release v0.8.0

Former-commit-id: 004db680b9e3996ec511ee818df6c0c02bf13603
add ultrafeedback and fineweb #4085 #4132
2024-06-08 05:20:54 +08:00 · 2024-06-08 02:42:34 +08:00 · 2024-06-08 02:00:44 +08:00 · 2024-06-08 01:57:36 +08:00 · 2024-06-08 01:48:30 +08:00 · 2024-06-08 01:35:58 +08:00
195 changed files with 4296 additions and 2422 deletions
--- a/.dockerignore
+++ b/.dockerignore
@@ -4,6 +4,8 @@
 .venv
 cache
 data
 hf_cache
 output
 examples
 .dockerignore
 .gitattributes
--- a/.github/ISSUE_TEMPLATE/bug-report.yml
+++ b/.github/ISSUE_TEMPLATE/bug-report.yml
@@ -13,6 +13,18 @@ body:
        - label: I have read the README and searched the existing issues.
          required: true
  - type: textarea
    id: system-info
    validations:
      required: true
    attributes:
      label: System Info
      description: |
        Please share your system info with us. You can run the command **llamafactory-cli env** and copy-paste its output below.
        请提供您的系统信息。您可以在命令行运行 **llamafactory-cli env** 并将其输出复制到该文本框中。
      placeholder: llamafactory version, platform, python version, ...
  - type: textarea
    id: reproduction
    validations:
@@ -26,7 +38,7 @@ body:
        请合理使用 Markdown 标签来格式化您的文本。
      placeholder: |
-        python src/train_bash.py ...
+        llamafactory-cli train ...
  - type: textarea
    id: expected-behavior
@@ -38,18 +50,6 @@ body:
        Please provide a clear and concise description of what you would expect to happen.
        请提供您原本的目的，即这段代码的期望行为。
  - type: textarea
    id: system-info
    validations:
      required: false
    attributes:
      label: System Info
      description: |
        Please share your system info with us. You can run the command **transformers-cli env** and copy-paste its output below.
        请提供您的系统信息。您可以在命令行运行 **transformers-cli env** 并将其输出复制到该文本框中。
      placeholder: transformers version, platform, python version, ...
  - type: textarea
    id: others
    validations:
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -2,28 +2,38 @@ name: tests
 on:
  push:
-    branches: [ "main" ]
+    branches:
      - main
    paths:
      - "**.py"
      - "requirements.txt"
      - ".github/workflows/*.yml"
  pull_request:
-    branches: [ "main" ]
+    branches:
      - main
    paths:
      - "**.py"
      - "requirements.txt"
      - ".github/workflows/*.yml"
 jobs:
-  check_code_quality:
+  tests:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.8"
-
+          cache: "pip"
          cache-dependency-path: "setup.py"
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
-          python -m pip install ruff
+          python -m pip install .[torch,dev]
      - name: Check quality
        run: |
          make style && make quality
      - name: Test with pytest
        run: |
          make test
--- a/2
+++ b/2
@@ -6,7 +6,7 @@ COPY requirements.txt /app/
 RUN pip install -r requirements.txt
 COPY . /app/
-RUN pip install -e .[deepspeed,metrics,bitsandbytes,qwen]
+RUN pip install -e .[metrics,bitsandbytes,qwen]
 VOLUME [ "/root/.cache/huggingface/", "/app/data", "/app/output" ]
 EXPOSE 7860
--- a/5
+++ b/5
@@ -1,4 +1,4 @@
-.PHONY: quality style
+.PHONY: quality style test
 check_dirs := scripts src tests
@@ -9,3 +9,6 @@ quality:
 style:
 	ruff check $(check_dirs) --fix
 	ruff format $(check_dirs)
 test:
 	pytest tests/
--- a/README.md
+++ b/README.md
@@ -3,15 +3,15 @@
 [![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers)
 [![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/llmtuner)](https://pypi.org/project/llmtuner/)
+[![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
 [![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
 [![Citation](https://img.shields.io/badge/citation-44-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)
 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
 [![Open in DSW](https://gallery.pai-ml.com/assets/open-in-dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
 [![Spaces](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
 [![Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
 [![GitHub Tread](https://trendshift.io/api/badge/repositories/4535)](https://trendshift.io/repositories/4535)
@@ -26,6 +26,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/9840a653-7e9c-41c8-ae89
 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
 - **Local machine**: Please refer to [usage](#getting-started)
 ## Table of Contents
@@ -46,7 +47,7 @@ Choose your path:
 ## Features
 - **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
- **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO and ORPO.
+- **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
 - **Scalable resources**: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA and 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8.
 - **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and Agent tuning.
 - **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA.
@@ -70,14 +71,22 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 ## Changelog
-[24/05/14] We supported training and inference on the Ascend NPU devices. Check [installation](#installation) section for details.
+[24/06/07] We supported fine-tuning the **[Qwen-2](https://qwenlm.github.io/blog/qwen2/)** series models.
-[24/05/13] We supported fine-tuning the **Yi-1.5** series models.
+[24/06/05] We supported fine-tuning the **[GLM-4-9B/GLM-4-9B-Chat](https://github.com/THUDM/GLM-4)** models.
-[24/04/26] We supported fine-tuning the **LLaVA-1.5** multimodal LLMs. See [examples](examples/README.md) for usage.
+[24/05/26] We supported **[SimPO](https://arxiv.org/abs/2405.14734)** algorithm for preference learning. See [examples](examples/README.md) for usage.
 <details><summary>Full Changelog</summary>
 [24/05/20] We supported fine-tuning the **PaliGemma** series models. Note that the PaliGemma models are pre-trained models, you need to fine-tune them with `gemma` template for chat completion.
 [24/05/18] We supported **[KTO](https://arxiv.org/abs/2402.01306)** algorithm for preference learning. See [examples](examples/README.md) for usage.
 [24/05/14] We supported training and inference on the Ascend NPU devices. Check [installation](#installation) section for details.
 [24/04/26] We supported fine-tuning the **LLaVA-1.5** multimodal LLMs. See [examples](examples/README.md) for usage.
 [24/04/22] We provided a **[Colab notebook](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)** for fine-tuning the Llama-3 model on a free T4 GPU. Two Llama-3-derived models fine-tuned using LLaMA Factory are available at Hugging Face, check [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) and [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese) for details.
 [24/04/21] We supported **[Mixture-of-Depths](https://arxiv.org/abs/2404.02258)** according to [AstraMindAI's implementation](https://github.com/astramind-ai/Mixture-of-depths). See [examples](examples/README.md) for usage.
@@ -104,7 +113,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 [24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this [blog post](https://qwenlm.github.io/blog/qwen1.5/) for details.
-[24/01/18] We supported **agent tuning** for most models, equipping model with tool using abilities by fine-tuning with `dataset: glaive_toolcall`.
+[24/01/18] We supported **agent tuning** for most models, equipping model with tool using abilities by fine-tuning with `dataset: glaive_toolcall_en`.
 [23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `use_unsloth: true` argument to activate unsloth patch. It achieves **170%** speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details.
@@ -142,43 +151,44 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 ## Supported Models
-| Model                                                    | Model size                       | Default module    | Template  |
+| Model                                                    | Model size                       | Template  |
-| -------------------------------------------------------- | -------------------------------- | ----------------- | --------- |
+| -------------------------------------------------------- | -------------------------------- | --------- |
-| [Baichuan2](https://huggingface.co/baichuan-inc)         | 7B/13B                           | W_pack            | baichuan2 |
+| [Baichuan2](https://huggingface.co/baichuan-inc)         | 7B/13B                           | baichuan2 |
-| [BLOOM](https://huggingface.co/bigscience)               | 560M/1.1B/1.7B/3B/7.1B/176B      | query_key_value   | -         |
+| [BLOOM](https://huggingface.co/bigscience)               | 560M/1.1B/1.7B/3B/7.1B/176B      | -         |
-| [BLOOMZ](https://huggingface.co/bigscience)              | 560M/1.1B/1.7B/3B/7.1B/176B      | query_key_value   | -         |
+| [BLOOMZ](https://huggingface.co/bigscience)              | 560M/1.1B/1.7B/3B/7.1B/176B      | -         |
-| [ChatGLM3](https://huggingface.co/THUDM)                 | 6B                               | query_key_value   | chatglm3  |
+| [ChatGLM3](https://huggingface.co/THUDM)                 | 6B                               | chatglm3  |
-| [Command-R](https://huggingface.co/CohereForAI)          | 35B/104B                         | q_proj,v_proj     | cohere    |
+| [Command-R](https://huggingface.co/CohereForAI)          | 35B/104B                         | cohere    |
-| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai)     | 7B/16B/67B/236B                  | q_proj,v_proj     | deepseek  |
+| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai)     | 7B/16B/67B/236B                  | deepseek  |
-| [Falcon](https://huggingface.co/tiiuae)                  | 7B/40B/180B                      | query_key_value   | falcon    |
+| [Falcon](https://huggingface.co/tiiuae)                  | 7B/11B/40B/180B                  | falcon    |
-| [Gemma/CodeGemma](https://huggingface.co/google)         | 2B/7B                            | q_proj,v_proj     | gemma     |
+| [Gemma/CodeGemma](https://huggingface.co/google)         | 2B/7B                            | gemma     |
-| [InternLM2](https://huggingface.co/internlm)             | 7B/20B                           | wqkv              | intern2   |
+| [GLM4](https://huggingface.co/THUDM)                     | 9B                               | glm4      |
-| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B                   | q_proj,v_proj     | -         |
+| [InternLM2](https://huggingface.co/internlm)             | 7B/20B                           | intern2   |
-| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                       | q_proj,v_proj     | llama2    |
+| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B                   | -         |
-| [LLaMA-3](https://huggingface.co/meta-llama)             | 8B/70B                           | q_proj,v_proj     | llama3    |
+| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                       | llama2    |
-| [LLaVA-1.5](https://huggingface.co/llava-hf)             | 7B/13B                           | q_proj,v_proj     | vicuna    |
+| [LLaMA-3](https://huggingface.co/meta-llama)             | 8B/70B                           | llama3    |
-| [Mistral/Mixtral](https://huggingface.co/mistralai)      | 7B/8x7B/8x22B                    | q_proj,v_proj     | mistral   |
+| [LLaVA-1.5](https://huggingface.co/llava-hf)             | 7B/13B                           | vicuna    |
-| [OLMo](https://huggingface.co/allenai)                   | 1B/7B                            | q_proj,v_proj     | -         |
+| [Mistral/Mixtral](https://huggingface.co/mistralai)      | 7B/8x7B/8x22B                    | mistral   |
-| [Phi-1.5/2](https://huggingface.co/microsoft)            | 1.3B/2.7B                        | q_proj,v_proj     | -         |
+| [OLMo](https://huggingface.co/allenai)                   | 1B/7B                            | -         |
-| [Phi-3](https://huggingface.co/microsoft)                | 3.8B                             | qkv_proj          | phi       |
+| [PaliGemma](https://huggingface.co/google)               | 3B                               | gemma     |
-| [Qwen](https://huggingface.co/Qwen)                      | 1.8B/7B/14B/72B                  | c_attn            | qwen      |
+| [Phi-1.5/2](https://huggingface.co/microsoft)            | 1.3B/2.7B                        | -         |
-| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen)        | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | q_proj,v_proj     | qwen      |
+| [Phi-3](https://huggingface.co/microsoft)                | 4B/7B/14B                        | phi       |
-| [StarCoder2](https://huggingface.co/bigcode)             | 3B/7B/15B                        | q_proj,v_proj     | -         |
+| [Qwen](https://huggingface.co/Qwen)                      | 1.8B/7B/14B/72B                  | qwen      |
-| [XVERSE](https://huggingface.co/xverse)                  | 7B/13B/65B                       | q_proj,v_proj     | xverse    |
+| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen)        | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | qwen      |
-| [Yi (1/1.5)](https://huggingface.co/01-ai)               | 6B/9B/34B                        | q_proj,v_proj     | yi        |
+| [Qwen2 (MoE)](https://huggingface.co/Qwen)               | 0.5B/1.5B/7B/57B/72B             | qwen      |
-| [Yi-VL](https://huggingface.co/01-ai)                    | 6B/34B                           | q_proj,v_proj     | yi_vl     |
+| [StarCoder2](https://huggingface.co/bigcode)             | 3B/7B/15B                        | -         |
-| [Yuan](https://huggingface.co/IEITYuan)                  | 2B/51B/102B                      | q_proj,v_proj     | yuan      |
+| [XVERSE](https://huggingface.co/xverse)                  | 7B/13B/65B                       | xverse    |
 | [Yi (1/1.5)](https://huggingface.co/01-ai)               | 6B/9B/34B                        | yi        |
 | [Yi-VL](https://huggingface.co/01-ai)                    | 6B/34B                           | yi_vl     |
 | [Yuan](https://huggingface.co/IEITYuan)                  | 2B/51B/102B                      | yuan      |
 > [!NOTE]
-> **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules for better convergence.
+> 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.
 >
 > 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.
 >
 > Remember to use the **SAME** template in training and inference.
-Please refer to [constants.py](src/llmtuner/extras/constants.py) for a full list of models we supported.
+Please refer to [constants.py](src/llamafactory/extras/constants.py) for a full list of models we supported.
-You also can add a custom chat template to [template.py](src/llmtuner/data/template.py).
+You also can add a custom chat template to [template.py](src/llamafactory/data/template.py).
 ## Supported Training Approaches
@@ -189,7 +199,9 @@ You also can add a custom chat template to [template.py](src/llmtuner/data/templ
 | Reward Modeling        | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | PPO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | DPO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | KTO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | ORPO Training          | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | SimPO Training         | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 ## Provided Datasets
@@ -202,6 +214,8 @@ You also can add a custom chat template to [template.py](src/llmtuner/data/templ
 - [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
 - [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile)
 - [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
 - [FineWeb (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
 - [FineWeb-Edu (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
 - [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
 - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
@@ -209,12 +223,12 @@ You also can add a custom chat template to [template.py](src/llmtuner/data/templ
 <details><summary>Supervised fine-tuning datasets</summary>
 - [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
 - [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
 - [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
 - [Identity (en&zh)](data/identity.json)
- [Open Assistant (zh)](https://huggingface.co/datasets/OpenAssistant/oasst1)
+- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
- [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
+- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3)
 - [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
 - [Glaive Function Calling V2 (en&zh)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
 - [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
 - [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
 - [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
 - [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
@@ -223,7 +237,6 @@ You also can add a custom chat template to [template.py](src/llmtuner/data/templ
 - [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
 - [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
 - [UltraChat (en)](https://github.com/thunlp/UltraChat)
 - [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
 - [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
 - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
 - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
@@ -236,15 +249,16 @@ You also can add a custom chat template to [template.py](src/llmtuner/data/templ
 - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
 - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
 - [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data)
- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
+- [Advertise Generating (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
 - [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
 - [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
 - [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
 - [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
 - [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
 - [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
 - [Glaive Function Calling V2 (en)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
 - [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
 - [STEM (zh)](https://huggingface.co/datasets/hfl/stem_zh_instruction)
 - [Ruozhiba (zh)](https://huggingface.co/datasets/hfl/ruozhiba_gpt4_turbo)
 - [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
 - [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de)
 - [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de)
@@ -260,13 +274,13 @@ You also can add a custom chat template to [template.py](src/llmtuner/data/templ
 <details><summary>Preference datasets</summary>
 - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
 - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
 - [Orca DPO (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
 - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
 - [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
- [Open Assistant (zh)](https://huggingface.co/datasets/OpenAssistant/oasst1)
+- [UltraFeedback (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)
 - [Orca DPO Pairs (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
 - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
 - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
 - [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)
 - [KTO mixed (en)](https://huggingface.co/datasets/argilla/kto-mix-15k)
 </details>
@@ -281,21 +295,21 @@ huggingface-cli login
 | Mandatory    | Minimum | Recommend |
 | ------------ | ------- | --------- |
-| python       | 3.8     | 3.10      |
+| python       | 3.8     | 3.11      |
-| torch        | 1.13.1  | 2.2.0     |
+| torch        | 1.13.1  | 2.3.0     |
-| transformers | 4.37.2  | 4.40.1    |
+| transformers | 4.41.2  | 4.41.2    |
-| datasets     | 2.14.3  | 2.19.1    |
+| datasets     | 2.16.0  | 2.19.2    |
-| accelerate   | 0.27.2  | 0.30.0    |
+| accelerate   | 0.30.1  | 0.30.1    |
-| peft         | 0.9.0   | 0.10.0    |
+| peft         | 0.11.1  | 0.11.1    |
-| trl          | 0.8.1   | 0.8.6     |
+| trl          | 0.8.6   | 0.9.4     |
 | Optional     | Minimum | Recommend |
 | ------------ | ------- | --------- |
 | CUDA         | 11.6    | 12.2      |
 | deepspeed    | 0.10.0  | 0.14.0    |
 | bitsandbytes | 0.39.0  | 0.43.1    |
-| vllm         | 0.4.0   | 0.4.2     |
+| vllm         | 0.4.3   | 0.4.3     |
-| flash-attn   | 2.3.0   | 2.5.8     |
+| flash-attn   | 2.3.0   | 2.5.9     |
 ### Hardware Requirement
@@ -319,12 +333,12 @@ huggingface-cli login
 > Installation is mandatory.
 ```bash
-git clone https://github.com/hiyouga/LLaMA-Factory.git
+git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
 cd LLaMA-Factory
-pip install -e .[torch,metrics]
+pip install -e '.[torch,metrics]'
 ```
-Extra dependencies available: torch, metrics, deepspeed, bitsandbytes, vllm, galore, badam, gptq, awq, aqlm, qwen, modelscope, quality
+Extra dependencies available: torch, torch_npu, metrics, deepspeed, bitsandbytes, vllm, galore, badam, gptq, awq, aqlm, qwen, modelscope, quality
 > [!TIP]
 > Use `pip install --no-deps -e .` to resolve package conflicts.
@@ -343,19 +357,35 @@ To enable FlashAttention-2 on the Windows platform, you need to install the prec
 <details><summary>For Ascend NPU users</summary>
-To utilize Ascend NPU devices for (distributed) training and inference, you need to install the **[torch-npu](https://gitee.com/ascend/pytorch)** library and the **[Ascend CANN Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**.
+Join [NPU user group](assets/wechat_npu.jpg).
 To install LLaMA Factory on Ascend NPU devices, please specify extra dependencies: `pip install -e '.[torch-npu,metrics]'`. Additionally, you need to install the **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**. Please follow the [installation tutorial](https://www.hiascend.com/document/detail/en/CANNCommunityEdition/600alphaX/softwareinstall/instg/atlasdeploy_03_0031.html) or use the following commands:
 ```bash
 # replace the url according to your CANN version and devices
 # install CANN Toolkit
 wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run
 bash Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run --install
 # install CANN Kernels
 wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run
 bash Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run --install
 # set env variables
 source /usr/local/Ascend/ascend-toolkit/set_env.sh
 ```
 | Requirement  | Minimum | Recommend   |
-| ------------ | ------- | --------- |
+| ------------ | ------- | ----------- |
 | CANN         | 8.0.RC1 | 8.0.RC1     |
-| torch        | 2.2.0   | 2.2.0     |
+| torch        | 2.1.0   | 2.1.0       |
-| torch-npu    | 2.2.0   | 2.2.0     |
+| torch-npu    | 2.1.0   | 2.1.0.post3 |
 | deepspeed    | 0.13.2  | 0.13.2      |
 Docker image:
 - 32GB: [Download page](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html)
- 64GB: Coming soon
+- 64GB: [Download page](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
 Remember to use `ASCEND_RT_VISIBLE_DEVICES` instead of `CUDA_VISIBLE_DEVICES` to specify the device to use.
