parallel_tool

24534501 · mashun1 · c4ba4563 · 24534501 · 24534501 · c4ba4563
Commit 24534501 authored May 21, 2025 by mashun1
20 changed files
--- a/.gitignore
+++ b/.gitignore
@@ -176,3 +176,4 @@ output/
 wandb/
 swanlog/
 generated_predictions.jsonl
+predictions_score.json
--- a/README_zh.md
+++ b/README_zh.md
@@ -5,7 +5,7 @@
 [![GitHub contributors](https://img.shields.io/github/contributors/hiyouga/LLaMA-Factory?color=orange)](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors)
 [![GitHub workflow](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml/badge.svg)](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml)
 [![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
-[![Citation](https://img.shields.io/badge/citation-429-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
+[![Citation](https://img.shields.io/badge/citation-476-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
 [![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
 [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
@@ -18,15 +18,27 @@
 [![Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
 [![SageMaker](https://img.shields.io/badge/SageMaker-Open%20in%20AWS-blue)](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)
-<h3 align="center">
+### 获得[亚马逊](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)、[英伟达](https://developer.nvidia.cn/rtx/ai-toolkit)、[阿里云](https://help.aliyun.com/zh/pai/use-cases/fine-tune-a-llama-3-model-with-llama-factory)等的应用。
-    使用零代码<a href="#快速开始">命令行</a>与 <a href="#llama-board-可视化微调由-gradio-驱动">Web UI</a> 轻松微调百余种大模型
-</h3>
-<p align="center">
-    <picture>
-        <img alt="Github trend" src="https://trendshift.io/api/badge/repositories/4535">
-    </picture>
-</p>
+<div align="center" markdown="1">
+### 赞助商 ❤️
+<a href="https://warp.dev/llama-factory">
+    <img alt="Warp sponsorship" width="400" src="https://github.com/user-attachments/assets/ab8dd143-b0fd-4904-bdc5-dd7ecac94eae">
+</a>
+#### [Warp，面向开发者的智能终端](https://warp.dev/llama-factory)
+[适用于 MacOS、Linux 和 Windows](https://warp.dev/llama-factory)
+----
+### 使用零代码[命令行](#快速开始)与 [Web UI](#llama-board-可视化微调由-gradio-驱动) 轻松微调百余种大模型
+![GitHub Trend](https://trendshift.io/api/badge/repositories/4535)
+</div>
 👋 加入我们的[微信群](assets/wechat.jpg)或 [NPU 用户群](assets/wechat_npu.jpg)。
@@ -43,9 +55,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 - **框架文档（昇腾 NPU）**：https://ascend.github.io/docs/sources/llamafactory/
 - **Colab（免费）**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
 - **本地机器**：请见[如何使用](#如何使用)
- **PAI-DSW（免费试用）**：[Llama3 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) | [Qwen2-VL 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl) | [DeepSeek-R1-Distill 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b)
+- **PAI-DSW（免费试用）**：https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
- **Amazon SageMaker**：[博客](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)
- **Easy Dataset**：[数据蒸馏微调](https://buaa-act.feishu.cn/wiki/KY9xwTGs1iqHrRkjXBwcZP9WnL9)
 > [!NOTE]
 > 除上述链接以外的其他网站均为未经许可的第三方网站，请小心甄别。
@@ -53,7 +63,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 ## 目录
 - [项目特色](#项目特色)
- [性能指标](#性能指标)
+- [官方博客](#官方博客)
 - [更新日志](#更新日志)
 - [模型](#模型)
 - [训练方法](#训练方法)
@@ -93,18 +103,17 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | Day 0        | Qwen3 / Qwen2.5-VL / Gemma 3 / InternLM 3 / MiniCPM-o-2.6    |
 | Day 1        | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4       |
-## 性能指标
+## 官方博客
-与 ChatGLM 官方的 [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning) 微调相比，LLaMA Factory 的 LoRA 微调提供了 **3.7 倍**的加速比，同时在广告文案生成任务上取得了更高的 Rouge 分数。结合 4 比特量化技术，LLaMA Factory 的 QLoRA 微调进一步降低了 GPU 显存消耗。
+- [通过亚马逊 SageMaker HyperPod 上的 LLaMA-Factory 增强多模态模型银行文档的视觉信息提取](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/)（英文）
+- [Easy Dataset × LLaMA Factory: 让大模型高效学习领域知识](https://buaa-act.feishu.cn/wiki/KY9xwTGs1iqHrRkjXBwcZP9WnL9)（中文）
+- [LLaMA Factory：微调 DeepSeek-R1-Distill-Qwen-7B 模型实现新闻标题分类器](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b)（中文）
-![benchmark](assets/benchmark.svg)
+<details><summary>全部博客</summary>
-<details><summary>变量定义</summary>
+- [基于 Amazon SageMaker 和 LLaMA-Factory 打造一站式无代码模型微调部署平台 Model Hub](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)（中文）
+- [LLaMA Factory 多模态微调实践：微调 Qwen2-VL 构建文旅大模型](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)（中文）
- **Training Speed**: 训练阶段每秒处理的样本数量。（批处理大小=4，截断长度=1024）
+- [LLaMA Factory：微调LLaMA3模型实现角色扮演](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)（中文）
- **Rouge Score**: [广告文案生成](https://aclanthology.org/D19-1321.pdf)任务验证集上的 Rouge-2 分数。（批处理大小=4，截断长度=1024）
- **GPU Memory**: 4 比特量化训练的 GPU 显存峰值。（批处理大小=1，截断长度=1024）
