attention.py
+++ b/src/llamafactory/model/model_utils/attention.py
@ -17,7 +17,7 @@ from typing import TYPE_CHECKING
 from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_available
 from transformers.utils.versions import require_version
-from ...extras.logging import get_logger
+from ...extras import logging
 if TYPE_CHECKING:
@ -26,7 +26,7 @@ if TYPE_CHECKING:
    from ...hparams import ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def configure_attn_implementation(
@ -37,13 +37,16 @@ def configure_attn_implementation(
            if is_flash_attn_2_available():
                require_version("transformers>=4.42.4", "To fix: pip install transformers>=4.42.4")
                require_version("flash_attn>=2.6.3", "To fix: pip install flash_attn>=2.6.3")
-                logger.warning("Gemma-2 should use flash attention 2, change `flash_attn` to fa2.")
+                if model_args.flash_attn != "fa2":
-                model_args.flash_attn = "fa2"
+                    logger.warning_rank0("Gemma-2 should use flash attention 2, change `flash_attn` to fa2.")
                    model_args.flash_attn = "fa2"
            else:
-                logger.warning("Gemma-2 should use eager attention, change `flash_attn` to disabled.")
+                logger.warning_rank0("FlashAttention-2 is not installed, use eager attention.")
                model_args.flash_attn = "disabled"
        elif model_args.flash_attn == "sdpa":
-            logger.warning("Gemma-2 should use soft-capping attention, while the SDPA attention does not support it.")
+            logger.warning_rank0(
                "Gemma-2 should use soft-capping attention, while the SDPA attention does not support it."
            )
    if model_args.flash_attn == "auto":
        return
@ -53,18 +56,18 @@ def configure_attn_implementation(
    elif model_args.flash_attn == "sdpa":
        if not is_torch_sdpa_available():
-            logger.warning("torch>=2.1.1 is required for SDPA attention.")
+            logger.warning_rank0("torch>=2.1.1 is required for SDPA attention.")
            return
        requested_attn_implementation = "sdpa"
    elif model_args.flash_attn == "fa2":
        if not is_flash_attn_2_available():
-            logger.warning("FlashAttention-2 is not installed.")
+            logger.warning_rank0("FlashAttention-2 is not installed.")
            return
        requested_attn_implementation = "flash_attention_2"
    else:
-        raise NotImplementedError("Unknown attention type: {}".format(model_args.flash_attn))
+        raise NotImplementedError(f"Unknown attention type: {model_args.flash_attn}")
    if getattr(config, "model_type", None) == "internlm2":  # special case for custom models
        setattr(config, "attn_implementation", requested_attn_implementation)
@ -79,8 +82,8 @@ def print_attn_implementation(config: "PretrainedConfig") -> None:
        attn_implementation = getattr(config, "_attn_implementation", None)
    if attn_implementation == "flash_attention_2":
-        logger.info("Using FlashAttention-2 for faster training and inference.")
+        logger.info_rank0("Using FlashAttention-2 for faster training and inference.")
    elif attn_implementation == "sdpa":
-        logger.info("Using torch SDPA for faster training and inference.")
+        logger.info_rank0("Using torch SDPA for faster training and inference.")
    else:
-        logger.info("Using vanilla attention implementation.")
+        logger.info_rank0("Using vanilla attention implementation.")
--- a/src/llamafactory/model/model_utils/checkpointing.py
+++ b/src/llamafactory/model/model_utils/checkpointing.py
@ -19,14 +19,14 @@
 # limitations under the License.
 import inspect
-from functools import partial, wraps
+from functools import WRAPPER_ASSIGNMENTS, partial, wraps
 from types import MethodType
 from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Union
 import torch
 from ...extras import logging
 from ...extras.constants import LAYERNORM_NAMES
 from ...extras.logging import get_logger
 if TYPE_CHECKING:
@ -35,7 +35,7 @@ if TYPE_CHECKING:
    from ...hparams import ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def get_unsloth_gradient_checkpointing_func() -> Callable:
@ -81,7 +81,7 @@ def get_custom_gradient_checkpointing_func(gradient_checkpointing_func: Callable
    Only applies gradient checkpointing to trainable layers.
    """
-    @wraps(gradient_checkpointing_func)
+    @wraps(gradient_checkpointing_func, assigned=WRAPPER_ASSIGNMENTS + ("__self__",))
    def custom_gradient_checkpointing_func(func: Callable, *args: Union["torch.Tensor", Any], **kwargs):
        module: "torch.nn.Module" = func.__self__
@ -92,9 +92,6 @@ def get_custom_gradient_checkpointing_func(gradient_checkpointing_func: Callable
        return gradient_checkpointing_func(func, *args, **kwargs)
    if hasattr(gradient_checkpointing_func, "__self__"):  # fix unsloth gc test case
        custom_gradient_checkpointing_func.__self__ = gradient_checkpointing_func.__self__
    return custom_gradient_checkpointing_func
@ -111,7 +108,7 @@ def _gradient_checkpointing_enable(
    from torch.utils.checkpoint import checkpoint
    if not self.supports_gradient_checkpointing:
-        raise ValueError("{} does not support gradient checkpointing.".format(self.__class__.__name__))
+        raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
    if gradient_checkpointing_kwargs is None:
        gradient_checkpointing_kwargs = {"use_reentrant": True}
@ -125,7 +122,7 @@ def _gradient_checkpointing_enable(
    if "value" in inspect.signature(self._set_gradient_checkpointing).parameters:  # old GC format
        self.apply(partial(self._set_gradient_checkpointing, value=True))
        self.enable_input_require_grads()
-        logger.warning("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
+        logger.warning_once("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
    else:  # have already enabled input require gradients
        self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=gradient_checkpointing_func)
@ -144,14 +141,14 @@ def prepare_model_for_training(model: "PreTrainedModel", model_args: "ModelArgum
        (3) add the upcasting of the lm_head in fp32
    """
    if model_args.upcast_layernorm:
-        logger.info("Upcasting layernorm weights in float32.")
+        logger.info_rank0("Upcasting layernorm weights in float32.")
        for name, param in model.named_parameters():
            if param.ndim == 1 and any(ln_name in name for ln_name in LAYERNORM_NAMES):
                param.data = param.data.to(torch.float32)
    if not model_args.disable_gradient_checkpointing:
        if not getattr(model, "supports_gradient_checkpointing", False):
-            logger.warning("Current model does not support gradient checkpointing.")
+            logger.warning_rank0("Current model does not support gradient checkpointing.")
        else:
            # use_reentrant=False might increase VRAM usage (have not been empirically verified yet)
            # According to: https://github.com/huggingface/transformers/issues/28339
@ -161,10 +158,10 @@ def prepare_model_for_training(model: "PreTrainedModel", model_args: "ModelArgum
            model.gradient_checkpointing_enable = MethodType(gradient_checkpointing_enable, model)
            model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": True})
            setattr(model.config, "use_cache", False)  # turn off when gradient checkpointing is enabled
-            logger.info("Gradient checkpointing enabled.")
+            logger.info_rank0("Gradient checkpointing enabled.")
    if model_args.upcast_lmhead_output:
        output_layer = model.get_output_embeddings()
        if isinstance(output_layer, torch.nn.Linear) and output_layer.weight.dtype != torch.float32:
-            logger.info("Upcasting lm_head outputs in float32.")
+            logger.info_rank0("Upcasting lm_head outputs in float32.")
            output_layer.register_forward_hook(_fp32_forward_post_hook)
--- a/src/llamafactory/model/model_utils/embedding.py
+++ b/src/llamafactory/model/model_utils/embedding.py
@ -19,14 +19,14 @@ from typing import TYPE_CHECKING
 import torch
 from transformers.integrations import is_deepspeed_zero3_enabled
-from ...extras.logging import get_logger
+from ...extras import logging
 if TYPE_CHECKING:
    from transformers import PreTrainedModel, PreTrainedTokenizer
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def _noisy_mean_initialization(embed_weight: "torch.Tensor", num_new_tokens: int) -> None:
@ -69,4 +69,4 @@ 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)
-        logger.info("Resized token embeddings from {} to {}.".format(current_embedding_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
@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import inspect
 from typing import TYPE_CHECKING
-from ...extras.logging import get_logger
+from ...extras import logging
 if TYPE_CHECKING:
@ -23,10 +24,15 @@ if TYPE_CHECKING:
    from ...hparams import ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
-def configure_liger_kernel(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
+def apply_liger_kernel(
    config: "PretrainedConfig",
    model_args: "ModelArguments",
    is_trainable: bool,
    require_logits: bool,
 ) -> None:
    if not is_trainable or not model_args.enable_liger_kernel:
        return
@ -48,8 +54,14 @@ def configure_liger_kernel(config: "PretrainedConfig", model_args: "ModelArgumen
    elif model_type == "qwen2_vl":
        from liger_kernel.transformers import apply_liger_kernel_to_qwen2_vl as apply_liger_kernel
    else:
-        logger.warning("Current model does not support liger kernel.")
+        logger.warning_rank0("Current model does not support liger kernel.")
        return
-    apply_liger_kernel()
+    if require_logits and "fused_linear_cross_entropy" in inspect.signature(apply_liger_kernel).parameters:
-    logger.info("Liger kernel has been applied to the model.")
+        logger.info_rank0("Current training stage does not support chunked cross entropy.")
        kwargs = {"fused_linear_cross_entropy": False}
    else:
        kwargs = {}
    apply_liger_kernel(**kwargs)
    logger.info_rank0("Liger kernel has been applied to the model.")
