Release v0.1.6

Former-commit-id: 43c8b3c3c8bfb2e32d17fb3e8b194938e37d54bd
Update README_zh.md
2023-08-11 23:25:57 +08:00 · 2023-08-11 14:06:02 +08:00 · 2023-08-11 13:56:26 +08:00 · 2023-08-11 13:51:46 +08:00 · 2023-08-11 13:45:47 +08:00 · 2023-08-11 03:02:53 +08:00
56 changed files with 1395 additions and 635 deletions
--- a/README.md
+++ b/README.md
@@ -12,6 +12,10 @@

 ## Changelog

+[23/08/11] Now we support **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [this example](#dpo-training) to train your models (experimental feature).
+
+[23/08/03] Now we support training the **Qwen-7B** model in this repo. Try `--model_name_or_path Qwen/Qwen-7B-Chat` and `--lora_target c_attn` arguments to train the Qwen-7B model. Remember to use `--template chatml` argument when you are using the Qwen-7B-Chat model.
+
 [23/07/31] Now we support dataset streaming. Try `--streaming` and `--max_steps 100` arguments to stream your dataset.

 [23/07/29] We release two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos ([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/baichuan-13b-sft)) for details.
@@ -40,28 +44,31 @@

 ## Supported Models

- [LLaMA](https://github.com/facebookresearch/llama) (7B/13B/33B/65B)
- [LLaMA-2](https://huggingface.co/meta-llama) (7B/13B/70B)
- [BLOOM](https://huggingface.co/bigscience/bloom) & [BLOOMZ](https://huggingface.co/bigscience/bloomz) (560M/1.1B/1.7B/3B/7.1B/176B)
- [Falcon](https://huggingface.co/tiiuae/falcon-7b) (7B/40B)
- [Baichuan](https://huggingface.co/baichuan-inc/baichuan-7B) (7B/13B)
- [InternLM](https://github.com/InternLM/InternLM) (7B)
+| Model                                                    | Model size                  | Default module    | Template |
+| -------------------------------------------------------- | --------------------------- | ----------------- |----------|
+| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B              | q_proj,v_proj     | -        |
+| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                  | q_proj,v_proj     | llama2   |
+| [BLOOM](https://huggingface.co/bigscience/bloom)         | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -        |
+| [BLOOMZ](https://huggingface.co/bigscience/bloomz)       | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -        |
+| [Falcon](https://huggingface.co/tiiuae/falcon-7b)        | 7B/40B                      | query_key_value   | -        |
+| [Baichuan](https://github.com/baichuan-inc/baichuan-13B) | 7B/13B                      | W_pack            | baichuan |
+| [InternLM](https://github.com/InternLM/InternLM)         | 7B                          | q_proj,v_proj     | intern   |
+| [Qwen](https://github.com/QwenLM/Qwen-7B)                | 7B                          | c_attn            | chatml   |
+| [XVERSE](https://github.com/xverse-ai/XVERSE-13B)        | 13B                         | q_proj,v_proj     | -        |
+| [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)         | 6B                          | query_key_value   | chatglm2 |
+
+- **Default module** is used for the `--lora_target` argument. Please use `python src/train_bash.py -h` to see all available options.
+- 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 "chat" models.

 ## Supported Training Approaches

- [(Continually) pre-training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
-  - Full-parameter tuning
-  - Partial-parameter tuning
-  - [LoRA](https://arxiv.org/abs/2106.09685)
-  - [QLoRA](https://arxiv.org/abs/2305.14314)
- [Supervised fine-tuning](https://arxiv.org/abs/2109.01652)
-  - Full-parameter tuning
-  - Partial-parameter tuning
-  - [LoRA](https://arxiv.org/abs/2106.09685)
-  - [QLoRA](https://arxiv.org/abs/2305.14314)
- [RLHF](https://arxiv.org/abs/2203.02155)
-  - [LoRA](https://arxiv.org/abs/2106.09685)
-  - [QLoRA](https://arxiv.org/abs/2305.14314)
+| Approach               | Full-parameter | Partial-parameter | LoRA | QLoRA |
+| ---------------------- | -------------- | ----------------- | ---- | ----- |
+| Pre-Training           | ✅            | ✅                | ✅   | ✅   |
+| Supervised Fine-Tuning | ✅            | ✅                | ✅   | ✅   |
+| Reward Model Training  |                |                   | ✅   | ✅   |
+| PPO Training           |                |                   | ✅   | ✅   |
+| DPO Training           | ✅            |                    | ✅   | ✅   |

 ## Provided Datasets

@@ -78,7 +85,6 @@
  - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
  - [Self-cognition (zh)](data/self_cognition.json)
  - [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
-  - [RefGPT (zh)](https://github.com/sufengniu/RefGPT)
  - [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)
@@ -93,7 +99,7 @@
  - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
  - [UltraChat (en)](https://github.com/thunlp/UltraChat)
  - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
- For reward modelling:
+- For reward modelling or DPO training:
  - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
  - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
  - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
@@ -111,6 +117,7 @@ huggingface-cli login

 - Python 3.8+ and PyTorch 1.13.1+
 - 🤗Transformers, Datasets, Accelerate, PEFT and TRL
+- sentencepiece and tiktoken
 - jieba, rouge-chinese and nltk (used at evaluation)
 - gradio and matplotlib (used in web_demo.py)
 - uvicorn, fastapi and sse-starlette (used in api_demo.py)
@@ -128,7 +135,6 @@ Note: please update `data/dataset_info.json` to use your custom dataset. About t
 ### Dependence Installation (optional)

 ```bash
-git lfs install
 git clone https://github.com/hiyouga/LLaMA-Efficient-Tuning.git
 conda create -n llama_etuning python=3.10
 conda activate llama_etuning
@@ -150,7 +156,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_web.py

 Currently the web UI only supports training on **a single GPU**.

-### (Continually) Pre-Training
+### Pre-Training

 ```bash
 CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
@@ -211,7 +217,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --resume_lora_training False \
    --checkpoint_dir path_to_sft_checkpoint \
    --output_dir path_to_rm_checkpoint \
-    --per_device_train_batch_size 4 \
+    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 4 \
    --lr_scheduler_type cosine \
    --logging_steps 10 \
@@ -222,7 +228,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --fp16
 ```

-### PPO Training (RLHF)
+### PPO Training

 ```bash
 CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
@@ -246,6 +252,30 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --plot_loss
 ```

+### DPO Training
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage dpo \
+    --model_name_or_path path_to_your_model \
+    --do_train \
+    --dataset comparison_gpt4_en \
+    --template default \
+    --finetuning_type lora \
+    --resume_lora_training False \
+    --checkpoint_dir path_to_sft_checkpoint \
+    --output_dir path_to_dpo_checkpoint \
+    --per_device_train_batch_size 2 \
+    --gradient_accumulation_steps 4 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --save_steps 1000 \
+    --learning_rate 1e-5 \
+    --num_train_epochs 1.0 \
+    --plot_loss \
+    --fp16
+```
+
 ### Distributed Training

 ```bash
@@ -378,6 +408,9 @@ Please follow the model licenses to use the corresponding model weights:
 - [Falcon](LICENSE)
 - [Baichuan](https://huggingface.co/baichuan-inc/baichuan-7B/resolve/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)
 - [InternLM](https://github.com/InternLM/InternLM#open-source-license)
+- [Qwen](https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/LICENSE)
+- [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf)
+- [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B/blob/main/MODEL_LICENSE)

 ## Citation

--- a/README_zh.md
+++ b/README_zh.md
@@ -12,6 +12,10 @@

 ## 更新日志

+[23/08/11] 现在我们支持了指令模型的 **[DPO 训练](https://arxiv.org/abs/2305.18290)**。详情请参阅[此示例](#dpo-训练)（实验性功能）。
+
+[23/08/03] 现在我们支持了 **Qwen-7B** 模型的训练。请尝试使用 `--model_name_or_path Qwen/Qwen-7B-Chat` 和 `--lora_target c_attn` 参数。使用 Qwen-7B-Chat 模型请添加 `--template chatml` 参数。
+
 [23/07/31] 现在我们支持了训练数据流式加载。请尝试使用 `--streaming` 和 `--max_steps 100` 参数来流式加载数据集。

 [23/07/29] 我们在 Hugging Face 发布了两个 13B 指令微调模型。详细内容请查阅我们的 Hugging Face 项目（[LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/baichuan-13b-sft)）。
@@ -20,7 +24,7 @@

 [23/07/18] 我们开发了支持训练和测试的浏览器一键微调界面。请尝试使用 `train_web.py` 在您的浏览器中微调模型。感谢 [@KanadeSiina](https://github.com/KanadeSiina) 和 [@codemayq](https://github.com/codemayq) 在该功能开发中付出的努力。

-[23/07/11] 现在我们支持了 **Baichuan-13B** 模型的训练。请尝试使用 `--model_name_or_path path_to_baichuan_model` 和 `--lora_target W_pack` 参数。请注意使用 Baichuan-13B-Chat 模型需要添加 `--template baichuan` 参数。
+[23/07/11] 现在我们支持了 **Baichuan-13B** 模型的训练。请尝试使用 `--model_name_or_path baichuan-inc/Baichuan-13B-Base` 和 `--lora_target W_pack` 参数。请注意使用 Baichuan-13B-Chat 模型需要添加 `--template baichuan` 参数。

 [23/07/09] 我们开源了 [FastEdit](https://github.com/hiyouga/FastEdit)⚡🩹，一个简单易用的、能迅速编辑大模型事实记忆的工具包。如果您感兴趣请关注我们的 [FastEdit](https://github.com/hiyouga/FastEdit) 项目。

@@ -40,45 +44,46 @@

 ## 模型

- [LLaMA](https://github.com/facebookresearch/llama) (7B/13B/33B/65B)
- [LLaMA-2](https://huggingface.co/meta-llama) (7B/13B/70B)
- [BLOOM](https://huggingface.co/bigscience/bloom) & [BLOOMZ](https://huggingface.co/bigscience/bloomz) (560M/1.1B/1.7B/3B/7.1B/176B)
- [Falcon](https://huggingface.co/tiiuae/falcon-7b) (7B/40B)
- [Baichuan](https://huggingface.co/baichuan-inc/baichuan-7B) (7B/13B)
- [InternLM](https://github.com/InternLM/InternLM) (7B)
+| 模型名                                                   | 模型大小                     | 默认模块           | Template |
+| -------------------------------------------------------- | --------------------------- | ----------------- |----------|
+| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B              | q_proj,v_proj     | -        |
+| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                  | q_proj,v_proj     | llama2   |
+| [BLOOM](https://huggingface.co/bigscience/bloom)         | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -        |
+| [BLOOMZ](https://huggingface.co/bigscience/bloomz)       | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -        |
+| [Falcon](https://huggingface.co/tiiuae/falcon-7b)        | 7B/40B                      | query_key_value   | -        |
+| [Baichuan](https://github.com/baichuan-inc/baichuan-13B) | 7B/13B                      | W_pack            | baichuan |
+| [InternLM](https://github.com/InternLM/InternLM)         | 7B                          | q_proj,v_proj     | intern   |
+| [Qwen](https://github.com/QwenLM/Qwen-7B)                | 7B                          | c_attn            | chatml   |
+| [XVERSE](https://github.com/xverse-ai/XVERSE-13B)        | 13B                         | q_proj,v_proj     | -        |

-## 微调方法
+- **默认模块**是 `--lora_target` 参数的部分可选项。请使用 `python src/train_bash.py -h` 查看全部可选项。
+- 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”（Chat）模型请务必使用对应的模板。

- [二次预训练](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
-  - 全参数微调
-  - 部分参数微调
-  - [LoRA](https://arxiv.org/abs/2106.09685)
-  - [QLoRA](https://arxiv.org/abs/2305.14314)
- [指令监督微调](https://arxiv.org/abs/2109.01652)
-  - 全参数微调
-  - 部分参数微调
-  - [LoRA](https://arxiv.org/abs/2106.09685)
-  - [QLoRA](https://arxiv.org/abs/2305.14314)
- [人类反馈的强化学习（RLHF）](https://arxiv.org/abs/2203.02155)
-  - [LoRA](https://arxiv.org/abs/2106.09685)
-  - [QLoRA](https://arxiv.org/abs/2305.14314)
+## 训练方法
+
+| 方法        | 全参数训练 | 部分参数训练 | LoRA | QLoRA |
+| ---------- | ---------- | ----------- | ---- | ----- |
+| 预训练      | ✅        | ✅         | ✅   | ✅   |
+| 指令监督微调 | ✅        | ✅         | ✅   | ✅   |
+| 奖励模型训练 |           |             | ✅   | ✅   |
+| PPO 训练    |           |             | ✅   | ✅   |
+| DPO 训练    | ✅        |             | ✅   | ✅   |

 ## 数据集

- 用于二次预训练:
+- 用于预训练：
  - [Wiki Demo (en)](data/wiki_demo.txt)
  - [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
  - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
  - [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220)
  - [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
- 用于指令监督微调:
+- 用于指令监督微调：
  - [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
  - [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
  - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
  - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
  - [Self-cognition (zh)](data/self_cognition.json)
  - [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
-  - [RefGPT (zh)](https://github.com/sufengniu/RefGPT)
  - [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)
@@ -93,7 +98,7 @@
  - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
  - [UltraChat (en)](https://github.com/thunlp/UltraChat)
  - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
- 用于奖励模型训练:
+- 用于奖励模型或 DPO 训练：
  - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
  - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
  - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
@@ -111,6 +116,7 @@ huggingface-cli login

 - Python 3.8+ 和 PyTorch 1.13.1+
 - 🤗Transformers, Datasets, Accelerate, PEFT 和 TRL
+- sentencepiece 和 tiktoken
 - jieba, rouge-chinese 和 nltk (用于评估)
 - gradio 和 matplotlib (用于网页端交互)
 - uvicorn, fastapi 和 sse-starlette (用于 API)
@@ -128,7 +134,6 @@ huggingface-cli login
 ### 环境搭建（可跳过）

 ```bash
-git lfs install
 git clone https://github.com/hiyouga/LLaMA-Efficient-Tuning.git
 conda create -n llama_etuning python=3.10
 conda activate llama_etuning
@@ -150,7 +155,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_web.py

 目前网页 UI 仅支持**单卡训练**。

-### 二次预训练
+### 预训练

 ```bash
 CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
@@ -211,7 +216,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --resume_lora_training False \
    --checkpoint_dir path_to_sft_checkpoint \
    --output_dir path_to_rm_checkpoint \
-    --per_device_train_batch_size 4 \
+    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 4 \
    --lr_scheduler_type cosine \
    --logging_steps 10 \
@@ -222,7 +227,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --fp16
 ```

-### RLHF 训练
+### PPO 训练

 ```bash
 CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
@@ -246,6 +251,30 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --plot_loss
 ```

+### DPO 训练
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage dpo \
+    --model_name_or_path path_to_your_model \
+    --do_train \
+    --dataset comparison_gpt4_zh \
+    --template default \
+    --finetuning_type lora \
+    --resume_lora_training False \
+    --checkpoint_dir path_to_sft_checkpoint \
+    --output_dir path_to_dpo_checkpoint \
+    --per_device_train_batch_size 2 \
+    --gradient_accumulation_steps 4 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --save_steps 1000 \
+    --learning_rate 1e-5 \
+    --num_train_epochs 1.0 \
+    --plot_loss \
+    --fp16
+```
+
 ### 多 GPU 分布式训练