- 我们在 ChatGLM 的 P-Tuning 中采用 `pre_seq_len=128`，在 LLaMA Factory 的 LoRA 微调中采用 `lora_rank=32`。
 </details>
@@ -236,6 +245,9 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 </details>
+> [!TIP]
+> 如果您无法使用最新的功能，请尝试重新拉取代码并再次安装 LLaMA-Factory。
 ## 模型
 | 模型名                                                             | 参数量                            | Template            |
@@ -246,17 +258,17 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | [Command R](https://huggingface.co/CohereForAI)                   | 35B/104B                         | cohere              |
 | [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai)         | 7B/16B/67B/236B                  | deepseek            |
 | [DeepSeek 2.5/3](https://huggingface.co/deepseek-ai)              | 236B/671B                        | deepseek3           |
-| [DeepSeek R1 (Distill)](https://huggingface.co/deepseek-ai)       | 1.5B/7B/8B/14B/32B/70B/671B      | deepseek3           |
+| [DeepSeek R1 (Distill)](https://huggingface.co/deepseek-ai)       | 1.5B/7B/8B/14B/32B/70B/671B      | deepseekr1          |
 | [Falcon](https://huggingface.co/tiiuae)                           | 7B/11B/40B/180B                  | falcon              |
 | [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google)          | 2B/7B/9B/27B                     | gemma               |
 | [Gemma 3](https://huggingface.co/google)                          | 1B/4B/12B/27B                    | gemma3/gemma (1B)   |
-| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM)           | 9B/32B                           | glm4                |
+| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM)           | 9B/32B                           | glm4/glmz1          |
 | [GPT-2](https://huggingface.co/openai-community)                  | 0.1B/0.4B/0.8B/1.5B              | -                   |
 | [Granite 3.0-3.3](https://huggingface.co/ibm-granite)             | 1B/2B/3B/8B                      | granite3            |
 | [Hunyuan](https://huggingface.co/tencent/)                        | 7B                               | hunyuan             |
 | [Index](https://huggingface.co/IndexTeam)                         | 1.9B                             | index               |
 | [InternLM 2-3](https://huggingface.co/internlm)                   | 7B/8B/20B                        | intern2             |
-| [InternVL 2.5-3](https://huggingface.co/OpenGVLab)\*              | 1B/2B/8B/14B/38B/78B             | intern_vl           |
+| [InternVL 2.5-3](https://huggingface.co/OpenGVLab)                | 1B/2B/8B/14B/38B/78B             | intern_vl           |
 | [Kimi-VL](https://huggingface.co/moonshotai)                      | 16B                              | kimi_vl             |
 | [Llama](https://github.com/facebookresearch/llama)                | 7B/13B/33B/65B                   | -                   |
 | [Llama 2](https://huggingface.co/meta-llama)                      | 7B/13B/70B                       | llama2              |
@@ -266,6 +278,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | [LLaVA-1.5](https://huggingface.co/llava-hf)                      | 7B/13B                           | llava               |
 | [LLaVA-NeXT](https://huggingface.co/llava-hf)                     | 7B/8B/13B/34B/72B/110B           | llava_next          |
 | [LLaVA-NeXT-Video](https://huggingface.co/llava-hf)               | 7B/34B                           | llava_next_video    |
+| [MiMo](https://huggingface.co/XiaomiMiMo)                         | 7B                               | mimo                |
 | [MiniCPM](https://huggingface.co/openbmb)                         | 1B/2B/4B                         | cpm/cpm3            |
 | [MiniCPM-o-2.6/MiniCPM-V-2.6](https://huggingface.co/openbmb)     | 8B                               | minicpm_o/minicpm_v |
 | [Ministral/Mistral-Nemo](https://huggingface.co/mistralai)        | 8B/12B                           | ministral           |
@@ -281,8 +294,9 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | [Qwen (1-2.5) (Code/Math/MoE/QwQ)](https://huggingface.co/Qwen)   | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen                |
 | [Qwen3 (MoE)](https://huggingface.co/Qwen)                        | 0.6B/1.7B/4B/8B/14B/32B/235B     | qwen3               |
 | [Qwen2-Audio](https://huggingface.co/Qwen)                        | 7B                               | qwen2_audio         |
-| [Qwen2.5-Omni](https://huggingface.co/Qwen)\*\*                   | 7B                               | qwen2_omni          |
+| [Qwen2.5-Omni](https://huggingface.co/Qwen)                       | 3B/7B                            | qwen2_omni          |