--- a/src/llamafactory/model/model_utils/longlora.py
+++ b/src/llamafactory/model/model_utils/longlora.py
@ -22,6 +22,7 @@ from typing import TYPE_CHECKING, Optional, Tuple
 import torch
 import torch.nn as nn
 import transformers
 from transformers.models.llama.modeling_llama import (
    Cache,
    LlamaAttention,
@ -30,11 +31,10 @@ from transformers.models.llama.modeling_llama import (
    apply_rotary_pos_emb,
    repeat_kv,
 )
 from transformers.utils import logging
 from transformers.utils.versions import require_version
 from ...extras import logging
 from ...extras.constants import SUPPORTED_CLASS_FOR_S2ATTN
 from ...extras.logging import get_logger
 from ...extras.packages import is_transformers_version_greater_than_4_43
@ -44,7 +44,7 @@ if TYPE_CHECKING:
    from ...hparams import ModelArguments
-transformers_logger = logging.get_logger(__name__)
+transformers_logger = transformers.utils.logging.get_logger(__name__)
 # Modified from:
@ -86,7 +86,7 @@ def llama_attention_forward(
    if getattr(self.config, "group_size_ratio", None) and self.training:  # shift
        groupsz = int(q_len * getattr(self.config, "group_size_ratio"))
-        assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz)
+        assert q_len % groupsz == 0, f"q_len {q_len} should be divisible by group size {groupsz}."
        num_groups = q_len // groupsz
        def shift(state: "torch.Tensor") -> "torch.Tensor":
@ -195,7 +195,7 @@ def llama_flash_attention_2_forward(
    if getattr(self.config, "group_size_ratio", None) and self.training:  # shift
        groupsz = int(q_len * getattr(self.config, "group_size_ratio"))
-        assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz)
+        assert q_len % groupsz == 0, f"q_len {q_len} should be divisible by group size {groupsz}."
        num_groups = q_len // groupsz
        def shift(state: "torch.Tensor") -> "torch.Tensor":
@ -301,7 +301,7 @@ def llama_sdpa_attention_forward(
    if getattr(self.config, "group_size_ratio", None) and self.training:  # shift
        groupsz = int(q_len * getattr(self.config, "group_size_ratio"))
-        assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz)
+        assert q_len % groupsz == 0, f"q_len {q_len} should be divisible by group size {groupsz}."
        num_groups = q_len // groupsz
        def shift(state: "torch.Tensor") -> "torch.Tensor":
@ -353,7 +353,7 @@ def llama_sdpa_attention_forward(
 def _apply_llama_patch() -> None:
-    require_version("transformers>=4.41.2,<=4.45.0", "To fix: pip install transformers>=4.41.2,<=4.45.0")
+    require_version("transformers>=4.41.2,<=4.46.1", "To fix: pip install transformers>=4.41.2,<=4.46.1")
    LlamaAttention.forward = llama_attention_forward
    LlamaFlashAttention2.forward = llama_flash_attention_2_forward
    LlamaSdpaAttention.forward = llama_sdpa_attention_forward
@ -363,11 +363,11 @@ def configure_longlora(config: "PretrainedConfig", model_args: "ModelArguments",
    if not is_trainable or not model_args.shift_attn:
        return
-    logger = get_logger(__name__)
+    logger = logging.get_logger(__name__)
    if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_S2ATTN:
        setattr(config, "group_size_ratio", 0.25)
        _apply_llama_patch()
-        logger.info("Using shift short attention with group_size_ratio=1/4.")
+        logger.info_rank0("Using shift short attention with group_size_ratio=1/4.")
    else:
-        logger.warning("Current model does not support shift short attention.")
+        logger.warning_rank0("Current model does not support shift short attention.")
--- a/src/llamafactory/model/model_utils/misc.py
+++ b/src/llamafactory/model/model_utils/misc.py
@ -14,14 +14,14 @@
 from typing import TYPE_CHECKING, List
-from ...extras.logging import get_logger
+from ...extras import logging
 if TYPE_CHECKING:
    from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool) -> List[str]:
@ -34,7 +34,7 @@ def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool)
        forbidden_modules.add("output_layer")
    elif model_type == "internlm2":
        forbidden_modules.add("output")
-    elif model_type in ["llava", "paligemma"]:
+    elif model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "video_llava"]:
        forbidden_modules.add("multi_modal_projector")
    elif model_type == "qwen2_vl":
        forbidden_modules.add("merger")
@ -53,7 +53,7 @@ def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool)
        if "Linear" in module.__class__.__name__ and "Embedding" not in module.__class__.__name__:
            module_names.add(name.split(".")[-1])
-    logger.info("Found linear modules: {}".format(",".join(module_names)))
+    logger.info_rank0("Found linear modules: {}".format(",".join(module_names)))
    return list(module_names)
@ -67,12 +67,12 @@ def find_expanded_modules(model: "PreTrainedModel", target_modules: List[str], n
    if num_layers % num_layer_trainable != 0:
        raise ValueError(
-            "`num_layers` {} should be divisible by `num_layer_trainable` {}.".format(num_layers, num_layer_trainable)
+            f"`num_layers` {num_layers} should be divisible by `num_layer_trainable` {num_layer_trainable}."
        )
    stride = num_layers // num_layer_trainable
    trainable_layer_ids = range(stride - 1, num_layers + stride - 1, stride)
-    trainable_layers = [".{:d}.".format(idx) for idx in trainable_layer_ids]
+    trainable_layers = [f".{idx:d}." for idx in trainable_layer_ids]
    module_names = []
    for name, _ in model.named_modules():
        if any(target_module in name for target_module in target_modules) and any(
@ -80,7 +80,7 @@ def find_expanded_modules(model: "PreTrainedModel", target_modules: List[str], n
        ):
            module_names.append(name)
-    logger.info("Apply lora to layers: {}".format(",".join(map(str, trainable_layer_ids))))
+    logger.info_rank0("Apply lora to layers: {}".format(",".join(map(str, trainable_layer_ids))))
    return module_names
--- a/src/llamafactory/model/model_utils/packing.py
+++ b/src/llamafactory/model/model_utils/packing.py
@ -43,8 +43,8 @@ import torch
 import torch.nn.functional as F
 from transformers.utils.versions import require_version
 from ...extras import logging
 from ...extras.constants import SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN
 from ...extras.logging import get_logger
 from ...extras.packages import is_transformers_version_greater_than_4_43
@ -54,7 +54,7 @@ if TYPE_CHECKING:
    from ...hparams import ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def get_seqlens_in_batch(attention_mask: "torch.Tensor") -> "torch.Tensor":
@ -114,7 +114,7 @@ def get_unpad_data(attention_mask: "torch.Tensor") -> Tuple["torch.Tensor", "tor
 def _patch_for_block_diag_attn(model_type: str) -> None:
-    require_version("transformers>=4.41.2,<=4.45.0", "To fix: pip install transformers>=4.41.2,<=4.45.0")
+    require_version("transformers>=4.41.2,<=4.46.1", "To fix: pip install transformers>=4.41.2,<=4.46.1")
    if is_transformers_version_greater_than_4_43():
        import transformers.modeling_flash_attention_utils
@ -152,6 +152,6 @@ def configure_packing(config: "PretrainedConfig", model_args: "ModelArguments",
    model_type = getattr(config, "model_type", None)
    if model_type in SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN:
        _patch_for_block_diag_attn(model_type)
-        logger.info("Using block diagonal attention for sequence packing without cross-attention.")
+        logger.info_rank0("Using block diagonal attention for sequence packing without cross-attention.")
    else:
        raise ValueError("Current model does not support block diagonal attention.")
--- a/src/llamafactory/model/model_utils/quantization.py
+++ b/src/llamafactory/model/model_utils/quantization.py
@ -28,8 +28,8 @@ from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.modeling_utils import is_fsdp_enabled
 from transformers.utils.versions import require_version
 from ...extras import logging
 from ...extras.constants import FILEEXT2TYPE
 from ...extras.logging import get_logger
 from ...extras.misc import get_current_device
@ -39,7 +39,7 @@ if TYPE_CHECKING:
    from ...hparams import ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
@unique
@ -109,7 +109,7 @@ def configure_quantization(
    """
    if getattr(config, "quantization_config", None):  # ptq
        if model_args.quantization_bit is not None:
-            logger.warning("`quantization_bit` will not affect on the PTQ-quantized models.")
+            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.")
@ -130,7 +130,7 @@ def configure_quantization(
            quantization_config["bits"] = 2
        quant_bits = quantization_config.get("bits", "?")
-        logger.info("Loading {}-bit {}-quantized model.".format(quant_bits, quant_method.upper()))
+        logger.info_rank0(f"Loading {quant_bits}-bit {quant_method.upper()}-quantized model.")
    elif model_args.export_quantization_bit is not None:  # auto-gptq
        if model_args.export_quantization_bit not in [8, 4, 3, 2]:
@ -149,7 +149,7 @@ def configure_quantization(
        )
        init_kwargs["device_map"] = "auto"
        init_kwargs["max_memory"] = get_max_memory()
-        logger.info("Quantizing model to {} bit with AutoGPTQ.".format(model_args.export_quantization_bit))
+        logger.info_rank0(f"Quantizing model to {model_args.export_quantization_bit} bit with AutoGPTQ.")
    elif model_args.quantization_bit is not None:  # on-the-fly
        if model_args.quantization_method == QuantizationMethod.BITS_AND_BYTES.value:
@ -179,7 +179,7 @@ def configure_quantization(
            else:
                init_kwargs["device_map"] = {"": get_current_device()}  # change auto device map for inference
-            logger.info("Quantizing model to {} bit with bitsandbytes.".format(model_args.quantization_bit))
+            logger.info_rank0(f"Quantizing model to {model_args.quantization_bit} bit with bitsandbytes.")
        elif model_args.quantization_method == QuantizationMethod.HQQ.value:
            if model_args.quantization_bit not in [8, 6, 5, 4, 3, 2, 1]:
                raise ValueError("HQQ only accepts 1/2/3/4/5/6/8-bit quantization.")
@ -191,7 +191,7 @@ def configure_quantization(
            init_kwargs["quantization_config"] = HqqConfig(
                nbits=model_args.quantization_bit, quant_zero=False, quant_scale=False, axis=0
            )  # use ATEN kernel (axis=0) for performance
-            logger.info("Quantizing model to {} bit with HQQ.".format(model_args.quantization_bit))
+            logger.info_rank0(f"Quantizing model to {model_args.quantization_bit} bit with HQQ.")
        elif model_args.quantization_method == QuantizationMethod.EETQ.value:
            if model_args.quantization_bit != 8:
                raise ValueError("EETQ only accepts 8-bit quantization.")
@ -201,4 +201,4 @@ def configure_quantization(
            require_version("eetq", "To fix: pip install eetq")
            init_kwargs["quantization_config"] = EetqConfig()
-            logger.info("Quantizing model to {} bit with EETQ.".format(model_args.quantization_bit))
+            logger.info_rank0(f"Quantizing model to {model_args.quantization_bit} bit with EETQ.")
--- a/src/llamafactory/model/model_utils/rope.py
+++ b/src/llamafactory/model/model_utils/rope.py
@ -19,7 +19,7 @@
 import math
 from typing import TYPE_CHECKING
-from ...extras.logging import get_logger
+from ...extras import logging
 if TYPE_CHECKING:
@ -28,7 +28,7 @@ if TYPE_CHECKING:
    from ...hparams import ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
@ -36,30 +36,28 @@ def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_
        return
    if not hasattr(config, "rope_scaling"):
-        logger.warning("Current model does not support RoPE scaling.")