@@ -387,29 +417,12 @@ See [examples/README.md](examples/README.md) for advanced usage (including distr
 ### Fine-Tuning with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio))
 > [!IMPORTANT]
 > LLaMA Board GUI only supports training on a single GPU.
 #### Use local environment
 ```bash
 CUDA_VISIBLE_DEVICES=0 GRADIO_SHARE=1 llamafactory-cli webui
 ```
 <details><summary>For Alibaba Cloud PAI or AutoDL users</summary>
 If you encountered display problems in LLaMA Board on Alibaba Cloud PAI, try using the following command to set environment variables before starting LLaMA Board:
 ```bash
 export GRADIO_SERVER_PORT=7860 GRADIO_ROOT_PATH=/${JUPYTER_NAME}/proxy/7860/
 ```
 If you are using AutoDL, please install a specific version of Gradio:
 ```bash
 pip install gradio==4.10.0
 ```
 </details>
 #### Use Docker
@@ -420,7 +433,6 @@ docker run --gpus=all \
    -v ./hf_cache:/root/.cache/huggingface/ \
    -v ./data:/app/data \
    -v ./output:/app/output \
    -e CUDA_VISIBLE_DEVICES=0 \
    -p 7860:7860 \
    --shm-size 16G \
    --name llama_factory \
@@ -447,6 +459,9 @@ docker compose -f ./docker-compose.yml up -d
 CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
 ```
 > [!TIP]
 > Visit https://platform.openai.com/docs/api-reference/chat/create for API document.
 ### Download from ModelScope Hub
 If you have trouble with downloading models and datasets from Hugging Face, you can use ModelScope.
@@ -455,7 +470,18 @@ If you have trouble with downloading models and datasets from Hugging Face, you
 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`.
+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`.
 ### Use W&B Logger
 To use [Weights & Biases](https://wandb.ai) for logging experimental results, you need to add the following arguments.
 ```yaml
 report_to: wandb
 run_name: test_run # optional
 ```
 Set `WANDB_API_KEY` to [your key](https://wandb.ai/authorize) when launching training tasks to log in with your W&B account.
 ## Projects using LLaMA Factory
@@ -502,7 +528,7 @@ If you have a project that should be incorporated, please contact via email or c
 1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)
 1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
 1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge.
-1. **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
+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)
@@ -514,7 +540,7 @@ 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: [Baichuan2](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) / [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/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](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) / [StarCoder2](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](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
+Please follow the model licenses to use the corresponding model weights: [Baichuan2](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) / [GLM4](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/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](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) / [StarCoder2](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](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
 ## Citation
--- a/README_zh.md
+++ b/README_zh.md
@@ -3,15 +3,15 @@
 [![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers)
 [![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/llmtuner)](https://pypi.org/project/llmtuner/)
+[![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
 [![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
 [![Citation](https://img.shields.io/badge/citation-44-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)
 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)
 [![Open in DSW](https://gallery.pai-ml.com/assets/open-in-dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
 [![Spaces](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
 [![Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)
 [![GitHub Tread](https://trendshift.io/api/badge/repositories/4535)](https://trendshift.io/repositories/4535)
@@ -26,6 +26,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 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
 - **本地机器**：请见[如何使用](#如何使用)
 ## 目录
@@ -46,7 +47,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 ## 项目特色
 - **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
- **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练和 ORPO 训练。
+- **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练、ORPO 训练等等。
 - **多种精度**：32 比特全参数微调、16 比特冻结微调、16 比特 LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8 的 2/4/8 比特 QLoRA 微调。
 - **先进算法**：GaLore、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ 和 Agent 微调。
 - **实用技巧**：FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。
@@ -70,14 +71,22 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 ## 更新日志
-[24/05/14] 我们支持了昇腾 NPU 设备的训练和推理。详情请查阅[安装](#安装-llama-factory)部分。
+[24/06/07] 我们支持了 **[Qwen-2](https://qwenlm.github.io/blog/qwen2/)** 系列模型的微调。
-[24/05/13] 我们支持了 Yi-1.5 系列模型的微调。
+[24/06/05] 我们支持了 **[GLM-4-9B/GLM-4-9B-Chat](https://github.com/THUDM/GLM-4)** 模型的微调。
-[24/04/26] 我们支持了多模态模型 **LLaVA-1.5** 的微调。详细用法请参照 [examples](examples/README_zh.md)。
+[24/05/26] 我们支持了 **[SimPO](https://arxiv.org/abs/2405.14734)** 偏好对齐算法。详细用法请参照 [examples](examples/README_zh.md)。
 <details><summary>展开日志</summary>
 [24/05/20] 我们支持了 **PaliGemma** 系列模型的微调。注意 PaliGemma 是预训练模型，你需要使用 `gemma` 模板进行微调使其获得对话能力。
 [24/05/18] 我们支持了 **[KTO](https://arxiv.org/abs/2402.01306)** 偏好对齐算法。详细用法请参照 [examples](examples/README_zh.md)。
 [24/05/14] 我们支持了昇腾 NPU 设备的训练和推理。详情请查阅[安装](#安装-llama-factory)部分。
 [24/04/26] 我们支持了多模态模型 **LLaVA-1.5** 的微调。详细用法请参照 [examples](examples/README_zh.md)。
 [24/04/22] 我们提供了在免费 T4 GPU 上微调 Llama-3 模型的 **[Colab 笔记本](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)**。Hugging Face 社区公开了两个利用 LLaMA Factory 微调的 Llama-3 模型，详情请见 [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) 和 [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese)。
 [24/04/21] 我们基于 [AstraMindAI 的仓库](https://github.com/astramind-ai/Mixture-of-depths)支持了 **[混合深度训练](https://arxiv.org/abs/2404.02258)**。详细用法请参照 [examples](examples/README_zh.md)。
@@ -104,7 +113,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 [24/02/05] Qwen1.5（Qwen2 测试版）系列模型已在 LLaMA-Factory 中实现微调支持。详情请查阅该[博客页面](https://qwenlm.github.io/zh/blog/qwen1.5/)。
-[24/01/18] 我们针对绝大多数模型实现了 **Agent 微调**，微调时指定 `dataset: glaive_toolcall` 即可使模型获得工具调用能力。
+[24/01/18] 我们针对绝大多数模型实现了 **Agent 微调**，微调时指定 `dataset: glaive_toolcall_zh` 即可使模型获得工具调用能力。
 [23/12/23] 我们针对 LLaMA, Mistral 和 Yi 模型支持了 **[unsloth](https://github.com/unslothai/unsloth)** 的 LoRA 训练加速。请使用 `use_unsloth: true` 参数启用 unsloth 优化。该方法可提供 **170%** 的训练速度，详情请查阅[此页面](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。
@@ -142,43 +151,44 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 ## 模型
-| 模型名                                                   | 模型大小                          | 默认模块           | Template  |
+| 模型名                                                   | 模型大小                          | Template  |
-| -------------------------------------------------------- | -------------------------------- | ----------------- | --------- |
+| -------------------------------------------------------- | -------------------------------- | --------- |
-| [Baichuan2](https://huggingface.co/baichuan-inc)         | 7B/13B                           | W_pack            | baichuan2 |
+| [Baichuan2](https://huggingface.co/baichuan-inc)         | 7B/13B                           | baichuan2 |
-| [BLOOM](https://huggingface.co/bigscience)               | 560M/1.1B/1.7B/3B/7.1B/176B      | query_key_value   | -         |
+| [BLOOM](https://huggingface.co/bigscience)               | 560M/1.1B/1.7B/3B/7.1B/176B      | -         |
-| [BLOOMZ](https://huggingface.co/bigscience)              | 560M/1.1B/1.7B/3B/7.1B/176B      | query_key_value   | -         |
+| [BLOOMZ](https://huggingface.co/bigscience)              | 560M/1.1B/1.7B/3B/7.1B/176B      | -         |
-| [ChatGLM3](https://huggingface.co/THUDM)                 | 6B                               | query_key_value   | chatglm3  |
+| [ChatGLM3](https://huggingface.co/THUDM)                 | 6B                               | chatglm3  |
-| [Command-R](https://huggingface.co/CohereForAI)          | 35B/104B                         | q_proj,v_proj     | cohere    |
+| [Command-R](https://huggingface.co/CohereForAI)          | 35B/104B                         | cohere    |
-| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai)     | 7B/16B/67B/236B                  | q_proj,v_proj     | deepseek  |
+| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai)     | 7B/16B/67B/236B                  | deepseek  |
-| [Falcon](https://huggingface.co/tiiuae)                  | 7B/40B/180B                      | query_key_value   | falcon    |
+| [Falcon](https://huggingface.co/tiiuae)                  | 7B/11B/40B/180B                  | falcon    |
-| [Gemma/CodeGemma](https://huggingface.co/google)         | 2B/7B                            | q_proj,v_proj     | gemma     |
+| [Gemma/CodeGemma](https://huggingface.co/google)         | 2B/7B                            | gemma     |
-| [InternLM2](https://huggingface.co/internlm)             | 7B/20B                           | wqkv              | intern2   |
+| [GLM4](https://huggingface.co/THUDM)                     | 9B                               | glm4      |
-| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B                   | q_proj,v_proj     | -         |
+| [InternLM2](https://huggingface.co/internlm)             | 7B/20B                           | intern2   |
-| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                       | q_proj,v_proj     | llama2    |
+| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B                   | -         |
-| [LLaMA-3](https://huggingface.co/meta-llama)             | 8B/70B                           | q_proj,v_proj     | llama3    |
+| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                       | llama2    |
-| [LLaVA-1.5](https://huggingface.co/llava-hf)             | 7B/13B                           | q_proj,v_proj     | vicuna    |
+| [LLaMA-3](https://huggingface.co/meta-llama)             | 8B/70B                           | llama3    |
-| [Mistral/Mixtral](https://huggingface.co/mistralai)      | 7B/8x7B/8x22B                    | q_proj,v_proj     | mistral   |
+| [LLaVA-1.5](https://huggingface.co/llava-hf)             | 7B/13B                           | vicuna    |
-| [OLMo](https://huggingface.co/allenai)                   | 1B/7B                            | q_proj,v_proj     | -         |
+| [Mistral/Mixtral](https://huggingface.co/mistralai)      | 7B/8x7B/8x22B                    | mistral   |
-| [Phi-1.5/2](https://huggingface.co/microsoft)            | 1.3B/2.7B                        | q_proj,v_proj     | -         |
+| [OLMo](https://huggingface.co/allenai)                   | 1B/7B                            | -         |
-| [Phi-3](https://huggingface.co/microsoft)                | 3.8B                             | qkv_proj          | phi       |
+| [PaliGemma](https://huggingface.co/google)               | 3B                               | gemma     |
-| [Qwen](https://huggingface.co/Qwen)                      | 1.8B/7B/14B/72B                  | c_attn            | qwen      |
+| [Phi-1.5/2](https://huggingface.co/microsoft)            | 1.3B/2.7B                        | -         |
-| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen)        | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | q_proj,v_proj     | qwen      |
+| [Phi-3](https://huggingface.co/microsoft)                | 4B/7B/14B                        | phi       |
-| [StarCoder2](https://huggingface.co/bigcode)             | 3B/7B/15B                        | q_proj,v_proj     | -         |
+| [Qwen](https://huggingface.co/Qwen)                      | 1.8B/7B/14B/72B                  | qwen      |
-| [XVERSE](https://huggingface.co/xverse)                  | 7B/13B/65B                       | q_proj,v_proj     | xverse    |
+| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen)        | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | qwen      |
-| [Yi (1/1.5)](https://huggingface.co/01-ai)               | 6B/9B/34B                        | q_proj,v_proj     | yi        |
+| [Qwen2 (MoE)](https://huggingface.co/Qwen)               | 0.5B/1.5B/7B/57B/72B             | qwen      |
-| [Yi-VL](https://huggingface.co/01-ai)                    | 6B/34B                           | q_proj,v_proj     | yi_vl     |
+| [StarCoder2](https://huggingface.co/bigcode)             | 3B/7B/15B                        | -         |
-| [Yuan](https://huggingface.co/IEITYuan)                  | 2B/51B/102B                      | q_proj,v_proj     | yuan      |
+| [XVERSE](https://huggingface.co/xverse)                  | 7B/13B/65B                       | xverse    |
 | [Yi (1/1.5)](https://huggingface.co/01-ai)               | 6B/9B/34B                        | yi        |
 | [Yi-VL](https://huggingface.co/01-ai)                    | 6B/34B                           | yi_vl     |
 | [Yuan](https://huggingface.co/IEITYuan)                  | 2B/51B/102B                      | yuan      |
 > [!NOTE]
-> **默认模块**应作为 `--lora_target` 参数的默认值，可使用 `--lora_target all` 参数指定全部模块以取得更好的效果。
+> 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**。
 >
-> 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**。
+> 请务必在训练和推理时采用**完全一致**的模板。
 >
 > 请务必在训练和推理时使用**完全一致**的模板。
-项目所支持模型的完整列表请参阅 [constants.py](src/llmtuner/extras/constants.py)。
+项目所支持模型的完整列表请参阅 [constants.py](src/llamafactory/extras/constants.py)。
-您也可以在 [template.py](src/llmtuner/data/template.py) 中添加自己的对话模板。
+您也可以在 [template.py](src/llamafactory/data/template.py) 中添加自己的对话模板。
 ## 训练方法
@@ -189,7 +199,9 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 | 奖励模型训练            | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | PPO 训练               | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | DPO 训练               | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | KTO 训练               | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | ORPO 训练              | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | SimPO 训练             | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 ## 数据集
@@ -202,6 +214,8 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 - [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
 - [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile)
 - [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
 - [FineWeb (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
 - [FineWeb-Edu (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
 - [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
 - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
@@ -209,12 +223,12 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 <details><summary>指令微调数据集</summary>
 - [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
 - [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
 - [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
 - [Identity (en&zh)](data/identity.json)
- [Open Assistant (zh)](https://huggingface.co/datasets/OpenAssistant/oasst1)
+- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
- [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
+- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3)
 - [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
 - [Glaive Function Calling V2 (en&zh)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
 - [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
 - [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
 - [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
 - [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
@@ -223,7 +237,6 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 - [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
 - [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
 - [UltraChat (en)](https://github.com/thunlp/UltraChat)
 - [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
 - [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
 - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
 - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
@@ -236,15 +249,16 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
 - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
 - [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data)
- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
+- [Advertise Generating (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
 - [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
 - [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
 - [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
 - [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
 - [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
 - [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
 - [Glaive Function Calling V2 (en)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
 - [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
 - [STEM (zh)](https://huggingface.co/datasets/hfl/stem_zh_instruction)
 - [Ruozhiba (zh)](https://huggingface.co/datasets/hfl/ruozhiba_gpt4_turbo)
 - [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
 - [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de)
 - [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de)
@@ -260,13 +274,13 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 <details><summary>偏好数据集</summary>
 - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
 - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
 - [Orca DPO (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
 - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
 - [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
- [Open Assistant (zh)](https://huggingface.co/datasets/OpenAssistant/oasst1)
+- [UltraFeedback (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)
 - [Orca DPO Pairs (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
 - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
 - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
 - [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)
 - [KTO mixed (en)](https://huggingface.co/datasets/argilla/kto-mix-15k)
 </details>
@@ -281,21 +295,21 @@ huggingface-cli login
 | 必需项       | 至少     | 推荐      |
 | ------------ | ------- | --------- |
-| python       | 3.8     | 3.10      |
+| python       | 3.8     | 3.11      |
-| torch        | 1.13.1  | 2.2.0     |
+| torch        | 1.13.1  | 2.3.0     |
-| transformers | 4.37.2  | 4.40.1    |
+| transformers | 4.41.2  | 4.41.2    |
-| datasets     | 2.14.3  | 2.19.1    |
+| datasets     | 2.16.0  | 2.19.2    |
-| accelerate   | 0.27.2  | 0.30.0    |
+| accelerate   | 0.30.1  | 0.30.1    |
-| peft         | 0.9.0   | 0.10.0    |
+| peft         | 0.11.1  | 0.11.1    |
-| trl          | 0.8.1   | 0.8.6     |
+| trl          | 0.8.6   | 0.9.4     |
 | 可选项       | 至少     | 推荐      |
 | ------------ | ------- | --------- |
 | CUDA         | 11.6    | 12.2      |
 | deepspeed    | 0.10.0  | 0.14.0    |
 | bitsandbytes | 0.39.0  | 0.43.1    |
-| vllm         | 0.4.0   | 0.4.2     |
+| vllm         | 0.4.3   | 0.4.3     |
-| flash-attn   | 2.3.0   | 2.5.8     |
+| flash-attn   | 2.3.0   | 2.5.9     |
 ### 硬件依赖
@@ -319,12 +333,12 @@ huggingface-cli login
 > 此步骤为必需。
 ```bash
-git clone https://github.com/hiyouga/LLaMA-Factory.git
+git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
 cd LLaMA-Factory
-pip install -e .[torch,metrics]
+pip install -e '.[torch,metrics]'
 ```
-可选的额外依赖项：torch、metrics、deepspeed、bitsandbytes、vllm、galore、badam、gptq、awq、aqlm、qwen、modelscope、quality
+可选的额外依赖项：torch、torch_npu、metrics、deepspeed、bitsandbytes、vllm、galore、badam、gptq、awq、aqlm、qwen、modelscope、quality
 > [!TIP]
 > 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
@@ -343,21 +357,37 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
 <details><summary>昇腾 NPU 用户指南</summary>
-如果使用昇腾 NPU 设备进行（分布式）训练或推理，需要安装 **[torch-npu](https://gitee.com/ascend/pytorch)** 库和 **[Ascend CANN Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**。
+加入 [NPU 用户群](assets/wechat_npu.jpg)。
 在昇腾 NPU 设备上安装 LLaMA Factory 时，需要指定额外依赖项，使用 `pip install -e '.[torch-npu,metrics]'` 命令安装。此外，还需要安装 **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**，安装方法请参考[安装教程](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC2alpha002/quickstart/quickstart/quickstart_18_0004.html)或使用以下命令：
 ```bash
 # 请替换 URL 为 CANN 版本和设备型号对应的 URL
 # 安装 CANN Toolkit
 wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run
 bash Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run --install
 # 安装 CANN Kernels
 wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run
 bash Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run --install
 # 设置环境变量
 source /usr/local/Ascend/ascend-toolkit/set_env.sh
 ```
 | 依赖项       | 至少     | 推荐        |
-| ------------ | ------- | --------- |
+| ------------ | ------- | ----------- |
 | CANN         | 8.0.RC1 | 8.0.RC1     |
-| torch        | 2.2.0   | 2.2.0     |
+| torch        | 2.1.0   | 2.1.0       |
-| torch-npu    | 2.2.0   | 2.2.0     |
+| torch-npu    | 2.1.0   | 2.1.0.post3 |
 | deepspeed    | 0.13.2  | 0.13.2      |
 Docker 镜像：
 - 32GB：[下载地址](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html)
- 64GB：敬请期待
+- 64GB：[下载地址](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
-请记得使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定您使用的设备。
+请使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定运算设备。
 如果遇到无法正常推理的情况，请尝试设置 `do_sample: false`。
@@ -387,31 +417,12 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_s
 ### LLaMA Board 可视化微调（由 [Gradio](https://github.com/gradio-app/gradio) 驱动）
 > [!IMPORTANT]
 > LLaMA Board 可视化界面目前仅支持单 GPU 训练。
 #### 使用本地环境
 ```bash
 CUDA_VISIBLE_DEVICES=0 GRADIO_SHARE=1 llamafactory-cli webui
 ```
 <details><summary>阿里云 PAI 和 AutoDL 用户指南</summary>
 如果您在阿里云 PAI 上使用 LLaMA Board 时遇到显示问题，请尝试在启动前使用以下命令设置环境变量：
 ```bash
 export GRADIO_SERVER_PORT=7860 GRADIO_ROOT_PATH=/${JUPYTER_NAME}/proxy/7860/
 ```
 如果您正在使用 AutoDL，请安装下述 Gradio 版本：
 ```bash
 pip install gradio==4.10.0
 ```
 </details>
 #### 使用 Docker
 ```bash
@@ -420,7 +431,6 @@ docker run --gpus=all \
    -v ./hf_cache:/root/.cache/huggingface/ \
    -v ./data:/app/data \
    -v ./output:/app/output \
    -e CUDA_VISIBLE_DEVICES=0 \
    -p 7860:7860 \
    --shm-size 16G \
    --name llama_factory \
@@ -447,6 +457,9 @@ docker compose -f ./docker-compose.yml up -d
 CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
 ```
 > [!TIP]
 > API 文档请查阅 https://platform.openai.com/docs/api-reference/chat/create。
 ### 从魔搭社区下载
 如果您在 Hugging Face 模型和数据集的下载中遇到了问题，可以通过下述方法使用魔搭社区。
@@ -455,7 +468,18 @@ CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/l
 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`。
+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`。
 ### 使用 W&B 面板
 若要使用 [Weights & Biases](https://wandb.ai) 记录实验数据，请添加下面的参数。
 ```yaml
 report_to: wandb
 run_name: test_run # 可选
 ```
 在启动训练任务时，将 `WANDB_API_KEY` 设置为[密钥](https://wandb.ai/authorize)来登录 W&B 账户。
 ## 使用了 LLaMA Factory 的项目
@@ -502,7 +526,7 @@ export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
 1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)
 1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: 天文大模型 StarWhisper，基于 ChatGLM2-6B 和 Qwen-14B 在天文数据上微调而得。
 1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: 中文法律领域大模型 DISC-LawLLM，基于 Baichuan-13B 微调而得，具有法律推理和知识检索能力。
-1. **[Sunsimiao](https://github.com/thomas-yanxin/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. **[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)
@@ -514,7 +538,7 @@ export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
 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：[Baichuan2](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) / [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/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](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) / [StarCoder2](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](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：[Baichuan2](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) / [GLM4](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/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](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) / [StarCoder2](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](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
 ## 引用
--- a/data/README.md
+++ b/data/README.md
@@ -1,16 +1,18 @@
-If you are using a custom dataset, please add your **dataset description** to `dataset_info.json` according to the following format. We also provide several examples in the next section.