 | [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen)            | 2B/3B/7B/32B/72B                 | qwen2_vl            |
+| [Seed Coder](https://huggingface.co/ByteDance-Seed)               | 8B                               | seed_coder          |
 | [Skywork o1](https://huggingface.co/Skywork)                      | 8B                               | skywork_o1          |
 | [StarCoder 2](https://huggingface.co/bigcode)                     | 3B/7B/15B                        | -                   |
 | [TeleChat2](https://huggingface.co/Tele-AI)                       | 3B/7B/35B/115B                   | telechat2           |
@@ -405,6 +419,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 - [COIG-P (en&zh)](https://huggingface.co/datasets/m-a-p/COIG-P)
 - [RLHF-V (en)](https://huggingface.co/datasets/openbmb/RLHF-V-Dataset)
 - [VLFeedback (en)](https://huggingface.co/datasets/Zhihui/VLFeedback)
+- [RLAIF-V (en)](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset)
 - [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)
@@ -426,6 +441,7 @@ huggingface-cli login
 | ------------ | ------- | --------- |
 | python       | 3.9     | 3.10      |
 | torch        | 2.0.0   | 2.6.0     |
+| torchvision  | 0.15.0  | 0.21.0    |
 | transformers | 4.45.0  | 4.50.0    |
 | datasets     | 2.16.0  | 3.2.0     |
 | accelerate   | 0.34.0  | 1.2.1     |
@@ -463,13 +479,13 @@ huggingface-cli login
 ```bash
 git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
 cd LLaMA-Factory
-pip install -e ".[torch,metrics]"
+pip install -e ".[torch,metrics]" --no-build-isolation
 ```
-可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、sglang、galore、apollo、badam、adam-mini、qwen、minicpm_v、modelscope、openmind、swanlab、quality
+可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、aqlm、vllm、sglang、galore、apollo、badam、adam-mini、qwen、minicpm_v、modelscope、openmind、swanlab、quality
 > [!TIP]
-> 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
+> 遇到包冲突时，可使用 `pip install -e . --no-deps --no-build-isolation` 解决。
 <details><summary>使用 <b>uv</b> 构建虚拟环境</summary>
@@ -487,9 +503,22 @@ uv run --prerelease=allow llamafactory-cli train examples/train_lora/llama3_lora
 </details>
 <details><summary>Windows 用户指南</summary>
+#### 安装 PyTorch
+Windows 平台需要额外手动安装 GPU 版本的 PyTorch 依赖包，您可以参考[官方网站](https://pytorch.org/get-started/locally/)和以下命令安装并测试 PyTorch 是否正确安装。
+```bash
+pip uninstall torch torchvision torchaudio
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
+python -c "import torch; print(torch.cuda.is_available())"
+```
+如果看到 `True` 则说明安装成功。
+若遇到类似 `Can't pickle local object` 的报错，请设置 `dataloader_num_workers: 0`。
 #### 安装 BitsAndBytes
 如果要在 Windows 平台上开启量化 LoRA（QLoRA），需要安装预编译的 `bitsandbytes` 库, 支持 CUDA 11.1 到 12.2, 请根据您的 CUDA 版本情况选择适合的[发布版本](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels)。
@@ -579,7 +608,7 @@ pip install .
 > [!NOTE]
 > 使用自定义数据集时，请更新 `data/dataset_info.json` 文件。
-您也可以使用 **[Easy Dataset](https://github.com/ConardLi/easy-dataset)** 构建用于微调的合成数据。
+您也可以使用 **[Easy Dataset](https://github.com/ConardLi/easy-dataset)** 或 **[GraphGen](https://github.com/open-sciencelab/GraphGen)** 构建用于微调的合成数据。
 ### 快速开始
@@ -890,6 +919,7 @@ swanlab_run_name: test_run # 可选
 1. **[RAG-Retrieval](https://github.com/NLPJCL/RAG-Retrieval)**：一个全链路 RAG 检索模型微调、推理和蒸馏代码库。[[blog]](https://zhuanlan.zhihu.com/p/987727357)
 1. **[360-LLaMA-Factory](https://github.com/Qihoo360/360-LLaMA-Factory)**：一个魔改后的代码库，通过 Ring Attention 支持长序列的 SFT 和 DPO 训练。
 1. **[Sky-T1](https://novasky-ai.github.io/posts/sky-t1/)**：由 NovaSky AI 微调的低成本类 o1 长推理模型。
+1. **[WeClone](https://github.com/xming521/WeClone)**：从聊天记录创造数字分身的一站式解决方案。
 </details>

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--- a/assets/wechat.jpg
+++ b/assets/wechat.jpg
--- a/assets/wechat_npu.jpg
+++ b/assets/wechat_npu.jpg
--- a/data/README.md
+++ b/data/README.md
 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.
+The `dataset_info.json` file should be put in the `dataset_dir` directory. You can change `dataset_dir` to use another directory. The default value is `./data`.
 Currently we support datasets in **alpaca** and **sharegpt** format.
 ```json
@@ -48,7 +50,9 @@ Currently we support datasets in **alpaca** and **sharegpt** 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.
+In supervised fine-tuning, the `instruction` column will be concatenated with the `input` column and used as the user prompt, then the user prompt would be `instruction\ninput`. The `output` column represents the model response.
+For reasoning models, if the dataset contains chain-of-thought (CoT), the CoT needs to be placed in the model responses, such as `<think>cot</think>output`.
 The `system` column will be used as the system prompt if specified.