+        logger.warning_rank0("Current model does not support RoPE scaling.")
        return
    if model_args.model_max_length is not None:
        if is_trainable and model_args.rope_scaling == "dynamic":
-            logger.warning(
+            logger.warning_rank0(
                "Dynamic NTK scaling may not work well with fine-tuning. "
                "See: https://github.com/huggingface/transformers/pull/24653"
            )
        current_max_length = getattr(config, "max_position_embeddings", None)
        if current_max_length and model_args.model_max_length > current_max_length:
-            logger.info(
+            logger.info_rank0(f"Enlarge max model length from {current_max_length} to {model_args.model_max_length}.")
                "Enlarge max model length from {} to {}.".format(current_max_length, model_args.model_max_length)
            )
            setattr(config, "max_position_embeddings", model_args.model_max_length)
            scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length))
        else:
-            logger.warning("Input length is smaller than max length. Consider increase input length.")
+            logger.warning_rank0("Input length is smaller than max length. Consider increase input length.")
            scaling_factor = 1.0
    else:
        scaling_factor = 2.0
    setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor})
-    logger.info(
+    logger.info_rank0(
-        "Using {} scaling strategy and setting scaling factor to {}".format(model_args.rope_scaling, scaling_factor)
+        f"Using {model_args.rope_scaling} scaling strategy and setting scaling factor to {scaling_factor}"
    )
--- a/src/llamafactory/model/model_utils/unsloth.py
+++ b/src/llamafactory/model/model_utils/unsloth.py
@ -14,7 +14,7 @@
 from typing import TYPE_CHECKING, Any, Dict, Optional
-from ...extras.logging import get_logger
+from ...extras import logging
 from ...extras.misc import get_current_device
@ -24,7 +24,7 @@ if TYPE_CHECKING:
    from ...hparams import ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def _get_unsloth_kwargs(
@ -56,7 +56,7 @@ def load_unsloth_pretrained_model(
    try:
        model, _ = FastLanguageModel.from_pretrained(**unsloth_kwargs)
    except NotImplementedError:
-        logger.warning("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
+        logger.warning_rank0("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
        model = None
        model_args.use_unsloth = False
--- a/src/llamafactory/model/model_utils/valuehead.py
+++ b/src/llamafactory/model/model_utils/valuehead.py
@ -17,8 +17,8 @@ from typing import TYPE_CHECKING, Dict
 import torch
 from transformers.utils import cached_file
 from ...extras import logging
 from ...extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
 from ...extras.logging import get_logger
 if TYPE_CHECKING:
@ -27,7 +27,7 @@ if TYPE_CHECKING:
    from ...hparams import ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") -> Dict[str, torch.Tensor]:
@ -54,8 +54,8 @@ def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") ->
    except Exception as err:
        err_text = str(err)
-    logger.info("Provided path ({}) does not contain value head weights: {}.".format(path_or_repo_id, err_text))
+    logger.info_rank0(f"Provided path ({path_or_repo_id}) does not contain value head weights: {err_text}.")
-    logger.info("Ignore the above message if you are not resuming the training of a value head model.")
+    logger.info_rank0("Ignore the above message if you are not resuming the training of a value head model.")
    return None
--- a/src/llamafactory/model/model_utils/visual.py
+++ b/src/llamafactory/model/model_utils/visual.py
@ -18,11 +18,11 @@
 from typing import TYPE_CHECKING, List, Sequence, Set, Tuple, Union
 import torch
 import transformers
 import transformers.models
 from transformers.activations import ACT2FN
 from transformers.utils import logging
-from ...extras.logging import get_logger
+from ...extras import logging
 if TYPE_CHECKING:
@ -31,8 +31,8 @@ if TYPE_CHECKING:
    from ...hparams import FinetuningArguments, ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
-transformers_logger = logging.get_logger(__name__)
+transformers_logger = transformers.utils.logging.get_logger(__name__)
 class LlavaMultiModalProjectorForYiVL(torch.nn.Module):
@ -92,14 +92,14 @@ def autocast_projector_dtype(model: "PreTrainedModel", model_args: "ModelArgumen
    if getattr(model, "quantization_method", None):
        model_type = getattr(model.config, "model_type", None)
-        if model_type in ["llava", "paligemma"]:
+        if model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "pixtral", "video_llava"]:
            mm_projector: "torch.nn.Module" = getattr(model, "multi_modal_projector")
        elif model_type == "qwen2_vl":
            mm_projector: "torch.nn.Module" = getattr(getattr(model, "visual"), "merger")
        else:
            return
-        logger.info("Casting multimodal projector outputs in {}.".format(model_args.compute_dtype))
+        logger.info_rank0(f"Casting multimodal projector outputs in {model_args.compute_dtype}.")
        mm_projector.register_forward_hook(_mm_projector_forward_post_hook)
@ -108,11 +108,18 @@ def configure_visual_model(config: "PretrainedConfig") -> None:
    Patches VLMs before loading them.
    """
    model_type = getattr(config, "model_type", None)
-    if model_type == "llava":  # required for ds zero3 and valuehead models
+    if model_type in [
        "llava",
        "llava_next",
        "llava_next_video",
        "paligemma",
        "pixtral",
        "video_llava",
    ]:  # required for ds zero3 and valuehead models
        setattr(config, "hidden_size", getattr(config.text_config, "hidden_size", None))
    if getattr(config, "is_yi_vl_derived_model", None):
-        logger.info("Detected Yi-VL model, applying projector patch.")
+        logger.info_rank0("Detected Yi-VL model, applying projector patch.")
        transformers.models.llava.modeling_llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVL
@ -122,7 +129,7 @@ def get_forbidden_modules(config: "PretrainedConfig", finetuning_args: "Finetuni
    """
    model_type = getattr(config, "model_type", None)
    forbidden_modules = set()
-    if model_type in ["llava", "paligemma"]:
+    if model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "pixtral", "video_llava"]:
        if finetuning_args.freeze_vision_tower:
            forbidden_modules.add("vision_tower")
@ -150,12 +157,28 @@ def get_image_seqlen(config: "PretrainedConfig") -> int:
            image_seqlen += 1
    elif model_type == "paligemma":
        image_seqlen = config.vision_config.num_image_tokens
-    elif model_type == "qwen2_vl":  # variable length
+    else:
        image_seqlen = -1
    return image_seqlen
 def get_patch_size(config: "PretrainedConfig") -> int:
    r"""
    Computes the patch size of the vit.
    """
    patch_size = getattr(config.vision_config, "patch_size", -1)
    return patch_size
 def get_vision_feature_select_strategy(config: "PretrainedConfig") -> int:
    r"""
    Get the vision_feature_select_strategy.
    """
    vision_feature_select_strategy = getattr(config, "vision_feature_select_strategy", "default")
    return vision_feature_select_strategy
 def patch_target_modules(
    config: "PretrainedConfig", finetuning_args: "FinetuningArguments", target_modules: Sequence[str]
 ) -> Union[str, List[str]]:
@ -164,7 +187,7 @@ def patch_target_modules(
    """
    model_type = getattr(config, "model_type", None)
    if finetuning_args.freeze_vision_tower:
-        if model_type in ["llava", "paligemma"]:
+        if model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "pixtral", "video_llava"]:
            return "^(?!.*vision_tower).*(?:{}).*".format("|".join(target_modules))
        elif model_type == "qwen2_vl":
            return "^(?!.*visual).*(?:{}).*".format("|".join(target_modules))
@ -173,5 +196,7 @@ def patch_target_modules(
    else:
        if model_type == "qwen2_vl":
            return "^(?!.*patch_embed).*(?:{}).*".format("|".join(target_modules))
        elif model_type == "pixtral":
            return "^(?!.*patch_conv).*(?:{}).*".format("|".join(target_modules))
        else:
            return target_modules
--- a/src/llamafactory/model/patcher.py
+++ b/src/llamafactory/model/patcher.py
@ -22,29 +22,34 @@ from transformers import PreTrainedModel, PreTrainedTokenizerBase, is_torch_npu_
 from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.modeling_utils import is_fsdp_enabled
-from ..extras.logging import get_logger
+from ..extras import logging
 from ..extras.misc import infer_optim_dtype
 from .model_utils.attention import configure_attn_implementation, print_attn_implementation
 from .model_utils.checkpointing import prepare_model_for_training
 from .model_utils.embedding import resize_embedding_layer
 from .model_utils.liger_kernel import configure_liger_kernel
 from .model_utils.longlora import configure_longlora
 from .model_utils.moe import add_z3_leaf_module, configure_moe
 from .model_utils.packing import configure_packing
 from .model_utils.quantization import configure_quantization
 from .model_utils.rope import configure_rope
 from .model_utils.valuehead import prepare_valuehead_model
-from .model_utils.visual import autocast_projector_dtype, configure_visual_model
+from .model_utils.visual import (
    autocast_projector_dtype,
    configure_visual_model,
    get_image_seqlen,
    get_patch_size,
    get_vision_feature_select_strategy,
 )
 if TYPE_CHECKING:
-    from transformers import PretrainedConfig, PreTrainedTokenizer
+    from transformers import PretrainedConfig, PreTrainedTokenizer, ProcessorMixin
    from trl import AutoModelForCausalLMWithValueHead
    from ..hparams import ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def patch_tokenizer(tokenizer: "PreTrainedTokenizer") -> None:
@ -52,6 +57,22 @@ def patch_tokenizer(tokenizer: "PreTrainedTokenizer") -> None:
        tokenizer._pad = MethodType(PreTrainedTokenizerBase._pad, tokenizer)
 def patch_processor(
    processor: "ProcessorMixin",
    config: "PretrainedConfig",
    tokenizer: "PreTrainedTokenizer",
    model_args: "ModelArguments",
 ) -> None:
    setattr(processor, "tokenizer", tokenizer)
    setattr(processor, "image_seqlen", get_image_seqlen(config))
    setattr(processor, "image_resolution", model_args.image_resolution)
    setattr(processor, "patch_size", get_patch_size(config))
    setattr(processor, "video_resolution", model_args.video_resolution)
    setattr(processor, "video_fps", model_args.video_fps)
    setattr(processor, "video_maxlen", model_args.video_maxlen)
    setattr(processor, "vision_feature_select_strategy", get_vision_feature_select_strategy(config))
 def patch_config(
    config: "PretrainedConfig",
    tokenizer: "PreTrainedTokenizer",
@ -71,7 +92,6 @@ def patch_config(
    configure_attn_implementation(config, model_args, is_trainable)
    configure_rope(config, model_args, is_trainable)
    configure_liger_kernel(config, model_args, is_trainable)
    configure_longlora(config, model_args, is_trainable)
    configure_quantization(config, tokenizer, model_args, init_kwargs)
    configure_moe(config, model_args, is_trainable)
@ -80,7 +100,7 @@ def patch_config(
    if model_args.use_cache and not is_trainable:
        setattr(config, "use_cache", True)
-        logger.info("Using KV cache for faster generation.")