 ```bash
@@ -378,6 +407,7 @@ python src/export_model.py \
 - [Falcon](LICENSE)
 - [Baichuan](https://huggingface.co/baichuan-inc/baichuan-7B/resolve/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)
 - [InternLM](https://github.com/InternLM/InternLM#open-source-license)
+- [Qwen](https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/LICENSE)

 ## 引用

--- a/data/refgpt_zh_50k_p1.json.REMOVED.git-id
+++ b/data/refgpt_zh_50k_p1.json.REMOVED.git-id
@@ -1 +0,0 @@
-f967a4f6d04a11308a15524aa9a846a19a8d1e83
--- a/data/refgpt_zh_50k_p2.json.REMOVED.git-id
+++ b/data/refgpt_zh_50k_p2.json.REMOVED.git-id
@@ -1 +0,0 @@
-0a4f0d74fd1c5cab2eb6d84a3a3fe669847becd8
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,8 +3,10 @@ transformers>=4.29.1
 datasets>=2.12.0
 accelerate>=0.21.0
 peft>=0.4.0
-trl>=0.4.7
+trl>=0.5.0
+scipy
 sentencepiece
+tiktoken
 jieba
 rouge-chinese
 nltk
--- a/src/api_demo.py
+++ b/src/api_demo.py
@@ -1,19 +1,13 @@
-# coding=utf-8
-# Implements API for fine-tuned models in OpenAI's format. (https://platform.openai.com/docs/api-reference/chat)
-# Usage: python api_demo.py --model_name_or_path path_to_model --checkpoint_dir path_to_checkpoint
-# Visit http://localhost:8000/docs for document.
-
 import uvicorn

-from llmtuner import ChatModel
-from llmtuner.api.app import create_app
-from llmtuner.tuner import get_infer_args
+from llmtuner import ChatModel, create_app


 def main():
-    chat_model = ChatModel(*get_infer_args())
+    chat_model = ChatModel()
    app = create_app(chat_model)
    uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
+    print("Visit http://localhost:8000/docs for API document.")


 if __name__ == "__main__":
--- a/src/cli_demo.py
+++ b/src/cli_demo.py
@@ -1,13 +1,8 @@
-# coding=utf-8
-# Implements stream chat in command line for fine-tuned models.
-# Usage: python cli_demo.py --model_name_or_path path_to_model --checkpoint_dir path_to_checkpoint
-
 from llmtuner import ChatModel
-from llmtuner.tuner import get_infer_args


 def main():
-    chat_model = ChatModel(*get_infer_args())
+    chat_model = ChatModel()
    history = []
    print("Welcome to the CLI application, use `clear` to remove the history, use `exit` to exit the application.")

--- a/src/export_model.py
+++ b/src/export_model.py
@@ -1,16 +1,8 @@
-# coding=utf-8
-# Exports the fine-tuned model.
-# Usage: python export_model.py --checkpoint_dir path_to_checkpoint --output_dir path_to_save_model
-
-from llmtuner.tuner import get_train_args, load_model_and_tokenizer
+from llmtuner import export_model


 def main():
-    model_args, _, training_args, finetuning_args, _ = get_train_args()
-    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
-    model.save_pretrained(training_args.output_dir, max_shard_size="10GB")
-    tokenizer.save_pretrained(training_args.output_dir)
-    print("model and tokenizer have been saved at:", training_args.output_dir)
+    export_model()


 if __name__ == "__main__":
--- a/src/llmtuner/init.py
+++ b/src/llmtuner/init.py
@@ -1,4 +1,9 @@
+# Level: api, webui > chat > tuner > dsets > extras, hparams
+
+from llmtuner.api import create_app
 from llmtuner.chat import ChatModel
+from llmtuner.tuner import export_model, run_exp
+from llmtuner.webui import create_ui, create_web_demo


-__version__ = "0.1.5"
+__version__ = "0.1.6"
--- a/src/llmtuner/api/init.py
+++ b/src/llmtuner/api/init.py
@@ -0,0 +1 @@
+from llmtuner.api.app import create_app
--- a/src/llmtuner/api/app.py
+++ b/src/llmtuner/api/app.py
@@ -5,9 +5,8 @@ from contextlib import asynccontextmanager
 from sse_starlette import EventSourceResponse
 from typing import List, Tuple

-from llmtuner.tuner import get_infer_args
 from llmtuner.extras.misc import torch_gc
-from llmtuner.chat.stream_chat import ChatModel
+from llmtuner.chat import ChatModel
 from llmtuner.api.protocol import (
    Role,
    Finish,
@@ -50,8 +49,8 @@ def create_app(chat_model: ChatModel) -> FastAPI:
    async def create_chat_completion(request: ChatCompletionRequest):
        if request.messages[-1].role != Role.USER:
            raise HTTPException(status_code=400, detail="Invalid request")
-        query = request.messages[-1].content

+        query = request.messages[-1].content
        prev_messages = request.messages[:-1]
        if len(prev_messages) > 0 and prev_messages[0].role == Role.SYSTEM:
            prefix = prev_messages.pop(0).content
@@ -122,6 +121,6 @@ def create_app(chat_model: ChatModel) -> FastAPI:


 if __name__ == "__main__":
-    chat_model = ChatModel(*get_infer_args())
+    chat_model = ChatModel()
    app = create_app(chat_model)
    uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
--- a/src/llmtuner/chat/stream_chat.py
+++ b/src/llmtuner/chat/stream_chat.py
@@ -1,30 +1,24 @@
 import torch
-from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Tuple
+from types import MethodType
+from typing import Any, Dict, Generator, List, Optional, Tuple
 from threading import Thread
-from transformers import TextIteratorStreamer
+from transformers import PreTrainedModel, TextIteratorStreamer

-from llmtuner.extras.misc import dispatch_model, get_logits_processor
-from llmtuner.extras.template import get_template
-from llmtuner.tuner import load_model_and_tokenizer
-
-if TYPE_CHECKING:
-    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
+from llmtuner.extras.misc import dispatch_model, get_logits_processor, get_stopping_criteria
+from llmtuner.extras.template import get_template_and_fix_tokenizer
+from llmtuner.tuner.core import get_infer_args, load_model_and_tokenizer


 class ChatModel:

-    def __init__(
-        self,
-        model_args: "ModelArguments",
-        data_args: "DataArguments",
-        finetuning_args: "FinetuningArguments",
-        generating_args: "GeneratingArguments"
-    ) -> None:
+    def __init__(self, args: Optional[Dict[str, Any]] = None) -> None:
+        model_args, data_args, finetuning_args, self.generating_args = get_infer_args(args)
        self.model, self.tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
        self.model = dispatch_model(self.model)
-        self.template = get_template(data_args.template)
+        self.model = self.model.eval() # change to eval mode
+        self.template = get_template_and_fix_tokenizer(data_args.template, self.tokenizer)
        self.source_prefix = data_args.source_prefix
-        self.generating_args = generating_args
+        self.model.generate = MethodType(PreTrainedModel.generate, self.model) # disable custom method (for Qwen)

    def process_args(
        self,
@@ -35,10 +29,11 @@ class ChatModel:
    ) -> Tuple[Dict[str, Any], int]:
        prefix = prefix or self.source_prefix

-        prompt = self.template.get_prompt(query, history, prefix, self.tokenizer.eos_token)
-        inputs = self.tokenizer([prompt], return_tensors="pt")
-        inputs = inputs.to(self.model.device)
-        prompt_length = len(inputs["input_ids"][0])
+        prompt, _ = self.template.encode_oneturn(
+            tokenizer=self.tokenizer, query=query, resp="", history=history, prefix=prefix
+        )
+        input_ids = torch.tensor([prompt], device=self.model.device)
+        prompt_length = len(input_ids[0])

        do_sample = input_kwargs.pop("do_sample", None)
        temperature = input_kwargs.pop("temperature", None)
@@ -50,13 +45,14 @@ class ChatModel:

        gen_kwargs = self.generating_args.to_dict()
        gen_kwargs.update(dict(
-            input_ids=inputs["input_ids"],
+            input_ids=input_ids,
            do_sample=do_sample if do_sample is not None else gen_kwargs["do_sample"],
            temperature=temperature or gen_kwargs["temperature"],
            top_p=top_p or gen_kwargs["top_p"],
            top_k=top_k or gen_kwargs["top_k"],
            repetition_penalty=repetition_penalty or gen_kwargs["repetition_penalty"],
-            logits_processor=get_logits_processor()
+            logits_processor=get_logits_processor(),
+            stopping_criteria=get_stopping_criteria(self.tokenizer.additional_special_tokens_ids)
        ))

        if max_length:
--- a/src/llmtuner/dsets/loader.py
+++ b/src/llmtuner/dsets/loader.py
@@ -93,11 +93,13 @@ def get_dataset(
                dataset = dataset.rename_column(getattr(dataset_attr, column_name), column_name)

        if dataset_attr.source_prefix: # add prefix
-            features = None
            if data_args.streaming:
                features = dataset.features
                features["prefix"] = Value(dtype="string", id=None)
                dataset = dataset.map(lambda _: {"prefix": dataset_attr.source_prefix}, features=features)
+            else:
+                prefix_data = [dataset_attr.source_prefix] * len(dataset)
+                dataset = dataset.add_column("prefix", prefix_data)

        all_datasets.append(dataset)

@@ -111,6 +113,6 @@ def get_dataset(
        if not data_args.streaming:
            logger.warning("We recommend using `mix_strategy=concat` in non-streaming mode.")
        stopping_strategy = "first_exhausted" if data_args.mix_strategy.endswith("under") else "all_exhausted"
-        return interleave_datasets(all_datasets, stopping_strategy=stopping_strategy)
+        return interleave_datasets(all_datasets, data_args.interleave_probs, stopping_strategy=stopping_strategy)
    else:
        raise ValueError("Unknown mixing strategy.")
--- a/src/llmtuner/dsets/preprocess.py
+++ b/src/llmtuner/dsets/preprocess.py
@@ -1,8 +1,9 @@
+import tiktoken
 from typing import TYPE_CHECKING, Any, Dict, Generator, List, Literal
 from itertools import chain

 from llmtuner.extras.constants import IGNORE_INDEX
-from llmtuner.extras.template import get_template
+from llmtuner.extras.template import get_template_and_fix_tokenizer

 if TYPE_CHECKING:
    from datasets import Dataset
@@ -19,7 +20,7 @@ def preprocess_dataset(
    stage: Literal["pt", "sft", "rm", "ppo"]
 ) -> "Dataset":
    column_names = list(dataset.column_names)
-    template = get_template(data_args.template)
+    template = get_template_and_fix_tokenizer(data_args.template, tokenizer)

    def construct_example(examples: Dict[str, List[Any]]) -> Generator[Any, None, None]:
        for i in range(len(examples["prompt"])):
@@ -30,8 +31,13 @@ def preprocess_dataset(
            yield query, response, history, prefix

    def preprocess_pretrain_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
-        # build grouped texts with format `<bos> X1 X2 X3 ...` (without <eos>)
-        tokenized_examples = tokenizer(examples["prompt"], add_special_tokens=False)
+        # build grouped texts with format `X1 X2 X3 ...` (without <eos>)
+        if isinstance(getattr(tokenizer, "tokenizer", None), tiktoken.Encoding): # for tiktoken tokenizer (Qwen)
+            kwargs = dict(allowed_special="all")
+        else:
+            kwargs = dict(add_special_tokens=False)
+
+        tokenized_examples = tokenizer(examples["prompt"], **kwargs)
        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.max_source_length
@@ -42,33 +48,28 @@ def preprocess_dataset(
            k: [t[i: i + block_size] for i in range(0, total_length, block_size)]
            for k, t in concatenated_examples.items()
        }
-        result["labels"] = result["input_ids"].copy()
        return result

    def preprocess_supervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
        # build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
-        # for input with history, we build multiple input-label pairs just like:
-        # https://github.com/lm-sys/FastChat/blob/f17c092f64840fa6354ed52789dccb2daa793d0b/fastchat/train/train.py#L112
+        # for multiturn examples, we only mask the prompt part in each prompt-response pair.
        model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
        max_length = data_args.max_source_length + data_args.max_target_length

        for query, response, history, prefix in construct_example(examples):
            input_ids, labels = [], []

-            for i, (query_i, resp_i) in enumerate(template.get_dialog(query, response, history, prefix)):
-                source_ids = tokenizer.encode(text=query_i, add_special_tokens=(i == 0))
-                target_ids = tokenizer.encode(text=resp_i, add_special_tokens=False)
-
+            for source_ids, target_ids in template.encode_multiturn(tokenizer, query, response, history, prefix):
                if len(source_ids) > data_args.max_source_length:
                    source_ids = source_ids[:data_args.max_source_length]
-                if len(target_ids) > data_args.max_target_length - 1: # eos token
-                    target_ids = target_ids[:data_args.max_target_length - 1]
+                if len(target_ids) > data_args.max_target_length:
+                    target_ids = target_ids[:data_args.max_target_length]

-                if len(input_ids) + len(source_ids) + len(target_ids) + 1 > max_length:
+                if len(input_ids) + len(source_ids) + len(target_ids) > max_length:
                    break

-                input_ids += source_ids + target_ids + [tokenizer.eos_token_id]
-                labels += [IGNORE_INDEX] * len(source_ids) + target_ids + [tokenizer.eos_token_id]
+                input_ids += source_ids + target_ids
+                labels += [IGNORE_INDEX] * len(source_ids) + target_ids

            model_inputs["input_ids"].append(input_ids)
            model_inputs["attention_mask"].append([1] * len(input_ids))
@@ -81,10 +82,7 @@ def preprocess_dataset(
        model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}

        for query, response, history, prefix in construct_example(examples):
-            prompt = template.get_prompt(query, history, prefix, tokenizer.eos_token)
-
-            source_ids = tokenizer.encode(text=prompt, add_special_tokens=True)
-            target_ids = tokenizer.encode(text=response, add_special_tokens=True)
+            source_ids, target_ids = template.encode_oneturn(tokenizer, query, response, history, prefix)

            if len(source_ids) > data_args.max_source_length:
                source_ids = source_ids[:data_args.max_source_length]
@@ -98,43 +96,39 @@ def preprocess_dataset(
        return model_inputs

    def preprocess_pairwise_dataset(examples):
-        # build input pairs with format `<bos> X Y1 <eos>` and `<bos> X Y2 <eos>`
-        model_inputs = {"accept_ids": [], "reject_ids": []}
+        # build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
+        model_inputs = {"prompt_ids": [], "chosen_ids": [], "rejected_ids": []}
        for query, response, history, prefix in construct_example(examples):
-            prompt = template.get_prompt(query, history, prefix, tokenizer.eos_token)
+            prompt_ids, chosen_ids = template.encode_oneturn(tokenizer, query, response[0], history, prefix)
+            _, rejected_ids = template.encode_oneturn(tokenizer, query, response[1], history, prefix)