@@ -57,13 +61,13 @@ The `history` column is a list consisting of string tuples representing prompt-r
 ```json
 [
  {
-    "instruction": "human instruction (required)",
+    "instruction": "user instruction (required)",
-    "input": "human input (optional)",
+    "input": "user input (optional)",
    "output": "model response (required)",
    "system": "system prompt (optional)",
    "history": [
-      ["human instruction in the first round (optional)", "model response in the first round (optional)"],
+      ["user instruction in the first round (optional)", "model response in the first round (optional)"],
-      ["human instruction in the second round (optional)", "model response in the second round (optional)"]
+      ["user instruction in the second round (optional)", "model response in the second round (optional)"]
    ]
  }
 ]
@@ -84,6 +88,11 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
 }
 ```
+> [!TIP]  
+> If the model has reasoning capabilities but the dataset does not contain chain-of-thought (CoT), LLaMA-Factory will automatically add empty CoT to the data. When `enable_thinking` is `True` (slow thinking), the empty CoT will be added to the model responses and loss computation will be considered; otherwise (fast thinking), it will be added to the user prompts and loss computation will be ignored. Please keep the `enable_thinking` parameter consistent during training and inference.
+>
+> If you want to train data containing CoT with slow thinking and data without CoT with fast thinking, you can set `enable_thinking` to `None`. However, this feature is relatively complicated and should be used with caution.
 ### Pre-training Dataset
 - [Example dataset](c4_demo.jsonl)
@@ -117,8 +126,8 @@ It requires a better response in `chosen` column and a worse response in `reject
 ```json
 [
  {
-    "instruction": "human instruction (required)",
+    "instruction": "user instruction (required)",
-    "input": "human input (optional)",
+    "input": "user input (optional)",
    "chosen": "chosen answer (required)",
    "rejected": "rejected answer (required)"
  }
@@ -172,7 +181,7 @@ Note that the human and observation should appear in odd positions, while gpt an
    "conversations": [
      {
        "from": "human",
-        "value": "human instruction"
+        "value": "user instruction"
      },
      {
        "from": "function_call",
@@ -223,7 +232,7 @@ Preference datasets in sharegpt format also require a better message in `chosen`
    "conversations": [
      {
        "from": "human",
-        "value": "human instruction"
+        "value": "user instruction"
      },
      {
        "from": "gpt",
@@ -231,7 +240,7 @@ Preference datasets in sharegpt format also require a better message in `chosen`
      },
      {
        "from": "human",
-        "value": "human instruction"
+        "value": "user instruction"
      }
    ],
    "chosen": {
@@ -273,7 +282,7 @@ KTO datasets require a extra `kto_tag` column containing the boolean human feedb
    "conversations": [
      {
        "from": "human",
-        "value": "human instruction"
+        "value": "user instruction"
      },
      {
        "from": "gpt",
@@ -312,7 +321,7 @@ The number of images should be identical to the `<image>` tokens in the conversa
    "conversations": [
      {
        "from": "human",
-        "value": "<image>human instruction"
+        "value": "<image>user instruction"
      },
      {
        "from": "gpt",
@@ -353,7 +362,7 @@ The number of videos should be identical to the `<video>` tokens in the conversa
    "conversations": [
      {
        "from": "human",
-        "value": "<video>human instruction"
+        "value": "<video>user instruction"
      },
      {
        "from": "gpt",
@@ -394,7 +403,7 @@ The number of audios should be identical to the `<audio>` tokens in the conversa
    "conversations": [
      {
        "from": "human",
-        "value": "<audio>human instruction"
+        "value": "<audio>user instruction"
      },
      {
        "from": "gpt",
@@ -435,7 +444,7 @@ The openai format is simply a special case of the sharegpt format, where the fir
      },
      {
        "role": "user",
-        "content": "human instruction"
+        "content": "user instruction"
      },
      {
        "role": "assistant",

--- a/data/README_zh.md
+++ b/data/README_zh.md
 [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。
+其中 `dataset_info.json` 文件应放置在 `dataset_dir` 目录下。您可以通过修改 `dataset_dir` 参数来使用其他目录。默认值为 `./data`。
 目前我们支持 **alpaca** 格式和 **sharegpt** 格式的数据集。
 ```json
@@ -47,7 +49,9 @@
 - [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。
+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提示词，即提示词为 `instruction\ninput`。而 `output` 列对应的内容为模型回答。
+对于推理类模型的微调，如果数据集包含思维链，则需要把思维链放在模型回答中，例如 `<think>cot</think>output`。
 如果指定，`system` 列对应的内容将被作为系统提示词。
@@ -56,8 +60,8 @@
 ```json
 [
  {
-    "instruction": "人类指令（必填）",
+    "instruction": "用户指令（必填）",
-    "input": "人类输入（选填）",
+    "input": "用户输入（选填）",