+The [dataset_info.json](dataset_info.json) contains all available datasets. If you are using a custom dataset, please **make sure** to add a *dataset description* in `dataset_info.json` and specify `dataset: dataset_name` before training to use it.
 Currently we support datasets in **alpaca** and **sharegpt** format.
 ```json
 "dataset_name": {
  "hf_hub_url": "the name of the dataset repository on the Hugging Face hub. (if specified, ignore script_url and file_name)",
  "ms_hub_url": "the name of the dataset repository on the Model Scope hub. (if specified, ignore script_url and file_name)",
  "script_url": "the name of the directory containing a dataset loading script. (if specified, ignore file_name)",
-  "file_name": "the name of the dataset file in this directory. (required if above are not specified)",
+  "file_name": "the name of the dataset folder or dataset file in this directory. (required if above are not specified)",
-  "file_sha1": "the SHA-1 hash value of the dataset file. (optional, does not affect training)",
+  "formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})",
  "ranking": "whether the dataset is a preference dataset or not. (default: False)",
  "subset": "the name of the subset. (optional, default: None)",
  "folder": "the name of the folder of the dataset repository on the Hugging Face hub. (optional, default: None)",
-  "ranking": "whether the dataset is a preference dataset or not. (default: false)",
+  "num_samples": "the number of samples in the dataset used for training. (optional, default: None)",
  "formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})",
  "columns (optional)": {
    "prompt": "the column name in the dataset containing the prompts. (default: instruction)",
    "query": "the column name in the dataset containing the queries. (default: input)",
@@ -19,7 +21,10 @@ If you are using a custom dataset, please add your **dataset description** to `d
    "messages": "the column name in the dataset containing the messages. (default: conversations)",
    "system": "the column name in the dataset containing the system prompts. (default: None)",
    "tools": "the column name in the dataset containing the tool description. (default: None)",
-    "images": "the column name in the dataset containing the image inputs. (default: None)"
+    "images": "the column name in the dataset containing the image inputs. (default: None)",
    "chosen": "the column name in the dataset containing the chosen answers. (default: None)",
    "rejected": "the column name in the dataset containing the rejected answers. (default: None)",
    "kto_tag": "the column name in the dataset containing the kto tags. (default: None)"
  },
  "tags (optional, used for the sharegpt format)": {
    "role_tag": "the key in the message represents the identity. (default: from)",
@@ -33,28 +38,34 @@ If you are using a custom dataset, please add your **dataset description** to `d
 }
 ```
-After that, you can load the custom dataset by specifying `--dataset dataset_name`.
+## Alpaca Format
----
+### Supervised Fine-Tuning Dataset
-Currently we support dataset in **alpaca** or **sharegpt** format, the dataset in alpaca format should follow the below format:
+* [Example dataset](alpaca_en_demo.json)
 In supervised fine-tuning, the `instruction` column will be concatenated with the `input` column and used as the human prompt, then the human prompt would be `instruction\ninput`. The `output` column represents the model response.
 The `system` column will be used as the system prompt if specified.
 The `history` column is a list consisting of string tuples representing prompt-response pairs in the history messages. Note that the responses in the history **will also be learned by the model** in supervised fine-tuning.
 ```json
 [
  {
-    "instruction": "user instruction (required)",
+    "instruction": "human instruction (required)",
-    "input": "user input (optional)",
+    "input": "human input (optional)",
    "output": "model response (required)",
    "system": "system prompt (optional)",
    "history": [
-      ["user instruction in the first round (optional)", "model response in the first round (optional)"],
+      ["human instruction in the first round (optional)", "model response in the first round (optional)"],
-      ["user instruction in the second round (optional)", "model response in the second round (optional)"]
+      ["human instruction in the second round (optional)", "model response in the second round (optional)"]
    ]
  }
 ]
 ```
-Regarding the above dataset, the description in `dataset_info.json` should be:
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
 ```json
 "dataset_name": {
@@ -69,11 +80,11 @@ Regarding the above dataset, the description in `dataset_info.json` should be:
 }
 ```
-The `query` column will be concatenated with the `prompt` column and used as the user prompt, then the user prompt would be `prompt\nquery`. The `response` column represents the model response.
+### Pre-training Dataset
-The `system` column will be used as the system prompt. The `history` column is a list consisting string tuples representing prompt-response pairs in the history. Note that the responses in the history **will also be used for training** in supervised fine-tuning.
+- [Example dataset](c4_demo.json)
-For the **pre-training datasets**, only the `prompt` column will be used for training, for example:
+In pre-training, only the `text` column will be used for model learning.
 ```json
 [
@@ -82,7 +93,7 @@ For the **pre-training datasets**, only the `prompt` column will be used for tra
 ]
 ```
-Regarding the above dataset, the description in `dataset_info.json` should be:
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
 ```json
 "dataset_name": {
@@ -93,22 +104,24 @@ Regarding the above dataset, the description in `dataset_info.json` should be:
 }
 ```
-For the **preference datasets**, the `response` column should be a string list whose length is 2, with the preferred answers appearing first, for example:
+### Preference Dataset
 Preference datasets are used for reward modeling, DPO training and ORPO training.
 It requires a better response in `chosen` column and a worse response in `rejected` column.
 ```json
 [
  {
-    "instruction": "user instruction",
+    "instruction": "human instruction (required)",
-    "input": "user input",
+    "input": "human input (optional)",
-    "output": [
+    "chosen": "chosen answer (required)",
-      "chosen answer",
+    "rejected": "rejected answer (required)"
      "rejected answer"
    ]
  }
 ]
 ```
-Regarding the above dataset, the description in `dataset_info.json` should be:
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
 ```json
 "dataset_name": {
@@ -117,14 +130,85 @@ Regarding the above dataset, the description in `dataset_info.json` should be:
  "columns": {
    "prompt": "instruction",
    "query": "input",
-    "response": "output",
+    "chosen": "chosen",
    "rejected": "rejected"
  }
 }
 ```
----
+### KTO Dataset
-The dataset in **sharegpt** format should follow the below format:
+- [Example dataset](kto_en_demo.json)
 KTO datasets require a extra `kto_tag` column containing the boolean human feedback.
 ```json
 [
  {
    "instruction": "human instruction (required)",
    "input": "human input (optional)",
    "output": "model response (required)",
    "kto_tag": "human feedback [true/false] (required)"
  }
 ]
 ```
 Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
 ```json
 "dataset_name": {
  "file_name": "data.json",
  "columns": {
    "prompt": "instruction",
    "query": "input",
    "response": "output",
    "kto_tag": "kto_tag"
  }
 }
 ```
 ### Multimodal Dataset
 - [Example dataset](mllm_demo.json)
 Multimodal datasets require a `images` column containing the paths to the input images. Currently we only support one image.
 ```json
 [
  {
    "instruction": "human instruction (required)",
    "input": "human input (optional)",
    "output": "model response (required)",
    "images": [
      "image path (required)"
    ]
  }
 ]
 ```
 Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
 ```json
 "dataset_name": {
  "file_name": "data.json",
  "columns": {
    "prompt": "instruction",
    "query": "input",
    "response": "output",
    "images": "images"
  }
 }
 ```
 ## Sharegpt Format
 ### Supervised Fine-Tuning Dataset
 - [Example dataset](glaive_toolcall_en_demo.json)
 Compared to the alpaca format, the sharegpt format allows the datasets have **more roles**, such as human, gpt, observation and function. They are presented in a list of objects in the `conversations` column.
 Note that the human and observation should appear in odd positions, while gpt and function should appear in even positions.
 ```json
 [
@@ -132,7 +216,15 @@ The dataset in **sharegpt** format should follow the below format:
    "conversations": [
      {
        "from": "human",
-        "value": "user instruction"
+        "value": "human instruction"
      },
      {
        "from": "function_call",
        "value": "tool arguments"
      },
      {
        "from": "observation",
        "value": "tool result"
      },
      {
        "from": "gpt",
@@ -145,7 +237,7 @@ The dataset in **sharegpt** format should follow the below format:
 ]
 ```
-Regarding the above dataset, the description in `dataset_info.json` should be:
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
 ```json
 "dataset_name": {
@@ -155,19 +247,63 @@ Regarding the above dataset, the description in `dataset_info.json` should be:
    "messages": "conversations",
    "system": "system",
    "tools": "tools"
  },
  "tags": {
    "role_tag": "from",
    "content_tag": "value",
    "user_tag": "human",
    "assistant_tag": "gpt"
  }
 }
 ```
-where the `messages` column should be a list following the `u/a/u/a/u/a` order.
+### Preference Dataset
-We also supports the dataset in the **openai** format:
+- [Example dataset](dpo_en_demo.json)
 Preference datasets in sharegpt format also require a better message in `chosen` column and a worse message in `rejected` column.
 ```json
 [
  {
    "conversations": [
      {
        "from": "human",
        "value": "human instruction"
      },
      {
        "from": "gpt",
        "value": "model response"
      },
      {
        "from": "human",
        "value": "human instruction"
      }
    ],
    "chosen": {
      "from": "gpt",
      "value": "chosen answer (required)"
    },
    "rejected": {
      "from": "gpt",
      "value": "rejected answer (required)"
    }
  }
 ]
 ```
 Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
 ```json
 "dataset_name": {
  "file_name": "data.json",
  "formatting": "sharegpt",
  "ranking": true,
  "columns": {
    "messages": "conversations",
    "chosen": "chosen",
    "rejected": "rejected"
  }
 }
 ```
 ### OpenAI Format
 The openai format is simply a special case of the sharegpt format, where the first message may be a system prompt.
 ```json
 [
@@ -179,7 +315,7 @@ We also supports the dataset in the **openai** format:
      },
      {
        "role": "user",
-        "content": "user instruction"
+        "content": "human instruction"
      },
      {
        "role": "assistant",
@@ -190,7 +326,7 @@ We also supports the dataset in the **openai** format:
 ]
 ```
-Regarding the above dataset, the description in `dataset_info.json` should be:
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
 ```json
 "dataset_name": {
@@ -209,4 +345,6 @@ Regarding the above dataset, the description in `dataset_info.json` should be:
 }
 ```
-Pre-training datasets and preference datasets are **incompatible** with the sharegpt format yet.
+The KTO datasets and multimodal datasets in sharegpt format are similar to the alpaca format.
 Pre-training datasets are **incompatible** with the sharegpt format.
--- a/data/README_zh.md
+++ b/data/README_zh.md
@@ -1,16 +1,18 @@
-如果您使用自定义数据集，请务必按照以下格式在 `dataset_info.json` 文件中添加**数据集描述**。我们在下面也提供了一些例子。
+[dataset_info.json](dataset_info.json) 包含了所有可用的数据集。如果您希望使用自定义数据集，请**务必**在 `dataset_info.json` 文件中添加*数据集描述*，并通过修改 `dataset: 数据集名称` 配置来使用数据集。
 目前我们支持 **alpaca** 格式和 **sharegpt** 格式的数据集。
 ```json
 "数据集名称": {
  "hf_hub_url": "Hugging Face 的数据集仓库地址（若指定，则忽略 script_url 和 file_name）",
  "ms_hub_url": "ModelScope 的数据集仓库地址（若指定，则忽略 script_url 和 file_name）",
  "script_url": "包含数据加载脚本的本地文件夹名称（若指定，则忽略 file_name）",
-  "file_name": "该目录下数据集文件的名称（若上述参数未指定，则此项必需）",
+  "file_name": "该目录下数据集文件夹或文件的名称（若上述参数未指定，则此项必需）",
-  "file_sha1": "数据集文件的 SHA-1 哈希值（可选，留空不影响训练）",
+  "formatting": "数据集格式（可选，默认：alpaca，可以为 alpaca 或 sharegpt）",
  "ranking": "是否为偏好数据集（可选，默认：False）",
  "subset": "数据集子集的名称（可选，默认：None）",
  "folder": "Hugging Face 仓库的文件夹名称（可选，默认：None）",
-  "ranking": "是否为偏好数据集（可选，默认：False）",
+  "num_samples": "该数据集中用于训练的样本数量。（可选，默认：None）",
  "formatting": "数据集格式（可选，默认：alpaca，可以为 alpaca 或 sharegpt）",
  "columns（可选）": {
    "prompt": "数据集代表提示词的表头名称（默认：instruction）",
    "query": "数据集代表请求的表头名称（默认：input）",
@@ -19,7 +21,10 @@
    "messages": "数据集代表消息列表的表头名称（默认：conversations）",
    "system": "数据集代表系统提示的表头名称（默认：None）",
    "tools": "数据集代表工具描述的表头名称（默认：None）",
-    "images": "数据集代表图像输入的表头名称（默认：None）"
+    "images": "数据集代表图像输入的表头名称（默认：None）",
    "chosen": "数据集代表更优回答的表头名称（默认：None）",
    "rejected": "数据集代表更差回答的表头名称（默认：None）",
    "kto_tag": "数据集代表 KTO 标签的表头名称（默认：None）"
  },
  "tags（可选，用于 sharegpt 格式）": {
    "role_tag": "消息中代表发送者身份的键名（默认：from）",
@@ -28,22 +33,28 @@
    "assistant_tag": "消息中代表助手的 role_tag（默认：gpt）",
    "observation_tag": "消息中代表工具返回结果的 role_tag（默认：observation）",
    "function_tag": "消息中代表工具调用的 role_tag（默认：function_call）",
-    "system_tag": "消息中代表系统提示的 role_tag（默认：system，会覆盖 system 列）"
+    "system_tag": "消息中代表系统提示的 role_tag（默认：system，会覆盖 system column）"
  }
 }
 ```
-然后，可通过使用 `--dataset 数据集名称` 参数加载自定义数据集。
+## Alpaca 格式
----
+### 指令监督微调数据集
-该项目目前支持两种格式的数据集：**alpaca** 和 **sharegpt**，其中 alpaca 格式的数据集按照以下方式组织：
+- [样例数据集](alpaca_zh_demo.json)
 在指令监督微调时，`instruction` 列对应的内容会与 `input` 列对应的内容拼接后作为人类指令，即人类指令为 `instruction\ninput`。而 `output` 列对应的内容为模型回答。
 如果指定，`system` 列对应的内容将被作为系统提示词。
 `history` 列是由多个字符串二元组构成的列表，分别代表历史消息中每轮对话的指令和回答。注意在指令监督微调时，历史消息中的回答内容**也会被用于模型学习**。
 ```json
 [
  {
-    "instruction": "用户指令（必填）",
+    "instruction": "人类指令（必填）",
-    "input": "用户输入（选填）",
+    "input": "人类输入（选填）",
    "output": "模型回答（必填）",
    "system": "系统提示词（选填）",
    "history": [
@@ -54,7 +65,7 @@
 ]
 ```
-对于上述格式的数据，`dataset_info.json` 中的描述应为：
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
 ```json
 "数据集名称": {
@@ -69,11 +80,11 @@
 }
 ```
-其中 `query` 列对应的内容会与 `prompt` 列对应的内容拼接后作为用户指令，即用户指令为 `prompt\nquery`。`response` 列对应的内容为模型回答。
+### 预训练数据集
-`system` 列对应的内容将被作为系统提示词。`history` 列是由多个字符串二元组构成的列表，分别代表历史消息中每轮的指令和回答。注意在指令监督学习时，历史消息中的回答**也会被用于训练**。
+- [样例数据集](c4_demo.json)
-对于**预训练数据集**，仅 `prompt` 列中的内容会用于模型训练，例如：
+在预训练时，只有 `text` 列中的内容会用于模型学习。
 ```json
 [
@@ -82,7 +93,7 @@
 ]
 ```
-对于上述格式的数据，`dataset_info.json` 中的描述应为：
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
 ```json
 "数据集名称": {
@@ -93,22 +104,24 @@
 }
 ```
-对于**偏好数据集**，`response` 列应当是一个长度为 2 的字符串列表，排在前面的代表更优的回答，例如：
+### 偏好数据集
 偏好数据集用于奖励模型训练、DPO 训练和 ORPO 训练。
 它需要在 `chosen` 列中提供更优的回答，并在 `rejected` 列中提供更差的回答。
 ```json
 [
  {
-    "instruction": "用户指令",
+    "instruction": "人类指令（必填）",
-    "input": "用户输入",
+    "input": "人类输入（选填）",
-    "output": [
+    "chosen": "优质回答（必填）",
-      "优质回答",
+    "rejected": "劣质回答（必填）"
      "劣质回答"
    ]
  }
 ]
 ```
-对于上述格式的数据，`dataset_info.json` 中的描述应为：
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
 ```json
 "数据集名称": {
@@ -117,14 +130,85 @@
  "columns": {
    "prompt": "instruction",
    "query": "input",
-    "response": "output",
+    "chosen": "chosen",
    "rejected": "rejected"
  }
 }
 ```
----
+### KTO 数据集
-而 **sharegpt** 格式的数据集按照以下方式组织：
+- [样例数据集](kto_en_demo.json)
 KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人类反馈。
 ```json
 [
  {
    "instruction": "人类指令（必填）",
    "input": "人类输入（选填）",
    "output": "模型回答（必填）",
    "kto_tag": "人类反馈 [true/false]（必填）"
  }
 ]
 ```
 对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
 ```json
 "数据集名称": {
  "file_name": "data.json",
  "columns": {
    "prompt": "instruction",
    "query": "input",
    "response": "output",
    "kto_tag": "kto_tag"
  }
 }
 ```
 ### 多模态数据集
 - [样例数据集](mllm_demo.json)
 多模态数据集需要额外添加一个 `images` 列，包含输入图像的路径。目前我们仅支持单张图像输入。
 ```json
 [
  {
    "instruction": "人类指令（必填）",
    "input": "人类输入（选填）",
    "output": "模型回答（必填）",
    "images": [
      "图像路径（必填）"
    ]
  }
 ]
 ```
 对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
 ```json
 "数据集名称": {
  "file_name": "data.json",
  "columns": {
    "prompt": "instruction",
    "query": "input",
    "response": "output",
    "images": "images"
  }
 }
 ```
 ## Sharegpt 格式
 ### 指令监督微调数据集
 - [样例数据集](glaive_toolcall_zh_demo.json)
 相比 alpaca 格式的数据集，sharegpt 格式支持**更多的角色种类**，例如 human、gpt、observation、function 等等。它们构成一个对象列表呈现在 `conversations` 列中。
 注意其中 human 和 observation 必须出现在奇数位置，gpt 和 function 必须出现在偶数位置。
 ```json
 [
@@ -132,7 +216,15 @@
    "conversations": [
      {
        "from": "human",
-        "value": "用户指令"
+        "value": "人类指令"
      },
      {
        "from": "function_call",
        "value": "工具参数"
      },
      {
        "from": "observation",
        "value": "工具结果"
      },
      {
        "from": "gpt",
@@ -145,7 +237,7 @@
 ]
 ```
-对于上述格式的数据，`dataset_info.json` 中的描述应为：
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
 ```json
 "数据集名称": {
@@ -155,19 +247,63 @@
    "messages": "conversations",
    "system": "system",
    "tools": "tools"
  },
  "tags": {
    "role_tag": "from",
    "content_tag": "value",
    "user_tag": "human",
    "assistant_tag": "gpt"
  }
 }
 ```
-其中 `messages` 列应当是一个列表，且符合 `用户/模型/用户/模型/用户/模型` 的顺序。
+### 偏好数据集
-我们同样支持 **openai** 格式的数据集：
+- [样例数据集](dpo_zh_demo.json)
 Sharegpt 格式的偏好数据集同样需要在 `chosen` 列中提供更优的消息，并在 `rejected` 列中提供更差的消息。
 ```json
 [
  {
    "conversations": [
      {
        "from": "human",
        "value": "人类指令"
      },
      {
        "from": "gpt",
        "value": "模型回答"
      },
      {
        "from": "human",
        "value": "人类指令"
      }
    ],
    "chosen": {
      "from": "gpt",
      "value": "优质回答"
    },
    "rejected": {
      "from": "gpt",
      "value": "劣质回答"
    }
  }
 ]
 ```
 对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
 ```json
 "数据集名称": {
  "file_name": "data.json",
  "formatting": "sharegpt",
  "ranking": true,
  "columns": {
    "messages": "conversations",
    "chosen": "chosen",
    "rejected": "rejected"
  }
 }
 ```
 ### OpenAI 格式
 OpenAI 格式仅仅是 sharegpt 格式的一种特殊情况，其中第一条消息可能是系统提示词。
 ```json
 [
@@ -179,7 +315,7 @@
      },
      {
        "role": "user",
-        "content": "用户指令"
+        "content": "人类指令"
      },
      {
        "role": "assistant",
@@ -190,7 +326,7 @@
 ]
 ```
-对于上述格式的数据，`dataset_info.json` 中的描述应为：
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
 ```json
 "数据集名称": {
@@ -209,4 +345,6 @@
 }
 ```
-预训练数据集和偏好数据集**尚不支持** sharegpt 格式。
+Sharegpt 格式中的 KTO 数据集和多模态数据集与 alpaca 格式的类似。