-            source_ids = tokenizer.encode(text=prompt, add_special_tokens=True)
-            accept_ids = tokenizer.encode(text=response[0], add_special_tokens=False)
-            reject_ids = tokenizer.encode(text=response[1], add_special_tokens=False)
+            if len(prompt_ids) > data_args.max_source_length:
+                prompt_ids = prompt_ids[:data_args.max_source_length]
+            if len(chosen_ids) > data_args.max_target_length:
+                chosen_ids = chosen_ids[:data_args.max_target_length]
+            if len(rejected_ids) > data_args.max_target_length:
+                rejected_ids = rejected_ids[:data_args.max_target_length]

-            if len(source_ids) > data_args.max_source_length:
-                source_ids = source_ids[:data_args.max_source_length]
-            if len(accept_ids) > data_args.max_target_length - 1: # eos token
-                accept_ids = accept_ids[:data_args.max_target_length - 1]
-            if len(reject_ids) > data_args.max_target_length - 1: # eos token
-                reject_ids = reject_ids[:data_args.max_target_length - 1]
-
-            accept_ids = source_ids + accept_ids + [tokenizer.eos_token_id]
-            reject_ids = source_ids + reject_ids + [tokenizer.eos_token_id]
-
-            model_inputs["accept_ids"].append(accept_ids)
-            model_inputs["reject_ids"].append(reject_ids)
+            model_inputs["prompt_ids"].append(prompt_ids)
+            model_inputs["chosen_ids"].append(chosen_ids)
+            model_inputs["rejected_ids"].append(rejected_ids)
        return model_inputs

    def print_supervised_dataset_example(example):
        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([d if d != IGNORE_INDEX else tokenizer.pad_token_id for d in example["labels"]],
-                             skip_special_tokens=False)
-        ))
+        print("labels:\n{}".format(tokenizer.decode([
+            token_id if token_id != IGNORE_INDEX else tokenizer.pad_token_id for token_id in example["labels"]
+        ], skip_special_tokens=False)))

    def print_pairwise_dataset_example(example):
-        print("accept_ids:\n{}".format(example["accept_ids"]))
-        print("accepts:\n{}".format(tokenizer.decode(example["accept_ids"], skip_special_tokens=False)))
-        print("reject_ids:\n{}".format(example["reject_ids"]))
-        print("rejects:\n{}".format(tokenizer.decode(example["reject_ids"], skip_special_tokens=False)))
+        print("prompt_ids:\n{}".format(example["prompt_ids"]))
+        print("prompt:\n{}".format(tokenizer.decode(example["prompt_ids"], skip_special_tokens=False)))
+        print("chosen_ids:\n{}".format(example["chosen_ids"]))
+        print("chosen:\n{}".format(tokenizer.decode(example["chosen_ids"], skip_special_tokens=False)))
+        print("rejected_ids:\n{}".format(example["rejected_ids"]))
+        print("rejected:\n{}".format(tokenizer.decode(example["rejected_ids"], skip_special_tokens=False)))

    def print_unsupervised_dataset_example(example):
        print("input_ids:\n{}".format(example["input_ids"]))
@@ -173,8 +167,5 @@ def preprocess_dataset(
            **kwargs
        )

-        if data_args.streaming:
-            dataset = dataset.shuffle(buffer_size=data_args.buffer_size)
-
        print_function(next(iter(dataset)))
        return dataset
--- a/src/llmtuner/dsets/utils.py
+++ b/src/llmtuner/dsets/utils.py
@@ -1,15 +1,30 @@
-from typing import TYPE_CHECKING, Dict
+from typing import TYPE_CHECKING, Dict, Union

 if TYPE_CHECKING:
-    from datasets import Dataset
+    from datasets import Dataset, IterableDataset
+    from transformers import TrainingArguments
+    from llmtuner.hparams import DataArguments


-def split_dataset(dataset: "Dataset", dev_ratio: float, do_train: bool) -> Dict[str, "Dataset"]:
-    if do_train:
-        if dev_ratio > 1e-6: # Split the dataset
-            dataset = dataset.train_test_split(test_size=dev_ratio)
+def split_dataset(
+    dataset: Union["Dataset", "IterableDataset"],
+    data_args: "DataArguments",
+    training_args: "TrainingArguments"
+) -> Dict[str, "Dataset"]:
+    if training_args.do_train:
+        if data_args.val_size > 1e-6: # Split the dataset
+            if data_args.streaming:
+                val_set = dataset.take(int(data_args.val_size))
+                train_set = dataset.skip(int(data_args.val_size))
+                dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
+                return {"train_dataset": train_set, "eval_dataset": val_set}
+            else:
+                val_size = int(data_args.val_size) if data_args.val_size > 1 else data_args.val_size
+                dataset = dataset.train_test_split(test_size=val_size, seed=training_args.seed)
                return {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
        else:
+            if data_args.streaming:
+                dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
            return {"train_dataset": dataset}
    else: # do_eval or do_predict
        return {"eval_dataset": dataset}
--- a/src/llmtuner/extras/callbacks.py
+++ b/src/llmtuner/extras/callbacks.py
@@ -5,67 +5,124 @@ from typing import TYPE_CHECKING
 from datetime import timedelta

 from transformers import TrainerCallback
+from transformers.trainer_utils import has_length
+
+from llmtuner.extras.constants import LOG_FILE_NAME
+from llmtuner.extras.logging import get_logger

 if TYPE_CHECKING:
    from transformers import TrainingArguments, TrainerState, TrainerControl


+logger = get_logger(__name__)
+
+
 class LogCallback(TrainerCallback):

    def __init__(self, runner=None):
        self.runner = runner
+        self.in_training = False
        self.start_time = time.time()
-        self.tracker = {}
+        self.cur_steps = 0
+        self.max_steps = 0
+        self.elapsed_time = ""
+        self.remaining_time = ""
+
+    def timing(self):
+        cur_time = time.time()
+        elapsed_time = cur_time - self.start_time
+        avg_time_per_step = elapsed_time / self.cur_steps if self.cur_steps != 0 else 0
+        remaining_time = (self.max_steps - self.cur_steps) * avg_time_per_step
+        self.elapsed_time = str(timedelta(seconds=int(elapsed_time)))
+        self.remaining_time = str(timedelta(seconds=int(remaining_time)))

    def on_train_begin(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the beginning of training.
        """
+        if state.is_local_process_zero:
+            self.in_training = True
            self.start_time = time.time()
+            self.max_steps = state.max_steps
+            if os.path.exists(os.path.join(args.output_dir, LOG_FILE_NAME)):
+                logger.warning("Previous log file in this folder will be deleted.")
+                os.remove(os.path.join(args.output_dir, LOG_FILE_NAME))

-    def on_step_begin(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+    def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
-        Event called at the beginning of a training step. If using gradient accumulation, one training step
-        might take several inputs.
+        Event called at the end of training.
        """
-        if self.runner is not None and self.runner.aborted:
-            control.should_epoch_stop = True
-            control.should_training_stop = True
+        if state.is_local_process_zero:
+            self.in_training = False
+            self.cur_steps = 0
+            self.max_steps = 0

    def on_substep_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the end of an substep during gradient accumulation.
        """
+        if state.is_local_process_zero and self.runner is not None and self.runner.aborted:
+            control.should_epoch_stop = True
+            control.should_training_stop = True
+
+    def on_step_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called at the end of a training step.
+        """
+        if state.is_local_process_zero:
+            self.cur_steps = state.global_step
+            self.timing()
            if self.runner is not None and self.runner.aborted:
                control.should_epoch_stop = True
                control.should_training_stop = True

+    def on_evaluate(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called after an evaluation phase.
+        """
+        if state.is_local_process_zero and not self.in_training:
+            self.cur_steps = 0
+            self.max_steps = 0
+
+    def on_predict(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", *other, **kwargs):
+        r"""
+        Event called after a successful prediction.
+        """
+        if state.is_local_process_zero and not self.in_training:
+            self.cur_steps = 0
+            self.max_steps = 0
+
    def on_log(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs) -> None:
        r"""
        Event called after logging the last logs.
        """
-        if not state.is_world_process_zero:
+        if not state.is_local_process_zero:
            return

-        cur_time = time.time()
-        cur_steps = state.log_history[-1].get("step")
-        elapsed_time = cur_time - self.start_time
-        avg_time_per_step = elapsed_time / cur_steps if cur_steps != 0 else 0
-        remaining_steps = state.max_steps - cur_steps
-        remaining_time = remaining_steps * avg_time_per_step
-        self.tracker = {
-            "current_steps": cur_steps,
-            "total_steps": state.max_steps,
-            "loss": state.log_history[-1].get("loss", None),
-            "eval_loss": state.log_history[-1].get("eval_loss", None),
-            "predict_loss": state.log_history[-1].get("predict_loss", None),
-            "reward": state.log_history[-1].get("reward", None),
-            "learning_rate": state.log_history[-1].get("learning_rate", None),
-            "epoch": state.log_history[-1].get("epoch", None),
-            "percentage": round(cur_steps / state.max_steps * 100, 2) if state.max_steps != 0 else 100,
-            "elapsed_time": str(timedelta(seconds=int(elapsed_time))),
-            "remaining_time": str(timedelta(seconds=int(remaining_time)))
-        }
+        logs = dict(
+            current_steps=self.cur_steps,
+            total_steps=self.max_steps,
+            loss=state.log_history[-1].get("loss", None),
+            eval_loss=state.log_history[-1].get("eval_loss", None),
+            predict_loss=state.log_history[-1].get("predict_loss", None),
+            reward=state.log_history[-1].get("reward", None),
+            learning_rate=state.log_history[-1].get("learning_rate", None),
+            epoch=state.log_history[-1].get("epoch", None),
+            percentage=round(self.cur_steps / self.max_steps * 100, 2) if self.max_steps != 0 else 100,
+            elapsed_time=self.elapsed_time,
+            remaining_time=self.remaining_time
+        )
        os.makedirs(args.output_dir, exist_ok=True)
        with open(os.path.join(args.output_dir, "trainer_log.jsonl"), "a", encoding="utf-8") as f:
-            f.write(json.dumps(self.tracker) + "\n")
+            f.write(json.dumps(logs) + "\n")
+
+    def on_prediction_step(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
+        r"""
+        Event called after a prediction step.
+        """
+        eval_dataloader = kwargs.pop("eval_dataloader", None)
+        if state.is_local_process_zero and has_length(eval_dataloader) and not self.in_training:
+            if self.max_steps == 0:
+                self.max_steps = len(eval_dataloader)
+            self.cur_steps += 1
+            self.timing()
--- a/src/llmtuner/extras/constants.py
+++ b/src/llmtuner/extras/constants.py
@@ -1,10 +1,12 @@
 IGNORE_INDEX = -100

+LOG_FILE_NAME = "trainer_log.jsonl"
+
 VALUE_HEAD_FILE_NAME = "value_head.bin"

 FINETUNING_ARGS_NAME = "finetuning_args.json"

-LAYERNORM_NAMES = ["norm", "ln_f", "ln_attn", "ln_mlp"] # for LLaMA, BLOOM and Falcon settings
+LAYERNORM_NAMES = ["norm", "ln_f", "ln_attn", "ln_mlp"]

 METHODS = ["full", "freeze", "lora"]

@@ -25,15 +27,19 @@ SUPPORTED_MODELS = {
    "BLOOMZ-560M": "bigscience/bloomz-560m",
    "BLOOMZ-3B": "bigscience/bloomz-3b",
    "BLOOMZ-7B1-mt": "bigscience/bloomz-7b1-mt",
-    "Falcon-7B-Base": "tiiuae/falcon-7b",
+    "Falcon-7B": "tiiuae/falcon-7b",
    "Falcon-7B-Chat": "tiiuae/falcon-7b-instruct",
-    "Falcon-40B-Base": "tiiuae/falcon-40b",
+    "Falcon-40B": "tiiuae/falcon-40b",
    "Falcon-40B-Chat": "tiiuae/falcon-40b-instruct",
    "Baichuan-7B": "baichuan-inc/Baichuan-7B",
-    "Baichuan-13B-Base": "baichuan-inc/Baichuan-13B-Base",
+    "Baichuan-13B": "baichuan-inc/Baichuan-13B-Base",
    "Baichuan-13B-Chat": "baichuan-inc/Baichuan-13B-Chat",
-    "InternLM-7B-Base": "internlm/internlm-7b",
-    "InternLM-7B-Chat": "internlm/internlm-chat-7b"
+    "InternLM-7B": "internlm/internlm-7b",
+    "InternLM-7B-Chat": "internlm/internlm-chat-7b",
+    "Qwen-7B": "Qwen/Qwen-7B",
+    "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
+    "XVERSE-13B": "xverse/XVERSE-13B",
+    "ChatGLM2-6B": "THUDM/chatglm2-6b"
 }

 DEFAULT_MODULE = {
@@ -43,5 +49,8 @@ DEFAULT_MODULE = {
    "BLOOMZ": "query_key_value",
    "Falcon": "query_key_value",
    "Baichuan": "W_pack",
-    "InternLM": "q_proj,v_proj"
+    "InternLM": "q_proj,v_proj",
+    "Qwen": "c_attn",
+    "XVERSE": "q_proj,v_proj",
+    "ChatGLM2": "query_key_value"
 }
--- a/src/llmtuner/extras/misc.py
+++ b/src/llmtuner/extras/misc.py
@@ -1,8 +1,11 @@
 import torch
 from typing import TYPE_CHECKING, List, Optional, Tuple
-
-from transformers.generation.utils import LogitsProcessorList
-from transformers.generation.logits_process import LogitsProcessor
+from transformers import (
+    LogitsProcessor,
+    LogitsProcessorList,
+    StoppingCriteria,
+    StoppingCriteriaList
+)

 from llmtuner.extras.constants import LAYERNORM_NAMES

@@ -30,7 +33,6 @@ class AverageMeter:
        self.avg = self.sum / self.count


-# Avoids runtime error in model.generate(do_sample=True).
 class InvalidScoreLogitsProcessor(LogitsProcessor):

    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
@@ -46,6 +48,22 @@ def get_logits_processor() -> LogitsProcessorList:
    return logits_processor


+class StopWordsCriteria(StoppingCriteria):
+
+    def __init__(self, stop_ids: List[int]) -> None:
+        super().__init__()
+        self.stop_ids = stop_ids
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        return any([stop_id in input_ids[:, -1] for stop_id in self.stop_ids])
+
+
+def get_stopping_criteria(stop_ids: List[int]) -> StoppingCriteriaList:
+    stopping_criteria = StoppingCriteriaList()
+    stopping_criteria.append(StopWordsCriteria(stop_ids))
+    return stopping_criteria
+
+
 def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
    r"""
    Returns the number of trainable parameters and number of all parameters in the model.
--- a/src/llmtuner/extras/template.py
+++ b/src/llmtuner/extras/template.py
@@ -1,92 +1,213 @@
-from typing import Dict, List, Optional, Tuple
+import tiktoken
 from dataclasses import dataclass
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+
+from llmtuner.extras.logging import get_logger
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer
+
+
+logger = get_logger(__name__)