    "output": "模型回答（必填）",
    "system": "系统提示词（选填）",
    "history": [
@@ -83,6 +87,11 @@
 }
 ```
+> [!TIP]
+> 如果模型本身具备推理能力，而数据集不包含思维链，LLaMA-Factory 会自动为数据添加空思维链。当 `enable_thinking` 为 `True` 时（慢思考），空思维链会添加到模型回答中并且计算损失，否则会添加到用户指令中并且不计算损失（快思考）。请在训练和推理时保持 `enable_thinking` 参数一致。
+>
+> 如果您希望训练包含思维链的数据时使用慢思考，训练不包含思维链的数据时使用快思考，可以设置 `enable_thinking` 为 `None`。但该功能较为复杂，请谨慎使用。
 ### 预训练数据集
 - [样例数据集](c4_demo.jsonl)
@@ -116,8 +125,8 @@
 ```json
 [
  {
-    "instruction": "人类指令（必填）",
+    "instruction": "用户指令（必填）",
-    "input": "人类输入（选填）",
+    "input": "用户输入（选填）",
    "chosen": "优质回答（必填）",
    "rejected": "劣质回答（必填）"
  }
@@ -171,7 +180,7 @@ KTO 数据集需要提供额外的 `kto_tag` 列。详情请参阅 [sharegpt](#s
    "conversations": [
      {
        "from": "human",
-        "value": "人类指令"
+        "value": "用户指令"
      },
      {
        "from": "function_call",
@@ -222,7 +231,7 @@ Sharegpt 格式的偏好数据集同样需要在 `chosen` 列中提供更优的
    "conversations": [
      {
        "from": "human",
-        "value": "人类指令"
+        "value": "用户指令"
      },
      {
        "from": "gpt",
@@ -230,7 +239,7 @@ Sharegpt 格式的偏好数据集同样需要在 `chosen` 列中提供更优的
      },
      {
        "from": "human",
-        "value": "人类指令"
+        "value": "用户指令"
      }
    ],
    "chosen": {
@@ -272,7 +281,7 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
    "conversations": [
      {
        "from": "human",
-        "value": "人类指令"
+        "value": "用户指令"
      },
      {
        "from": "gpt",
@@ -311,7 +320,7 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
    "conversations": [
      {
        "from": "human",
-        "value": "<image>人类指令"
+        "value": "<image><image>用户指令"
      },
      {
        "from": "gpt",
@@ -319,6 +328,7 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
      }
    ],
    "images": [
+      "图像路径（必填）",
      "图像路径（必填）"
    ]
  }
@@ -352,7 +362,7 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
    "conversations": [
      {
        "from": "human",
-        "value": "<video>人类指令"
+        "value": "<video><video>用户指令"
      },
      {
        "from": "gpt",
@@ -360,6 +370,7 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
      }
    ],
    "videos": [
+      "视频路径（必填）",
      "视频路径（必填）"
    ]
  }
@@ -393,7 +404,7 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
    "conversations": [
      {
        "from": "human",
-        "value": "<audio>人类指令"
+        "value": "<audio><audio>用户指令"
      },
      {
        "from": "gpt",
@@ -401,6 +412,7 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
      }
    ],
    "audios": [
+      "音频路径（必填）",
      "音频路径（必填）"
    ]
  }
@@ -435,7 +447,7 @@ OpenAI 格式仅仅是 sharegpt 格式的一种特殊情况，其中第一条消
      },
      {
        "role": "user",
-        "content": "人类指令"
+        "content": "用户指令"
      },
      {
        "role": "assistant",

--- a/data/dataset_info.json
+++ b/data/dataset_info.json
@@ -559,6 +559,16 @@
      "images": "images"
    }
  },
+  "rlaif_v": {
+    "hf_hub_url": "openbmb/RLAIF-V-Dataset",
+    "ranking": true,
+    "columns": {
+      "prompt": "question",
+      "chosen": "chosen",
+      "rejected": "rejected",
+      "images": "image"
+    }
+  },
  "orca_pairs": {
    "hf_hub_url": "Intel/orca_dpo_pairs",
    "ranking": true,

--- a/examples/README.md
+++ b/examples/README.md
@@ -52,7 +52,7 @@ llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
 #### Multimodal Supervised Fine-Tuning
 ```bash
-llamafactory-cli train examples/train_lora/qwen2vl_lora_sft.yaml
+llamafactory-cli train examples/train_lora/qwen2_5vl_lora_sft.yaml
 ```
 #### DPO/ORPO/SimPO Training
@@ -64,7 +64,7 @@ llamafactory-cli train examples/train_lora/llama3_lora_dpo.yaml
 #### Multimodal DPO/ORPO/SimPO Training
 ```bash
-llamafactory-cli train examples/train_lora/qwen2vl_lora_dpo.yaml
+llamafactory-cli train examples/train_lora/qwen2_5vl_lora_dpo.yaml
 ```
 #### Reward Modeling
@@ -168,7 +168,7 @@ FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500
 #### Multimodal Supervised Fine-Tuning
 ```bash
-FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/qwen2vl_full_sft.yaml
+FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/qwen2_5vl_full_sft.yaml
 ```
 ### Merging LoRA Adapters and Quantization
@@ -195,10 +195,11 @@ llamafactory-cli export examples/merge_lora/llama3_full_sft.yaml
 ### Inferring LoRA Fine-Tuned Models
-#### Batch Generation using vLLM Tensor Parallel
+#### Evaluation using vLLM's Multi-GPU Inference
 ```
-python scripts/vllm_infer.py --model_name_or_path path_to_merged_model --dataset alpaca_en_demo
+python scripts/vllm_infer.py --model_name_or_path meta-llama/Meta-Llama-3-8B-Instruct --template llama3 --dataset alpaca_en_demo
+python scripts/eval_bleu_rouge.py generated_predictions.jsonl
 ```
 #### Use CLI ChatBox
@@ -281,9 +282,3 @@ llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
 ```bash
 bash examples/extras/fsdp_qlora/train.sh
 ```
-#### Computing BLEU and ROUGE Scores
-```bash
-llamafactory-cli train examples/extras/nlg_eval/llama3_lora_predict.yaml