 预训练数据集**不支持** sharegpt 格式。
--- a/data/alpaca_data_en_52k.json.REMOVED.git-id
+++ b/data/alpaca_data_en_52k.json.REMOVED.git-id
@@ -1 +0,0 @@
 3779ddbc040543ab1834ef216c983d6fcc06cc9a
--- a/data/alpaca_data_zh_51k.json.REMOVED.git-id
+++ b/data/alpaca_data_zh_51k.json.REMOVED.git-id
@@ -1 +0,0 @@
 a97cf9475291591843976554878568e046d8a46d
--- a/data/alpaca_gpt4_data_en.json.REMOVED.git-id
+++ b/data/alpaca_gpt4_data_en.json.REMOVED.git-id
@@ -1 +0,0 @@
 25508714b7879a1e5a6764ba7f979a980f549f1a
--- a/data/alpaca_gpt4_data_zh.json.REMOVED.git-id
+++ b/data/alpaca_gpt4_data_zh.json.REMOVED.git-id
@@ -1 +0,0 @@
 7cb6a7d11455bddc3d495750a2392683d775b184
--- a/data/comparison_gpt4_data_en.json.REMOVED.git-id
+++ b/data/comparison_gpt4_data_en.json.REMOVED.git-id
@@ -1 +0,0 @@
 f5cb08305ff5dc9c17a09809c54c8c8834aadc70
--- a/data/comparison_gpt4_data_zh.json.REMOVED.git-id
+++ b/data/comparison_gpt4_data_zh.json.REMOVED.git-id
@@ -1 +0,0 @@
 aee47b7b443496e37808d7f34ef10403ff99bcc3
--- a/data/example_dataset/example_dataset.py
+++ b/data/example_dataset/example_dataset.py
@@ -1,37 +0,0 @@
 import json
 from typing import Any, Dict, Generator, List, Tuple
 import datasets
 _DESCRIPTION = "An example of dataset."
 _CITATION = ""
 _HOMEPAGE = ""
 _LICENSE = ""
 _URL = "examples.json"
 class ExampleDataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("0.0.0")
    def _info(self) -> datasets.DatasetInfo:
        features = datasets.Features(
            {
                "instruction": datasets.Value("string"),
                "input": datasets.Value("string"),
                "output": datasets.Value("string"),
                "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
        )
    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        file_path = dl_manager.download(_URL)
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": file_path})]
    def _generate_examples(self, filepath: str) -> Generator[Tuple[int, Dict[str, Any]], None, None]:
        example_dataset = json.load(open(filepath, "r", encoding="utf-8"))
        for key, example in enumerate(example_dataset):
            yield key, example
--- a/data/glaive_toolcall_10k.json.REMOVED.git-id
+++ b/data/glaive_toolcall_10k.json.REMOVED.git-id
@@ -1 +0,0 @@
 4748dff00d1dc42768a5b6cc772143c313017812
--- a/data/hh_rlhf_en/hh_rlhf_en.py
+++ b/data/hh_rlhf_en/hh_rlhf_en.py
@@ -34,7 +34,8 @@ class HhRlhfEn(datasets.GeneratorBasedBuilder):
        features = datasets.Features(
            {
                "instruction": datasets.Value("string"),
-                "output": datasets.Sequence(datasets.Value("string")),
+                "chosen": datasets.Value("string"),
                "rejected": datasets.Value("string"),
                "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
            }
        )
@@ -79,5 +80,5 @@ class HhRlhfEn(datasets.GeneratorBasedBuilder):
                            break
                        prompt = prompt[:human_idx]
-                    yield key, {"instruction": query, "output": [r_accept, r_reject], "history": history}
+                    yield key, {"instruction": query, "chosen": r_accept, "rejected": r_reject, "history": history}
                    key += 1
--- a/data/orca_rlhf.json.REMOVED.git-id
+++ b/data/orca_rlhf.json.REMOVED.git-id
@@ -1 +0,0 @@
 736bcedea2b24a1414765c6d69cbdafaea839f3c
--- a/data/wiki_demo.txt
+++ b/data/wiki_demo.txt
--- a/data/wiki_demo.txt.REMOVED.git-id
+++ b/data/wiki_demo.txt.REMOVED.git-id
@@ -1 +0,0 @@
 c9cf509b7fdac5490cfd6dae72c2d7b8a60af6cb
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -10,8 +10,6 @@ services:
      - ./hf_cache:/root/.cache/huggingface/
      - ./data:/app/data
      - ./output:/app/output
    environment:
      - CUDA_VISIBLE_DEVICES=0
    ports:
      - "7860:7860"
    ipc: host
--- a/evaluation/mmlu/mmlu.py
+++ b/evaluation/mmlu/mmlu.py
@@ -154,7 +154,7 @@ class MMLU(datasets.GeneratorBasedBuilder):
        ]
    def _generate_examples(self, filepath):
-        df = pd.read_csv(filepath)
+        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")):
--- a/examples/README.md
+++ b/examples/README.md
@@ -47,16 +47,16 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lo
 CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_ppo.yaml
 ```
-#### DPO Training
+#### DPO/ORPO/SimPO Training
 ```bash
 CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_dpo.yaml
 ```
-#### ORPO Training
+#### KTO Training
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_orpo.yaml
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_kto.yaml
 ```
 #### Preprocess Dataset
@@ -107,22 +107,23 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_l
 ### LoRA Fine-Tuning on Multiple GPUs
-#### Supervised Fine-Tuning with Accelerate on Single Node
+#### Supervised Fine-Tuning on Single Node
 ```bash
-bash examples/lora_multi_gpu/single_node.sh
+CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
 ```
-#### Supervised Fine-Tuning with Accelerate on Multiple Nodes
+#### Supervised Fine-Tuning on Multiple Nodes
 ```bash
-bash examples/lora_multi_gpu/multi_node.sh
+CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
 CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
 ```
 #### Supervised Fine-Tuning with DeepSpeed ZeRO-3 (Weight Sharding)
 ```bash
-bash examples/lora_multi_gpu/ds_zero3.sh
+CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft_ds.yaml
 ```
 ### LoRA Fine-Tuning on Multiple NPUs
@@ -130,27 +131,28 @@ bash examples/lora_multi_gpu/ds_zero3.sh
 #### Supervised Fine-Tuning with DeepSpeed ZeRO-0
 ```bash
-bash examples/lora_multi_npu/ds_zero0.sh
+ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_npu/llama3_lora_sft_ds.yaml
 ```
 ### Full-Parameter Fine-Tuning on Multiple GPUs
-#### Supervised Fine-Tuning with Accelerate on Single Node
+#### Supervised Fine-Tuning on Single Node
 ```bash
-bash examples/full_multi_gpu/single_node.sh
+CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
 ```
-#### Supervised Fine-Tuning with Accelerate on Multiple Nodes
+#### Supervised Fine-Tuning on Multiple Nodes
 ```bash
-bash examples/full_multi_gpu/multi_node.sh
+CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
 CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
 ```
 #### Batch Predicting and Computing BLEU and ROUGE Scores
 ```bash
-bash examples/full_multi_gpu/predict.sh
+CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_predict.yaml
 ```
 ### Merging LoRA Adapters and Quantization
@@ -171,22 +173,24 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_gptq.y
 ### Inferring LoRA Fine-Tuned Models
 Use `CUDA_VISIBLE_DEVICES=0,1` to infer models on multiple devices.
 #### Use CLI
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/merge_lora/llama3_lora_sft.yaml
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
 ```
 #### Use Web UI
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli webchat examples/merge_lora/llama3_lora_sft.yaml
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli webchat examples/inference/llama3_lora_sft.yaml
 ```
 #### Launch OpenAI-style API
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/merge_lora/llama3_lora_sft.yaml
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/inference/llama3_lora_sft.yaml
 ```
 ### Extras
--- a/examples/README_zh.md
+++ b/examples/README_zh.md
@@ -47,16 +47,16 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lo
 CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_ppo.yaml
 ```
-#### DPO 训练
+#### DPO/ORPO/SimPO 训练
 ```bash
 CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_dpo.yaml
 ```
-#### ORPO 训练
+#### KTO 训练
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_orpo.yaml
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_kto.yaml
 ```
 #### 预处理数据集
@@ -107,50 +107,52 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_l
 ### 多 GPU LoRA 微调
-#### 使用 Accelerate 进行单节点训练
+#### 在单机上进行指令监督微调
 ```bash
-bash examples/lora_multi_gpu/single_node.sh
+CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
 ```
-#### 使用 Accelerate 进行多节点训练
+#### 在多机上进行指令监督微调
 ```bash
-bash examples/lora_multi_gpu/multi_node.sh
+CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
 CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
 ```
 #### 使用 DeepSpeed ZeRO-3 平均分配显存
 ```bash
-bash examples/lora_multi_gpu/ds_zero3.sh
+CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft_ds.yaml
 ```
 ### 多 NPU LoRA 微调
-#### 使用 DeepSpeed ZeRO-0 训练
+#### 使用 DeepSpeed ZeRO-0 进行指令监督微调
 ```bash
-bash examples/lora_multi_npu/ds_zero0.sh
+ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_npu/llama3_lora_sft_ds.yaml
 ```
 ### 多 GPU 全参数微调
-#### 使用 DeepSpeed 进行单节点训练
+#### 在单机上进行指令监督微调
 ```bash
-bash examples/full_multi_gpu/single_node.sh
+CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
 ```
-#### 使用 DeepSpeed 进行多节点训练
+#### 在多机上进行指令监督微调
 ```bash
-bash examples/full_multi_gpu/multi_node.sh
+CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
 CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
 ```
 #### 批量预测并计算 BLEU 和 ROUGE 分数
 ```bash
-bash examples/full_multi_gpu/predict.sh
+CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_predict.yaml
 ```
 ### 合并 LoRA 适配器与模型量化
@@ -171,22 +173,24 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_gptq.y
 ### 推理 LoRA 模型
 使用 `CUDA_VISIBLE_DEVICES=0,1` 进行多卡推理。
 #### 使用命令行接口
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/merge_lora/llama3_lora_sft.yaml
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
 ```
 #### 使用浏览器界面
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli webchat examples/merge_lora/llama3_lora_sft.yaml
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli webchat examples/inference/llama3_lora_sft.yaml
 ```
 #### 启动 OpenAI 风格 API
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/merge_lora/llama3_lora_sft.yaml
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/inference/llama3_lora_sft.yaml
 ```
 ### 杂项
--- a/examples/accelerate/fsdp_config.yaml
+++ b/examples/accelerate/fsdp_config.yaml
@@ -5,16 +5,16 @@ downcast_bf16: 'no'
 fsdp_config:
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_backward_prefetch: BACKWARD_PRE
  fsdp_cpu_ram_efficient_loading: true
  fsdp_forward_prefetch: false
-  fsdp_offload_params: true
+  fsdp_cpu_ram_efficient_loading: true
  fsdp_offload_params: true # offload may affect training speed
  fsdp_sharding_strategy: FULL_SHARD
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_sync_module_states: true
-  fsdp_use_orig_params: false
+  fsdp_use_orig_params: true
 machine_rank: 0
 main_training_function: main
-mixed_precision: fp16
+mixed_precision: fp16 # or bf16
 num_machines: 1 # the number of nodes
 num_processes: 2 # the number of GPUs in all nodes
 rdzv_backend: static
--- a/examples/accelerate/master_config.yaml
+++ b/examples/accelerate/master_config.yaml
@@ -1,18 +0,0 @@
 compute_environment: LOCAL_MACHINE
 debug: false
 distributed_type: MULTI_GPU
 downcast_bf16: 'no'
 gpu_ids: all
 machine_rank: 0
 main_process_ip: 192.168.0.1
 main_process_port: 29555
 main_training_function: main
 mixed_precision: fp16
 num_machines: 2 # the number of nodes
 num_processes: 8 # the number of GPUs in all nodes
 rdzv_backend: static
 same_network: true
 tpu_env: []
 tpu_use_cluster: false
 tpu_use_sudo: false
 use_cpu: false
--- a/examples/accelerate/single_config.yaml
+++ b/examples/accelerate/single_config.yaml
@@ -1,16 +0,0 @@
 compute_environment: LOCAL_MACHINE
 debug: false
 distributed_type: MULTI_GPU
 downcast_bf16: 'no'
 gpu_ids: all
 machine_rank: 0
 main_training_function: main
 mixed_precision: fp16
 num_machines: 1 # the number of nodes
 num_processes: 4 # the number of GPUs in all nodes
 rdzv_backend: static
 same_network: true
 tpu_env: []
 tpu_use_cluster: false
 tpu_use_sudo: false
 use_cpu: false
--- a/examples/accelerate/slave_config.yaml
+++ b/examples/accelerate/slave_config.yaml
@@ -1,18 +0,0 @@
 compute_environment: LOCAL_MACHINE
 debug: false
 distributed_type: MULTI_GPU
 downcast_bf16: 'no'
 gpu_ids: all
 machine_rank: 1
 main_process_ip: 192.168.0.1
 main_process_port: 29555
 main_training_function: main
 mixed_precision: fp16
 num_machines: 2 # the number of nodes
 num_processes: 8 # the number of GPUs in all nodes
 rdzv_backend: static
 same_network: true
 tpu_env: []
 tpu_use_cluster: false
 tpu_use_sudo: false
 use_cpu: false
--- a/examples/extras/badam/llama3_lora_sft.yaml
+++ b/examples/extras/badam/llama3_lora_sft.yaml
@@ -1,41 +1,41 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: full
 use_badam: true
-badam_switch_mode: descending
+badam_switch_mode: ascending
 badam_switch_interval: 50
 badam_verbose: 2
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/full/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 pure_bf16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/extras/fsdp_qlora/llama3_lora_sft.yaml
+++ b/examples/extras/fsdp_qlora/llama3_lora_sft.yaml
@@ -1,42 +1,42 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
 quantization_bit: 4
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# ddp
+### ddp
 ddp_timeout: 180000000
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/extras/fsdp_qlora/single_node.sh
+++ b/examples/extras/fsdp_qlora/single_node.sh
@@ -1,10 +1,6 @@
 #!/bin/bash
 # DO NOT use GPTQ/AWQ model in FSDP+QLoRA
 pip install "transformers>=4.39.1"
 pip install "accelerate>=0.28.0"
 pip install "bitsandbytes>=0.43.0"
 CUDA_VISIBLE_DEVICES=0,1 accelerate launch \
    --config_file examples/accelerate/fsdp_config.yaml \
    src/train.py examples/extras/fsdp_qlora/llama3_lora_sft.yaml
--- a/examples/extras/galore/llama3_full_sft.yaml
+++ b/examples/extras/galore/llama3_full_sft.yaml
@@ -1,7 +1,7 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: full
@@ -11,32 +11,32 @@ galore_target: mlp,self_attn
 galore_rank: 128
 galore_scale: 2.0
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/full/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 1
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 pure_bf16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/extras/llama_pro/llama3_freeze_sft.yaml
+++ b/examples/extras/llama_pro/llama3_freeze_sft.yaml
@@ -1,7 +1,7 @@
-# model
+### model
 model_name_or_path: models/llama3-8b-instruct-pro
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: freeze
@@ -9,32 +9,32 @@ freeze_trainable_layers: 8
 freeze_trainable_modules: all
 use_llama_pro: true
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b-instruct-pro/freeze/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/extras/loraplus/llama3_lora_sft.yaml
+++ b/examples/extras/loraplus/llama3_lora_sft.yaml
@@ -1,39 +1,39 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
 loraplus_lr_ratio: 16.0
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/extras/mod/llama3_full_sft.yaml
+++ b/examples/extras/mod/llama3_full_sft.yaml
@@ -1,39 +1,39 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: full
 mixture_of_depths: convert
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b-mod/full/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
 optim: paged_adamw_8bit
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 pure_bf16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/full_multi_gpu/llama3_full_predict.yaml
+++ b/examples/full_multi_gpu/llama3_full_predict.yaml
@@ -1,23 +1,23 @@
-# model
+### model
 model_name_or_path: saves/llama3-8b/full/sft
-# method
+### method
 stage: sft
 do_predict: true
 finetuning_type: full
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 50
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/full/predict
 overwrite_output_dir: true
-# eval
+### eval
 per_device_eval_batch_size: 1
 predict_with_generate: true
--- a/examples/full_multi_gpu/llama3_full_sft.yaml
+++ b/examples/full_multi_gpu/llama3_full_sft.yaml
@@ -1,41 +1,41 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: full
-# ddp
+### ddp
 ddp_timeout: 180000000
 deepspeed: examples/deepspeed/ds_z3_config.json
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/full/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 2
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/full_multi_gpu/multi_node.sh
+++ b/examples/full_multi_gpu/multi_node.sh
@@ -1,15 +0,0 @@
 #!/bin/bash
 NPROC_PER_NODE=4
 NNODES=2
 RANK=0
 MASTER_ADDR=192.168.0.1
 MASTER_PORT=29500
 CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun \
    --nproc_per_node $NPROC_PER_NODE \
    --nnodes $NNODES \
    --node_rank $RANK \
    --master_addr $MASTER_ADDR \
    --master_port $MASTER_PORT \
    src/train.py examples/full_multi_gpu/llama3_full_sft.yaml
--- a/examples/full_multi_gpu/predict.sh
+++ b/examples/full_multi_gpu/predict.sh
@@ -1,5 +0,0 @@
 #!/bin/bash
 CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch \
    --config_file examples/accelerate/single_config.yaml \
    src/train.py examples/full_multi_gpu/llama3_full_predict.yaml
--- a/examples/full_multi_gpu/single_node.sh
+++ b/examples/full_multi_gpu/single_node.sh
@@ -1,15 +0,0 @@
 #!/bin/bash
 NPROC_PER_NODE=4
 NNODES=1
 RANK=0
 MASTER_ADDR=127.0.0.1
 MASTER_PORT=29500
 CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun \
    --nproc_per_node $NPROC_PER_NODE \
    --nnodes $NNODES \
    --node_rank $RANK \
    --master_addr $MASTER_ADDR \
    --master_port $MASTER_PORT \
    src/train.py examples/full_multi_gpu/llama3_full_sft.yaml
--- a/examples/lora_multi_gpu/ds_zero3.sh
+++ b/examples/lora_multi_gpu/ds_zero3.sh
@@ -1,15 +0,0 @@
 #!/bin/bash
 NPROC_PER_NODE=4
 NNODES=1
 RANK=0
 MASTER_ADDR=127.0.0.1
 MASTER_PORT=29500
 CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun \
    --nproc_per_node $NPROC_PER_NODE \
    --nnodes $NNODES \
    --node_rank $RANK \
    --master_addr $MASTER_ADDR \
    --master_port $MASTER_PORT \
    src/train.py examples/lora_multi_gpu/llama3_lora_sft_ds.yaml
--- a/examples/lora_multi_gpu/llama3_lora_sft.yaml
+++ b/examples/lora_multi_gpu/llama3_lora_sft.yaml
@@ -1,41 +1,41 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# ddp
+### ddp
 ddp_timeout: 180000000
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 2
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/lora_multi_gpu/llama3_lora_sft_ds.yaml
+++ b/examples/lora_multi_gpu/llama3_lora_sft_ds.yaml
@@ -1,42 +1,42 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# ddp
+### ddp
 ddp_timeout: 180000000
 deepspeed: examples/deepspeed/ds_z3_config.json
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 2
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/lora_multi_gpu/multi_node.sh
+++ b/examples/lora_multi_gpu/multi_node.sh
@@ -1,6 +0,0 @@
 #!/bin/bash
 # also launch it on slave machine using slave_config.yaml
 CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch \
    --config_file examples/accelerate/master_config.yaml \
    src/train.py examples/lora_multi_gpu/llama3_lora_sft.yaml
--- a/examples/lora_multi_gpu/single_node.sh
+++ b/examples/lora_multi_gpu/single_node.sh