@dataclass
 class Template:

-    prefix: str
-    prompt: str
-    sep: str
+    prefix: List[Union[str, Dict[str, str]]]
+    prompt: List[Union[str, Dict[str, str]]]
+    sep: List[Union[str, Dict[str, str]]]
+    stop_words: List[str]
    use_history: bool

-    def get_prompt(
+    def encode_oneturn(
        self,
+        tokenizer: "PreTrainedTokenizer",
        query: str,
+        resp: str,
        history: Optional[List[Tuple[str, str]]] = None,
-        prefix: Optional[str] = "",
-        eos_token: Optional[str] = "</s>"
-    ) -> str:
+        prefix: Optional[str] = None
+    ) -> Tuple[List[int], List[int]]:
        r"""
-        Returns a string containing prompt without response.
+        Returns a single pair of token ids representing prompt and response respectively.
        """
-        return eos_token.join(map(lambda x: x[0] + x[1], self._format_example(query, history, prefix)))
+        prefix, history = self._format(query, resp, history, prefix)
+        encoded_pairs = self._encode(tokenizer, prefix, history)
+        prompt_ids = []
+        for query_ids, resp_ids in encoded_pairs[:-1]:
+            prompt_ids = prompt_ids + query_ids + resp_ids
+        prompt_ids = prompt_ids + encoded_pairs[-1][0]
+        return prompt_ids, encoded_pairs[-1][1]

-    def get_dialog(
+    def encode_multiturn(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        query: str,
+        resp: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        prefix: Optional[str] = None
+    ) -> List[Tuple[List[int], List[int]]]:
+        r"""
+        Returns multiple pairs of token ids representing prompts and responses respectively.
+        """
+        prefix, history = self._format(query, resp, history, prefix)
+        encoded_pairs = self._encode(tokenizer, prefix, history)
+        return encoded_pairs
+
+    def _format(
        self,
        query: str,
        resp: str,
        history: Optional[List[Tuple[str, str]]] = None,
-        prefix: Optional[str] = ""
-    ) -> List[Tuple[str, str]]:
+        prefix: Optional[str] = None
+    ) -> Tuple[List[Union[str, Dict[str, str]]], List[Tuple[str, str]]]:
        r"""
-        Returns a list containing prompt-response pairs.
+        Aligns inputs to the standard format.
        """
-        result = self._format_example(query, history, prefix)
-        result[-1][-1] = resp
-        return result
-
-    def _format_example(
-        self,
-        query: str,
-        history: Optional[List[Tuple[str, str]]] = None,
-        prefix: Optional[str] = ""
-    ) -> List[Tuple[str, str]]:
-        prefix = prefix or self.prefix # use prefix if provided
-        prefix = prefix + self.sep if prefix else "" # add separator for non-empty prefix
+        prefix = [prefix] if prefix else self.prefix # use prefix if provided
        history = history if (history and self.use_history) else []
-        history = history + [(query, "")]
-        return [
-            [(self.sep if i else prefix) + self.prompt.format(query=q), r]
-            for i, (q, r) in enumerate(history)
-        ]
+        history = history + [(query, resp)]
+        return prefix, history
+
+    def _get_special_ids(
+        self,
+        tokenizer: "PreTrainedTokenizer"
+    ) -> Tuple[List[int], List[int]]:
+        if tokenizer.bos_token_id:
+            bos_ids = [tokenizer.bos_token_id]
+        else:
+            bos_ids = [] # bos token is optional
+
+        if tokenizer.eos_token_id:
+            eos_ids = [tokenizer.eos_token_id]
+        else:
+            raise ValueError("EOS token is required.")
+
+        return bos_ids, eos_ids
+
+    def _encode(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        prefix: List[Union[str, Dict[str, str]]],
+        history: List[Tuple[str, str]]
+    ) -> List[Tuple[List[int], List[int]]]:
+        r"""
+        Encodes formatted inputs to pairs of token ids.
+        Turn 0: bos + prefix + sep + query    resp + eos
+        Turn t: sep + bos + query             resp + eos
+        """
+        bos_ids, eos_ids = self._get_special_ids(tokenizer)
+        sep_ids = self._convert_inputs_to_ids(tokenizer, context=self.sep)
+        encoded_pairs = []
+        for turn_idx, (query, resp) in enumerate(history):
+            if turn_idx == 0:
+                if prefix: # has prefix
+                    prefix_ids = bos_ids + self._convert_inputs_to_ids(tokenizer, context=prefix) + sep_ids
+                else:
+                    prefix_ids = bos_ids
+            else:
+                prefix_ids = sep_ids + bos_ids
+
+            query_ids = self._convert_inputs_to_ids(tokenizer, context=self.prompt, query=query, idx=str(turn_idx))
+            resp_ids = self._convert_inputs_to_ids(tokenizer, context=[resp])
+            encoded_pairs.append((prefix_ids + query_ids, resp_ids + eos_ids))
+        return encoded_pairs
+
+    def _convert_inputs_to_ids(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        context: List[Union[str, Dict[str, str]]],
+        query: Optional[str] = "",
+        idx: Optional[str] = ""
+    ) -> List[int]:
+        r"""
+        Converts context to token ids.
+        """
+        if isinstance(getattr(tokenizer, "tokenizer", None), tiktoken.Encoding): # for tiktoken tokenizer (Qwen)
+            kwargs = dict(allowed_special="all")
+        else:
+            kwargs = dict(add_special_tokens=False)
+
+        token_ids = []
+        for elem in context:
+            if isinstance(elem, str):
+                elem = elem.replace("{{query}}", query, 1)
+                elem = elem.replace("{{idx}}", idx, 1)
+                token_ids = token_ids + tokenizer.encode(elem, **kwargs)
+            elif isinstance(elem, dict):
+                token_ids = token_ids + [tokenizer.convert_tokens_to_ids(elem.get("token"))]
+            else:
+                raise NotImplementedError
+        return token_ids


@dataclass
 class Llama2Template(Template):

-    def _format_example(
+    def _encode(
        self,
-        query: str,
-        history: Optional[List[Tuple[str, str]]] = None,
-        prefix: Optional[str] = ""
-    ) -> List[Tuple[str, str]]:
-        prefix = prefix or self.prefix # use prefix if provided
-        prefix = prefix if prefix.startswith("<<SYS>>") else "<<SYS>>\n{}\n<</SYS>>\n\n".format(prefix)
-        history = history if (history and self.use_history) else []
-        history = history + [(query, "")]
-        return [
-            [(self.sep if i else "") + self.prompt.format(query=(q if i else prefix + q)), r]
-            for i, (q, r) in enumerate(history)
-        ]
+        tokenizer: "PreTrainedTokenizer",
+        prefix: List[Union[str, Dict[str, str]]],
+        history: List[Tuple[str, str]]
+    ) -> List[Tuple[List[int], List[int]]]:
+        r"""
+        Encodes formatted inputs to pairs of token ids.
+        Turn 0: bos + prefix + query    resp + eos
+        Turn t: bos + query             resp + eos
+        """
+        bos_ids, eos_ids = self._get_special_ids(tokenizer)
+        encoded_pairs = []
+        assert isinstance(prefix[0], str), "LLaMA-2 template only accepts list containing a single string."
+        for turn_idx, (query, resp) in enumerate(history):
+            if turn_idx == 0: # llama2 template has not sep_ids
+                query = prefix[0] + query
+            query_ids = self._convert_inputs_to_ids(tokenizer, context=self.prompt, query=query)
+            resp_ids = self._convert_inputs_to_ids(tokenizer, context=[resp])
+            encoded_pairs.append((bos_ids + query_ids, resp_ids + eos_ids))
+        return encoded_pairs


 templates: Dict[str, Template] = {}


-def register_template(name: str, prefix: str, prompt: str, sep: str, use_history: bool) -> None:
-    template_class = Llama2Template if name == "llama2" else Template
+def register_template(
+    name: str,
+    prefix: List[Union[str, Dict[str, str]]],
+    prompt: List[Union[str, Dict[str, str]]],
+    sep: List[Union[str, Dict[str, str]]],
+    stop_words: List[str],
+    use_history: bool
+) -> None:
+    template_class = Llama2Template if "llama2" in name else Template
    templates[name] = template_class(
        prefix=prefix,
        prompt=prompt,
        sep=sep,
+        stop_words=stop_words,
        use_history=use_history
    )


-def get_template(name: str) -> Template:
+def get_template_and_fix_tokenizer(
+    name: str,
+    tokenizer: "PreTrainedTokenizer"
+) -> Template:
    template = templates.get(name, None)
    assert template is not None, "Template {} does not exist.".format(name)
+
+    if len(template.stop_words): # inplace method
+        tokenizer.eos_token = template.stop_words[0]
+        logger.info("Replace eos token: {}".format(tokenizer.eos_token))
+
+    if tokenizer.eos_token_id is None:
+        tokenizer.eos_token = "<|endoftext|>"
+        logger.info("Add eos token: {}".format(tokenizer.eos_token))
+
+    if tokenizer.pad_token_id is None:
+        tokenizer.pad_token = tokenizer.eos_token
+        logger.info("Add pad token: {}".format(tokenizer.pad_token))
+
+    tokenizer.add_special_tokens(dict(additional_special_tokens=template.stop_words))
    return template


@@ -95,9 +216,12 @@ Supports language model inference without histories.
 """
 register_template(
    name="vanilla",
-    prefix="",
-    prompt="{query}",
-    sep="",
+    prefix=[],
+    prompt=[
+        "{{query}}"
+    ],
+    sep=[],
+    stop_words=[],
    use_history=False
 )

@@ -107,10 +231,17 @@ Default template.
 """
 register_template(
    name="default",
-    prefix="A chat between a curious user and an artificial intelligence assistant. "
-           "The assistant gives helpful, detailed, and polite answers to the user's questions.",
-    prompt="Human: {query}\nAssistant: ",
-    sep="\n",
+    prefix=[
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ],
+    prompt=[
+        "Human: {{query}}\nAssistant: "
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[],
    use_history=True
 )

@@ -122,16 +253,38 @@ Supports: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
 """
 register_template(
    name="llama2",
-    prefix="<<SYS>>\nYou are a helpful, respectful and honest assistant. "
+    prefix=[
+        "<<SYS>>\nYou are a helpful, respectful and honest assistant. "
        "Always answer as helpfully as possible, while being safe.  "
        "Your answers should not include any harmful, unethical, "
        "racist, sexist, toxic, dangerous, or illegal content. "
        "Please ensure that your responses are socially unbiased and positive in nature.\n"
        "If a question does not make any sense, or is not factually coherent, "
        "explain why instead of answering something not correct. "
-           "If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n",
-    prompt="[INST] {query} [/INST] ",
-    sep="<s>",
+        "If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"
+    ],
+    prompt=[
+        "[INST] {{query}} [/INST] "
+    ],
+    sep=[],
+    stop_words=[],
+    use_history=True
+)
+
+
+r"""
+Supports: https://github.com/ymcui/Chinese-LLaMA-Alpaca-2
+"""
+register_template(
+    name="llama2_zh",
+    prefix=[
+        "<<SYS>>\nYou are a helpful assistant. 你是一个乐于助人的助手。\n<</SYS>>\n\n"
+    ],
+    prompt=[
+        "[INST] {{query}} [/INST] "
+    ],
+    sep=[],
+    stop_words=[],
    use_history=True
 )

@@ -142,10 +295,17 @@ Supports: https://huggingface.co/tatsu-lab/alpaca-7b-wdiff
 """
 register_template(
    name="alpaca",
-    prefix="Below is an instruction that describes a task. "
-           "Write a response that appropriately completes the request.",
-    prompt="### Instruction:\n{query}\n\n### Response:\n",
-    sep="\n\n",
+    prefix=[
+        "Below is an instruction that describes a task. "
+        "Write a response that appropriately completes the request."
+    ],
+    prompt=[
+        "### Instruction:\n{{query}}\n\n### Response:\n"
+    ],
+    sep=[
+        "\n\n"
+    ],
+    stop_words=[],
    use_history=True
 )

@@ -156,10 +316,15 @@ Supports: https://huggingface.co/lmsys/vicuna-7b-delta-v1.1
 """
 register_template(
    name="vicuna",
-    prefix="A chat between a curious user and an artificial intelligence assistant. "
-           "The assistant gives helpful, detailed, and polite answers to the user's questions.",
-    prompt="USER: {query} ASSISTANT: ",
-    sep="",
+    prefix=[
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ],
+    prompt=[
+        "USER: {{query}} ASSISTANT: "
+    ],
+    sep=[],
+    stop_words=[],
    use_history=True
 )

@@ -169,9 +334,14 @@ Supports: https://huggingface.co/BelleGroup/BELLE-LLaMA-EXT-13B
 """
 register_template(
    name="belle",
-    prefix="",
-    prompt="Human: {query}\n\nBelle: ",
-    sep="\n\n",
+    prefix=[],
+    prompt=[
+        "Human: {{query}}\n\nBelle: "
+    ],
+    sep=[
+        "\n\n"
+    ],
+    stop_words=[],
    use_history=True
 )

@@ -181,9 +351,14 @@ Supports: https://github.com/CVI-SZU/Linly
 """
 register_template(
    name="linly",
-    prefix="",
-    prompt="User: {query}\nBot: ",
-    sep="\n",
+    prefix=[],
+    prompt=[
+        "User: {{query}}\nBot: "
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[],
    use_history=True
 )

@@ -193,9 +368,14 @@ Supports: https://github.com/Neutralzz/BiLLa
 """
 register_template(
    name="billa",
-    prefix="",
-    prompt="Human: {query}\nAssistant: ",
-    sep="\n",
+    prefix=[],
+    prompt=[
+        "Human: {{query}}\nAssistant: "
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[],
    use_history=True
 )

@@ -205,9 +385,17 @@ Supports: https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1
 """
 register_template(
    name="ziya",
-    prefix="",
-    prompt="<human>:{query}\n<bot>:",
-    sep="\n",
+    prefix=[],
+    prompt=[
+        {"token": "<human>"},
+        ":{{query}}\n",
+        {"token": "<bot>"},
+        ":"
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[],
    use_history=True
 )

@@ -217,10 +405,17 @@ Supports: https://huggingface.co/qhduan/aquilachat-7b
 """
 register_template(
    name="aquila",
-    prefix="A chat between a curious human and an artificial intelligence assistant. "
-           "The assistant gives helpful, detailed, and polite answers to the human's questions.",
-    prompt="Human: {query}###Assistant: ",
-    sep="###",
+    prefix=[
+        "A chat between a curious human and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the human's questions."
+    ],
+    prompt=[
+        "Human: {{query}}###Assistant: "
+    ],
+    sep=[
+        "###"
+    ],
+    stop_words=[],
    use_history=True
 )

@@ -230,9 +425,18 @@ Supports: https://huggingface.co/internlm/internlm-chat-7b
 """
 register_template(
    name="intern",
-    prefix="",
-    prompt="<|User|>:{query}<eoh>\n<|Bot|>:",
-    sep="<eoa>\n",
+    prefix=[],
+    prompt=[
+        "<|User|>:{{query}}",
+        {"token": "<eoh>"},
+        "\n<|Bot|>:"
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[
+        "<eoa>"
+    ],
    use_history=True
 )