-```
--- a/examples/README_zh.md
+++ b/examples/README_zh.md
@@ -52,7 +52,7 @@ llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
 #### 多模态指令监督微调
 ```bash
-llamafactory-cli train examples/train_lora/qwen2vl_lora_sft.yaml
+llamafactory-cli train examples/train_lora/qwen2_5vl_lora_sft.yaml
 ```
 #### DPO/ORPO/SimPO 训练
@@ -64,7 +64,7 @@ llamafactory-cli train examples/train_lora/llama3_lora_dpo.yaml
 #### 多模态 DPO/ORPO/SimPO 训练
 ```bash
-llamafactory-cli train examples/train_lora/qwen2vl_lora_dpo.yaml
+llamafactory-cli train examples/train_lora/qwen2_5vl_lora_dpo.yaml
 ```
 #### 奖励模型训练
@@ -168,7 +168,7 @@ FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500
 #### 多模态指令监督微调
 ```bash
-FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/qwen2vl_full_sft.yaml
+FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/qwen2_5vl_full_sft.yaml
 ```
 ### 合并 LoRA 适配器与模型量化
@@ -195,10 +195,11 @@ llamafactory-cli export examples/merge_lora/llama3_full_sft.yaml
 ### 推理 LoRA 模型
-#### 使用 vLLM+TP 批量推理
+#### 使用 vLLM 多卡推理评估
 ```
-python scripts/vllm_infer.py --model_name_or_path path_to_merged_model --dataset alpaca_en_demo
+python scripts/vllm_infer.py --model_name_or_path meta-llama/Meta-Llama-3-8B-Instruct --template llama3 --dataset alpaca_en_demo
+python scripts/eval_bleu_rouge.py generated_predictions.jsonl
 ```
 #### 使用命令行对话框
@@ -281,9 +282,3 @@ llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
 ```bash
 bash examples/extras/fsdp_qlora/train.sh
 ```
-#### 计算 BLEU 和 ROUGE 分数
-```bash
-llamafactory-cli train examples/extras/nlg_eval/llama3_lora_predict.yaml
-```
--- a/examples/inference/qwen2_vl.yaml
+++ b/examples/inference/qwen2_vl.yaml
--- a/examples/merge_lora/qwen2vl_lora_sft.yaml
+++ b/examples/merge_lora/qwen2vl_lora_sft.yaml
@@ -2,12 +2,12 @@
 ### model
 model_name_or_path: Qwen/Qwen2.5-VL-7B-Instruct
-adapter_name_or_path: saves/qwen2_vl-7b/lora/sft
+adapter_name_or_path: saves/qwen2_5vl-7b/lora/sft
 template: qwen2_vl
 trust_remote_code: true
 ### export
-export_dir: output/qwen2_vl_lora_sft
+export_dir: output/qwen2_5vl_lora_sft
 export_size: 5
 export_device: cpu  # choices: [cpu, auto]
 export_legacy_format: false
--- a/examples/train_full/qwen2vl_full_sft.yaml
+++ b/examples/train_full/qwen2vl_full_sft.yaml
 ### model
-model_name_or_path: Qwen/Qwen2-VL-7B-Instruct
+model_name_or_path: Qwen/Qwen2.5-VL-7B-Instruct
 image_max_pixels: 262144
 video_max_pixels: 16384
 trust_remote_code: true
@@ -23,7 +23,7 @@ preprocessing_num_workers: 16
 dataloader_num_workers: 4
 ### output
-output_dir: saves/qwen2_vl-7b/full/sft
+output_dir: saves/qwen2_5vl-7b/full/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true

--- a/examples/train_lora/qwen2vl_lora_dpo.yaml
+++ b/examples/train_lora/qwen2vl_lora_dpo.yaml
@@ -23,7 +23,7 @@ preprocessing_num_workers: 16
 dataloader_num_workers: 4
 ### output
-output_dir: saves/qwen2_vl-7b/lora/dpo
+output_dir: saves/qwen2_5vl-7b/lora/dpo
 logging_steps: 10
 save_steps: 500
 plot_loss: true

--- a/examples/train_lora/qwen2vl_lora_sft.yaml
+++ b/examples/train_lora/qwen2vl_lora_sft.yaml
@@ -21,7 +21,7 @@ preprocessing_num_workers: 16
 dataloader_num_workers: 4
 ### output
-output_dir: saves/qwen2_vl-7b/lora/sft
+output_dir: saves/qwen2_5vl-7b/lora/sft
 logging_steps: 10
 save_steps: 500
 plot_loss: true

--- a/pyproject.toml
+++ b/pyproject.toml
@@ -88,6 +88,14 @@ conflicts = [
        { extra = "torch-npu" },
        { extra = "vllm" },
    ],
+    [
+        { extra = "torch-npu" },
+        { extra = "sglang" },
+    ],
+    [
+        { extra = "vllm" },
+        { extra = "sglang" },
+    ],
    [
        { extra = "sglang" },
        { extra = "minicpm_v" },

--- a/requirements.txt
+++ b/requirements.txt
-transformers>=4.45.0,<=4.51.3,!=4.46.*,!=4.47.*,!=4.48.0
+transformers>=4.45.0,<=4.52.1,!=4.46.*,!=4.47.*,!=4.48.0,!=4.52.0
-datasets>=2.16.0,<=3.5.0
+datasets>=2.16.0,<=3.6.0
-accelerate>=0.34.0,<=1.6.0
+accelerate>=0.34.0,<=1.7.0
-peft>=0.14.0,<=0.15.1
+peft>=0.14.0,<=0.15.2
 trl>=0.8.6,<=0.9.6
 tokenizers>=0.19.0,<=0.21.1
-gradio>=4.38.0,<=5.25.0
+gradio>=4.38.0,<=5.29.1
 scipy
 einops
 sentencepiece

--- a/scripts/vllm_infer.py
+++ b/scripts/vllm_infer.py
@@ -12,10 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import gc
 import json
 from typing import Optional
 import fire
+from tqdm import tqdm
 from transformers import Seq2SeqTrainingArguments
 from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
@@ -47,10 +49,15 @@ def vllm_infer(
    max_new_tokens: int = 1024,
    repetition_penalty: float = 1.0,
    skip_special_tokens: bool = True,
+    default_system: Optional[str] = None,
+    enable_thinking: bool = True,
    seed: Optional[int] = None,
    pipeline_parallel_size: int = 1,
    image_max_pixels: int = 768 * 768,
    image_min_pixels: int = 32 * 32,
+    video_fps: float = 2.0,
+    video_maxlen: int = 128,
+    batch_size: int = 1024,
 ):
    r"""Perform batch generation using vLLM engine, which supports tensor parallelism.