+        logger.info_rank0("Using KV cache for faster generation.")
    if getattr(config, "model_type", None) == "qwen":
        setattr(config, "use_flash_attn", model_args.flash_attn == "fa2")
@ -90,6 +110,9 @@ def patch_config(
    if getattr(config, "model_type", None) == "qwen2" and is_trainable and model_args.flash_attn == "fa2":
        setattr(config, "use_cache", False)  # qwen2 does not support use_cache when using flash attn
    if "LlavaLlamaForCausalLM" in getattr(config, "architectures", []):
        raise ValueError("Please download llava models with hf-compatible format: https://huggingface.co/llava-hf")
    # deepspeed zero3 is not compatible with low_cpu_mem_usage
    init_kwargs["low_cpu_mem_usage"] = model_args.low_cpu_mem_usage and (not is_deepspeed_zero3_enabled())
@ -142,7 +165,7 @@ def patch_model(
    try:
        model.add_model_tags(["llama-factory"])
    except Exception:
-        logger.warning("Cannot properly tag the model.")
+        logger.warning_rank0("Cannot properly tag the model.")
 def patch_valuehead_model(model: "AutoModelForCausalLMWithValueHead") -> None:
--- a/src/llamafactory/train/callbacks.py
+++ b/src/llamafactory/train/callbacks.py
@ -13,7 +13,6 @@
 # limitations under the License.
 import json
 import logging
 import os
 import signal
 import sys
@ -34,8 +33,8 @@ from transformers.utils import (
 )
 from typing_extensions import override
 from ..extras import logging
 from ..extras.constants import TRAINER_LOG, V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
 from ..extras.logging import LoggerHandler, get_logger
 from ..extras.misc import get_peak_memory
@ -48,7 +47,7 @@ if TYPE_CHECKING:
    from trl import AutoModelForCausalLMWithValueHead
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def fix_valuehead_checkpoint(
@ -92,7 +91,7 @@ def fix_valuehead_checkpoint(
    else:
        torch.save(v_head_state_dict, os.path.join(output_dir, V_HEAD_WEIGHTS_NAME))
-    logger.info("Value head model saved at: {}".format(output_dir))
+    logger.info_rank0(f"Value head model saved at: {output_dir}")
 class FixValueHeadModelCallback(TrainerCallback):
@ -106,7 +105,7 @@ class FixValueHeadModelCallback(TrainerCallback):
        Event called after a checkpoint save.
        """
        if args.should_save:
-            output_dir = os.path.join(args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, state.global_step))
+            output_dir = os.path.join(args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}")
            fix_valuehead_checkpoint(
                model=kwargs.pop("model"), output_dir=output_dir, safe_serialization=args.save_safetensors
            )
@ -123,13 +122,13 @@ class SaveProcessorCallback(TrainerCallback):
    @override
    def on_save(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        if args.should_save:
-            output_dir = os.path.join(args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, state.global_step))
+            output_dir = os.path.join(args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}")
-            getattr(self.processor, "image_processor").save_pretrained(output_dir)
+            self.processor.save_pretrained(output_dir)
    @override
    def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        if args.should_save:
-            getattr(self.processor, "image_processor").save_pretrained(args.output_dir)
+            self.processor.save_pretrained(args.output_dir)
 class PissaConvertCallback(TrainerCallback):
@ -145,7 +144,7 @@ class PissaConvertCallback(TrainerCallback):
        if args.should_save:
            model = kwargs.pop("model")
            pissa_init_dir = os.path.join(args.output_dir, "pissa_init")
-            logger.info("Initial PiSSA adapter will be saved at: {}.".format(pissa_init_dir))
+            logger.info_rank0(f"Initial PiSSA adapter will be saved at: {pissa_init_dir}.")
            if isinstance(model, PeftModel):
                init_lora_weights = getattr(model.peft_config["default"], "init_lora_weights")
                setattr(model.peft_config["default"], "init_lora_weights", True)
@ -159,7 +158,7 @@ class PissaConvertCallback(TrainerCallback):
            pissa_init_dir = os.path.join(args.output_dir, "pissa_init")
            pissa_backup_dir = os.path.join(args.output_dir, "pissa_backup")
            pissa_convert_dir = os.path.join(args.output_dir, "pissa_converted")
-            logger.info("Converted PiSSA adapter will be saved at: {}.".format(pissa_convert_dir))
+            logger.info_rank0(f"Converted PiSSA adapter will be saved at: {pissa_convert_dir}.")
            # 1. save a pissa backup with init_lora_weights: True
            # 2. save a converted lora with init_lora_weights: pissa
            # 3. load the pissa backup with init_lora_weights: True
@ -200,8 +199,8 @@ class LogCallback(TrainerCallback):
        self.webui_mode = os.environ.get("LLAMABOARD_ENABLED", "0").lower() in ["true", "1"]
        if self.webui_mode:
            signal.signal(signal.SIGABRT, self._set_abort)
-            self.logger_handler = LoggerHandler(os.environ.get("LLAMABOARD_WORKDIR"))
+            self.logger_handler = logging.LoggerHandler(os.environ.get("LLAMABOARD_WORKDIR"))
-            logging.root.addHandler(self.logger_handler)
+            logging.add_handler(self.logger_handler)
            transformers.logging.add_handler(self.logger_handler)
    def _set_abort(self, signum, frame) -> None:
@ -243,7 +242,7 @@ class LogCallback(TrainerCallback):
            and os.path.exists(os.path.join(args.output_dir, TRAINER_LOG))
            and args.overwrite_output_dir
        ):
-            logger.warning("Previous trainer log in this folder will be deleted.")
+            logger.warning_once("Previous trainer log in this folder will be deleted.")
            os.remove(os.path.join(args.output_dir, TRAINER_LOG))
    @override
@ -288,13 +287,13 @@ class LogCallback(TrainerCallback):
        logs = dict(
            current_steps=self.cur_steps,
            total_steps=self.max_steps,
-            loss=state.log_history[-1].get("loss", None),
+            loss=state.log_history[-1].get("loss"),
-            eval_loss=state.log_history[-1].get("eval_loss", None),
+            eval_loss=state.log_history[-1].get("eval_loss"),
-            predict_loss=state.log_history[-1].get("predict_loss", None),
+            predict_loss=state.log_history[-1].get("predict_loss"),
-            reward=state.log_history[-1].get("reward", None),
+            reward=state.log_history[-1].get("reward"),
-            accuracy=state.log_history[-1].get("rewards/accuracies", None),
+            accuracy=state.log_history[-1].get("rewards/accuracies"),
-            learning_rate=state.log_history[-1].get("learning_rate", None),
+            lr=state.log_history[-1].get("learning_rate"),
-            epoch=state.log_history[-1].get("epoch", None),
+            epoch=state.log_history[-1].get("epoch"),
            percentage=round(self.cur_steps / self.max_steps * 100, 2) if self.max_steps != 0 else 100,
            elapsed_time=self.elapsed_time,
            remaining_time=self.remaining_time,
@ -305,16 +304,17 @@ class LogCallback(TrainerCallback):
        if os.environ.get("RECORD_VRAM", "0").lower() in ["true", "1"]:
            vram_allocated, vram_reserved = get_peak_memory()
-            logs["vram_allocated"] = round(vram_allocated / 1024 / 1024 / 1024, 2)
+            logs["vram_allocated"] = round(vram_allocated / (1024**3), 2)
-            logs["vram_reserved"] = round(vram_reserved / 1024 / 1024 / 1024, 2)
+            logs["vram_reserved"] = round(vram_reserved / (1024**3), 2)
        logs = {k: v for k, v in logs.items() if v is not None}
-        if self.webui_mode and all(key in logs for key in ["loss", "learning_rate", "epoch"]):
+        if self.webui_mode and all(key in logs for key in ("loss", "lr", "epoch")):
-            logger.info(
+            log_str = f"'loss': {logs['loss']:.4f}, 'learning_rate': {logs['lr']:2.4e}, 'epoch': {logs['epoch']:.2f}"
-                "{{'loss': {:.4f}, 'learning_rate': {:2.4e}, 'epoch': {:.2f}, 'throughput': {}}}".format(
+            for extra_key in ("reward", "accuracy", "throughput"):
-                    logs["loss"], logs["learning_rate"], logs["epoch"], logs.get("throughput", "N/A")
+                if logs.get(extra_key):
-                )
+                    log_str += f", '{extra_key}': {logs[extra_key]:.2f}"
-            )
+
            logger.info_rank0("{" + log_str + "}")
        if self.thread_pool is not None:
            self.thread_pool.submit(self._write_log, args.output_dir, logs)
--- a/src/llamafactory/train/dpo/trainer.py
+++ b/src/llamafactory/train/dpo/trainer.py
@ -29,6 +29,7 @@ from trl.trainer import disable_dropout_in_model
 from typing_extensions import override
 from ...extras.constants import IGNORE_INDEX
 from ...extras.packages import is_transformers_version_equal_to_4_46
 from ..callbacks import PissaConvertCallback, SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps
@ -100,7 +101,7 @@ class CustomDPOTrainer(DPOTrainer):
            self.callback_handler.add_callback(PissaConvertCallback)
        if finetuning_args.use_badam:
-            from badam import BAdamCallback, clip_grad_norm_old_version
+            from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore
            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
            self.add_callback(BAdamCallback)
@ -118,6 +119,13 @@ class CustomDPOTrainer(DPOTrainer):
        create_custom_scheduler(self.args, num_training_steps, optimizer)
        return super().create_scheduler(num_training_steps, optimizer)
    @override
    def get_batch_samples(self, epoch_iterator, num_batches):
        r"""
        Replaces the method of KTO Trainer with the one of the standard Trainer.
        """
        return Trainer.get_batch_samples(self, epoch_iterator, num_batches)
    def odds_ratio_loss(self, chosen_logps: "torch.Tensor", rejected_logps: "torch.Tensor") -> "torch.Tensor":
        r"""
        Computes ORPO's odds ratio (OR) loss for batched log probabilities of the policy model.