@@ -242,9 +446,17 @@ Supports: https://huggingface.co/baichuan-inc/Baichuan-13B-Chat
 """
 register_template(
    name="baichuan",
-    prefix="",
-    prompt="<reserved_102>{query}<reserved_103>",
-    sep="",
+    prefix=[
+        {"token": "<reserved_102>"} # user token (a little difference in the first turn)
+    ],
+    prompt=[
+        "{{query}}",
+        {"token": "<reserved_103>"} # assistant token
+    ],
+    sep=[],
+    stop_words=[
+        "<reserved_102>" # user token
+    ],
    use_history=True
 )

@@ -255,8 +467,71 @@ Supports: https://huggingface.co/HuggingFaceH4/starchat-alpha
 """
 register_template(
    name="starchat",
-    prefix="<|system|>\n",
-    prompt="<|user|>\n{query}<|end|>\n<|assistant|>\n",
-    sep="<|end|>\n",
+    prefix=[
+        {"token": "<|system|>"},
+        "\n",
+        {"token": "<|end|>"}
+    ],
+    prompt=[
+        {"token": "<|user|>"},
+        "\n{{query}}",
+        {"token": "<|end|>"},
+        "\n",
+        {"token": "<|assistant|>"}
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[
+        "<|end|>"
+    ],
+    use_history=True
+)
+
+
+r"""
+Supports: https://huggingface.co/Qwen/Qwen-7B-Chat
+"""
+register_template(
+    name="chatml",
+    prefix=[
+        {"token": "<|im_start|>"},
+        "system\nYou are a helpful assistant.",
+        {"token": "<|im_end|>"}
+    ],
+    prompt=[
+        {"token": "<|im_start|>"},
+        "user\n{{query}}",
+        {"token": "<|im_end|>"},
+        "\n",
+        {"token": "<|im_start|>"},
+        "assistant\n"
+    ],
+    sep=[
+        "\n"
+    ],
+    stop_words=[
+        "<|im_end|>"
+    ],
+    use_history=True
+)
+
+
+r"""
+Supports: https://huggingface.co/THUDM/chatglm2-6b
+"""
+register_template(
+    name="chatglm2",
+    prefix=[
+        {"token": "[gMASK]"},
+        {"token": "sop"}
+    ],
+    prompt=[
+        "[Round {{idx}}]\n\n问：{{query}}\n\n答："
+    ],
+    sep=[
+        "\n\n"
+    ],
+    stop_words=[],
    use_history=True
 )
--- a/src/llmtuner/hparams/data_args.py
+++ b/src/llmtuner/hparams/data_args.py
@@ -24,7 +24,7 @@ class DatasetAttr:

@dataclass
 class DataArguments:
-    """
+    r"""
    Arguments pertaining to what data we are going to input our model for training and evaluation.
    """
    template: str = field(
@@ -54,6 +54,10 @@ class DataArguments:
        default="concat",
        metadata={"help": "Strategy to use in dataset mixing."}
    )
+    interleave_probs: Optional[str] = field(
+        default=None,
+        metadata={"help": "Probabilities to sample data from datasets. Use commas to separate multiple datasets."}
+    )
    overwrite_cache: Optional[bool] = field(
        default=False,
        metadata={"help": "Overwrite the cached training and evaluation sets."}
@@ -86,9 +90,9 @@ class DataArguments:
        default=None,
        metadata={"help": "A prefix to add before every source text. Use `|` to separate multiple prefixes in training."}
    )
-    dev_ratio: Optional[float] = field(
+    val_size: Optional[float] = field(
        default=0,
-        metadata={"help": "Proportion of the dataset to include in the development set, should be between 0.0 and 1.0."}
+        metadata={"help": "Size of the development set, should be an integer or a float in range `[0,1)`."}
    )

    def init_for_training(self): # support mixing multiple datasets
@@ -103,6 +107,9 @@ class DataArguments:
        else:
            prefix_list = [None] * len(dataset_names)

+        if self.interleave_probs is not None:
+            self.interleave_probs = [float(prob.strip()) for prob in self.interleave_probs.split(",")]
+
        self.dataset_list: List[DatasetAttr] = []
        for i, name in enumerate(dataset_names):
            if name not in dataset_info:
--- a/src/llmtuner/hparams/finetuning_args.py
+++ b/src/llmtuner/hparams/finetuning_args.py
@@ -5,7 +5,7 @@ from dataclasses import asdict, dataclass, field

@dataclass
 class FinetuningArguments:
-    """
+    r"""
    Arguments pertaining to which techniques we are going to fine-tuning with.
    """
    finetuning_type: Optional[Literal["none", "freeze", "lora", "full"]] = field(
@@ -14,23 +14,27 @@ class FinetuningArguments:
    )
    num_hidden_layers: Optional[int] = field(
        default=32,
-        metadata={"help": "Number of decoder blocks in the model. \
+        metadata={"help": "Number of decoder blocks in the model for partial-parameter (freeze) fine-tuning. \
                  LLaMA choices: [\"32\", \"40\", \"60\", \"80\"], \
                  LLaMA-2 choices: [\"32\", \"40\", \"80\"], \
                  BLOOM choices: [\"24\", \"30\", \"70\"], \
                  Falcon choices: [\"32\", \"60\"], \
-                  Baichuan choices: [\"32\", \"40\"]"}
+                  Baichuan choices: [\"32\", \"40\"] \
+                  Qwen choices: [\"32\"], \
+                  XVERSE choices: [\"40\"]"}
    )
    num_layer_trainable: Optional[int] = field(
        default=3,
-        metadata={"help": "Number of trainable layers for Freeze fine-tuning."}
+        metadata={"help": "Number of trainable layers for partial-parameter (freeze) fine-tuning."}
    )
    name_module_trainable: Optional[Literal["mlp", "self_attn", "self_attention"]] = field(
        default="mlp",
-        metadata={"help": "Name of trainable modules for Freeze fine-tuning. \
-                  LLaMA & LLaMA-2 choices: [\"mlp\", \"self_attn\"], \
+        metadata={"help": "Name of trainable modules for partial-parameter (freeze) fine-tuning. \
+                  LLaMA choices: [\"mlp\", \"self_attn\"], \
                  BLOOM & Falcon choices: [\"mlp\", \"self_attention\"], \
-                  Baichuan choices: [\"mlp\", \"self_attn\"]"}
+                  Baichuan choices: [\"mlp\", \"self_attn\"], \
+                  Qwen choices: [\"mlp\", \"attn\"], \
+                  LLaMA-2, InternLM, XVERSE choices: the same as LLaMA."}
    )
    lora_rank: Optional[int] = field(
        default=8,
@@ -47,9 +51,19 @@ class FinetuningArguments:
    lora_target: Optional[str] = field(
        default="q_proj,v_proj",
        metadata={"help": "Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. \
-                  LLaMA & LLaMA-2 choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
+                  LLaMA choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
                  BLOOM & Falcon choices: [\"query_key_value\", \"self_attention.dense\", \"mlp.dense\"], \
-                  Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"]"}
+                  Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
+                  Qwen choices: [\"c_attn\", \"attn.c_proj\", \"w1\", \"w2\", \"mlp.c_proj\"], \
+                  LLaMA-2, InternLM, XVERSE choices: the same as LLaMA."}
+    )
+    resume_lora_training: Optional[bool] = field(
+        default=True,
+        metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
+    )
+    dpo_beta: Optional[float] = field(
+        default=0.1,
+        metadata={"help": "The beta parameter for the DPO loss."}
    )

    def __post_init__(self):
@@ -66,14 +80,14 @@ class FinetuningArguments:
        assert self.finetuning_type in ["none", "freeze", "lora", "full"], "Invalid fine-tuning method."

    def save_to_json(self, json_path: str):
-        """Saves the content of this instance in JSON format inside `json_path`."""
+        r"""Saves the content of this instance in JSON format inside `json_path`."""
        json_string = json.dumps(asdict(self), indent=2, sort_keys=True) + "\n"
        with open(json_path, "w", encoding="utf-8") as f:
            f.write(json_string)

    @classmethod
    def load_from_json(cls, json_path: str):
-        """Creates an instance from the content of `json_path`."""
+        r"""Creates an instance from the content of `json_path`."""
        with open(json_path, "r", encoding="utf-8") as f:
            text = f.read()
        return cls(**json.loads(text))
--- a/src/llmtuner/hparams/general_args.py
+++ b/src/llmtuner/hparams/general_args.py
@@ -4,10 +4,10 @@ from dataclasses import dataclass, field

@dataclass
 class GeneralArguments:
-    """
+    r"""
    Arguments pertaining to which stage we are going to perform.
    """
-    stage: Optional[Literal["pt", "sft", "rm", "ppo"]] = field(
+    stage: Optional[Literal["pt", "sft", "rm", "ppo", "dpo"]] = field(
        default="sft",
        metadata={"help": "Which stage will be performed in training."}
    )
--- a/src/llmtuner/hparams/generating_args.py
+++ b/src/llmtuner/hparams/generating_args.py
@@ -4,7 +4,7 @@ from dataclasses import asdict, dataclass, field

@dataclass
 class GeneratingArguments:
-    """
+    r"""
    Arguments pertaining to specify the decoding parameters.
    """
    do_sample: Optional[bool] = field(
--- a/src/llmtuner/hparams/model_args.py
+++ b/src/llmtuner/hparams/model_args.py
@@ -5,7 +5,7 @@ from dataclasses import dataclass, field

@dataclass
 class ModelArguments:
-    """
+    r"""
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune.
    """
    model_name_or_path: str = field(
@@ -55,14 +55,14 @@ class ModelArguments:
        default=None,
        metadata={"help": "Path to the directory containing the checkpoints of the reward model."}
    )
-    resume_lora_training: Optional[bool] = field(
-        default=True,
-        metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
-    )
    plot_loss: Optional[bool] = field(
        default=False,
        metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
    )
+    hf_auth_token: Optional[str] = field(
+        default=None,
+        metadata={"help": "Auth token to log in with Hugging Face Hub."}
+    )

    def __post_init__(self):
        if self.checkpoint_dir is not None: # support merging multiple lora weights
@@ -70,3 +70,7 @@ class ModelArguments:

        if self.quantization_bit is not None:
            assert self.quantization_bit in [4, 8], "We only accept 4-bit or 8-bit quantization."
+
+        if self.use_auth_token == True and self.hf_auth_token is not None:
+            from huggingface_hub.hf_api import HfFolder # lazy load
+            HfFolder.save_token(self.hf_auth_token)
--- a/src/llmtuner/tuner/init.py
+++ b/src/llmtuner/tuner/init.py
@@ -1,5 +1 @@
-from llmtuner.tuner.core import get_train_args, get_infer_args, load_model_and_tokenizer
-from llmtuner.tuner.pt import run_pt
-from llmtuner.tuner.sft import run_sft
-from llmtuner.tuner.rm import run_rm
-from llmtuner.tuner.ppo import run_ppo
+from llmtuner.tuner.tune import export_model, run_exp
--- a/src/llmtuner/tuner/core/adapter.py
+++ b/src/llmtuner/tuner/core/adapter.py
@@ -39,7 +39,7 @@ def init_adapter(
    if finetuning_args.finetuning_type == "none" and is_trainable:
        raise ValueError("You cannot use finetuning_type=none while training.")

-    if finetuning_args.finetuning_type == "full":
+    if finetuning_args.finetuning_type == "full" and is_trainable:
        logger.info("Fine-tuning method: Full")
        model = model.float()

@@ -65,7 +65,7 @@ def init_adapter(
            assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], CONFIG_NAME)), \
                "The given checkpoint may be not a LoRA checkpoint, please specify `--finetuning_type full/freeze` instead."

-            if (is_trainable and model_args.resume_lora_training) or (not is_mergeable): # continually train on the lora weights
+            if (is_trainable and finetuning_args.resume_lora_training) or (not is_mergeable): # continually fine-tuning
                checkpoints_to_merge, latest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
            else:
                checkpoints_to_merge = model_args.checkpoint_dir
--- a/src/llmtuner/tuner/core/loader.py
+++ b/src/llmtuner/tuner/core/loader.py
@@ -6,13 +6,14 @@ from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
-    BitsAndBytesConfig
+    BitsAndBytesConfig,
+    PretrainedConfig,
+    PreTrainedModel,
+    PreTrainedTokenizerBase
 )
 from transformers.utils import check_min_version
 from transformers.utils.versions import require_version
 from transformers.deepspeed import is_deepspeed_zero3_enabled
-from transformers.modeling_utils import PretrainedConfig, PreTrainedModel
-from transformers.tokenization_utils import PreTrainedTokenizerBase
 from trl import AutoModelForCausalLMWithValueHead

 from llmtuner.extras.logging import reset_logging, get_logger
@@ -22,6 +23,7 @@ from llmtuner.hparams import FinetuningArguments
 from llmtuner.tuner.core.adapter import init_adapter

 if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer
    from llmtuner.hparams import ModelArguments


@@ -32,7 +34,7 @@ check_min_version("4.29.1")
 require_version("datasets>=2.12.0", "To fix: pip install datasets>=2.12.0")
 require_version("accelerate>=0.21.0", "To fix: pip install accelerate>=0.21.0")
 require_version("peft>=0.4.0", "To fix: pip install peft>=0.4.0")
-require_version("trl>=0.4.7", "To fix: pip install trl>=0.4.7")
+require_version("trl>=0.5.0", "To fix: pip install trl>=0.5.0")


 def load_model_and_tokenizer(
@@ -40,7 +42,7 @@ def load_model_and_tokenizer(
    finetuning_args: "FinetuningArguments",
    is_trainable: Optional[bool] = False,
    stage: Optional[Literal["pt", "sft", "rm", "ppo"]] = "sft"
-) -> Tuple[PreTrainedModel, PreTrainedTokenizerBase]:
+) -> Tuple[PreTrainedModel, "PreTrainedTokenizer"]:
    r"""
    Loads pretrained model and tokenizer.