@@ -1,5 +0,0 @@
 #!/bin/bash
 CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch \
    --config_file examples/accelerate/single_config.yaml \
    src/train.py examples/lora_multi_gpu/llama3_lora_sft.yaml
--- a/examples/lora_multi_npu/ds_zero0.sh
+++ b/examples/lora_multi_npu/ds_zero0.sh
@@ -1,15 +0,0 @@
 #!/bin/bash
 NPROC_PER_NODE=4
 NNODES=1
 RANK=0
 MASTER_ADDR=127.0.0.1
 MASTER_PORT=29500
 ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 torchrun \
    --nproc_per_node $NPROC_PER_NODE \
    --nnodes $NNODES \
    --node_rank $RANK \
    --master_addr $MASTER_ADDR \
    --master_port $MASTER_PORT \
    src/train.py examples/lora_multi_npu/llama3_lora_sft_ds.yaml
--- a/examples/lora_multi_npu/llama3_lora_sft_ds.yaml
+++ b/examples/lora_multi_npu/llama3_lora_sft_ds.yaml
@@ -1,42 +1,42 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# ddp
+### ddp
 ddp_timeout: 180000000
 deepspeed: examples/deepspeed/ds_z0_config.json
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 2
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/lora_single_gpu/llama3_lora_dpo.yaml
+++ b/examples/lora_single_gpu/llama3_lora_dpo.yaml
@@ -1,39 +1,40 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
 stage: dpo
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-dpo_ftx: 1.0
+pref_beta: 0.1
 pref_loss: sigmoid  # [sigmoid (dpo), orpo, simpo]
-# dataset
+### dataset
-dataset: orca_rlhf
+dataset: dpo_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/dpo
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.00001
+learning_rate: 5.0e-6
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/lora_single_gpu/llama3_lora_eval.yaml
+++ b/examples/lora_single_gpu/llama3_lora_eval.yaml
@@ -1,19 +1,19 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
 adapter_name_or_path: saves/llama3-8b/lora/sft
-# method
+### method
 finetuning_type: lora
-# dataset
+### dataset
 task: mmlu
 split: test
 template: fewshot
 lang: en
 n_shot: 5
-# output
+### output
 save_dir: saves/llama3-8b/lora/eval
-# eval
+### eval
 batch_size: 4
--- a/examples/lora_single_gpu/llama3_lora_orpo.yaml
+++ b/examples/lora_single_gpu/llama3_lora_orpo.yaml
@@ -1,38 +1,38 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
-stage: orpo
+stage: kto
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# dataset
+### dataset
-dataset: orca_rlhf
+dataset: kto_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
-output_dir: saves/llama3-8b/lora/orpo
+output_dir: saves/llama3-8b/lora/kto
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.00001
+learning_rate: 5.0e-6
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/lora_single_gpu/llama3_lora_ppo.yaml
+++ b/examples/lora_single_gpu/llama3_lora_ppo.yaml
@@ -1,38 +1,38 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
 reward_model: saves/llama3-8b/lora/reward
-# method
+### method
 stage: ppo
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/ppo
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.00001
+learning_rate: 1.0e-5
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# generate
+### generate
 max_new_tokens: 512
 top_k: 0
 top_p: 0.9
--- a/examples/lora_single_gpu/llama3_lora_predict.yaml
+++ b/examples/lora_single_gpu/llama3_lora_predict.yaml
@@ -1,24 +1,24 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
 adapter_name_or_path: saves/llama3-8b/lora/sft
-# method
+### method
 stage: sft
 do_predict: true
 finetuning_type: lora
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 50
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/predict
 overwrite_output_dir: true
-# eval
+### eval
 per_device_eval_batch_size: 1
 predict_with_generate: true
--- a/examples/lora_single_gpu/llama3_lora_pretrain.yaml
+++ b/examples/lora_single_gpu/llama3_lora_pretrain.yaml
@@ -1,37 +1,37 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
 stage: pt
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# dataset
+### dataset
 dataset: c4_demo
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/lora_single_gpu/llama3_lora_reward.yaml
+++ b/examples/lora_single_gpu/llama3_lora_reward.yaml
@@ -1,38 +1,38 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
 stage: rm
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# dataset
+### dataset
-dataset: orca_rlhf
+dataset: dpo_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/reward
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.00001
+learning_rate: 1.0e-5
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/lora_single_gpu/llama3_lora_sft.yaml
+++ b/examples/lora_single_gpu/llama3_lora_sft.yaml
@@ -1,38 +1,38 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/lora_single_gpu/llama3_preprocess.yaml
+++ b/examples/lora_single_gpu/llama3_preprocess.yaml
@@ -1,14 +1,14 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
@@ -16,6 +16,6 @@ overwrite_cache: true
 preprocessing_num_workers: 16
 tokenized_path: saves/llama3-8b/dataset/sft
-# output
+### output
 output_dir: saves/llama3-8b/lora/sft
 overwrite_output_dir: true
--- a/examples/lora_single_gpu/llava1_5_lora_sft.yaml
+++ b/examples/lora_single_gpu/llava1_5_lora_sft.yaml
@@ -1,14 +1,14 @@
-# model
+### model
 model_name_or_path: llava-hf/llava-1.5-7b-hf
 visual_inputs: true
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# dataset
+### dataset
 dataset: mllm_demo
 template: vicuna
 cutoff_len: 1024
@@ -16,24 +16,24 @@ max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llava1_5-7b/lora/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/merge_lora/llama3_gptq.yaml
+++ b/examples/merge_lora/llama3_gptq.yaml
@@ -1,8 +1,8 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
 template: llama3
-# export
+### export
 export_dir: models/llama3_gptq
 export_quantization_bit: 4
 export_quantization_dataset: data/c4_demo.json
--- a/examples/merge_lora/llama3_lora_sft.yaml
+++ b/examples/merge_lora/llama3_lora_sft.yaml
@@ -1,12 +1,12 @@
-# Note: DO NOT use quantized model or quantization_bit when merging lora adapters
+### Note: DO NOT use quantized model or quantization_bit when merging lora adapters
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
 adapter_name_or_path: saves/llama3-8b/lora/sft
 template: llama3
 finetuning_type: lora
-# export
+### export
 export_dir: models/llama3_lora_sft
 export_size: 2
 export_device: cpu
--- a/examples/qlora_single_gpu/llama3_lora_sft_aqlm.yaml
+++ b/examples/qlora_single_gpu/llama3_lora_sft_aqlm.yaml
@@ -1,38 +1,38 @@
-# model
+### model
 model_name_or_path: ISTA-DASLab/Meta-Llama-3-8B-Instruct-AQLM-2Bit-1x16
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/qlora_single_gpu/llama3_lora_sft_awq.yaml
+++ b/examples/qlora_single_gpu/llama3_lora_sft_awq.yaml
@@ -1,38 +1,38 @@
-# model
+### model
 model_name_or_path: TechxGenus/Meta-Llama-3-8B-Instruct-AWQ
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/qlora_single_gpu/llama3_lora_sft_bitsandbytes.yaml
+++ b/examples/qlora_single_gpu/llama3_lora_sft_bitsandbytes.yaml
@@ -1,39 +1,39 @@
-# model
+### model
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
 quantization_bit: 4
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/examples/qlora_single_gpu/llama3_lora_sft_gptq.yaml
+++ b/examples/qlora_single_gpu/llama3_lora_sft_gptq.yaml
@@ -1,38 +1,38 @@
-# model
+### model
 model_name_or_path: TechxGenus/Meta-Llama-3-8B-Instruct-GPTQ
-# method
+### method
 stage: sft
 do_train: true
 finetuning_type: lora
-lora_target: q_proj,v_proj
+lora_target: all
-# dataset
+### dataset
-dataset: identity,alpaca_gpt4_en
+dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 1000
 overwrite_cache: true
 preprocessing_num_workers: 16
-# output
+### output
 output_dir: saves/llama3-8b/lora/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true
 overwrite_output_dir: true
-# train
+### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 0.0001
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
-warmup_steps: 0.1
+warmup_ratio: 0.1
 fp16: true
-# eval
+### eval
 val_size: 0.1
 per_device_eval_batch_size: 1
-evaluation_strategy: steps
+eval_strategy: steps
 eval_steps: 500
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,7 @@ select = ["C", "E", "F", "I", "W"]
 [tool.ruff.lint.isort]
 lines-after-imports = 2
-known-first-party = ["llmtuner"]
+known-first-party = ["llamafactory"]
 known-third-party = [
    "accelerate",
    "datasets",
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,12 +1,13 @@
-transformers>=4.37.2
+transformers>=4.41.2
-datasets>=2.14.3
+datasets>=2.16.0
-accelerate>=0.27.2
+accelerate>=0.30.1
-peft>=0.10.0
+peft>=0.11.1
-trl>=0.8.1
+trl>=0.8.6
 gradio>=4.0.0
 scipy
 einops
 sentencepiece
 tiktoken
 protobuf
 uvicorn
 pydantic
--- a/scripts/cal_flops.py
+++ b/scripts/cal_flops.py
@@ -8,7 +8,7 @@ import torch
 from deepspeed.accelerator import get_accelerator  # type: ignore
 from deepspeed.profiling.flops_profiler import get_model_profile  # type: ignore
-from llmtuner.chat import ChatModel
+from llamafactory.chat import ChatModel
 def calculate_flops(
--- a/scripts/cal_lr.py
+++ b/scripts/cal_lr.py
@@ -12,10 +12,10 @@ from torch.utils.data import DataLoader
 from tqdm import tqdm
 from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq
-from llmtuner.data import get_dataset
+from llamafactory.data import get_dataset
-from llmtuner.extras.constants import IGNORE_INDEX
+from llamafactory.extras.constants import IGNORE_INDEX
-from llmtuner.hparams import get_train_args
+from llamafactory.hparams import get_train_args
-from llmtuner.model import load_tokenizer
+from llamafactory.model import load_tokenizer
 BASE_LR = 3e-4  # 1.5e-4 for 30B-70B models
--- a/scripts/cal_ppl.py
+++ b/scripts/cal_ppl.py
@@ -12,10 +12,10 @@ from torch.utils.data import DataLoader
 from tqdm import tqdm
 from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq
-from llmtuner.data import get_dataset
+from llamafactory.data import get_dataset
-from llmtuner.extras.constants import IGNORE_INDEX
+from llamafactory.extras.constants import IGNORE_INDEX
-from llmtuner.hparams import get_train_args
+from llamafactory.hparams import get_train_args
-from llmtuner.model import load_model, load_tokenizer
+from llamafactory.model import load_model, load_tokenizer
@dataclass
--- a/scripts/length_cdf.py
+++ b/scripts/length_cdf.py
@@ -7,9 +7,9 @@ from collections import defaultdict
 import fire
 from tqdm import tqdm
-from llmtuner.data import get_dataset
+from llamafactory.data import get_dataset
-from llmtuner.hparams import get_train_args
+from llamafactory.hparams import get_train_args
-from llmtuner.model import load_tokenizer
+from llamafactory.model import load_tokenizer
 def length_cdf(
--- a/scripts/llama_pro.py
+++ b/scripts/llama_pro.py
@@ -104,10 +104,10 @@ def block_expansion(
        print("Model weights saved in {}".format(output_dir))
    print("Fine-tune this model with:")
-    print("  --model_name_or_path {} \\".format(output_dir))
+    print("model_name_or_path: {}".format(output_dir))
-    print("  --finetuning_type freeze \\")
+    print("finetuning_type: freeze")
-    print("  --freeze_trainable_layers {} \\".format(num_expand))
+    print("freeze_trainable_layers: {}".format(num_expand))
-    print("  --use_llama_pro")
+    print("use_llama_pro: true")
 if __name__ == "__main__":
--- a/scripts/test_toolcall.py
+++ b/scripts/test_toolcall.py
@@ -20,7 +20,7 @@ def calculate_gpa(grades: Sequence[str], hours: Sequence[int]) -> float:
 def main():
    client = OpenAI(
-        api_key="0",
+        api_key="{}".format(os.environ.get("API_KEY", "0")),
        base_url="http://localhost:{}/v1".format(os.environ.get("API_PORT", 8000)),
    )
    tools = [
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@ from setuptools import find_packages, setup
 def get_version():
-    with open(os.path.join("src", "llmtuner", "cli.py"), "r", encoding="utf-8") as f:
+    with open(os.path.join("src", "llamafactory", "extras", "env.py"), "r", encoding="utf-8") as f:
        file_content = f.read()
        pattern = r"{}\W*=\W*\"([^\"]+)\"".format("VERSION")
        (version,) = re.findall(pattern, file_content)
@@ -21,24 +21,25 @@ def get_requires():
 extra_require = {
    "torch": ["torch>=1.13.1"],
    "torch-npu": ["torch==2.1.0", "torch-npu==2.1.0.post3", "decorator"],
    "metrics": ["nltk", "jieba", "rouge-chinese"],
    "deepspeed": ["deepspeed>=0.10.0,<=0.14.0"],
    "bitsandbytes": ["bitsandbytes>=0.39.0"],
-    "vllm": ["vllm>=0.4.0"],
+    "vllm": ["vllm>=0.4.3"],
    "galore": ["galore-torch"],
    "badam": ["badam"],
    "gptq": ["optimum>=1.16.0", "auto-gptq>=0.5.0"],
    "awq": ["autoawq"],
    "aqlm": ["aqlm[gpu]>=1.1.0"],
-    "qwen": ["tiktoken", "transformers_stream_generator"],
+    "qwen": ["transformers_stream_generator"],
    "modelscope": ["modelscope"],
-    "quality": ["ruff"],
+    "dev": ["ruff", "pytest"],
 }
 def main():
    setup(
-        name="llmtuner",
+        name="llamafactory",
        version=get_version(),
        author="hiyouga",
        author_email="hiyouga" "@" "buaa.edu.cn",
@@ -53,7 +54,7 @@ def main():
        python_requires=">=3.8.0",
        install_requires=get_requires(),
        extras_require=extra_require,
-        entry_points={"console_scripts": ["llamafactory-cli = llmtuner.cli:main"]},
+        entry_points={"console_scripts": ["llamafactory-cli = llamafactory.cli:main"]},
        classifiers=[
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
--- a/src/api.py
+++ b/src/api.py
@@ -2,8 +2,8 @@ import os
 import uvicorn
-from llmtuner.api.app import create_app
+from llamafactory.api.app import create_app
-from llmtuner.chat import ChatModel
+from llamafactory.chat import ChatModel
 def main():
--- a/src/llamafactory/init.py
+++ b/src/llamafactory/init.py
@@ -0,0 +1,6 @@
 # Level: api, webui > chat, eval, train > data, model > hparams > extras
 from .cli import VERSION
 __version__ = VERSION
--- a/src/llamafactory/api/init.py
+++ b/src/llamafactory/api/init.py
--- a/src/llamafactory/api/app.py
+++ b/src/llamafactory/api/app.py
--- a/src/llamafactory/api/chat.py
+++ b/src/llamafactory/api/chat.py
@@ -1,10 +1,13 @@
 import base64
 import io
 import json
 import os
 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.packages import is_fastapi_available
+from ..extras.packages import is_fastapi_available, is_pillow_available, is_requests_available
 from .common import dictify, jsonify
 from .protocol import (
    ChatCompletionMessage,
@@ -25,7 +28,17 @@ if is_fastapi_available():
    from fastapi import HTTPException, status
 if is_pillow_available():
    from PIL import Image
 if is_requests_available():
    import requests
 if TYPE_CHECKING:
    from numpy.typing import NDArray
    from ..chat import ChatModel
    from .protocol import ChatCompletionRequest, ScoreEvaluationRequest
@@ -40,7 +53,9 @@ ROLE_MAPPING = {
 }
-def _process_request(request: "ChatCompletionRequest") -> Tuple[List[Dict[str, str]], str, str]:
+def _process_request(
    request: "ChatCompletionRequest",
 ) -> Tuple[List[Dict[str, str]], Optional[str], Optional[str], Optional["NDArray"]]:
    logger.info("==== request ====\n{}".format(json.dumps(dictify(request), indent=2, ensure_ascii=False)))
    if len(request.messages) == 0:
@@ -49,12 +64,13 @@ def _process_request(request: "ChatCompletionRequest") -> Tuple[List[Dict[str, s
    if request.messages[0].role == Role.SYSTEM:
        system = request.messages.pop(0).content
    else:
-        system = ""
+        system = None
    if len(request.messages) % 2 == 0:
        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
    input_messages = []
    image = None
    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")
@@ -66,6 +82,21 @@ def _process_request(request: "ChatCompletionRequest") -> Tuple[List[Dict[str, s
            arguments = message.tool_calls[0].function.arguments
            content = json.dumps({"name": name, "argument": arguments}, ensure_ascii=False)
            input_messages.append({"role": ROLE_MAPPING[Role.FUNCTION], "content": content})
        elif isinstance(message.content, list):
            for input_item in message.content:
                if input_item.type == "text":
                    input_messages.append({"role": ROLE_MAPPING[message.role], "content": input_item.text})
                else:
                    image_url = input_item.image_url.url
                    if image_url.startswith("data:image"):  # base64 image
                        image_data = base64.b64decode(image_url.split(",", maxsplit=1)[1])
                        image_path = io.BytesIO(image_data)
                    elif os.path.isfile(image_url):  # local file
                        image_path = open(image_url, "rb")
                    else:  # web uri
                        image_path = requests.get(image_url, stream=True).raw
                    image = Image.open(image_path).convert("RGB")
        else:
            input_messages.append({"role": ROLE_MAPPING[message.role], "content": message.content})
@@ -76,9 +107,9 @@ def _process_request(request: "ChatCompletionRequest") -> Tuple[List[Dict[str, s
        except Exception:
            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid tools")
    else:
-        tools = ""
+        tools = None
-    return input_messages, system, tools
+    return input_messages, system, tools, image
 def _create_stream_chat_completion_chunk(
@@ -97,11 +128,12 @@ async def create_chat_completion_response(
    request: "ChatCompletionRequest", chat_model: "ChatModel"
 ) -> "ChatCompletionResponse":
    completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
-    input_messages, system, tools = _process_request(request)
+    input_messages, system, tools, image = _process_request(request)
    responses = await chat_model.achat(
        input_messages,
        system,
        tools,
        image,
        do_sample=request.do_sample,
        temperature=request.temperature,
        top_p=request.top_p,
@@ -145,7 +177,7 @@ async def create_stream_chat_completion_response(
    request: "ChatCompletionRequest", chat_model: "ChatModel"
 ) -> AsyncGenerator[str, None]:
    completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
-    input_messages, system, tools = _process_request(request)
+    input_messages, system, tools, image = _process_request(request)
    if tools:
        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
@@ -159,6 +191,7 @@ async def create_stream_chat_completion_response(
        input_messages,
        system,
        tools,
        image,
        do_sample=request.do_sample,
        temperature=request.temperature,
        top_p=request.top_p,
--- a/src/llamafactory/api/common.py
+++ b/src/llamafactory/api/common.py
--- a/src/llamafactory/api/protocol.py
+++ b/src/llamafactory/api/protocol.py
@@ -56,9 +56,19 @@ class FunctionCall(BaseModel):
    function: Function
 class ImageURL(BaseModel):
    url: str
 class MultimodalInputItem(BaseModel):