@ -156,7 +164,7 @@ class CustomDPOTrainer(DPOTrainer):
            elif self.loss_type == "simpo":
                losses = self.simpo_loss(policy_chosen_logps, policy_rejected_logps)
            else:
-                raise NotImplementedError("Unknown loss type: {}.".format(self.loss_type))
+                raise NotImplementedError(f"Unknown loss type: {self.loss_type}.")
            chosen_rewards = self.beta * policy_chosen_logps.to(self.accelerator.device).detach()
            rejected_rewards = self.beta * policy_rejected_logps.to(self.accelerator.device).detach()
@ -242,19 +250,59 @@ class CustomDPOTrainer(DPOTrainer):
        if self.ftx_gamma > 1e-6:
            losses += self.ftx_gamma * sft_loss
        reward_accuracies = (chosen_rewards > rejected_rewards).float()
        prefix = "eval_" if train_eval == "eval" else ""
-        metrics["{}rewards/chosen".format(prefix)] = chosen_rewards.mean().cpu()
+        metrics[f"{prefix}rewards/chosen"] = chosen_rewards.mean().item()
-        metrics["{}rewards/rejected".format(prefix)] = rejected_rewards.mean().cpu()
+        metrics[f"{prefix}rewards/rejected"] = rejected_rewards.mean().item()
-        metrics["{}rewards/accuracies".format(prefix)] = reward_accuracies.mean().cpu()
+        metrics[f"{prefix}rewards/accuracies"] = (chosen_rewards > rejected_rewards).float().mean().item()
-        metrics["{}rewards/margins".format(prefix)] = (chosen_rewards - rejected_rewards).mean().cpu()
+        metrics[f"{prefix}rewards/margins"] = (chosen_rewards - rejected_rewards).mean().item()
-        metrics["{}logps/rejected".format(prefix)] = policy_rejected_logps.detach().mean().cpu()
+        metrics[f"{prefix}logps/rejected"] = policy_chosen_logps.mean().item()
-        metrics["{}logps/chosen".format(prefix)] = policy_chosen_logps.detach().mean().cpu()
+        metrics[f"{prefix}logps/chosen"] = policy_rejected_logps.mean().item()
-        metrics["{}logits/rejected".format(prefix)] = policy_rejected_logits.detach().mean().cpu()
+        metrics[f"{prefix}logits/rejected"] = policy_chosen_logits.mean().item()
-        metrics["{}logits/chosen".format(prefix)] = policy_chosen_logits.detach().mean().cpu()
+        metrics[f"{prefix}logits/chosen"] = policy_rejected_logits.mean().item()
        if self.loss_type == "orpo":
-            metrics["{}sft_loss".format(prefix)] = sft_loss.detach().mean().cpu()
+            metrics[f"{prefix}sft_loss"] = sft_loss.mean().item()
-            metrics["{}odds_ratio_loss".format(prefix)] = ((losses - sft_loss) / self.beta).detach().mean().cpu()
+            metrics[f"{prefix}odds_ratio_loss"] = ((losses - sft_loss) / self.beta).mean().item()
        return losses.mean(), metrics
    @override
    def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
        r"""
        Fixes the loss value for transformers 4.46.0.
        https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
        """
        loss = super().compute_loss(model, inputs, return_outputs)
        if is_transformers_version_equal_to_4_46() and kwargs.pop("num_items_in_batch", False):
            if return_outputs:
                return (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
            else:
                return loss / self.args.gradient_accumulation_steps
        return loss
    @override
    def log(self, logs: Dict[str, float]) -> None:
        r"""
        Log `logs` on the various objects watching training, including stored metrics.
        """
        # logs either has "loss" or "eval_loss"
        train_eval = "train" if "loss" in logs else "eval"
        # Add averaged stored metrics to logs
        key_list, metric_list = [], []
        for key, metrics in self._stored_metrics[train_eval].items():
            key_list.append(key)
            metric_list.append(torch.tensor(metrics, dtype=torch.float).to(self.accelerator.device).mean().item())
        del self._stored_metrics[train_eval]
        if len(metric_list) < 10:  # pad to for all reduce
            for i in range(10 - len(metric_list)):
                key_list.append(f"dummy_{i}")
                metric_list.append(0.0)
        metric_list = torch.tensor(metric_list, dtype=torch.float).to(self.accelerator.device)
        metric_list = self.accelerator.reduce(metric_list, "mean").tolist()
        for key, metric in zip(key_list, metric_list):  # add remaining items
            if not key.startswith("dummy_"):
                logs[key] = metric
        return Trainer.log(self, logs)
--- a/src/llamafactory/train/kto/trainer.py
+++ b/src/llamafactory/train/kto/trainer.py
@ -28,6 +28,7 @@ from trl.trainer import disable_dropout_in_model
 from typing_extensions import override
 from ...extras.constants import IGNORE_INDEX
 from ...extras.packages import is_transformers_version_equal_to_4_46
 from ..callbacks import SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps
@ -95,7 +96,7 @@ class CustomKTOTrainer(KTOTrainer):
            self.add_callback(SaveProcessorCallback(processor))
        if finetuning_args.use_badam:
-            from badam import BAdamCallback, clip_grad_norm_old_version
+            from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore
            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
            self.add_callback(BAdamCallback)
@ -120,20 +121,27 @@ class CustomKTOTrainer(KTOTrainer):
        """
        return Trainer._get_train_sampler(self)
    @override
    def get_batch_samples(self, epoch_iterator, num_batches):
        r"""
        Replaces the method of KTO Trainer with the one of the standard Trainer.
        """
        return Trainer.get_batch_samples(self, epoch_iterator, num_batches)
    @override
    def forward(
        self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"], prefix: Literal["", "kl_"] = ""
-    ) -> Tuple["torch.Tensor", "torch.Tensor"]:
+    ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor"]:
        r"""
        Runs forward pass and computes the log probabilities.
        """
        batch = {k: v.detach().clone() for k, v in batch.items()}  # avoid error
        model_inputs = {
-            "input_ids": batch["{}input_ids".format(prefix)],
+            "input_ids": batch[f"{prefix}input_ids"],
-            "attention_mask": batch["{}attention_mask".format(prefix)],
+            "attention_mask": batch[f"{prefix}attention_mask"],
        }
-        if "{}token_type_ids".format(prefix) in batch:
+        if f"{prefix}token_type_ids" in batch:
-            model_inputs["token_type_ids"] = batch["{}token_type_ids".format(prefix)]
+            model_inputs["token_type_ids"] = batch[f"{prefix}token_type_ids"]
        if "pixel_values" in batch:
            model_inputs["pixel_values"] = batch["pixel_values"]
@ -142,24 +150,26 @@ class CustomKTOTrainer(KTOTrainer):
            model_inputs["image_grid_thw"] = batch["image_grid_thw"]
        logits = model(**model_inputs, return_dict=True, use_cache=False).logits.to(torch.float32)
-        logps, valid_length = get_batch_logps(logits=logits, labels=batch["{}labels".format(prefix)])
+        logps, valid_length = get_batch_logps(logits=logits, labels=batch[f"{prefix}labels"])
-        return logps, logps / valid_length
+        return logits, logps, logps / valid_length
    @override
    def concatenated_forward(
        self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"]
-    ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
+    ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
-        target_logps, target_logps_avg = self.forward(model, batch)
+        target_logits, target_logps, target_logps_avg = self.forward(model, batch)
        with torch.no_grad():
-            kl_logps, _ = self.forward(model, batch, prefix="kl_")
+            _, kl_logps, _ = self.forward(model, batch, prefix="kl_")
        if len(target_logps) != len(batch["kto_tags"]):
            raise ValueError("Mismatched shape of inputs and labels.")
        chosen_logits = target_logits[batch["kto_tags"]]
        chosen_logps = target_logps[batch["kto_tags"]]
        rejected_logits = target_logits[~batch["kto_tags"]]
        rejected_logps = target_logps[~batch["kto_tags"]]
        chosen_logps_avg = target_logps_avg[batch["kto_tags"]]
-        return chosen_logps, rejected_logps, kl_logps, chosen_logps_avg
+        return chosen_logps, rejected_logps, chosen_logits, rejected_logits, kl_logps, chosen_logps_avg
    @override
    def compute_reference_log_probs(
@ -176,7 +186,7 @@ class CustomKTOTrainer(KTOTrainer):
            ref_context = nullcontext()
        with torch.no_grad(), ref_context:
-            reference_chosen_logps, reference_rejected_logps, reference_kl_logps, _ = self.concatenated_forward(
+            reference_chosen_logps, reference_rejected_logps, _, _, reference_kl_logps, _ = self.concatenated_forward(
                ref_model, batch
            )
@ -192,9 +202,14 @@ class CustomKTOTrainer(KTOTrainer):
        Computes the DPO loss and other metrics for the given batch of inputs for train or test.
        """
        metrics = {}
-        policy_chosen_logps, policy_rejected_logps, policy_kl_logps, policy_chosen_logps_avg = (
+        (
-            self.concatenated_forward(model, batch)
+            policy_chosen_logps,
-        )
+            policy_rejected_logps,
            policy_chosen_logits,
            policy_rejected_logits,
            policy_kl_logps,
            policy_chosen_logps_avg,
        ) = self.concatenated_forward(model, batch)
        reference_chosen_logps, reference_rejected_logps, reference_kl_logps = self.compute_reference_log_probs(
            model, batch
        )
@ -212,22 +227,73 @@ class CustomKTOTrainer(KTOTrainer):
            sft_loss = -policy_chosen_logps_avg
            losses += self.ftx_gamma * sft_loss.nanmean() / len(policy_chosen_logps) * len(batch["labels"])
-        num_chosen = torch.Tensor([len(chosen_rewards)]).to(self.accelerator.device)
+        num_chosen = len(chosen_rewards)
-        num_rejected = torch.Tensor([len(rejected_rewards)]).to(self.accelerator.device)
+        num_rejected = len(rejected_rewards)
        if num_chosen > 0:
            metrics["rewards/chosen_sum"] = chosen_rewards.nansum().item()
            metrics["logps/chosen_sum"] = policy_chosen_logps.nansum().item()
            metrics["logits/chosen_sum"] = policy_chosen_logits.nansum().item()
            metrics["count/chosen"] = float(num_chosen)
-        all_num_chosen = self.accelerator.gather(num_chosen).sum().item()
+        if num_rejected > 0:
-        all_num_rejected = self.accelerator.gather(num_rejected).sum().item()
+            metrics["rewards/rejected_sum"] = rejected_rewards.nansum().item()
-
+            metrics["logps/rejected_sum"] = policy_rejected_logps.nansum().item()
-        if all_num_chosen > 0:
+            metrics["logits/rejected_sum"] = policy_rejected_logits.nansum().item()
-            metrics["rewards/chosen_sum"] = self.accelerator.gather(chosen_rewards.nansum()).nansum().item()
+            metrics["count/rejected"] = float(num_rejected)
            metrics["logps/chosen_sum"] = self.accelerator.gather(policy_chosen_logps.nansum()).nansum().item()
            metrics["count/chosen"] = all_num_chosen
        if all_num_rejected > 0:
            metrics["rewards/rejected_sum"] = self.accelerator.gather(rejected_rewards.nansum()).nansum().item()
            metrics["logps/rejected_sum"] = self.accelerator.gather(policy_rejected_logps.nansum()).nansum().item()
            metrics["count/rejected"] = all_num_rejected
        metrics["kl"] = kl.item()
        return losses, metrics
    @override
    def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
        r"""
        Fixes the loss value for transformers 4.46.0.
        https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
        """
        loss = super().compute_loss(model, inputs, return_outputs)
        if is_transformers_version_equal_to_4_46() and kwargs.pop("num_items_in_batch", False):
            if return_outputs:
                return (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
            else:
                return loss / self.args.gradient_accumulation_steps
        return loss
    @override
    def log(self, logs: Dict[str, float]) -> None:
        r"""
        Log `logs` on the various objects watching training, including stored metrics.