@@ -50,9 +52,6 @@ def load_model_and_tokenizer(
        logger.warning("Checkpoint is not found at evaluation, load the original model.")
        finetuning_args = FinetuningArguments(finetuning_type="none")

-    assert stage in ["pt", "sft"] or finetuning_args.finetuning_type == "lora", \
-        "RM and PPO training can only be performed with the LoRA method."
-
    config_kwargs = {
        "trust_remote_code": True,
        "cache_dir": model_args.cache_dir,
@@ -66,10 +65,13 @@ def load_model_and_tokenizer(
        padding_side=model_args.padding_side,
        **config_kwargs
    )
-    if tokenizer.pad_token_id is None or tokenizer.pad_token_id == 64000: # 64000 for baichuan model (older version)
-        tokenizer.pad_token_id = 0 # set as the <unk> token

-    config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
+    if model_args.checkpoint_dir is not None and finetuning_args.finetuning_type == "full":
+        model_to_load = model_args.checkpoint_dir[0]
+    else:
+        model_to_load = model_args.model_name_or_path
+
+    config = AutoConfig.from_pretrained(model_to_load, **config_kwargs)
    is_mergeable = True

    # Quantization configurations (using bitsandbytes library).
@@ -77,10 +79,7 @@ def load_model_and_tokenizer(
        if model_args.quantization_bit == 8:
            require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
            config_kwargs["load_in_8bit"] = True
-            config_kwargs["quantization_config"] = BitsAndBytesConfig(
-                load_in_8bit=True,
-                llm_int8_threshold=6.0
-            )
+            config_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)

        elif model_args.quantization_bit == 4:
            require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
@@ -93,18 +92,8 @@ def load_model_and_tokenizer(
            )

        is_mergeable = False
-        logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
-
-    if (
-        model_args.quantization_bit is not None
-        or (os.environ.get('LOCAL_RANK') is not None and not is_deepspeed_zero3_enabled())
-    ):
        config_kwargs["device_map"] = {"": int(os.environ.get("LOCAL_RANK", "0"))}
-
-    if model_args.checkpoint_dir is not None and finetuning_args.finetuning_type == "full":
-        model_to_load = model_args.checkpoint_dir[0]
-    else:
-        model_to_load = model_args.model_name_or_path
+        logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))

    # Load and prepare pretrained models (without valuehead).
    model = AutoModelForCausalLM.from_pretrained(
@@ -128,7 +117,7 @@ def load_model_and_tokenizer(
    model = init_adapter(model, model_args, finetuning_args, is_trainable, is_mergeable)

    if stage == "rm" or stage == "ppo": # add value head
-        model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
+        model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(model)
        reset_logging()

        if stage == "rm" and model_args.checkpoint_dir is not None: # load valuehead weights to evaluate reward model
@@ -140,8 +129,6 @@ def load_model_and_tokenizer(
                })

        if stage == "ppo": # load reward model
-            assert is_trainable, "PPO stage cannot be performed at evaluation."
-            assert model_args.reward_model is not None, "Reward model is necessary for PPO training."
            logger.info("Load reward model from {}".format(model_args.reward_model))
            model.pretrained_model.load_adapter(model_args.reward_model, "reward", is_trainable=False)
            assert load_valuehead_params(model, model_args.reward_model), "Reward model is not correctly loaded."
--- a/src/llmtuner/tuner/core/parser.py
+++ b/src/llmtuner/tuner/core/parser.py
@@ -19,7 +19,7 @@ from llmtuner.hparams import (
 logger = get_logger(__name__)


-def _parse_args(parser: HfArgumentParser, args: Optional[Dict[str, Any]] = None):
+def _parse_args(parser: HfArgumentParser, args: Optional[Dict[str, Any]] = None) -> Tuple[Any]:
    if args is not None:
        return parser.parse_dict(args)
    elif len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
@@ -32,26 +32,53 @@ def _parse_args(parser: HfArgumentParser, args: Optional[Dict[str, Any]] = None)

 def parse_train_args(
    args: Optional[Dict[str, Any]] = None
-) -> Tuple[ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneralArguments]:
+) -> Tuple[
+    ModelArguments,
+    DataArguments,
+    Seq2SeqTrainingArguments,
+    FinetuningArguments,
+    GeneratingArguments,
+    GeneralArguments
+]:
    parser = HfArgumentParser((
-        ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneralArguments
+        ModelArguments,
+        DataArguments,
+        Seq2SeqTrainingArguments,
+        FinetuningArguments,
+        GeneratingArguments,
+        GeneralArguments
    ))
    return _parse_args(parser, args)


 def parse_infer_args(
    args: Optional[Dict[str, Any]] = None
-) -> Tuple[ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]:
+) -> Tuple[
+    ModelArguments,
+    DataArguments,
+    FinetuningArguments,
+    GeneratingArguments
+]:
    parser = HfArgumentParser((
-        ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
+        ModelArguments,
+        DataArguments,
+        FinetuningArguments,
+        GeneratingArguments
    ))
    return _parse_args(parser, args)


 def get_train_args(
    args: Optional[Dict[str, Any]] = None
-) -> Tuple[ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneralArguments]:
-    model_args, data_args, training_args, finetuning_args, general_args = parse_train_args(args)
+) -> Tuple[
+    ModelArguments,
+    DataArguments,
+    Seq2SeqTrainingArguments,
+    FinetuningArguments,
+    GeneratingArguments,
+    GeneralArguments
+]:
+    model_args, data_args, training_args, finetuning_args, generating_args, general_args = parse_train_args(args)

    # Setup logging
    if training_args.should_log:
@@ -67,33 +94,42 @@ def get_train_args(
    # Check arguments (do not check finetuning_args since it may be loaded from checkpoints)
    data_args.init_for_training()

-    assert general_args.stage == "sft" or (not training_args.predict_with_generate), \
-        "`predict_with_generate` cannot be set as True at PT, RM and PPO stages."
+    if general_args.stage != "sft" and training_args.predict_with_generate:
+        raise ValueError("`predict_with_generate` cannot be set as True except SFT.")

-    assert not (training_args.do_train and training_args.predict_with_generate), \
-        "`predict_with_generate` cannot be set as True while training."
+    if training_args.do_train and training_args.predict_with_generate:
+        raise ValueError("`predict_with_generate` cannot be set as True while training.")

-    assert general_args.stage != "sft" or (not training_args.do_predict) or training_args.predict_with_generate, \
-        "Please enable `predict_with_generate` to save model predictions."
+    if general_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate:
+        raise ValueError("Please enable `predict_with_generate` to save model predictions.")

-    assert model_args.quantization_bit is None or finetuning_args.finetuning_type == "lora", \
-        "Quantization is only compatible with the LoRA method."
+    if general_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type != "lora":
+        raise ValueError("RM and PPO training can only be performed with the LoRA method.")

-    assert not (training_args.max_steps == -1 and data_args.streaming), \
-        "Please specify `max_steps` in streaming mode."
+    if general_args.stage in ["ppo", "dpo"] and not training_args.do_train:
+        raise ValueError("PPO and DPO stage can only be performed at training.")

-    assert training_args.evaluation_strategy == "no" or (not data_args.streaming), \
-        "Streaming mode does not support evaluation currently."
+    if general_args.stage == "ppo" and model_args.reward_model is None:
+        raise ValueError("Reward model is necessary for PPO training.")

-    assert not (general_args.stage == "ppo" and data_args.streaming), \
-        "Streaming mode does not suppport PPO training currently."
+    if training_args.max_steps == -1 and data_args.streaming:
+        raise ValueError("Please specify `max_steps` in streaming mode.")
+
+    if general_args.stage == "ppo" and data_args.streaming:
+        raise ValueError("Streaming mode does not suppport PPO training currently.")
+
+    if data_args.val_size > 1e-6 and data_args.val_size < 1 and data_args.streaming:
+        raise ValueError("Streaming mode should have an integer val size.")
+
+    if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
+        raise ValueError("Quantization is only compatible with the LoRA method.")

    if model_args.checkpoint_dir is not None:
        if finetuning_args.finetuning_type != "lora":
-            assert len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
-        else:
-            assert model_args.quantization_bit is None or len(model_args.checkpoint_dir) == 1, \
-                "Quantized model only accepts a single checkpoint."
+            if len(model_args.checkpoint_dir) != 1:
+                raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
+        elif model_args.quantization_bit is not None and len(model_args.checkpoint_dir) != 1:
+                raise ValueError("Quantized model only accepts a single checkpoint.")

    if model_args.quantization_bit is not None and (not training_args.do_train):
        logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
@@ -113,10 +149,6 @@ def get_train_args(
        logger.warning("`max_samples` is incompatible with `streaming`. Disabling max_samples.")
        data_args.max_samples = None

-    if data_args.dev_ratio > 1e-6 and data_args.streaming:
-        logger.warning("`dev_ratio` is incompatible with `streaming`. Disabling development set.")
-        data_args.dev_ratio = 0
-
    training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim # suppress warning

    if model_args.quantization_bit is not None:
@@ -137,22 +169,27 @@ def get_train_args(
    # Set seed before initializing model.
    transformers.set_seed(training_args.seed)

-    return model_args, data_args, training_args, finetuning_args, general_args
+    return model_args, data_args, training_args, finetuning_args, generating_args, general_args


 def get_infer_args(
    args: Optional[Dict[str, Any]] = None
-) -> Tuple[ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]:
+) -> Tuple[
+    ModelArguments,
+    DataArguments,
+    FinetuningArguments,
+    GeneratingArguments
+]:
    model_args, data_args, finetuning_args, generating_args = parse_infer_args(args)

-    assert model_args.quantization_bit is None or finetuning_args.finetuning_type == "lora", \
-        "Quantization is only compatible with the LoRA method."
+    if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
+        raise ValueError("Quantization is only compatible with the LoRA method.")

    if model_args.checkpoint_dir is not None:
        if finetuning_args.finetuning_type != "lora":
-            assert len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
-        else:
-            assert model_args.quantization_bit is None or len(model_args.checkpoint_dir) == 1, \
-                "Quantized model only accepts a single checkpoint."
+            if len(model_args.checkpoint_dir) != 1:
+                raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
+        elif model_args.quantization_bit is not None and len(model_args.checkpoint_dir) != 1:
+                raise ValueError("Quantized model only accepts a single checkpoint.")

    return model_args, data_args, finetuning_args, generating_args
--- a/src/llmtuner/tuner/core/trainer.py
+++ b/src/llmtuner/tuner/core/trainer.py
@@ -13,26 +13,25 @@ from llmtuner.extras.logging import get_logger
 from llmtuner.extras.save_and_load import get_state_dict, load_trainable_params

 if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer, Seq2SeqTrainingArguments, TrainerState
    from llmtuner.hparams import FinetuningArguments


 logger = get_logger(__name__)


-class PeftTrainer(Seq2SeqTrainer):
+class PeftModelMixin:
    r"""
-    Inherits Seq2SeqTrainer to support parameter-efficient checkpoints.
+    Patches the save and load methods in Hugging Face Trainer for PeftModel and ModelWithValueHead.
    """

-    def __init__(self, finetuning_args: "FinetuningArguments", **kwargs):
-        super().__init__(**kwargs)
-        self.finetuning_args = finetuning_args
-        self._remove_log()
-
-    def _remove_log(self):
-        if self.is_world_process_zero() and os.path.exists(os.path.join(self.args.output_dir, "trainer_log.jsonl")):
-            logger.warning("Previous log file in this folder will be deleted.")
-            os.remove(os.path.join(self.args.output_dir, "trainer_log.jsonl"))
+    def __init__(self) -> None: # for type checking
+        self.model: PreTrainedModel = None
+        self.tokenizer: "PreTrainedTokenizer" = None
+        self.args: "Seq2SeqTrainingArguments" = None
+        self.finetuning_args: "FinetuningArguments" = None
+        self.state: "TrainerState" = None
+        raise AssertionError("Mixin should not be initialized.")

    def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, torch.Tensor]] = None) -> None:
        r"""
@@ -47,7 +46,6 @@ class PeftTrainer(Seq2SeqTrainer):
        logger.info(f"Saving model checkpoint to {output_dir}")

        model = unwrap_model(self.model)
-
        if isinstance(model, PreTrainedModelWrapper):
            # Custom state dict: https://github.com/lvwerra/trl/blob/v0.4.7/trl/models/modeling_value_head.py#L200
            model_state_dict = state_dict or model.state_dict()
@@ -68,7 +66,10 @@ class PeftTrainer(Seq2SeqTrainer):
            torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))

        if self.finetuning_args.finetuning_type == "full" and self.tokenizer is not None:
+            try:
                self.tokenizer.save_pretrained(output_dir)
+            except:
+                logger.warning("Cannot save tokenizer, copy the files manually.")