    type: Literal["text", "image_url"]
    text: Optional[str] = None
    image_url: Optional[ImageURL] = None
 class ChatMessage(BaseModel):
    role: Role
-    content: Optional[str] = None
+    content: Optional[Union[str, List[MultimodalInputItem]]] = None
    tool_calls: Optional[List[FunctionCall]] = None
--- a/src/llamafactory/chat/init.py
+++ b/src/llamafactory/chat/init.py
--- a/src/llamafactory/chat/base_engine.py
+++ b/src/llamafactory/chat/base_engine.py
--- a/src/llamafactory/chat/chat_model.py
+++ b/src/llamafactory/chat/chat_model.py
--- a/src/llamafactory/chat/hf_engine.py
+++ b/src/llamafactory/chat/hf_engine.py
@@ -2,12 +2,13 @@ import asyncio
 import concurrent.futures
 import os
 from threading import Thread
-from typing import TYPE_CHECKING, Any, AsyncGenerator, Callable, Dict, List, Optional, Sequence, Tuple
+from typing import TYPE_CHECKING, Any, AsyncGenerator, Callable, Dict, List, Optional, Sequence, Tuple, Union
 import torch
 from transformers import GenerationConfig, TextIteratorStreamer
 from ..data import get_template_and_fix_tokenizer
 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
@@ -23,6 +24,9 @@ if TYPE_CHECKING:
    from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
 logger = get_logger(__name__)
 class HuggingfaceEngine(BaseEngine):
    def __init__(
        self,
@@ -55,47 +59,69 @@ class HuggingfaceEngine(BaseEngine):
        image: Optional["NDArray"] = None,
        input_kwargs: Optional[Dict[str, Any]] = {},
    ) -> Tuple[Dict[str, Any], int]:
-        if processor is not None and image is not None and "<image>" not in messages[0]["content"]:
+        if (
-            messages[0]["content"] = "<image>" + messages[0]["content"]
+            processor is not None
            and image is not None
            and not hasattr(processor, "image_seq_length")
            and template.image_token not in messages[0]["content"]
        ):  # llava-like models
            messages[0]["content"] = template.image_token + messages[0]["content"]
        paired_messages = messages + [{"role": "assistant", "content": ""}]
        system = system or generating_args["default_system"]
        pixel_values = None
        prompt_ids, _ = template.encode_oneturn(
            tokenizer=tokenizer, messages=paired_messages, system=system, tools=tools
        )
        if processor is not None and image is not None:  # add image features
            image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
            batch_feature = image_processor(image, return_tensors="pt")
            pixel_values = batch_feature.to(model.device)["pixel_values"]  # shape (B, C, H, W)
            if hasattr(processor, "image_seq_length"):  # paligemma models
                image_token_id = tokenizer.convert_tokens_to_ids(template.image_token)
                prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids
        prompt_length = len(prompt_ids)
        inputs = torch.tensor([prompt_ids], device=model.device)
        attention_mask = torch.ones_like(inputs, dtype=torch.bool)
-        do_sample = input_kwargs.pop("do_sample", generating_args["do_sample"])
+        do_sample: Optional[bool] = input_kwargs.pop("do_sample", None)
-        temperature = input_kwargs.pop("temperature", generating_args["temperature"])
+        temperature: Optional[float] = input_kwargs.pop("temperature", None)
-        top_p = input_kwargs.pop("top_p", generating_args["top_p"])
+        top_p: Optional[float] = input_kwargs.pop("top_p", None)
-        top_k = input_kwargs.pop("top_k", generating_args["top_k"])
+        top_k: Optional[float] = input_kwargs.pop("top_k", None)
-        num_return_sequences = input_kwargs.pop("num_return_sequences", 1)
+        num_return_sequences: int = input_kwargs.pop("num_return_sequences", 1)
-        repetition_penalty = input_kwargs.pop("repetition_penalty", generating_args["repetition_penalty"])
+        repetition_penalty: Optional[float] = input_kwargs.pop("repetition_penalty", None)
-        length_penalty = input_kwargs.pop("length_penalty", generating_args["length_penalty"])
+        length_penalty: Optional[float] = input_kwargs.pop("length_penalty", None)
-        max_length = input_kwargs.pop("max_length", None)
+        max_length: Optional[int] = input_kwargs.pop("max_length", None)
-        max_new_tokens = input_kwargs.pop("max_new_tokens", None)
+        max_new_tokens: Optional[int] = input_kwargs.pop("max_new_tokens", None)
-        stop = input_kwargs.pop("stop", None)
+        stop: Optional[Union[str, List[str]]] = input_kwargs.pop("stop", None)
        if stop is not None:
-            raise ValueError("Stop parameter is not supported in Huggingface engine yet.")
+            logger.warning("Stop parameter is not supported in Huggingface engine yet.")
        generating_args = generating_args.copy()
        generating_args.update(
            dict(
-                do_sample=do_sample,
+                do_sample=do_sample if do_sample is not None else generating_args["do_sample"],
-                temperature=temperature,
+                temperature=temperature if temperature is not None else generating_args["temperature"],
-                top_p=top_p,
+                top_p=top_p if top_p is not None else generating_args["top_p"],
-                top_k=top_k,
+                top_k=top_k if top_k is not None else generating_args["top_k"],
                num_return_sequences=num_return_sequences,
-                repetition_penalty=repetition_penalty,
+                repetition_penalty=repetition_penalty
-                length_penalty=length_penalty,
+                if repetition_penalty is not None
                else generating_args["repetition_penalty"],
                length_penalty=length_penalty if length_penalty is not None else generating_args["length_penalty"],
                eos_token_id=[tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids,
                pad_token_id=tokenizer.pad_token_id,
            )
        )
-        if isinstance(num_return_sequences, int) and num_return_sequences > 1:
+        if isinstance(num_return_sequences, int) and num_return_sequences > 1:  # do_sample needs temperature > 0
            generating_args["do_sample"] = True
            generating_args["temperature"] = generating_args["temperature"] or 1.0
        if not generating_args["temperature"]:
            generating_args["do_sample"] = False
        if not generating_args["do_sample"]:
            generating_args.pop("temperature", None)
@@ -111,14 +137,13 @@ class HuggingfaceEngine(BaseEngine):
        gen_kwargs = dict(
            inputs=inputs,
            attention_mask=attention_mask,
            generation_config=GenerationConfig(**generating_args),
            logits_processor=get_logits_processor(),
        )
-        if processor is not None and image is not None:
+        if pixel_values is not None:
-            image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
+            gen_kwargs["pixel_values"] = pixel_values
            pixel_values: "torch.Tensor" = image_processor(image, return_tensors="pt")["pixel_values"]
            gen_kwargs["pixel_values"] = pixel_values.to(model.device)
        return gen_kwargs, prompt_length
--- a/src/llamafactory/chat/vllm_engine.py
+++ b/src/llamafactory/chat/vllm_engine.py
@@ -1,12 +1,12 @@
 import uuid
-from typing import TYPE_CHECKING, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence
+from typing import TYPE_CHECKING, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence, Union
 from ..data import get_template_and_fix_tokenizer
 from ..extras.logging import get_logger
-from ..extras.misc import get_device_count, infer_optim_dtype
+from ..extras.misc import get_device_count
 from ..extras.packages import is_vllm_available
 from ..model import load_config, load_tokenizer
-from ..model.utils.visual import LlavaMultiModalProjectorForYiVLForVLLM
+from ..model.model_utils.visual import LlavaMultiModalProjectorForYiVLForVLLM
 from .base_engine import BaseEngine, Response
@@ -17,7 +17,6 @@ if is_vllm_available():
 if TYPE_CHECKING:
    import torch
    from numpy.typing import NDArray
    from transformers.image_processing_utils import BaseImageProcessor
@@ -36,8 +35,6 @@ class VllmEngine(BaseEngine):
        generating_args: "GeneratingArguments",
    ) -> None:
        config = load_config(model_args)  # may download model from ms hub
        infer_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))
        infer_dtype = str(infer_dtype).split(".")[-1]
        self.can_generate = finetuning_args.stage == "sft"
        tokenizer_module = load_tokenizer(model_args)
@@ -51,7 +48,7 @@ class VllmEngine(BaseEngine):
            "model": model_args.model_name_or_path,
            "trust_remote_code": True,
            "download_dir": model_args.cache_dir,
-            "dtype": infer_dtype,
+            "dtype": model_args.vllm_dtype,
            "max_model_len": model_args.vllm_maxlen,
            "tensor_parallel_size": get_device_count() or 1,
            "gpu_memory_utilization": model_args.vllm_gpu_util,
@@ -59,6 +56,7 @@ class VllmEngine(BaseEngine):
            "disable_log_requests": True,
            "enforce_eager": model_args.vllm_enforce_eager,
            "enable_lora": model_args.adapter_name_or_path is not None,
            "max_lora_rank": model_args.vllm_max_lora_rank,
        }
        if model_args.visual_inputs:
@@ -66,11 +64,10 @@ class VllmEngine(BaseEngine):
            patch_size = config.vision_config.patch_size
            self.image_feature_size = (image_size // patch_size) ** 2
            engine_args["image_input_type"] = "pixel_values"
-            engine_args["image_token_id"] = self.tokenizer.convert_tokens_to_ids("<image>")
+            engine_args["image_token_id"] = self.tokenizer.convert_tokens_to_ids(self.template.image_token)
            engine_args["image_input_shape"] = "1,3,{},{}".format(image_size, image_size)
            engine_args["image_feature_size"] = self.image_feature_size
            if getattr(config, "is_yi_vl_derived_model", None):
                # bug in vllm 0.4.2, see: https://github.com/vllm-project/vllm/pull/4828
                import vllm.model_executor.models.llava
                logger.info("Detected Yi-VL model, applying projector patch.")
@@ -91,27 +88,49 @@ class VllmEngine(BaseEngine):
        **input_kwargs,
    ) -> AsyncIterator["RequestOutput"]:
        request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
-        if self.processor is not None and image is not None and "<image>" not in messages[0]["content"]:
+
-            messages[0]["content"] = "<image>" * self.image_feature_size + messages[0]["content"]
+        if (
            self.processor is not None
            and image is not None
            and not hasattr(self.processor, "image_seq_length")
            and self.template.image_token not in messages[0]["content"]
        ):  # llava-like models (TODO: paligemma models)
            messages[0]["content"] = self.template.image_token * self.image_feature_size + messages[0]["content"]
        paired_messages = messages + [{"role": "assistant", "content": ""}]
        system = system or self.generating_args["default_system"]
        prompt_ids, _ = self.template.encode_oneturn(
            tokenizer=self.tokenizer, messages=paired_messages, system=system, tools=tools
        )
        if self.processor is not None and image is not None:  # add image features
            image_processor: "BaseImageProcessor" = getattr(self.processor, "image_processor")
            pixel_values = image_processor(image, return_tensors="pt")["pixel_values"]
            multi_modal_data = MultiModalData(type=MultiModalData.Type.IMAGE, data=pixel_values)
        else:
            multi_modal_data = None
        prompt_length = len(prompt_ids)
-        use_beam_search = self.generating_args["num_beams"] > 1
+        use_beam_search: bool = self.generating_args["num_beams"] > 1
-        temperature = input_kwargs.pop("temperature", self.generating_args["temperature"])
+        temperature: Optional[float] = input_kwargs.pop("temperature", None)
-        top_p = input_kwargs.pop("top_p", self.generating_args["top_p"])
+        top_p: Optional[float] = input_kwargs.pop("top_p", None)
-        top_k = input_kwargs.pop("top_k", self.generating_args["top_k"])
+        top_k: Optional[float] = input_kwargs.pop("top_k", None)
-        num_return_sequences = input_kwargs.pop("num_return_sequences", 1)
+        num_return_sequences: int = input_kwargs.pop("num_return_sequences", 1)
-        repetition_penalty = input_kwargs.pop("repetition_penalty", self.generating_args["repetition_penalty"])
+        repetition_penalty: Optional[float] = input_kwargs.pop("repetition_penalty", None)
-        length_penalty = input_kwargs.pop("length_penalty", self.generating_args["length_penalty"])
+        length_penalty: Optional[float] = input_kwargs.pop("length_penalty", None)
-        max_length = input_kwargs.pop("max_length", None)
+        max_length: Optional[int] = input_kwargs.pop("max_length", None)
-        max_new_tokens = input_kwargs.pop("max_new_tokens", None)
+        max_new_tokens: Optional[int] = input_kwargs.pop("max_new_tokens", None)
-        stop = input_kwargs.pop("stop", None)
+        stop: Optional[Union[str, List[str]]] = input_kwargs.pop("stop", None)
        if "max_new_tokens" in self.generating_args:
            max_tokens = self.generating_args["max_new_tokens"]
        elif "max_length" in self.generating_args:
            if self.generating_args["max_length"] > prompt_length:
                max_tokens = self.generating_args["max_length"] - prompt_length
            else:
                max_tokens = 1
        max_tokens = self.generating_args["max_new_tokens"] or self.generating_args["max_length"]
        if max_length:
            max_tokens = max_length - prompt_length if max_length > prompt_length else 1
@@ -120,32 +139,26 @@ class VllmEngine(BaseEngine):
        sampling_params = SamplingParams(
            n=num_return_sequences,
-            repetition_penalty=repetition_penalty,
+            repetition_penalty=(
-            temperature=temperature,
+                repetition_penalty if repetition_penalty is not None else self.generating_args["repetition_penalty"]
-            top_p=top_p,
+            )
-            top_k=top_k,
+            or 1.0,  # repetition_penalty must > 0
            temperature=temperature if temperature is not None else self.generating_args["temperature"],
            top_p=(top_p if top_p is not None else self.generating_args["top_p"]) or 1.0,  # top_p must > 0
            top_k=top_k if top_k is not None else self.generating_args["top_k"],
            use_beam_search=use_beam_search,
-            length_penalty=length_penalty,
+            length_penalty=length_penalty if length_penalty is not None else self.generating_args["length_penalty"],
            stop=stop,
            stop_token_ids=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
            max_tokens=max_tokens,
            skip_special_tokens=True,
        )
        if self.processor is not None and image is not None:
            image_processor: "BaseImageProcessor" = getattr(self.processor, "image_processor")
            pixel_values: "torch.Tensor" = image_processor(image, return_tensors="pt")["pixel_values"]
            multi_modal_data = MultiModalData(type=MultiModalData.Type.IMAGE, data=pixel_values)
        else:
            multi_modal_data = None
        result_generator = self.model.generate(
-            prompt=None,
+            inputs={"prompt_token_ids": prompt_ids, "multi_modal_data": multi_modal_data},
            sampling_params=sampling_params,
            request_id=request_id,
            prompt_token_ids=prompt_ids,
            lora_request=self.lora_request,
            multi_modal_data=multi_modal_data,
        )
        return result_generator
--- a/src/llamafactory/cli.py
+++ b/src/llamafactory/cli.py
@@ -1,9 +1,16 @@
 import os
 import random
 import subprocess
 import sys
 from enum import Enum, unique
 from . import launcher
 from .api.app import run_api
 from .chat.chat_model import run_chat
 from .eval.evaluator import run_eval
 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
@@ -23,8 +30,6 @@ USAGE = (
    + "-" * 70
 )
 VERSION = "0.7.1"
 WELCOME = (
    "-" * 58
    + "\n"
@@ -37,11 +42,14 @@ WELCOME = (
    + "-" * 58
 )
 logger = get_logger(__name__)
@unique
 class Command(str, Enum):
    API = "api"
    CHAT = "chat"
    ENV = "env"
    EVAL = "eval"
    EXPORT = "export"
    TRAIN = "train"
@@ -57,11 +65,34 @@ def main():
        run_api()
    elif command == Command.CHAT:
        run_chat()
    elif command == Command.ENV:
        print_env()
    elif command == Command.EVAL:
        run_eval()
    elif command == Command.EXPORT:
        export_model()
    elif command == Command.TRAIN:
        force_torchrun = os.environ.get("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_port = os.environ.get("MASTER_PORT", str(random.randint(20001, 29999)))
            logger.info("Initializing distributed tasks at: {}:{}".format(master_addr, master_port))
            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"),
                    node_rank=os.environ.get("RANK", "0"),
                    nproc_per_node=os.environ.get("NPROC_PER_NODE", str(get_device_count())),
                    master_addr=master_addr,
                    master_port=master_port,
                    file_name=launcher.__file__,
                    args=" ".join(sys.argv[1:]),
                ),
                shell=True,
            )
        else:
            run_exp()
    elif command == Command.WEBDEMO:
        run_web_demo()
--- a/src/llamafactory/data/init.py
+++ b/src/llamafactory/data/init.py
@@ -0,0 +1,16 @@
 from .collator import KTODataCollatorWithPadding, PairwiseDataCollatorWithPadding
 from .data_utils import Role, split_dataset
 from .loader import get_dataset
 from .template import TEMPLATES, Template, get_template_and_fix_tokenizer
 __all__ = [
    "KTODataCollatorWithPadding",
    "PairwiseDataCollatorWithPadding",
    "Role",
    "split_dataset",
    "get_dataset",
    "TEMPLATES",
    "Template",
    "get_template_and_fix_tokenizer",
 ]
--- a/src/llamafactory/data/aligner.py
+++ b/src/llamafactory/data/aligner.py
@@ -4,7 +4,8 @@ from typing import TYPE_CHECKING, Any, Dict, List, Union
 from datasets import Features
-from .utils import Role
+from ..extras.logging import get_logger
 from .data_utils import Role
 if TYPE_CHECKING:
@@ -14,7 +15,13 @@ if TYPE_CHECKING:
    from .parser import DatasetAttr
 logger = get_logger(__name__)
 def _convert_images(images: List[Any], dataset_attr: "DatasetAttr", data_args: "DataArguments") -> List[Any]:
    r"""
    Optionally concatenates image path to dataset dir when loading from local disk.