        """
        # logs either has "loss" or "eval_loss"
        train_eval = "train" if "loss" in logs else "eval"
        prefix = "eval_" if train_eval == "eval" else ""
        # Add averaged stored metrics to logs
        key_list, metric_list = [], []
        for key, metrics in self._stored_metrics[train_eval].items():
            key_list.append(key)
            metric_list.append(torch.tensor(metrics, dtype=torch.float).to(self.accelerator.device).sum().item())
        del self._stored_metrics[train_eval]
        if len(metric_list) < 9:  # pad to for all reduce
            for i in range(9 - len(metric_list)):
                key_list.append(f"dummy_{i}")
                metric_list.append(0.0)
        metric_list = torch.tensor(metric_list, dtype=torch.float).to(self.accelerator.device)
        metric_list = self.accelerator.reduce(metric_list, "sum").tolist()
        metric_dict: Dict[str, float] = dict(zip(key_list, metric_list))
        for split in ["chosen", "rejected"]:  # accumulate average metrics from sums and lengths
            if f"count/{split}" in metric_dict:
                for key in ("rewards", "logps", "logits"):
                    logs[f"{prefix}{key}/{split}"] = metric_dict[f"{key}/{split}_sum"] / metric_dict[f"count/{split}"]
                    del metric_dict[f"{key}/{split}_sum"]
                del metric_dict[f"count/{split}"]
        if f"{prefix}rewards/chosen" in logs and f"{prefix}rewards/rejected" in logs:  # calculate reward margin
            logs[f"{prefix}rewards/margins"] = logs[f"{prefix}rewards/chosen"] - logs[f"{prefix}rewards/rejected"]
        for key, metric in metric_dict.items():  # add remaining items
            if not key.startswith("dummy_"):
                logs[key] = metric
        return Trainer.log(self, logs)
--- a/src/llamafactory/train/kto/workflow.py
+++ b/src/llamafactory/train/kto/workflow.py
@ -81,7 +81,7 @@ def run_kto(
        trainer.save_metrics("train", train_result.metrics)
        trainer.save_state()
        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
-            plot_loss(training_args.output_dir, keys=["loss", "eval_loss", "train/rewards/chosen"])
+            plot_loss(training_args.output_dir, keys=["loss", "eval_loss", "rewards/chosen"])
    # Evaluation
    if training_args.do_eval:
--- a/src/llamafactory/train/ppo/ppo_utils.py
+++ b/src/llamafactory/train/ppo/ppo_utils.py
@ -62,8 +62,8 @@ def replace_model(model: "AutoModelForCausalLMWithValueHead", target: Literal["d
            setattr(model, "default_head_bias", v_head_layer.bias.data.detach().clone())
        device = v_head_layer.weight.device
-        v_head_layer.weight.data = model.get_buffer("{}_head_weight".format(target)).detach().clone().to(device)
+        v_head_layer.weight.data = model.get_buffer(f"{target}_head_weight").detach().clone().to(device)
-        v_head_layer.bias.data = model.get_buffer("{}_head_bias".format(target)).detach().clone().to(device)
+        v_head_layer.bias.data = model.get_buffer(f"{target}_head_bias").detach().clone().to(device)
 def dump_layernorm(model: "PreTrainedModel") -> Dict[str, "torch.Tensor"]:
--- a/src/llamafactory/train/ppo/trainer.py
+++ b/src/llamafactory/train/ppo/trainer.py
@ -37,7 +37,7 @@ from trl.core import PPODecorators, logprobs_from_logits
 from trl.models.utils import unwrap_model_for_generation
 from typing_extensions import override
-from ...extras.logging import get_logger
+from ...extras import logging
 from ...extras.misc import AverageMeter, count_parameters, get_current_device, get_logits_processor
 from ..callbacks import FixValueHeadModelCallback, SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@ -58,7 +58,7 @@ if TYPE_CHECKING:
    from ...hparams import FinetuningArguments, GeneratingArguments, ModelArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 class CustomPPOTrainer(PPOTrainer, Trainer):
@ -112,7 +112,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
            ]
            ppo_config.accelerator_kwargs["deepspeed_plugin"] = training_args.deepspeed_plugin
            if ppo_config.log_with is not None:
-                logger.warning("PPOTrainer cannot use external logger when DeepSpeed is enabled.")
+                logger.warning_rank0("PPOTrainer cannot use external logger when DeepSpeed is enabled.")
                ppo_config.log_with = None
        # Create optimizer and scheduler
@ -160,7 +160,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
            callbacks, self.accelerator.unwrap_model(self.model), self.tokenizer, self.optimizer, self.lr_scheduler
        )
        if self.args.max_steps > 0:
-            logger.info("max_steps is given, it will override any value given in num_train_epochs")
+            logger.info_rank0("max_steps is given, it will override any value given in num_train_epochs")
        self.amp_context = torch.autocast(self.current_device.type)
        warnings.simplefilter("ignore")  # remove gc warnings on ref model
@ -181,7 +181,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
            self.add_callback(SaveProcessorCallback(processor))
        if finetuning_args.use_badam:
-            from badam import BAdamCallback, clip_grad_norm_old_version
+            from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore
            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
            self.add_callback(BAdamCallback)
@ -216,20 +216,19 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
        self.state.is_local_process_zero = self.is_local_process_zero()
        self.state.is_world_process_zero = self.is_world_process_zero()
-        if self.is_world_process_zero():
+        logger.info_rank0("***** Running training *****")
-            logger.info("***** Running training *****")
+        logger.info_rank0(f"  Num examples = {num_examples:,}")
-            logger.info("  Num examples = {:,}".format(num_examples))
+        logger.info_rank0(f"  Num Epochs = {num_train_epochs:,}")
-            logger.info("  Num Epochs = {:,}".format(num_train_epochs))
+        logger.info_rank0(f"  Instantaneous batch size per device = {self.args.per_device_train_batch_size:,}")
-            logger.info("  Instantaneous batch size per device = {:,}".format(self.args.per_device_train_batch_size))
+        logger.info_rank0(
-            logger.info(
+            "  Total train batch size (w. parallel, buffer, distributed & accumulation) = {:,}".format(
-                "  Total train batch size (w. parallel, buffer, distributed & accumulation) = {:,}".format(
+                total_train_batch_size
                    total_train_batch_size
                )
            )
-            logger.info("  Gradient Accumulation steps = {:,}".format(self.args.gradient_accumulation_steps))
+        )
-            logger.info("  Num optimization epochs per batch = {:,}".format(self.finetuning_args.ppo_epochs))
+        logger.info_rank0(f"  Gradient Accumulation steps = {self.args.gradient_accumulation_steps:,}")
-            logger.info("  Total training steps = {:,}".format(max_steps))
+        logger.info_rank0(f"  Num optimization epochs per batch = {self.finetuning_args.ppo_epochs:,}")
-            logger.info("  Number of trainable parameters = {:,}".format(count_parameters(self.model)[0]))
+        logger.info_rank0(f"  Total training steps = {max_steps:,}")
        logger.info_rank0(f"  Number of trainable parameters = {count_parameters(self.model)[0]:,}")
        dataiter = iter(self.dataloader)
        loss_meter = AverageMeter()
@ -269,7 +268,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
                    batch["response"] = self.tokenizer.batch_decode(responses, skip_special_tokens=True)
                    self.log_stats(stats, batch, rewards)
                except Exception:
-                    logger.warning("Failed to save stats due to unknown errors.")
+                    logger.warning_rank0("Failed to save stats due to unknown errors.")
            self.state.global_step += 1
            self.callback_handler.on_step_end(self.args, self.state, self.control)
@ -290,7 +289,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
            if (step + 1) % self.args.save_steps == 0:  # save checkpoint
                self.save_model(
-                    os.path.join(self.args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, self.state.global_step))
+                    os.path.join(self.args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}")
                )
                self.callback_handler.on_save(self.args, self.state, self.control)
@ -498,7 +497,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
                if self.args.should_save:
                    self._save(output_dir, state_dict=state_dict)
            except ValueError:
-                logger.warning(
+                logger.warning_rank0(
                    " stage3_gather_16bit_weights_on_model_save=false. Saving the full checkpoint instead,"
                    " use zero_to_fp32.py to recover weights"
                )
--- a/src/llamafactory/train/pt/trainer.py
+++ b/src/llamafactory/train/pt/trainer.py
@ -18,7 +18,7 @@ from typing import TYPE_CHECKING, Optional
 from transformers import Trainer
 from typing_extensions import override
-from ...extras.logging import get_logger
+from ...extras.packages import is_transformers_version_equal_to_4_46
 from ..callbacks import PissaConvertCallback, SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@ -30,9 +30,6 @@ if TYPE_CHECKING:
    from ...hparams import FinetuningArguments
 logger = get_logger(__name__)
 class CustomTrainer(Trainer):
    r"""
    Inherits Trainer for custom optimizer.