        with open(os.path.join(output_dir, TRAINING_ARGS_NAME), "w", encoding="utf-8") as f:
            f.write(self.args.to_json_string() + "\n")
@@ -94,3 +95,13 @@ class PeftTrainer(Seq2SeqTrainer):
            model.load_adapter(self.state.best_model_checkpoint, model.active_adapter)
        else: # freeze/full-tuning
            load_trainable_params(model, self.state.best_model_checkpoint)
+
+
+class PeftTrainer(PeftModelMixin, Seq2SeqTrainer):
+    r"""
+    Inherits Seq2SeqTrainer to support parameter-efficient checkpoints.
+    """
+
+    def __init__(self, finetuning_args: "FinetuningArguments", **kwargs):
+        Seq2SeqTrainer.__init__(self, **kwargs)
+        self.finetuning_args = finetuning_args
--- a/src/llmtuner/tuner/dpo/init.py
+++ b/src/llmtuner/tuner/dpo/init.py
@@ -0,0 +1 @@
+from llmtuner.tuner.dpo.workflow import run_dpo
--- a/src/llmtuner/tuner/dpo/collator.py
+++ b/src/llmtuner/tuner/dpo/collator.py
@@ -0,0 +1,51 @@
+import torch
+from dataclasses import dataclass
+from typing import Any, Dict, List, Sequence, Tuple
+from transformers import DataCollatorForSeq2Seq
+
+
+@dataclass
+class DPODataCollatorWithPadding(DataCollatorForSeq2Seq):
+    r"""
+    Data collator for pairwise data.
+    """
+
+    def _pad_labels(self, batch: torch.Tensor, positions: List[Tuple[int, int]]) -> torch.Tensor:
+        padded_labels = []
+        for feature, (prompt_len, answer_len) in zip(batch, positions):
+            if self.tokenizer.padding_side == "left":
+                start, end = feature.size(0) - answer_len, feature.size(0)
+            else:
+                start, end = prompt_len, answer_len
+            padded_tensor = self.label_pad_token_id * torch.ones_like(feature)
+            padded_tensor[start:end] = feature[start:end]
+            padded_labels.append(padded_tensor)
+        return torch.stack(padded_labels, dim=0).contiguous() # in contiguous memory
+
+    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 = []
+        label_positions = []
+        for key in ("chosen_ids", "rejected_ids"):
+            for feature in features:
+                prompt_len, answer_len = len(feature["prompt_ids"]), len(feature[key])
+                concatenated_features.append({
+                    "input_ids": feature["prompt_ids"] + feature[key],
+                    "attention_mask": [1] * (prompt_len + answer_len)
+                })
+                label_positions.append((prompt_len, answer_len))
+
+        batch = self.tokenizer.pad(
+            concatenated_features,
+            padding=self.padding,
+            max_length=self.max_length,
+            pad_to_multiple_of=self.pad_to_multiple_of,
+            return_tensors=self.return_tensors,
+        )
+        batch["labels"] = self._pad_labels(batch["input_ids"], label_positions)
+        return batch
--- a/src/llmtuner/tuner/dpo/trainer.py
+++ b/src/llmtuner/tuner/dpo/trainer.py
@@ -0,0 +1,75 @@
+import torch
+from collections import defaultdict
+from peft import PeftModel
+from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
+from transformers import Trainer
+from trl import DPOTrainer
+
+from llmtuner.extras.constants import IGNORE_INDEX
+from llmtuner.tuner.core.trainer import PeftModelMixin
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel
+    from llmtuner.hparams import FinetuningArguments, GeneratingArguments
+
+
+class DPOPeftTrainer(PeftModelMixin, DPOTrainer):
+
+    def __init__(
+        self,
+        finetuning_args: "FinetuningArguments",
+        generating_args: "GeneratingArguments",
+        ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]] = None,
+        **kwargs
+    ):
+        self.finetuning_args = finetuning_args
+        self.generating_args = generating_args
+        self.ref_model = ref_model
+        self.use_dpo_data_collator = True # hack to avoid warning
+        self.label_pad_token_id = IGNORE_INDEX
+        self.padding_value = 0
+        self.beta = finetuning_args.dpo_beta
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+
+        Trainer.__init__(self, **kwargs)
+        if ref_model is not None:
+            if hasattr(self, "accelerator"):
+                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
+            else:
+                raise AttributeError("Please update `transformers`.")
+
+    def concatenated_forward(
+        self,
+        model: Optional[torch.nn.Module] = None,
+        batch: Optional[Dict[str, torch.Tensor]] = None
+    ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+        unwrapped_model: "PreTrainedModel" = self.accelerator.unwrap_model(self.model)
+        if not torch.is_grad_enabled():
+            unwrapped_model.gradient_checkpointing_disable()
+
+        if model is None and isinstance(unwrapped_model, PeftModel): # peft model has no ref_model
+            with unwrapped_model.disable_adapter():
+                all_logits: torch.Tensor = self.model(
+                    batch["input_ids"],
+                    attention_mask=batch["attention_mask"],
+                    return_dict=True
+                ).logits.to(torch.float32)
+        else:
+            all_logits: torch.Tensor = model(
+                batch["input_ids"],
+                attention_mask=batch["attention_mask"],
+                return_dict=True
+            ).logits.to(torch.float32)
+
+        if not torch.is_grad_enabled():
+            unwrapped_model.gradient_checkpointing_enable()
+
+        all_logps = self._get_batch_logps(
+            all_logits,
+            batch["labels"],
+            average_log_prob=False
+        )
+        batch_size = batch["input_ids"].size(0) // 2
+        chosen_logps, rejected_logps = all_logps.split(batch_size, dim=0)
+        chosen_logits, rejected_logits = all_logits.split(batch_size, dim=0)
+        return chosen_logps, rejected_logps, chosen_logits, rejected_logits
--- a/src/llmtuner/tuner/dpo/workflow.py
+++ b/src/llmtuner/tuner/dpo/workflow.py
@@ -0,0 +1,59 @@
+# Inspired by: https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py
+
+from copy import deepcopy
+from peft import PeftModel
+from typing import TYPE_CHECKING, Optional, List
+
+from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
+from llmtuner.extras.constants import IGNORE_INDEX
+from llmtuner.extras.ploting import plot_loss
+from llmtuner.tuner.core import load_model_and_tokenizer
+from llmtuner.tuner.dpo.collator import DPODataCollatorWithPadding
+from llmtuner.tuner.dpo.trainer import DPOPeftTrainer
+
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
+
+
+def run_dpo(
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    generating_args: "GeneratingArguments",
+    callbacks: Optional[List["TrainerCallback"]] = None
+):
+    dataset = get_dataset(model_args, data_args)
+    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="sft")
+    dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="rm")
+    data_collator = DPODataCollatorWithPadding(
+        tokenizer=tokenizer,
+        label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
+    )
+
+    training_args.remove_unused_columns = False # important for pairwise dataset
+    ref_model = deepcopy(model) if not isinstance(model, PeftModel) else None
+
+    # Initialize our Trainer
+    trainer = DPOPeftTrainer(
+        finetuning_args=finetuning_args,
+        generating_args=generating_args,
+        ref_model=ref_model,
+        model=model,
+        args=training_args,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        callbacks=callbacks,
+        **split_dataset(dataset, data_args, training_args)
+    )
+
+    # Training
+    if training_args.do_train:
+        train_result = trainer.train()
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+        trainer.save_model()
+        if trainer.is_world_process_zero() and model_args.plot_loss:
+            plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
--- a/src/llmtuner/tuner/ppo/trainer.py
+++ b/src/llmtuner/tuner/ppo/trainer.py
@@ -2,24 +2,23 @@ import os
 import math
 import torch
 from tqdm import tqdm
-from typing import TYPE_CHECKING, Callable, Dict, List, Optional
+from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple

 from transformers import TrainerState, TrainerControl
-from transformers.modeling_utils import PreTrainedModel

 from trl import PPOTrainer
 from trl.core import LengthSampler

 from llmtuner.extras.logging import get_logger
-from llmtuner.extras.misc import AverageMeter, count_parameters, get_logits_processor
-
+from llmtuner.extras.misc import AverageMeter, count_parameters, get_logits_processor, get_stopping_criteria
 from llmtuner.tuner.core.trainer import PeftTrainer
 from llmtuner.tuner.ppo.utils import cast_layernorm_dtype, replace_model

 if TYPE_CHECKING:
    from transformers import Seq2SeqTrainingArguments
+    from trl import AutoModelForCausalLMWithValueHead
    from llmtuner.extras.callbacks import LogCallback
-    from llmtuner.hparams import FinetuningArguments
+    from llmtuner.hparams import FinetuningArguments, GeneratingArguments


 logger = get_logger(__name__)
@@ -34,17 +33,17 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
        self,
        training_args: "Seq2SeqTrainingArguments",
        finetuning_args: "FinetuningArguments",
+        generating_args: "GeneratingArguments",
        callbacks: List["LogCallback"],
        **kwargs
    ):
        PPOTrainer.__init__(self, **kwargs)
        self.args = training_args
        self.finetuning_args = finetuning_args
+        self.generating_args = generating_args
        self.log_callback = callbacks[0]
        self.state = TrainerState()
        self.control = TrainerControl()
-        self.data_collator = self.accelerator.prepare(kwargs["data_collator"]) # override the data collator of PPOTrainer
-        self._remove_log()

    def ppo_train(self, max_target_length: int) -> None:
        r"""
@@ -74,16 +73,12 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
            logger.info(f"  Number of trainable parameters = {count_parameters(self.model)[0]}")

        # Keyword arguments for `model.generate`
-        gen_kwargs = {
-            "top_k": 0.0,
-            "top_p": 1.0,
-            "do_sample": True,
-            "pad_token_id": self.tokenizer.pad_token_id,
-            "eos_token_id": self.tokenizer.eos_token_id,
-            "logits_processor": get_logits_processor()
-        }
+        gen_kwargs = self.generating_args.to_dict()
+        gen_kwargs["logits_processor"] = get_logits_processor()
+        gen_kwargs["stopping_criteria"] = get_stopping_criteria(self.tokenizer.additional_special_tokens_ids)
+
        length_sampler = LengthSampler(max_target_length // 2, max_target_length)
-        unwrapped_model: PreTrainedModel = self.accelerator.unwrap_model(self.model)
+        unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model)

        dataiter = iter(self.dataloader)
        steps_trained = 0
@@ -91,51 +86,38 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
        reward_meter = AverageMeter()
        self.log_callback.on_train_begin(self.args, self.state, self.control)

-        for step in tqdm(range(max_steps), disable=not self.is_world_process_zero(), leave=False):
+        for step in tqdm(range(max_steps), disable=not self.is_local_process_zero()):
            batch = next(dataiter)
            steps_trained += 1

+            # Cast to inference mode
            unwrapped_model.gradient_checkpointing_disable()
            unwrapped_model.config.use_cache = True

-            # Get responses
-            query_tensors = batch["input_ids"]
-            response_tensors = self.generate(batch, length_sampler, return_prompt=False, **gen_kwargs)
+            # Get inputs
+            queries, responses = self.get_inputs(batch, length_sampler, **gen_kwargs)
+            rewards = self.get_rewards(queries, responses, unwrapped_model)

-            queries, responses = [], []
-            for i in range(len(query_tensors)):
-                query_length = (query_tensors[i] != self.tokenizer.pad_token_id).nonzero()[0]
-                response_length = (response_tensors[i] != self.tokenizer.pad_token_id).nonzero()[-1] + 1
-                queries.append(query_tensors[i, query_length:]) # remove padding from left
-                responses.append(response_tensors[i, :response_length]) # remove padding from right
-
-            # Compute rewards
-            replace_model(unwrapped_model, target="reward")
-            with torch.no_grad():
-                _, _, values = self.model(
-                    **self.prepare_model_inputs(queries, responses),
-                    output_hidden_states=True,
-                    return_dict=True
-                )
-            rewards = [reward for reward in values[:, -1].to(torch.float32)] # use float32 type
-            replace_model(unwrapped_model, target="default")
-
-            # Run PPO step
+            # Cast to training mode
            unwrapped_model.gradient_checkpointing_enable()
            unwrapped_model.config.use_cache = False
-            stats = self.step(queries, responses, rewards)

+            # Run PPO step
+            stats = self.step(queries, responses, rewards)
            loss_meter.update(stats["ppo/loss/total"], n=len(rewards))
            reward_meter.update(torch.stack(rewards).mean().item(), n=len(rewards))

-            if self.is_world_process_zero() and (step+1) % self.args.logging_steps == 0:
+            self.state.global_step += 1
+            self.log_callback.on_step_end(self.args, self.state, self.control)
+
+            if self.is_local_process_zero() and (step+1) % self.args.logging_steps == 0:
                logs = dict(
                    loss=round(loss_meter.avg, 4),
                    reward=round(reward_meter.avg, 4),
                    learning_rate=stats["ppo/learning_rate"],
                    epoch=round(step / len_dataloader, 2)
                )
-                print(logs)
+                tqdm.write(str(logs))
                logs["step"] = step
                self.state.log_history.append(logs)
                self.log_callback.on_log(self.args, self.state, self.control)
@@ -152,38 +134,57 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
                dataiter = iter(self.dataloader)
                steps_trained = 0

+        self.log_callback.on_train_end(self.args, self.state, self.control)
+
    @torch.no_grad()
-    def generate(
+    def get_inputs(
        self,
-        inputs: Dict[str, torch.Tensor],
+        batch: Dict[str, torch.Tensor],
        length_sampler: Optional[Callable] = None,
-        return_prompt: Optional[bool] = True,
        **generation_kwargs
-    ) -> torch.Tensor:
+    ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
        r"""
        Generates model's responses given queries.
-
-        Subclass and override to inject custom behavior.
        """
-        self.model, layer_norm_params = cast_layernorm_dtype(self.model)
-
        if length_sampler is not None:
            generation_kwargs["max_new_tokens"] = length_sampler()

-        unwrapped_model = self.accelerator.unwrap_model(self.model)
-
-        response = unwrapped_model.generate(**inputs, **generation_kwargs)
+        self.model, layer_norm_params = cast_layernorm_dtype(self.model)
+        unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model)
+        response: torch.Tensor = unwrapped_model.generate(**batch, **generation_kwargs)
+        self.model, _ = cast_layernorm_dtype(self.model, layer_norm_params)

        # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
        # Inspired by: https://github.com/huggingface/transformers/blob/v4.28.1/src/transformers/trainer_seq2seq.py#L273
        if unwrapped_model.pretrained_model.generation_config._from_model_config:
            unwrapped_model.pretrained_model.generation_config._from_model_config = False

-        self.model, _ = cast_layernorm_dtype(self.model, layer_norm_params)
+        queries, responses = [], []
+        query, response = batch["input_ids"].detach().cpu(), response[:, batch["input_ids"].size(-1):].detach().cpu()
+        for i in range(len(query)):
+            query_length = (query[i] != self.tokenizer.pad_token_id).nonzero()[0]
+            response_length = (response[i] != self.tokenizer.pad_token_id).nonzero()[-1] + 1
+            queries.append(query[i, query_length:]) # remove padding from left
+            responses.append(response[i, :response_length]) # remove padding from right

-        if not return_prompt and not self.is_encoder_decoder:
-            return response[:, inputs["input_ids"].size(1):]
-        return response
+        return queries, responses
+
+    @torch.no_grad()
+    def get_rewards(
+        self,
+        queries: List[torch.Tensor],
+        responses: List[torch.Tensor],
+        unwrapped_model: "AutoModelForCausalLMWithValueHead"
+    ) -> List[torch.Tensor]:
+        r"""
+        Computes scores using given reward model.
+        """
+        replace_model(unwrapped_model, target="reward")
+        batch = self.prepare_model_inputs(queries, responses)
+        _, _, values = self.model(**batch, output_hidden_states=True, return_dict=True)
+        rewards = [reward for reward in values[:, -1].float().detach().cpu()] # use fp32 type
+        replace_model(unwrapped_model, target="default")
+        return rewards

    def save_model(self, output_dir: Optional[str] = None) -> None:
        r"""
--- a/src/llmtuner/tuner/ppo/workflow.py
+++ b/src/llmtuner/tuner/ppo/workflow.py
@@ -1,23 +1,20 @@
-# Inspired by:
-# https://github.com/lvwerra/trl/blob/main/examples/sentiment/scripts/gpt-neox-20b_peft/gpt-neo-20b_sentiment_peft.py
+# Inspired by: https://github.com/lvwerra/trl/blob/main/examples/research_projects/stack_llama/scripts/rl_training.py

 import math
-from typing import TYPE_CHECKING
 from trl import PPOConfig
 from torch.optim import AdamW
-from typing import Optional, List
+from typing import TYPE_CHECKING, Optional, List
 from transformers import DataCollatorForSeq2Seq
 from transformers.optimization import get_scheduler

 from llmtuner.dsets import get_dataset, preprocess_dataset
-from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.ploting import plot_loss
 from llmtuner.tuner.core import load_model_and_tokenizer
 from llmtuner.tuner.ppo.trainer import PPOPeftTrainer

 if TYPE_CHECKING:
    from transformers import Seq2SeqTrainingArguments, TrainerCallback
-    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
+    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments


 def run_ppo(
@@ -25,7 +22,8 @@ def run_ppo(
    data_args: "DataArguments",
    training_args: "Seq2SeqTrainingArguments",
    finetuning_args: "FinetuningArguments",
-    callbacks: Optional[List["TrainerCallback"]] = [LogCallback()]
+    generating_args: "GeneratingArguments",
+    callbacks: Optional[List["TrainerCallback"]] = None
 ):
    dataset = get_dataset(model_args, data_args)
    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="ppo")
@@ -42,20 +40,23 @@ def run_ppo(
        max_grad_norm=training_args.max_grad_norm
    )

-    optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=ppo_config.learning_rate)
-    total_train_batch_size = \
+    optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=training_args.learning_rate)
+    total_train_batch_size = (
        training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
+    )
+    num_training_steps = training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size)
    lr_scheduler = get_scheduler(
        training_args.lr_scheduler_type,
        optimizer=optimizer,
-        num_warmup_steps=training_args.warmup_steps,
-        num_training_steps=(training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size))
+        num_warmup_steps=training_args.get_warmup_steps(num_training_steps),
+        num_training_steps=num_training_steps
    )