@@ -69,6 +76,8 @@ def vllm_infer(
            cutoff_len=cutoff_len,
            max_samples=max_samples,
            preprocessing_num_workers=16,
+            default_system=default_system,
+            enable_thinking=enable_thinking,
            vllm_config=vllm_config,
            temperature=temperature,
            top_p=top_p,
@@ -83,38 +92,28 @@ def vllm_infer(
    tokenizer = tokenizer_module["tokenizer"]
    template_obj = get_template_and_fix_tokenizer(tokenizer, data_args)
    template_obj.mm_plugin.expand_mm_tokens = False  # for vllm generate
-    dataset_module = get_dataset(template_obj, model_args, data_args, training_args, "ppo", **tokenizer_module)
-    inputs, prompts, labels = [], [], []
+    engine_args = {
-    for sample in dataset_module["train_dataset"]:
+        "model": model_args.model_name_or_path,
-        if sample["images"]:
+        "trust_remote_code": True,
-            multi_modal_data = {
+        "dtype": model_args.infer_dtype,
-                "image": template_obj.mm_plugin._regularize_images(
+        "max_model_len": cutoff_len + max_new_tokens,
-                    sample["images"], image_max_pixels=image_max_pixels, image_min_pixels=image_min_pixels
+        "tensor_parallel_size": (get_device_count() // pipeline_parallel_size) or 1,
-                )["images"]
+        "pipeline_parallel_size": pipeline_parallel_size,
-            }
+        "disable_log_stats": True,
-        elif sample["videos"]:
+        "enable_lora": model_args.adapter_name_or_path is not None,
-            multi_modal_data = {
+    }
-                "video": template_obj.mm_plugin._regularize_videos(
+    if template_obj.mm_plugin.__class__.__name__ != "BasePlugin":
-                    sample["videos"], image_max_pixels=image_max_pixels, image_min_pixels=image_min_pixels
+        engine_args["limit_mm_per_prompt"] = {"image": 4, "video": 2, "audio": 2}
-                )["videos"]
-            }
+    if isinstance(model_args.vllm_config, dict):
-        elif sample["audios"]:
+        engine_args.update(model_args.vllm_config)
-            audio_data = template_obj.mm_plugin._regularize_audios(
-                sample["audios"],
+    llm = LLM(**engine_args)
-                sampling_rate=16000,
-            )
+    # load datasets
-            multi_modal_data = {"audio": zip(audio_data["audios"], audio_data["sampling_rates"])}
+    dataset_module = get_dataset(template_obj, model_args, data_args, training_args, "ppo", **tokenizer_module)
-        else:
+    train_dataset = dataset_module["train_dataset"]
-            multi_modal_data = None
-        inputs.append({"prompt_token_ids": sample["input_ids"], "multi_modal_data": multi_modal_data})
-        prompts.append(tokenizer.decode(sample["input_ids"], skip_special_tokens=skip_special_tokens))
-        labels.append(
-            tokenizer.decode(
-                list(filter(lambda x: x != IGNORE_INDEX, sample["labels"])), skip_special_tokens=skip_special_tokens
-            )
-        )
    sampling_params = SamplingParams(
        repetition_penalty=generating_args.repetition_penalty or 1.0,  # repetition_penalty must > 0
@@ -131,30 +130,68 @@ def vllm_infer(
    else:
        lora_request = None
-    engine_args = {
+    # Store all results in these lists
-        "model": model_args.model_name_or_path,
+    all_prompts, all_preds, all_labels = [], [], []
-        "trust_remote_code": True,
-        "dtype": model_args.infer_dtype,
+    # Add batch process to avoid the issue of too many files opened
-        "max_model_len": cutoff_len + max_new_tokens,
+    for i in tqdm(range(0, len(train_dataset), batch_size), desc="Processing batched inference"):
-        "tensor_parallel_size": (get_device_count() // pipeline_parallel_size) or 1,
+        vllm_inputs, prompts, labels = [], [], []
-        "pipeline_parallel_size": pipeline_parallel_size,
+        batch = train_dataset[i : min(i + batch_size, len(train_dataset))]
-        "disable_log_stats": True,
-        "enable_lora": model_args.adapter_name_or_path is not None,
+        for j in range(len(batch["input_ids"])):
-    }
+            if batch["images"][j] is not None:
-    if template_obj.mm_plugin.__class__.__name__ != "BasePlugin":
+                image = batch["images"][j]
-        engine_args["limit_mm_per_prompt"] = {"image": 4, "video": 2, "audio": 2}
+                multi_modal_data = {
+                    "image": template_obj.mm_plugin._regularize_images(