    """
    outputs = []
    if dataset_attr.load_from in ["script", "file"]:
        for image in images:
@@ -29,6 +36,9 @@ def _convert_images(images: List[Any], dataset_attr: "DatasetAttr", data_args: "
 def convert_alpaca(
    examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr", data_args: "DataArguments"
 ) -> Dict[str, List[Any]]:
    r"""
    Converts alpaca format dataset to the standard format.
    """
    outputs = {"prompt": [], "response": [], "system": [], "tools": [], "images": []}
    convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)
    for i in range(len(examples[dataset_attr.prompt])):
@@ -45,21 +55,32 @@ def convert_alpaca(
        if dataset_attr.query and examples[dataset_attr.query][i]:
            content.append(examples[dataset_attr.query][i])
-        prompt.append({"role": Role.USER.value, "content": "\n".join(content)})
+        prompt.append({"role": Role.USER.value, "content": "\n".join(content)})  # "prompt\nquery"
-        if dataset_attr.response and isinstance(examples[dataset_attr.response][i], list):
+        if dataset_attr.kto_tag and isinstance(examples[dataset_attr.kto_tag][i], bool):  # kto example
            response = [
                {"role": Role.ASSISTANT.value, "content": content} for content in examples[dataset_attr.response][i]
            ]
        elif dataset_attr.response and isinstance(examples[dataset_attr.response][i], str):
            response = [{"role": Role.ASSISTANT.value, "content": examples[dataset_attr.response][i]}]
            if examples[dataset_attr.kto_tag][i]:
                response = response + [{"role": Role.ASSISTANT.value, "content": ""}]
            else:
                response = [{"role": Role.ASSISTANT.value, "content": ""}] + response
        elif (
            dataset_attr.ranking
            and isinstance(examples[dataset_attr.chosen][i], str)
            and isinstance(examples[dataset_attr.rejected][i], str)
        ):  # pairwise example
            response = [
                {"role": Role.ASSISTANT.value, "content": examples[dataset_attr.chosen][i]},
                {"role": Role.ASSISTANT.value, "content": examples[dataset_attr.rejected][i]},
            ]
        elif dataset_attr.response and isinstance(examples[dataset_attr.response][i], str):  # normal example
            response = [{"role": Role.ASSISTANT.value, "content": examples[dataset_attr.response][i]}]
        else:  # unsupervised
            response = []
        outputs["prompt"].append(prompt)
        outputs["response"].append(response)
        outputs["system"].append(examples[dataset_attr.system][i] if dataset_attr.system else "")
-        outputs["tools"].append("")
+        outputs["tools"].append(examples[dataset_attr.tools][i] if dataset_attr.tools else "")
        outputs["images"].append(convert_images(examples[dataset_attr.images][i]) if dataset_attr.images else [])
    return outputs
@@ -68,6 +89,9 @@ def convert_alpaca(
 def convert_sharegpt(
    examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr", data_args: "DataArguments"
 ) -> Dict[str, List[Any]]:
    r"""
    Converts sharegpt format dataset to the standard format.
    """
    outputs = {"prompt": [], "response": [], "system": [], "tools": [], "images": []}
    convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)
    tag_mapping = {
@@ -87,21 +111,62 @@ def convert_sharegpt(
        else:
            system = examples[dataset_attr.system][i] if dataset_attr.system else ""
        messages = messages[: len(messages) // 2 * 2]  # should be multiples of 2
        if len(messages) == 0:
            continue
        aligned_messages = []
        broken_data = False
        for turn_idx, message in enumerate(messages):
            if message[dataset_attr.role_tag] not in accept_tags[turn_idx % 2]:
-                raise ValueError("Invalid role tag in {}.".format(messages))
+                logger.warning("Invalid role tag in {}.".format(messages))
                broken_data = True
            aligned_messages.append(
                {"role": tag_mapping[message[dataset_attr.role_tag]], "content": message[dataset_attr.content_tag]}
            )
-        outputs["prompt"].append(aligned_messages[:-1])
+        if (not dataset_attr.ranking and len(aligned_messages) % 2 != 0) or (
-        outputs["response"].append(aligned_messages[-1:])
+            dataset_attr.ranking and len(aligned_messages) % 2 == 0
        ):
            logger.warning("Invalid message count in {}.".format(messages))
            broken_data = True
        if dataset_attr.kto_tag and isinstance(examples[dataset_attr.kto_tag][i], bool):  # kto example
            prompt = aligned_messages[:-1]
            response = aligned_messages[-1:]
            if examples[dataset_attr.kto_tag][i]:
                response = response + [{"role": Role.ASSISTANT.value, "content": ""}]
            else:
                response = [{"role": Role.ASSISTANT.value, "content": ""}] + response
        elif (
            dataset_attr.ranking
            and isinstance(examples[dataset_attr.chosen][i], dict)
            and isinstance(examples[dataset_attr.rejected][i], dict)
        ):  # pairwise example
            chosen = examples[dataset_attr.chosen][i]
            rejected = examples[dataset_attr.rejected][i]
            if (
                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]))
                broken_data = True
            prompt = aligned_messages
            response = [
                {"role": tag_mapping[chosen[dataset_attr.role_tag]], "content": chosen[dataset_attr.content_tag]},
                {"role": tag_mapping[rejected[dataset_attr.role_tag]], "content": rejected[dataset_attr.content_tag]},
            ]
        else:  # normal example
            prompt = aligned_messages[:-1]
            response = aligned_messages[-1:]
        if broken_data:
            logger.warning("Skipping this abnormal example.")
            continue
        outputs["prompt"].append(prompt)
        outputs["response"].append(response)
        outputs["system"].append(system)
        outputs["tools"].append(examples[dataset_attr.tools][i] if dataset_attr.tools else "")
        outputs["images"].append(convert_images(examples[dataset_attr.images][i]) if dataset_attr.images else [])
--- a/src/llamafactory/data/collator.py
+++ b/src/llamafactory/data/collator.py
@@ -0,0 +1,81 @@
 from dataclasses import dataclass
 from typing import Any, Dict, Sequence
 import torch
 from transformers import DataCollatorForSeq2Seq
@dataclass
 class PairwiseDataCollatorWithPadding(DataCollatorForSeq2Seq):
    r"""
    Data collator for pairwise data.
    """
    def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
        r"""
        Pads batched data to the longest sequence in the batch.
        We generate 2 * n examples where the first n examples represent chosen examples and
        the last n examples represent rejected examples.
        """
        concatenated_features = []
        for key in ("chosen", "rejected"):
            for feature in features:
                target_feature = {
                    "input_ids": feature["{}_input_ids".format(key)],
                    "attention_mask": feature["{}_attention_mask".format(key)],
                    "labels": feature["{}_labels".format(key)],
                }
                if "pixel_values" in feature:
                    target_feature["pixel_values"] = feature["pixel_values"]
                if "{}_token_type_ids".format(key) in feature:
                    target_feature["token_type_ids"] = feature["{}_token_type_ids".format(key)]
                concatenated_features.append(target_feature)
        return super().__call__(concatenated_features)
@dataclass
 class KTODataCollatorWithPadding(DataCollatorForSeq2Seq):
    r"""
    Data collator for KTO data.
    """
    def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
        target_features = []
        kl_features = []
        kto_tags = []
        for feature in features:
            target_feature = {
                "input_ids": feature["input_ids"],
                "attention_mask": feature["attention_mask"],
                "labels": feature["labels"],
            }
            kl_feature = {
                "input_ids": feature["kl_input_ids"],
                "attention_mask": feature["kl_attention_mask"],
                "labels": feature["kl_labels"],
            }
            if "pixel_values" in feature:
                target_feature["pixel_values"] = feature["pixel_values"]
            if "token_type_ids" in feature:
                target_feature["token_type_ids"] = feature["token_type_ids"]
                kl_feature["token_type_ids"] = feature["kl_token_type_ids"]
            target_features.append(target_feature)
            kl_features.append(kl_feature)
            kto_tags.append(feature["kto_tags"])
        batch = super().__call__(target_features)
        kl_batch = super().__call__(kl_features)
        batch["kl_input_ids"] = kl_batch["input_ids"]
        batch["kl_attention_mask"] = kl_batch["attention_mask"]
        batch["kl_labels"] = kl_batch["labels"]
        if "token_type_ids" in batch:
            batch["kl_token_type_ids"] = kl_batch["token_type_ids"]
        batch["kto_tags"] = torch.tensor(kto_tags)
        return batch
--- a/src/llamafactory/data/data_utils.py
+++ b/src/llamafactory/data/data_utils.py
@@ -10,7 +10,7 @@ if TYPE_CHECKING:
    from datasets import Dataset, IterableDataset
    from transformers import Seq2SeqTrainingArguments
-    from llmtuner.hparams import DataArguments
+    from ..hparams import DataArguments
 logger = get_logger(__name__)
--- a/src/llamafactory/data/formatter.py
+++ b/src/llamafactory/data/formatter.py
--- a/src/llamafactory/data/loader.py
+++ b/src/llamafactory/data/loader.py
@@ -1,17 +1,19 @@
 import inspect
 import os
 import sys
 from typing import TYPE_CHECKING, Literal, Optional, Union
 import numpy as np
 from datasets import load_dataset, load_from_disk
 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
 from .parser import get_dataset_list
 from .preprocess import get_preprocess_and_print_func
 from .template import get_template_and_fix_tokenizer
 from .utils import merge_dataset
 if TYPE_CHECKING:
@@ -57,12 +59,12 @@ def load_single_dataset(
            data_files.append(local_path)
            data_path = FILEEXT2TYPE.get(local_path.split(".")[-1], None)
        else:
-            raise ValueError("File not found.")
+            raise ValueError("File {} not found.".format(local_path))
        if data_path is None:
-            raise ValueError("File extension must be txt, csv, json or jsonl.")
+            raise ValueError("Allowed file types: {}.".format(",".join(FILEEXT2TYPE.keys())))
    else:
-        raise NotImplementedError
+        raise NotImplementedError("Unknown load type: {}.".format(dataset_attr.load_from))
    if dataset_attr.load_from == "ms_hub":
        try:
@@ -105,9 +107,21 @@ def load_single_dataset(
    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]
        target_num -= len(indexes)
        if target_num > 0:
            expand_indexes = np.random.choice(len(dataset), target_num)
            indexes = np.concatenate((indexes, expand_indexes), axis=0)
        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))
    if data_args.max_samples is not None:  # truncate dataset
-        num_samples = min(data_args.max_samples, len(dataset))
+        max_samples = min(data_args.max_samples, len(dataset))
-        dataset = dataset.select(range(num_samples))
+        dataset = dataset.select(range(max_samples))
    return align_dataset(dataset, dataset_attr, data_args)
@@ -116,7 +130,7 @@ def get_dataset(
    model_args: "ModelArguments",
    data_args: "DataArguments",
    training_args: "Seq2SeqTrainingArguments",
-    stage: Literal["pt", "sft", "rm", "ppo"],
+    stage: Literal["pt", "sft", "rm", "ppo", "kto"],
    tokenizer: "PreTrainedTokenizer",
    processor: Optional["ProcessorMixin"] = None,
 ) -> Union["Dataset", "IterableDataset"]:
@@ -165,14 +179,17 @@ def get_dataset(
            if training_args.should_save:
                dataset.save_to_disk(data_args.tokenized_path)
                logger.info("Tokenized dataset saved at {}.".format(data_args.tokenized_path))
-                logger.info("Please restart the training with `--tokenized_path {}`.".format(data_args.tokenized_path))
+                logger.info("Please restart the training with `tokenized_path: {}`.".format(data_args.tokenized_path))
-            exit(0)
+            sys.exit(0)
        if training_args.should_log:
            try:
                print_function(next(iter(dataset)))
            except StopIteration:
                if stage == "pt":
                    raise RuntimeError("Cannot find sufficient samples, consider increasing dataset size.")
                else:
                    raise RuntimeError("Cannot find valid samples, check `data/README.md` for the data format.")