@ -51,7 +48,7 @@ class CustomTrainer(Trainer):
            self.add_callback(PissaConvertCallback)
        if finetuning_args.use_badam:
-            from badam import BAdamCallback, clip_grad_norm_old_version
+            from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore
            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
            self.add_callback(BAdamCallback)
@ -68,3 +65,19 @@ class CustomTrainer(Trainer):
    ) -> "torch.optim.lr_scheduler.LRScheduler":
        create_custom_scheduler(self.args, num_training_steps, optimizer)
        return super().create_scheduler(num_training_steps, optimizer)
    @override
    def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
        r"""
        Fixes the loss value for transformers 4.46.0.
        https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
        """
        loss = super().compute_loss(model, inputs, return_outputs, **kwargs)
        if is_transformers_version_equal_to_4_46() and not getattr(self, "model_accepts_loss_kwargs", False):
            # other model should not scale the loss
            if return_outputs:
                return (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
            else:
                return loss / self.args.gradient_accumulation_steps
        return loss
--- a/src/llamafactory/train/rm/trainer.py
+++ b/src/llamafactory/train/rm/trainer.py
@ -24,7 +24,8 @@ import torch
 from transformers import Trainer
 from typing_extensions import override
-from ...extras.logging import get_logger
+from ...extras import logging
 from ...extras.packages import is_transformers_version_equal_to_4_46
 from ..callbacks import FixValueHeadModelCallback, PissaConvertCallback, SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@ -36,7 +37,7 @@ if TYPE_CHECKING:
    from ...hparams import FinetuningArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 class PairwiseTrainer(Trainer):
@ -59,7 +60,7 @@ class PairwiseTrainer(Trainer):
            self.add_callback(PissaConvertCallback)
        if finetuning_args.use_badam:
-            from badam import BAdamCallback, clip_grad_norm_old_version
+            from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore
            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
            self.add_callback(BAdamCallback)
@ -79,7 +80,7 @@ class PairwiseTrainer(Trainer):
    @override
    def compute_loss(
-        self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False
+        self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
    ) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
        r"""
        Computes pairwise loss. The first n examples are chosen and the last n examples are rejected.
@ -98,6 +99,10 @@ class PairwiseTrainer(Trainer):
        chosen_scores, rejected_scores = chosen_scores.squeeze(), rejected_scores.squeeze()
        loss = -torch.nn.functional.logsigmoid(chosen_scores.float() - rejected_scores.float()).mean()
        if is_transformers_version_equal_to_4_46() and kwargs.pop("num_items_in_batch", False):
            loss /= self.args.gradient_accumulation_steps  # fixes the loss value for transformers 4.46.0
        if return_outputs:
            return loss, (loss, chosen_scores, rejected_scores)
        else:
@ -113,7 +118,7 @@ class PairwiseTrainer(Trainer):
            return
        output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
-        logger.info(f"Saving prediction results to {output_prediction_file}")
+        logger.info_rank0(f"Saving prediction results to {output_prediction_file}")
        chosen_scores, rejected_scores = predict_results.predictions
        with open(output_prediction_file, "w", encoding="utf-8") as writer:
--- a/src/llamafactory/train/sft/trainer.py
+++ b/src/llamafactory/train/sft/trainer.py
@ -25,8 +25,9 @@ import torch
 from transformers import Seq2SeqTrainer
 from typing_extensions import override
 from ...extras import logging
 from ...extras.constants import IGNORE_INDEX
-from ...extras.logging import get_logger
+from ...extras.packages import is_transformers_version_equal_to_4_46
 from ..callbacks import PissaConvertCallback, SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@ -39,7 +40,7 @@ if TYPE_CHECKING:
    from ...hparams import FinetuningArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 class CustomSeq2SeqTrainer(Seq2SeqTrainer):
@ -60,7 +61,7 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
            self.add_callback(PissaConvertCallback)
        if finetuning_args.use_badam:
-            from badam import BAdamCallback, clip_grad_norm_old_version
+            from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore
            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
            self.add_callback(BAdamCallback)
@ -78,6 +79,22 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
        create_custom_scheduler(self.args, num_training_steps, optimizer)
        return super().create_scheduler(num_training_steps, optimizer)
    @override
    def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
        r"""
        Fixes the loss value for transformers 4.46.0.
        https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
        """
        loss = super().compute_loss(model, inputs, return_outputs, **kwargs)
        if is_transformers_version_equal_to_4_46() and not getattr(self, "model_accepts_loss_kwargs", False):
            # other model should not scale the loss
            if return_outputs:
                return (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
            else:
                return loss / self.args.gradient_accumulation_steps
        return loss
    @override
    def prediction_step(
        self,
@ -129,7 +146,7 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
            return
        output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
-        logger.info(f"Saving prediction results to {output_prediction_file}")
+        logger.info_rank0(f"Saving prediction results to {output_prediction_file}")
        labels = np.where(
            predict_results.label_ids != IGNORE_INDEX, predict_results.label_ids, self.tokenizer.pad_token_id
--- a/src/llamafactory/train/test_utils.py
+++ b/src/llamafactory/train/test_utils.py
@ -37,9 +37,9 @@ def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_k
    assert set(state_dict_a.keys()) == set(state_dict_b.keys())
    for name in state_dict_a.keys():
        if any(key in name for key in diff_keys):
-            assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-3, atol=1e-4) is False
+            assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5) is False
        else:
-            assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-3, atol=1e-4) is True
+            assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5) is True
 def check_lora_model(model: "LoraModel") -> Tuple[Set[str], Set[str]]:
@ -80,18 +80,17 @@ def load_reference_model(
    is_trainable: bool = False,
    add_valuehead: bool = False,
 ) -> Union["PreTrainedModel", "LoraModel"]:
    current_device = get_current_device()
    if add_valuehead:
        model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(
-            model_path, torch_dtype=torch.float16, device_map=get_current_device()
+            model_path, torch_dtype=torch.float16, device_map=current_device
        )
        if not is_trainable:
            model.v_head = model.v_head.to(torch.float16)
        return model
-    model = AutoModelForCausalLM.from_pretrained(
+    model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map=current_device)
        model_path, torch_dtype=torch.float16, device_map=get_current_device()
    )
    if use_lora or use_pissa:
        model = PeftModel.from_pretrained(
            model, lora_path, subfolder="pissa_init" if use_pissa else None, is_trainable=is_trainable
@ -110,7 +109,7 @@ def load_train_dataset(**kwargs) -> "Dataset":
    return dataset_module["train_dataset"]
-def patch_valuehead_model():
+def patch_valuehead_model() -> None:
    def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: Dict[str, "torch.Tensor"]) -> None:
        state_dict = {k[7:]: state_dict[k] for k in state_dict.keys() if k.startswith("v_head.")}
        self.v_head.load_state_dict(state_dict, strict=False)
--- a/src/llamafactory/train/trainer_utils.py
+++ b/src/llamafactory/train/trainer_utils.py
@ -28,8 +28,8 @@ from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 from transformers.trainer_pt_utils import get_parameter_names
 from typing_extensions import override
 from ..extras import logging
 from ..extras.constants import IGNORE_INDEX
 from ..extras.logging import get_logger
 from ..extras.packages import is_galore_available
 from ..hparams import FinetuningArguments, ModelArguments
 from ..model import find_all_linear_modules, load_model, load_tokenizer, load_valuehead_params
@ -46,7 +46,7 @@ if TYPE_CHECKING:
    from ..hparams import DataArguments
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 class DummyOptimizer(torch.optim.Optimizer):
@ -116,7 +116,7 @@ def create_ref_model(
        ref_model = load_model(
            tokenizer, ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
        )
-        logger.info("Created reference model from {}".format(finetuning_args.ref_model))
+        logger.info_rank0(f"Created reference model from {finetuning_args.ref_model}")
    else:
        if finetuning_args.finetuning_type == "lora":
            ref_model = None
@ -127,7 +127,7 @@ def create_ref_model(
            ref_model = load_model(
                tokenizer, ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
            )
-            logger.info("Created reference model from the model itself.")
+            logger.info_rank0("Created reference model from the model itself.")
    return ref_model
@ -140,7 +140,7 @@ def create_reward_model(
    """
    if finetuning_args.reward_model_type == "api":
        assert finetuning_args.reward_model.startswith("http"), "Please provide full url."
-        logger.info("Use reward server {}".format(finetuning_args.reward_model))
+        logger.info_rank0(f"Use reward server {finetuning_args.reward_model}")
        return finetuning_args.reward_model
    elif finetuning_args.reward_model_type == "lora":
        model.pretrained_model.load_adapter(finetuning_args.reward_model, "reward")
@ -157,7 +157,7 @@ def create_reward_model(
        model.register_buffer(
            "default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False
        )
-        logger.info("Loaded adapter weights of reward model from {}".format(finetuning_args.reward_model))
+        logger.info_rank0(f"Loaded adapter weights of reward model from {finetuning_args.reward_model}")
        return None
    else:
        reward_model_args = ModelArguments.copyfrom(
@ -171,8 +171,8 @@ def create_reward_model(
        reward_model = load_model(
            tokenizer, reward_model_args, reward_finetuning_args, is_trainable=False, add_valuehead=True
        )
-        logger.info("Loaded full weights of reward model from {}".format(finetuning_args.reward_model))
+        logger.info_rank0(f"Loaded full weights of reward model from {finetuning_args.reward_model}")
-        logger.warning("Please ensure the ppo model and reward model share SAME tokenizer and vocabulary.")
+        logger.warning_rank0("Please ensure the ppo model and reward model share SAME tokenizer and vocabulary.")
        return reward_model
@ -231,7 +231,7 @@ def _create_galore_optimizer(
    elif training_args.optim == "adafactor":
        optim_class = GaLoreAdafactor
    else:
-        raise NotImplementedError("Unknow optim: {}".format(training_args.optim))
+        raise NotImplementedError(f"Unknow optim: {training_args.optim}")
    if finetuning_args.galore_layerwise:
        if training_args.gradient_accumulation_steps != 1:
@ -265,7 +265,7 @@ def _create_galore_optimizer(
        ]
        optimizer = optim_class(param_groups, **optim_kwargs)
-    logger.info("Using GaLore optimizer, may cause hanging at the start of training, wait patiently.")
+    logger.info_rank0("Using GaLore optimizer, may cause hanging at the start of training, wait patiently.")
    return optimizer
@ -305,7 +305,7 @@ def _create_loraplus_optimizer(
        dict(params=param_dict["embedding"], lr=embedding_lr, weight_decay=training_args.weight_decay),
    ]
    optimizer = optim_class(param_groups, **optim_kwargs)
-    logger.info("Using LoRA+ optimizer with loraplus lr ratio {:.2f}.".format(finetuning_args.loraplus_lr_ratio))
+    logger.info_rank0(f"Using LoRA+ optimizer with loraplus lr ratio {finetuning_args.loraplus_lr_ratio:.2f}.")
    return optimizer
@ -343,7 +343,7 @@ def _create_badam_optimizer(
            verbose=finetuning_args.badam_verbose,
            ds_zero3_enabled=is_deepspeed_zero3_enabled(),
        )
-        logger.info(
+        logger.info_rank0(
            f"Using BAdam optimizer with layer-wise update, switch mode is {finetuning_args.badam_switch_mode}, "
            f"switch block every {finetuning_args.badam_switch_interval} steps, "
            f"default start block is {finetuning_args.badam_start_block}"
@ -362,7 +362,7 @@ def _create_badam_optimizer(
            include_embedding=False,
            **optim_kwargs,
        )
-        logger.info(
+        logger.info_rank0(
            f"Using BAdam optimizer with ratio-based update, update ratio is {finetuning_args.badam_update_ratio}, "
            f"mask mode is {finetuning_args.badam_mask_mode}"
        )
@ -391,7 +391,7 @@ def _create_adam_mini_optimizer(
        n_heads=num_q_head,
        n_kv_heads=num_kv_head,
    )
-    logger.info("Using Adam-mini optimizer.")