+                        image, image_max_pixels=image_max_pixels, image_min_pixels=image_min_pixels
+                    )["images"]
+                }
+            elif batch["videos"][j] is not None:
+                video = batch["videos"][j]
+                multi_modal_data = {
+                    "video": template_obj.mm_plugin._regularize_videos(
+                        video,
+                        image_max_pixels=image_max_pixels,
+                        image_min_pixels=image_min_pixels,
+                        video_fps=video_fps,
+                        video_maxlen=video_maxlen,
+                    )["videos"]
+                }
+            elif batch["audios"][j] is not None:
+                audio = batch["audios"][j]
+                audio_data = template_obj.mm_plugin._regularize_audios(
+                    audio,
+                    sampling_rate=16000,
+                )
+                multi_modal_data = {"audio": zip(audio_data["audios"], audio_data["sampling_rates"])}
+            else:
+                multi_modal_data = None
+            vllm_inputs.append({"prompt_token_ids": batch["input_ids"][j], "multi_modal_data": multi_modal_data})
+            prompts.append(tokenizer.decode(batch["input_ids"][j], skip_special_tokens=skip_special_tokens))
+            labels.append(
+                tokenizer.decode(
+                    list(filter(lambda x: x != IGNORE_INDEX, batch["labels"][j])),
+                    skip_special_tokens=skip_special_tokens,
+                )
+            )
-    if isinstance(model_args.vllm_config, dict):
+        results = llm.generate(vllm_inputs, sampling_params, lora_request=lora_request)
-        engine_args.update(model_args.vllm_config)
+        preds = [result.outputs[0].text for result in results]
+        # Accumulate results
+        all_prompts.extend(prompts)
+        all_preds.extend(preds)
+        all_labels.extend(labels)
+        gc.collect()
-    results = LLM(**engine_args).generate(inputs, sampling_params, lora_request=lora_request)
+    # Write all results at once outside the loop
-    preds = [result.outputs[0].text for result in results]
    with open(save_name, "w", encoding="utf-8") as f:
-        for text, pred, label in zip(prompts, preds, labels):
+        for text, pred, label in zip(all_prompts, all_preds, all_labels):
            f.write(json.dumps({"prompt": text, "predict": pred, "label": label}, ensure_ascii=False) + "\n")
    print("*" * 70)
-    print(f"{len(prompts)} generated results have been saved at {save_name}.")
+    print(f"{len(all_prompts)} total generated results have been saved at {save_name}.")
    print("*" * 70)

--- a/setup.py
+++ b/setup.py
@@ -42,7 +42,7 @@ def get_console_scripts() -> list[str]:
 extra_require = {
-    "torch": ["torch>=1.13.1"],
+    "torch": ["torch>=2.0.0", "torchvision>=0.15.0"],
    "torch-npu": ["torch==2.4.0", "torch-npu==2.4.0.post2", "decorator"],
    "metrics": ["nltk", "jieba", "rouge-chinese"],
    "deepspeed": ["deepspeed>=0.10.0,<=0.16.5"],
@@ -50,10 +50,9 @@ extra_require = {
    "bitsandbytes": ["bitsandbytes>=0.39.0"],
    "hqq": ["hqq"],
    "eetq": ["eetq"],
-    "gptq": ["optimum>=1.17.0", "auto-gptq>=0.5.0"],
+    "gptq": ["optimum>=1.24.0", "gptqmodel>=2.0.0"],
-    "awq": ["autoawq"],
    "aqlm": ["aqlm[gpu]>=1.1.0"],
-    "vllm": ["vllm>=0.4.3,<=0.8.4"],
+    "vllm": ["vllm>=0.4.3,<=0.8.5"],
    "sglang": ["sglang[srt]>=0.4.5", "transformers==4.51.1"],
    "galore": ["galore-torch"],
    "apollo": ["apollo-torch"],

--- a/src/llamafactory/chat/hf_engine.py
+++ b/src/llamafactory/chat/hf_engine.py
@@ -104,7 +104,6 @@ class HuggingfaceEngine(BaseEngine):
            messages, mm_input_dict["images"], mm_input_dict["videos"], mm_input_dict["audios"], processor
        )
        paired_messages = messages + [{"role": "assistant", "content": ""}]
-        system = system or generating_args["default_system"]
        prompt_ids, _ = template.encode_oneturn(tokenizer, paired_messages, system, tools)
        prompt_ids, _ = template.mm_plugin.process_token_ids(
            prompt_ids,
@@ -117,7 +116,7 @@ class HuggingfaceEngine(BaseEngine):
        )
        prompt_length = len(prompt_ids)
        inputs = torch.tensor([prompt_ids], device=model.device)
-        attention_mask = torch.ones_like(inputs, dtype=torch.bool)
+        attention_mask = torch.ones_like(inputs, dtype=torch.long)
        do_sample: Optional[bool] = input_kwargs.pop("do_sample", None)
        temperature: Optional[float] = input_kwargs.pop("temperature", None)