        return dataset
--- a/src/llamafactory/data/parser.py
+++ b/src/llamafactory/data/parser.py
@@ -20,23 +20,28 @@ class DatasetAttr:
    """ basic configs """
    load_from: Literal["hf_hub", "ms_hub", "script", "file"]
    dataset_name: str
    formatting: Literal["alpaca", "sharegpt"] = "alpaca"
    ranking: bool = False
    """ extra configs """
    subset: Optional[str] = None
    folder: Optional[str] = None
-    ranking: bool = False
+    num_samples: Optional[int] = None
-    formatting: Literal["alpaca", "sharegpt"] = "alpaca"
+    """ common columns """
    """ columns """
    system: Optional[str] = None
    tools: Optional[str] = None
    images: Optional[str] = None
-    """ columns for the alpaca format """
+    """ rlhf columns """
    chosen: Optional[str] = None
    rejected: Optional[str] = None
    kto_tag: Optional[str] = None
    """ alpaca columns """
    prompt: Optional[str] = "instruction"
    query: Optional[str] = "input"
    response: Optional[str] = "output"
    history: Optional[str] = None
-    """ columns for the sharegpt format """
+    """ sharegpt columns """
    messages: Optional[str] = "conversations"
-    tools: Optional[str] = None
+    """ sharegpt tags """
    """ tags for the sharegpt format """
    role_tag: Optional[str] = "from"
    content_tag: Optional[str] = "value"
    user_tag: Optional[str] = "human"
@@ -98,17 +103,18 @@ def get_dataset_list(data_args: "DataArguments") -> List["DatasetAttr"]:
        else:
            dataset_attr = DatasetAttr("file", dataset_name=dataset_info[name]["file_name"])
        dataset_attr.set_attr("formatting", dataset_info[name], default="alpaca")
        dataset_attr.set_attr("ranking", dataset_info[name], default=False)
        dataset_attr.set_attr("subset", dataset_info[name])
        dataset_attr.set_attr("folder", dataset_info[name])
-        dataset_attr.set_attr("ranking", dataset_info[name], default=False)
+        dataset_attr.set_attr("num_samples", dataset_info[name])
        dataset_attr.set_attr("formatting", dataset_info[name], default="alpaca")
        if "columns" in dataset_info[name]:
-            column_names = ["system", "images"]
+            column_names = ["system", "tools", "images", "chosen", "rejected", "kto_tag"]
            if dataset_attr.formatting == "alpaca":
                column_names.extend(["prompt", "query", "response", "history"])
            else:
-                column_names.extend(["messages", "tools"])
+                column_names.extend(["messages"])
            for column_name in column_names:
                dataset_attr.set_attr(column_name, dataset_info[name]["columns"])
--- a/src/llamafactory/data/preprocess.py
+++ b/src/llamafactory/data/preprocess.py
@@ -0,0 +1,84 @@
 from functools import partial
 from typing import TYPE_CHECKING, Callable, Literal, Optional, Tuple
 from .processors.feedback import preprocess_feedback_dataset
 from .processors.pairwise import preprocess_pairwise_dataset, print_pairwise_dataset_example
 from .processors.pretrain import preprocess_pretrain_dataset
 from .processors.supervised import (
    preprocess_packed_supervised_dataset,
    preprocess_supervised_dataset,
    print_supervised_dataset_example,
 )
 from .processors.unsupervised import preprocess_unsupervised_dataset, print_unsupervised_dataset_example
 if TYPE_CHECKING:
    from transformers import ProcessorMixin, Seq2SeqTrainingArguments
    from transformers.tokenization_utils import PreTrainedTokenizer
    from ..hparams import DataArguments
    from .template import Template
 def get_preprocess_and_print_func(
    data_args: "DataArguments",
    training_args: "Seq2SeqTrainingArguments",
    stage: Literal["pt", "sft", "rm", "ppo", "kto"],
    template: "Template",
    tokenizer: "PreTrainedTokenizer",
    processor: Optional["ProcessorMixin"],
 ) -> Tuple[Callable, Callable]:
    if stage == "pt":
        preprocess_func = partial(
            preprocess_pretrain_dataset,
            tokenizer=tokenizer,
            data_args=data_args,
        )
        print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
    elif stage == "sft" and not training_args.predict_with_generate:
        if data_args.packing:
            preprocess_func = partial(
                preprocess_packed_supervised_dataset,
                template=template,
                tokenizer=tokenizer,
                data_args=data_args,
            )
        else:
            preprocess_func = partial(
                preprocess_supervised_dataset,
                template=template,
                tokenizer=tokenizer,
                processor=processor,
                data_args=data_args,
            )
        print_function = partial(print_supervised_dataset_example, tokenizer=tokenizer)
    elif stage == "rm":
        preprocess_func = partial(
            preprocess_pairwise_dataset,
            template=template,
            tokenizer=tokenizer,
            processor=processor,
            data_args=data_args,
        )
        print_function = partial(print_pairwise_dataset_example, tokenizer=tokenizer)
    elif stage == "kto":
        preprocess_func = partial(
            preprocess_feedback_dataset,
            template=template,
            tokenizer=tokenizer,
            processor=processor,
            data_args=data_args,
        )
        print_function = partial(print_supervised_dataset_example, tokenizer=tokenizer)
    else:
        preprocess_func = partial(
            preprocess_unsupervised_dataset,
            template=template,
            tokenizer=tokenizer,
            processor=processor,
            data_args=data_args,
        )
        print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
    return preprocess_func, print_function
--- a/src/llamafactory/data/processors/init.py
+++ b/src/llamafactory/data/processors/init.py
--- a/src/llamafactory/data/processors/feedback.py
+++ b/src/llamafactory/data/processors/feedback.py
@@ -0,0 +1,126 @@
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
 from ...extras.constants import IGNORE_INDEX
 from ...extras.logging import get_logger
 from .processor_utils import get_paligemma_token_type_ids, get_pixel_values
 if TYPE_CHECKING:
    from transformers import ProcessorMixin
    from transformers.tokenization_utils import PreTrainedTokenizer
    from ...hparams import DataArguments
    from ..template import Template
 logger = get_logger(__name__)
 def _encode_feedback_example(
    prompt: Sequence[Dict[str, str]],
    response: Sequence[Dict[str, str]],
    kl_response: Sequence[Dict[str, str]],
    system: Optional[str],
    tools: Optional[str],
    template: "Template",
    tokenizer: "PreTrainedTokenizer",
    processor: Optional["ProcessorMixin"],
    data_args: "DataArguments",
 ) -> Tuple[List[int], List[int], List[int], List[int], bool]:
    if processor is not None and not hasattr(processor, "image_seq_length"):  # llava-like models
        prompt[0]["content"] = template.image_token + prompt[0]["content"]
    if response[0]["content"]:  # desired example
        kto_tag = True
        messages = prompt + [response[0]]
    else:  # undesired example
        kto_tag = False
        messages = prompt + [response[1]]
    if kl_response[0]["content"]:
        kl_messages = prompt + [kl_response[0]]
    else:
        kl_messages = prompt + [kl_response[1]]
    prompt_ids, response_ids = template.encode_oneturn(
        tokenizer, messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
    )
    _, kl_response_ids = template.encode_oneturn(
        tokenizer, kl_messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
    )
    if template.efficient_eos:
        response_ids += [tokenizer.eos_token_id]
        kl_response_ids += [tokenizer.eos_token_id]
    if processor is not None and hasattr(processor, "image_seq_length"):  # paligemma models
        image_token_id = tokenizer.convert_tokens_to_ids(template.image_token)
        prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids
    input_ids = prompt_ids + response_ids
    labels = [IGNORE_INDEX] * len(prompt_ids) + response_ids
    kl_input_ids = prompt_ids + kl_response_ids
    kl_labels = [IGNORE_INDEX] * len(prompt_ids) + kl_response_ids
    return input_ids, labels, kl_input_ids, kl_labels, kto_tag
 def preprocess_feedback_dataset(
    examples: Dict[str, List[Any]],
    template: "Template",
    tokenizer: "PreTrainedTokenizer",
    processor: Optional["ProcessorMixin"],
    data_args: "DataArguments",
 ) -> Dict[str, List[List[int]]]:
    # create unrelated input-output pairs for estimating the KL term by flipping the matched pairs
    kl_response = examples["response"][::-1]
    model_inputs = {
        "input_ids": [],
        "attention_mask": [],
        "labels": [],
        "kl_input_ids": [],
        "kl_attention_mask": [],
        "kl_labels": [],
        "kto_tags": [],
    }
    if processor is not None:
        model_inputs["pixel_values"] = []
        if hasattr(processor, "image_seq_length"):  # paligemma models
            model_inputs["token_type_ids"] = []
            model_inputs["kl_token_type_ids"] = []
    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]))
            continue
        input_ids, labels, kl_input_ids, kl_labels, kto_tag = _encode_feedback_example(
            prompt=examples["prompt"][i],
            response=examples["response"][i],
            kl_response=kl_response[i],
            system=examples["system"][i],
            tools=examples["tools"][i],
            template=template,
            tokenizer=tokenizer,
            processor=processor,
            data_args=data_args,
        )
        model_inputs["input_ids"].append(input_ids)
        model_inputs["attention_mask"].append([1] * len(input_ids))
        model_inputs["labels"].append(labels)
        model_inputs["kl_input_ids"].append(kl_input_ids)
        model_inputs["kl_attention_mask"].append([1] * len(kl_input_ids))
        model_inputs["kl_labels"].append(kl_labels)
        model_inputs["kto_tags"].append(kto_tag)
        if processor is not None:
            model_inputs["pixel_values"].append(get_pixel_values(examples["images"][i], processor))
            if hasattr(processor, "image_seq_length"):  # paligemma models
                model_inputs["token_type_ids"].append(get_paligemma_token_type_ids(len(input_ids), processor))
                model_inputs["kl_token_type_ids"].append(get_paligemma_token_type_ids(len(kl_input_ids), processor))
    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.")
    return model_inputs
--- a/src/llamafactory/data/processors/pairwise.py
+++ b/src/llamafactory/data/processors/pairwise.py
@@ -0,0 +1,123 @@
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
 from ...extras.constants import IGNORE_INDEX
 from ...extras.logging import get_logger
 from .processor_utils import get_paligemma_token_type_ids, get_pixel_values
 if TYPE_CHECKING:
    from transformers import ProcessorMixin
    from transformers.tokenization_utils import PreTrainedTokenizer
    from ...hparams import DataArguments
    from ..template import Template
 logger = get_logger(__name__)
 def _encode_pairwise_example(
    prompt: Sequence[Dict[str, str]],
    response: Sequence[Dict[str, str]],
    system: Optional[str],
    tools: Optional[str],
    template: "Template",
    tokenizer: "PreTrainedTokenizer",
    processor: Optional["ProcessorMixin"],
    data_args: "DataArguments",
 ) -> Tuple[List[int], List[int], List[int], List[int]]:
    if processor is not None and not hasattr(processor, "image_seq_length"):  # llava-like models
        prompt[0]["content"] = template.image_token + prompt[0]["content"]
    chosen_messages = prompt + [response[0]]
    rejected_messages = prompt + [response[1]]
    prompt_ids, chosen_ids = template.encode_oneturn(
        tokenizer, chosen_messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
    )
    _, rejected_ids = template.encode_oneturn(
        tokenizer, rejected_messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
    )
    if template.efficient_eos:
        chosen_ids += [tokenizer.eos_token_id]
        rejected_ids += [tokenizer.eos_token_id]
    if processor is not None and hasattr(processor, "image_seq_length"):  # paligemma models
        image_token_id = tokenizer.convert_tokens_to_ids(template.image_token)
        prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids
    chosen_input_ids = prompt_ids + chosen_ids
    chosen_labels = [IGNORE_INDEX] * len(prompt_ids) + chosen_ids
    rejected_input_ids = prompt_ids + rejected_ids
    rejected_labels = [IGNORE_INDEX] * len(prompt_ids) + rejected_ids
    return chosen_input_ids, chosen_labels, rejected_input_ids, rejected_labels
 def preprocess_pairwise_dataset(
    examples: Dict[str, List[Any]],
    template: "Template",
    tokenizer: "PreTrainedTokenizer",
    processor: Optional["ProcessorMixin"],
    data_args: "DataArguments",
 ) -> Dict[str, List[List[int]]]:
    # build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
    model_inputs = {
        "chosen_input_ids": [],
        "chosen_attention_mask": [],
        "chosen_labels": [],
        "rejected_input_ids": [],
        "rejected_attention_mask": [],
        "rejected_labels": [],
    }
    if processor is not None:
        model_inputs["pixel_values"] = []
        if hasattr(processor, "image_seq_length"):  # paligemma models
            model_inputs["chosen_token_type_ids"] = []
            model_inputs["rejected_token_type_ids"] = []
    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]))
            continue
        chosen_input_ids, chosen_labels, rejected_input_ids, rejected_labels = _encode_pairwise_example(
            prompt=examples["prompt"][i],
            response=examples["response"][i],
            system=examples["system"][i],
            tools=examples["tools"][i],
            template=template,
            tokenizer=tokenizer,
            processor=processor,
            data_args=data_args,
        )
        model_inputs["chosen_input_ids"].append(chosen_input_ids)
        model_inputs["chosen_attention_mask"].append([1] * len(chosen_input_ids))
        model_inputs["chosen_labels"].append(chosen_labels)
        model_inputs["rejected_input_ids"].append(rejected_input_ids)
        model_inputs["rejected_attention_mask"].append([1] * len(rejected_input_ids))
        model_inputs["rejected_labels"].append(rejected_labels)
        if processor is not None:
            model_inputs["pixel_values"].append(get_pixel_values(examples["images"][i], processor))
            if hasattr(processor, "image_seq_length"):  # paligemma models
                model_inputs["chosen_token_type_ids"].append(
                    get_paligemma_token_type_ids(len(chosen_input_ids), processor)
                )
                model_inputs["rejected_token_type_ids"].append(
                    get_paligemma_token_type_ids(len(rejected_input_ids), processor)
                )
    return model_inputs
 def print_pairwise_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
    valid_chosen_labels = list(filter(lambda x: x != IGNORE_INDEX, example["chosen_labels"]))
    valid_rejected_labels = list(filter(lambda x: x != IGNORE_INDEX, example["rejected_labels"]))
    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("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)))
--- a/src/llamafactory/data/processors/pretrain.py
+++ b/src/llamafactory/data/processors/pretrain.py
@@ -0,0 +1,36 @@
 from itertools import chain
 from typing import TYPE_CHECKING, Any, Dict, List
 if TYPE_CHECKING:
    from transformers.tokenization_utils import PreTrainedTokenizer
    from ...hparams import DataArguments
 def preprocess_pretrain_dataset(
    examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
 ) -> Dict[str, List[List[int]]]:
    # build grouped texts with format `X1 X2 X3 ...` if packing is enabled
    text_examples = [messages[0]["content"] + tokenizer.eos_token for messages in examples["prompt"]]
    if not data_args.packing:
        if data_args.template == "gemma":
            text_examples = [tokenizer.bos_token + example for example in text_examples]
        result = tokenizer(text_examples, add_special_tokens=False, max_length=data_args.cutoff_len, truncation=True)
    else:
        tokenized_examples = tokenizer(text_examples, add_special_tokens=False)
        concatenated_examples = {k: list(chain(*tokenized_examples[k])) for k in tokenized_examples.keys()}
        total_length = len(concatenated_examples[list(concatenated_examples.keys())[0]])
        block_size = data_args.cutoff_len
        total_length = (total_length // block_size) * block_size
        result = {
            k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
            for k, t in concatenated_examples.items()
        }
        if data_args.template == "gemma":
            for i in range(len(result["input_ids"])):
                result["input_ids"][i][0] = tokenizer.bos_token_id
    return result
--- a/src/llamafactory/data/processors/processor_utils.py
+++ b/src/llamafactory/data/processors/processor_utils.py
@@ -0,0 +1,64 @@
 import bisect
 from typing import TYPE_CHECKING, List, Sequence
 from ...extras.packages import is_pillow_available
 if is_pillow_available():
    from PIL import Image
 if TYPE_CHECKING:
    from numpy.typing import NDArray
    from PIL.Image import Image as ImageObject
    from transformers import ProcessorMixin
    from transformers.image_processing_utils import BaseImageProcessor
 def search_for_fit(numbers: Sequence[int], capacity: int) -> int:
    r"""
    Finds the index of largest number that fits into the knapsack with the given capacity.
    """
    index = bisect.bisect(numbers, capacity)
    return -1 if index == 0 else (index - 1)
 def greedy_knapsack(numbers: List[int], capacity: int) -> List[List[int]]:
    r"""
    An efficient greedy algorithm with binary search for the knapsack problem.
    """
    numbers.sort()  # sort numbers in ascending order for binary search
    knapsacks = []
    while numbers:
        current_knapsack = []
        remaining_capacity = capacity
        while True:
            index = search_for_fit(numbers, remaining_capacity)
            if index == -1:
                break  # no more numbers fit in this knapsack
            remaining_capacity -= numbers[index]  # update the remaining capacity
            current_knapsack.append(numbers.pop(index))  # add the number to knapsack
        knapsacks.append(current_knapsack)
    return knapsacks
 def get_pixel_values(images: Sequence["ImageObject"], processor: "ProcessorMixin") -> "NDArray":
    r"""
    Processes visual inputs. (currently only supports a single image)
    """
    image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
    image = images[0] if len(images) != 0 else Image.new("RGB", (100, 100), (255, 255, 255))
    return image_processor(image, return_tensors="pt")["pixel_values"][0]  # shape (C, H, W)
 def get_paligemma_token_type_ids(input_len: int, processor: "ProcessorMixin") -> List[int]:
    r"""
    Gets paligemma token type ids for computing loss.
    """
    image_seq_length = getattr(processor, "image_seq_length")
    return [0] * image_seq_length + [1] * (input_len - image_seq_length)
--- a/src/llamafactory/data/processors/supervised.py
+++ b/src/llamafactory/data/processors/supervised.py
@@ -0,0 +1,169 @@
 from collections import defaultdict
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
 from ...extras.constants import IGNORE_INDEX
 from ...extras.logging import get_logger
 from .processor_utils import get_paligemma_token_type_ids, get_pixel_values, greedy_knapsack
 if TYPE_CHECKING:
    from transformers import ProcessorMixin
    from transformers.tokenization_utils import PreTrainedTokenizer
    from ...hparams import DataArguments
    from ..template import Template
 logger = get_logger(__name__)
 def _encode_supervised_example(
    prompt: Sequence[Dict[str, str]],
    response: Sequence[Dict[str, str]],
    system: Optional[str],
    tools: Optional[str],
    template: "Template",
    tokenizer: "PreTrainedTokenizer",
    processor: Optional["ProcessorMixin"],
    data_args: "DataArguments",
 ) -> Tuple[List[int], List[int]]:
    if processor is not None and not hasattr(processor, "image_seq_length"):  # llava-like models
        prompt[0]["content"] = template.image_token + prompt[0]["content"]
    messages = prompt + response
    input_ids, labels = [], []
    if processor is not None and hasattr(processor, "image_seq_length"):  # paligemma models
        image_token_id = tokenizer.convert_tokens_to_ids(template.image_token)
        input_ids += [image_token_id] * getattr(processor, "image_seq_length")
        labels += [IGNORE_INDEX] * getattr(processor, "image_seq_length")
    encoded_pairs = template.encode_multiturn(
        tokenizer, messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
    )
    for turn_idx, (source_ids, target_ids) in enumerate(encoded_pairs):
        if data_args.train_on_prompt:
            source_mask = source_ids
        elif turn_idx != 0 and template.efficient_eos:
            source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
        else:
            source_mask = [IGNORE_INDEX] * len(source_ids)
        input_ids += source_ids + target_ids
        labels += source_mask + target_ids
    if template.efficient_eos:
        input_ids += [tokenizer.eos_token_id]
        labels += [tokenizer.eos_token_id]
    return input_ids, labels
 def preprocess_supervised_dataset(
    examples: Dict[str, List[Any]],
    template: "Template",
    tokenizer: "PreTrainedTokenizer",
    processor: Optional["ProcessorMixin"],
    data_args: "DataArguments",
 ) -> Dict[str, List[List[int]]]:
    # build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
    # for multiturn examples, we only mask the prompt part in each prompt-response pair.
    model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
    if processor is not None:
        model_inputs["pixel_values"] = []
        if hasattr(processor, "image_seq_length"):  # paligemma models
            model_inputs["token_type_ids"] = []
    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]))
            continue
        input_ids, labels = _encode_supervised_example(
            prompt=examples["prompt"][i],
            response=examples["response"][i],
            system=examples["system"][i],
            tools=examples["tools"][i],
            template=template,
            tokenizer=tokenizer,
            processor=processor,
            data_args=data_args,
        )
        model_inputs["input_ids"].append(input_ids)
        model_inputs["attention_mask"].append([1] * len(input_ids))
        model_inputs["labels"].append(labels)
        if processor is not None:
            model_inputs["pixel_values"].append(get_pixel_values(examples["images"][i], processor))
            if hasattr(processor, "image_seq_length"):  # paligemma models
                model_inputs["token_type_ids"].append(get_paligemma_token_type_ids(len(input_ids), processor))
    return model_inputs
 def preprocess_packed_supervised_dataset(
    examples: Dict[str, List[Any]],
    template: "Template",
    tokenizer: "PreTrainedTokenizer",
    data_args: "DataArguments",
 ) -> Dict[str, List[List[int]]]:
    # build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
    # and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>`
    valid_num = 0
    batch_input_ids, batch_labels = [], []
    lengths = []
    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]))
            continue
        input_ids, labels = _encode_supervised_example(
            prompt=examples["prompt"][i],
            response=examples["response"][i],
            system=examples["system"][i],
            tools=examples["tools"][i],
            template=template,
            tokenizer=tokenizer,
            processor=None,
            data_args=data_args,
        )
        length = len(input_ids)
        if length > data_args.cutoff_len:
            logger.warning("Dropped lengthy example with length {} > {}.".format(length, data_args.cutoff_len))
        else:
            lengths.append(length)
            length2indexes[length].append(valid_num)
            batch_input_ids.append(input_ids)
            batch_labels.append(labels)
            valid_num += 1
    model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
    knapsacks = greedy_knapsack(lengths, data_args.cutoff_len)
    for knapsack in knapsacks:
        packed_input_ids, packed_labels = [], []
        for length in knapsack:
            index = length2indexes[length].pop()
            packed_input_ids += batch_input_ids[index]
            packed_labels += batch_labels[index]
        if len(packed_input_ids) < data_args.cutoff_len:
            pad_length = data_args.cutoff_len - len(packed_input_ids)
            packed_input_ids += [tokenizer.pad_token_id] * pad_length
            packed_labels += [IGNORE_INDEX] * pad_length
        if len(packed_input_ids) != data_args.cutoff_len:
            raise ValueError("The length of packed example should be identical to the cutoff length.")
        model_inputs["input_ids"].append(packed_input_ids)
        model_inputs["attention_mask"].append([1] * data_args.cutoff_len)
        model_inputs["labels"].append(packed_labels)
    return model_inputs
 def print_supervised_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
    valid_labels = list(filter(lambda x: x != IGNORE_INDEX, example["labels"]))
    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)))
--- a/Show More
+++ b/Show More