+    logger.info_rank0("Using Adam-mini optimizer.")
    return optimizer
--- a/src/llamafactory/train/tuner.py
+++ b/src/llamafactory/train/tuner.py
@ -20,8 +20,8 @@ import torch
 from transformers import PreTrainedModel
 from ..data import get_template_and_fix_tokenizer
 from ..extras import logging
 from ..extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
 from ..extras.logging import get_logger
 from ..hparams import get_infer_args, get_train_args
 from ..model import load_model, load_tokenizer
 from .callbacks import LogCallback
@ -37,7 +37,7 @@ if TYPE_CHECKING:
    from transformers import TrainerCallback
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: List["TrainerCallback"] = []) -> None:
@ -57,7 +57,7 @@ def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: List["TrainerCallb
    elif finetuning_args.stage == "kto":
        run_kto(model_args, data_args, training_args, finetuning_args, callbacks)
    else:
-        raise ValueError("Unknown task: {}.".format(finetuning_args.stage))
+        raise ValueError(f"Unknown task: {finetuning_args.stage}.")
 def export_model(args: Optional[Dict[str, Any]] = None) -> None:
@ -91,18 +91,18 @@ def export_model(args: Optional[Dict[str, Any]] = None) -> None:
        setattr(model.config, "torch_dtype", output_dtype)
        model = model.to(output_dtype)
-        logger.info("Convert model dtype to: {}.".format(output_dtype))
+        logger.info_rank0(f"Convert model dtype to: {output_dtype}.")
    model.save_pretrained(
        save_directory=model_args.export_dir,
-        max_shard_size="{}GB".format(model_args.export_size),
+        max_shard_size=f"{model_args.export_size}GB",
        safe_serialization=(not model_args.export_legacy_format),
    )
    if model_args.export_hub_model_id is not None:
        model.push_to_hub(
            model_args.export_hub_model_id,
            token=model_args.hf_hub_token,
-            max_shard_size="{}GB".format(model_args.export_size),
+            max_shard_size=f"{model_args.export_size}GB",
            safe_serialization=(not model_args.export_legacy_format),
        )
@ -117,13 +117,13 @@ def export_model(args: Optional[Dict[str, Any]] = None) -> None:
                os.path.join(vhead_path, V_HEAD_SAFE_WEIGHTS_NAME),
                os.path.join(model_args.export_dir, V_HEAD_SAFE_WEIGHTS_NAME),
            )
-            logger.info("Copied valuehead to {}.".format(model_args.export_dir))
+            logger.info_rank0(f"Copied valuehead to {model_args.export_dir}.")
        elif os.path.exists(os.path.join(vhead_path, V_HEAD_WEIGHTS_NAME)):
            shutil.copy(
                os.path.join(vhead_path, V_HEAD_WEIGHTS_NAME),
                os.path.join(model_args.export_dir, V_HEAD_WEIGHTS_NAME),
            )
-            logger.info("Copied valuehead to {}.".format(model_args.export_dir))
+            logger.info_rank0(f"Copied valuehead to {model_args.export_dir}.")
    try:
        tokenizer.padding_side = "left"  # restore padding side
@ -133,11 +133,9 @@ def export_model(args: Optional[Dict[str, Any]] = None) -> None:
            tokenizer.push_to_hub(model_args.export_hub_model_id, token=model_args.hf_hub_token)
        if processor is not None:
-            getattr(processor, "image_processor").save_pretrained(model_args.export_dir)
+            processor.save_pretrained(model_args.export_dir)
            if model_args.export_hub_model_id is not None:
-                getattr(processor, "image_processor").push_to_hub(
+                processor.push_to_hub(model_args.export_hub_model_id, token=model_args.hf_hub_token)
                    model_args.export_hub_model_id, token=model_args.hf_hub_token
                )
-    except Exception:
+    except Exception as e:
-        logger.warning("Cannot save tokenizer, please copy the files manually.")
+        logger.warning_rank0(f"Cannot save tokenizer, please copy the files manually: {e}.")
--- a/src/llamafactory/webui/chatter.py
+++ b/src/llamafactory/webui/chatter.py
@ -141,7 +141,14 @@ class WebChatModel(ChatModel):
        chatbot[-1][1] = ""
        response = ""
        for new_text in self.stream_chat(
-            messages, system, tools, image, video, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
+            messages,
            system,
            tools,
            images=[image] if image else None,
            videos=[video] if video else None,
            max_new_tokens=max_new_tokens,
            top_p=top_p,
            temperature=temperature,
        ):
            response += new_text
            if tools:
--- a/src/llamafactory/webui/common.py
+++ b/src/llamafactory/webui/common.py
@ -19,6 +19,7 @@ from typing import Any, Dict, Optional, Tuple
 from yaml import safe_dump, safe_load
 from ..extras import logging
 from ..extras.constants import (
    CHECKPOINT_NAMES,
    DATA_CONFIG,
@ -30,8 +31,7 @@ from ..extras.constants import (
    VISION_MODELS,
    DownloadSource,
 )
-from ..extras.logging import get_logger
+from ..extras.misc import use_modelscope, use_openmind
 from ..extras.misc import use_modelscope
 from ..extras.packages import is_gradio_available
@ -39,7 +39,7 @@ if is_gradio_available():
    import gradio as gr
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 DEFAULT_CACHE_DIR = "cache"
@ -56,7 +56,7 @@ def get_save_dir(*paths: str) -> os.PathLike:
    Gets the path to saved model checkpoints.
    """
    if os.path.sep in paths[-1]:
-        logger.warning("Found complex path, some features may be not available.")
+        logger.warning_rank0("Found complex path, some features may be not available.")
        return paths[-1]
    paths = (path.replace(" ", "").strip() for path in paths)
@ -75,7 +75,7 @@ def load_config() -> Dict[str, Any]:
    Loads user config if exists.
    """
    try:
-        with open(get_config_path(), "r", encoding="utf-8") as f:
+        with open(get_config_path(), encoding="utf-8") as f:
            return safe_load(f)
    except Exception:
        return {"lang": None, "last_model": None, "path_dict": {}, "cache_dir": None}
@ -109,19 +109,19 @@ def get_model_path(model_name: str) -> str:
        use_modelscope()
        and path_dict.get(DownloadSource.MODELSCOPE)
        and model_path == path_dict.get(DownloadSource.DEFAULT)
-    ):  # replace path
+    ):  # replace hf path with ms path
        model_path = path_dict.get(DownloadSource.MODELSCOPE)
    if (
        use_openmind()
        and path_dict.get(DownloadSource.OPENMIND)
        and model_path == path_dict.get(DownloadSource.DEFAULT)
    ):  # replace hf path with om path
        model_path = path_dict.get(DownloadSource.OPENMIND)
    return model_path
 def get_prefix(model_name: str) -> str:
    r"""
    Gets the prefix of the model name to obtain the model family.
    """
    return model_name.split("-")[0]
 def get_model_info(model_name: str) -> Tuple[str, str]:
    r"""
    Gets the necessary information of this model.
@ -137,21 +137,14 @@ def get_template(model_name: str) -> str:
    r"""
    Gets the template name if the model is a chat model.
    """
-    if (
+    return DEFAULT_TEMPLATE.get(model_name, "default")
        model_name
        and any(suffix in model_name for suffix in ("-Chat", "-Instruct"))
        and get_prefix(model_name) in DEFAULT_TEMPLATE
    ):
        return DEFAULT_TEMPLATE[get_prefix(model_name)]
    return "default"
 def get_visual(model_name: str) -> bool:
    r"""
    Judges if the model is a vision language model.
    """
-    return get_prefix(model_name) in VISION_MODELS
+    return model_name in VISION_MODELS
 def list_checkpoints(model_name: str, finetuning_type: str) -> "gr.Dropdown":
@ -179,14 +172,14 @@ def load_dataset_info(dataset_dir: str) -> Dict[str, Dict[str, Any]]:
    Loads dataset_info.json.
    """
    if dataset_dir == "ONLINE" or dataset_dir.startswith("REMOTE:"):
-        logger.info("dataset_dir is {}, using online dataset.".format(dataset_dir))
+        logger.info_rank0(f"dataset_dir is {dataset_dir}, using online dataset.")
        return {}
    try:
-        with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
+        with open(os.path.join(dataset_dir, DATA_CONFIG), encoding="utf-8") as f:
            return json.load(f)
    except Exception as err:
-        logger.warning("Cannot open {} due to {}.".format(os.path.join(dataset_dir, DATA_CONFIG), str(err)))
+        logger.warning_rank0(f"Cannot open {os.path.join(dataset_dir, DATA_CONFIG)} due to {str(err)}.")
        return {}
--- a/src/llamafactory/webui/components/data.py
+++ b/src/llamafactory/webui/components/data.py
@ -41,7 +41,7 @@ def next_page(page_index: int, total_num: int) -> int:
 def can_preview(dataset_dir: str, dataset: list) -> "gr.Button":
    try:
-        with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
+        with open(os.path.join(dataset_dir, DATA_CONFIG), encoding="utf-8") as f:
            dataset_info = json.load(f)
    except Exception:
        return gr.Button(interactive=False)
@ -57,7 +57,7 @@ def can_preview(dataset_dir: str, dataset: list) -> "gr.Button":
 def _load_data_file(file_path: str) -> List[Any]:
-    with open(file_path, "r", encoding="utf-8") as f:
+    with open(file_path, encoding="utf-8") as f:
        if file_path.endswith(".json"):
            return json.load(f)
        elif file_path.endswith(".jsonl"):
@ -67,7 +67,7 @@ def _load_data_file(file_path: str) -> List[Any]:
 def get_preview(dataset_dir: str, dataset: list, page_index: int) -> Tuple[int, list, "gr.Column"]:
-    with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
+    with open(os.path.join(dataset_dir, DATA_CONFIG), encoding="utf-8") as f:
        dataset_info = json.load(f)
    data_path = os.path.join(dataset_dir, dataset_info[dataset[0]]["file_name"])
--- a/Show More
+++ b/Show More