    # Initialize our Trainer
    ppo_trainer = PPOPeftTrainer(
        training_args=training_args,
        finetuning_args=finetuning_args,
+        generating_args=generating_args,
        callbacks=callbacks,
        config=ppo_config,
        model=model,
@@ -67,8 +68,10 @@ def run_ppo(
        lr_scheduler=lr_scheduler
    )

+    # Training
+    if training_args.do_train:
        ppo_trainer.ppo_train(max_target_length=data_args.max_target_length)
        ppo_trainer.save_model()
-    ppo_trainer.save_state() # must be after save_model
+        ppo_trainer.save_state() # must be called after save_model to have a folder
        if ppo_trainer.is_world_process_zero() and model_args.plot_loss:
            plot_loss(training_args.output_dir, keys=["loss", "reward"])
--- a/src/llmtuner/tuner/pt/workflow.py
+++ b/src/llmtuner/tuner/pt/workflow.py
@@ -2,11 +2,9 @@

 import math
 from typing import TYPE_CHECKING, Optional, List
-from transformers import DataCollatorForSeq2Seq
+from transformers import DataCollatorForLanguageModeling

 from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
-from llmtuner.extras.callbacks import LogCallback
-from llmtuner.extras.constants import IGNORE_INDEX
 from llmtuner.extras.ploting import plot_loss
 from llmtuner.tuner.core import load_model_and_tokenizer
 from llmtuner.tuner.core.trainer import PeftTrainer
@@ -21,15 +19,12 @@ def run_pt(
    data_args: "DataArguments",
    training_args: "Seq2SeqTrainingArguments",
    finetuning_args: "FinetuningArguments",
-    callbacks: Optional[List["TrainerCallback"]] = [LogCallback()]
+    callbacks: Optional[List["TrainerCallback"]] = None
 ):
    dataset = get_dataset(model_args, data_args)
    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="pt")
    dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="pt")
-    data_collator = DataCollatorForSeq2Seq(
-        tokenizer=tokenizer,
-        label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
-    )
+    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

    # Initialize our Trainer
    trainer = PeftTrainer(
@@ -39,7 +34,7 @@ def run_pt(
        tokenizer=tokenizer,
        data_collator=data_collator,
        callbacks=callbacks,
-        **split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
+        **split_dataset(dataset, data_args, training_args)
    )

    # Training
--- a/src/llmtuner/tuner/rm/collator.py
+++ b/src/llmtuner/tuner/rm/collator.py
@@ -1,8 +1,10 @@
 import torch
+from dataclasses import dataclass
 from typing import Any, Dict, Sequence
 from transformers import DataCollatorWithPadding


+@dataclass
 class PairwiseDataCollatorWithPadding(DataCollatorWithPadding):
    r"""
    Data collator for pairwise data.
@@ -16,7 +18,10 @@ class PairwiseDataCollatorWithPadding(DataCollatorWithPadding):
        the last n examples represent rejected examples.
        """
        features = [
-            {"input_ids": feature[key], "attention_mask": [1] * len(feature[key])}
-            for key in ("accept_ids", "reject_ids") for feature in features
+            {
+                "input_ids": feature["prompt_ids"] + feature[key],
+                "attention_mask": [1] * (len(feature["prompt_ids"]) + len(feature[key]))
+            }
+            for key in ("chosen_ids", "rejected_ids") for feature in features
        ]
        return super().__call__(features)
--- a/src/llmtuner/tuner/rm/workflow.py
+++ b/src/llmtuner/tuner/rm/workflow.py
@@ -5,7 +5,6 @@
 from typing import TYPE_CHECKING, Optional, List

 from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
-from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.ploting import plot_loss
 from llmtuner.tuner.core import load_model_and_tokenizer
 from llmtuner.tuner.rm.metric import compute_accuracy
@@ -22,7 +21,7 @@ def run_rm(
    data_args: "DataArguments",
    training_args: "Seq2SeqTrainingArguments",
    finetuning_args: "FinetuningArguments",
-    callbacks: Optional[List["TrainerCallback"]] = [LogCallback()]
+    callbacks: Optional[List["TrainerCallback"]] = None
 ):
    dataset = get_dataset(model_args, data_args)
    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="rm")
@@ -40,7 +39,7 @@ def run_rm(
        data_collator=data_collator,
        callbacks=callbacks,
        compute_metrics=compute_accuracy,
-        **split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
+        **split_dataset(dataset, data_args, training_args)
    )

    # Training
--- a/src/llmtuner/tuner/sft/trainer.py
+++ b/src/llmtuner/tuner/sft/trainer.py
@@ -79,7 +79,7 @@ class Seq2SeqPeftTrainer(PeftTrainer):

        padded_tensor = pad_token_id * torch.ones_like(tgt_tensor)
        padded_tensor[:, -src_tensor.shape[-1]:] = src_tensor # adopt left-padding
-        return padded_tensor
+        return padded_tensor.contiguous() # in contiguous memory

    def save_predictions(
        self,
--- a/src/llmtuner/tuner/sft/workflow.py
+++ b/src/llmtuner/tuner/sft/workflow.py
@@ -4,9 +4,8 @@ from typing import TYPE_CHECKING, Optional, List
 from transformers import DataCollatorForSeq2Seq

 from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
-from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.constants import IGNORE_INDEX
-from llmtuner.extras.misc import get_logits_processor
+from llmtuner.extras.misc import get_logits_processor, get_stopping_criteria
 from llmtuner.extras.ploting import plot_loss
 from llmtuner.tuner.core import load_model_and_tokenizer
 from llmtuner.tuner.sft.metric import ComputeMetrics
@@ -14,7 +13,7 @@ from llmtuner.tuner.sft.trainer import Seq2SeqPeftTrainer

 if TYPE_CHECKING:
    from transformers import Seq2SeqTrainingArguments, TrainerCallback
-    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
+    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments


 def run_sft(
@@ -22,7 +21,8 @@ def run_sft(
    data_args: "DataArguments",
    training_args: "Seq2SeqTrainingArguments",
    finetuning_args: "FinetuningArguments",
-    callbacks: Optional[List["TrainerCallback"]] = [LogCallback()]
+    generating_args: "GeneratingArguments",
+    callbacks: Optional[List["TrainerCallback"]] = None
 ):
    dataset = get_dataset(model_args, data_args)
    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="sft")
@@ -47,17 +47,13 @@ def run_sft(
        data_collator=data_collator,
        callbacks=callbacks,
        compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None,
-        **split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
+        **split_dataset(dataset, data_args, training_args)
    )

    # Keyword arguments for `model.generate`
-    gen_kwargs = {
-        "do_sample": True,
-        "top_p": 0.7,
-        "max_new_tokens": data_args.max_target_length + 1,
-        "temperature": 0.95,
-        "logits_processor": get_logits_processor()
-    }
+    gen_kwargs = generating_args.to_dict()
+    gen_kwargs["logits_processor"] = get_logits_processor()
+    gen_kwargs["stopping_criteria"] = get_stopping_criteria(tokenizer.additional_special_tokens_ids)

    # Training
    if training_args.do_train:
--- a/src/llmtuner/tuner/tune.py
+++ b/src/llmtuner/tuner/tune.py
@@ -0,0 +1,48 @@
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+from llmtuner.extras.callbacks import LogCallback
+from llmtuner.extras.logging import get_logger
+from llmtuner.tuner.core import get_train_args, load_model_and_tokenizer
+from llmtuner.tuner.pt import run_pt
+from llmtuner.tuner.sft import run_sft
+from llmtuner.tuner.rm import run_rm
+from llmtuner.tuner.ppo import run_ppo
+from llmtuner.tuner.dpo import run_dpo
+
+if TYPE_CHECKING:
+    from transformers import TrainerCallback
+
+
+logger = get_logger(__name__)
+
+
+def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: Optional[List["TrainerCallback"]] = None):
+    model_args, data_args, training_args, finetuning_args, generating_args, general_args = get_train_args(args)
+    callbacks = [LogCallback()] if callbacks is None else callbacks
+
+    if general_args.stage == "pt":
+        run_pt(model_args, data_args, training_args, finetuning_args, callbacks)
+    elif general_args.stage == "sft":
+        run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+    elif general_args.stage == "rm":
+        run_rm(model_args, data_args, training_args, finetuning_args, callbacks)
+    elif general_args.stage == "ppo":
+        run_ppo(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+    elif general_args.stage == "dpo":
+        run_dpo(model_args, data_args, training_args, finetuning_args, callbacks)
+    else:
+        raise ValueError("Unknown task.")
+
+
+def export_model(args: Optional[Dict[str, Any]] = None, max_shard_size: Optional[str] = "10GB"):
+    model_args, _, training_args, finetuning_args, _ = get_train_args(args)
+    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
+    model.save_pretrained(training_args.output_dir, max_shard_size=max_shard_size)
+    try:
+        tokenizer.save_pretrained(training_args.output_dir)
+    except:
+        logger.warning("Cannot save tokenizer, please copy the files manually.")
+
+
+if __name__ == "__main__":
+    run_exp()
--- a/src/llmtuner/webui/init.py
+++ b/src/llmtuner/webui/init.py
@@ -0,0 +1 @@
+from llmtuner.webui.interface import create_ui, create_web_demo
--- a/src/llmtuner/webui/chat.py
+++ b/src/llmtuner/webui/chat.py
@@ -1,22 +1,22 @@
 import os
-from typing import List, Tuple
+from typing import Any, Dict, List, Optional, Tuple

 from llmtuner.chat.stream_chat import ChatModel
 from llmtuner.extras.misc import torch_gc
 from llmtuner.hparams import GeneratingArguments
-from llmtuner.tuner import get_infer_args
 from llmtuner.webui.common import get_model_path, get_save_dir
 from llmtuner.webui.locales import ALERTS


 class WebChatModel(ChatModel):

-    def __init__(self, *args):
+    def __init__(self, args: Optional[Dict[str, Any]] = None, lazy_init: Optional[bool] = True) -> None:
+        if lazy_init:
            self.model = None
            self.tokenizer = None
            self.generating_args = GeneratingArguments()
-        if len(args) != 0:
-            super().__init__(*args)
+        else:
+            super().__init__(args)

    def load_model(
        self,
@@ -57,7 +57,7 @@ class WebChatModel(ChatModel):
            template=template,
            source_prefix=source_prefix
        )
-        super().__init__(*get_infer_args(args))
+        super().__init__(args)

        yield ALERTS["info_loaded"][lang]

--- a/src/llmtuner/webui/components/init.py
+++ b/src/llmtuner/webui/components/init.py
@@ -3,3 +3,4 @@ from llmtuner.webui.components.sft import create_sft_tab
 from llmtuner.webui.components.eval import create_eval_tab
 from llmtuner.webui.components.infer import create_infer_tab
 from llmtuner.webui.components.export import create_export_tab
+from llmtuner.webui.components.chatbot import create_chat_box
--- a/src/llmtuner/webui/components/data.py
+++ b/src/llmtuner/webui/components/data.py
@@ -16,6 +16,6 @@ def create_preview_box() -> Tuple["Block", "Component", "Component", "Component"

        close_btn = gr.Button()

-    close_btn.click(lambda: gr.update(visible=False), outputs=[preview_box])
+    close_btn.click(lambda: gr.update(visible=False), outputs=[preview_box], queue=False)

    return preview_box, preview_count, preview_samples, close_btn
--- a/src/llmtuner/webui/components/eval.py
+++ b/src/llmtuner/webui/components/eval.py
@@ -20,7 +20,12 @@ def create_eval_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict

    dataset_dir.change(list_dataset, [dataset_dir], [dataset])
    dataset.change(can_preview, [dataset_dir, dataset], [preview_btn])
-    preview_btn.click(get_preview, [dataset_dir, dataset], [preview_count, preview_samples, preview_box])
+    preview_btn.click(
+        get_preview,
+        [dataset_dir, dataset],
+        [preview_count, preview_samples, preview_box],
+        queue=False
+    )

    with gr.Row():
        max_source_length = gr.Slider(value=512, minimum=4, maximum=4096, step=1)
@@ -33,6 +38,9 @@ def create_eval_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict
        start_btn = gr.Button()
        stop_btn = gr.Button()

+    with gr.Row():
+        process_bar = gr.Slider(visible=False, interactive=False)
+
    with gr.Box():
        output_box = gr.Markdown()

@@ -54,7 +62,10 @@ def create_eval_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict
            batch_size,
            predict
        ],
-        [output_box]
+        [
+            output_box,
+            process_bar
+        ]
    )
    stop_btn.click(runner.set_abort, queue=False)

--- a/src/llmtuner/webui/components/export.py
+++ b/src/llmtuner/webui/components/export.py
@@ -1,7 +1,7 @@
 from typing import TYPE_CHECKING, Dict
 import gradio as gr

-from llmtuner.webui.utils import export_model
+from llmtuner.webui.utils import save_model

 if TYPE_CHECKING:
    from gradio.components import Component
@@ -16,12 +16,13 @@ def create_export_tab(top_elems: Dict[str, "Component"]) -> Dict[str, "Component
    info_box = gr.Textbox(show_label=False, interactive=False)

    export_btn.click(
-        export_model,
+        save_model,
        [
            top_elems["lang"],
            top_elems["model_name"],
            top_elems["checkpoints"],
            top_elems["finetuning_type"],
+            top_elems["template"],
            max_shard_size,
            save_dir
        ],
--- a/src/llmtuner/webui/components/sft.py
+++ b/src/llmtuner/webui/components/sft.py
@@ -22,7 +22,12 @@ def create_sft_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[

    dataset_dir.change(list_dataset, [dataset_dir], [dataset])
    dataset.change(can_preview, [dataset_dir, dataset], [preview_btn])
-    preview_btn.click(get_preview, [dataset_dir, dataset], [preview_count, preview_samples, preview_box])
+    preview_btn.click(
+        get_preview,
+        [dataset_dir, dataset],
+        [preview_count, preview_samples, preview_box],
+        queue=False
+    )

    with gr.Row():
        max_source_length = gr.Slider(value=512, minimum=4, maximum=4096, step=1)
@@ -38,7 +43,7 @@ def create_sft_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[
            value="cosine", choices=[scheduler.value for scheduler in SchedulerType]
        )
        max_grad_norm = gr.Textbox(value="1.0")
-        dev_ratio = gr.Slider(value=0, minimum=0, maximum=1, step=0.001)
+        val_size = gr.Slider(value=0, minimum=0, maximum=1, step=0.001)

    with gr.Accordion(label="Advanced config", open=False) as advanced_tab:
        with gr.Row():
@@ -46,12 +51,14 @@ def create_sft_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[
            save_steps = gr.Slider(value=100, minimum=10, maximum=5000, step=10)
            warmup_steps = gr.Slider(value=0, minimum=0, maximum=5000, step=1)
            compute_type = gr.Radio(choices=["fp16", "bf16"], value="fp16")
+            padding_side = gr.Radio(choices=["left", "right"], value="left")

    with gr.Accordion(label="LoRA config", open=False) as lora_tab:
        with gr.Row():
            lora_rank = gr.Slider(value=8, minimum=1, maximum=1024, step=1, scale=1)
-            lora_dropout = gr.Slider(value=0, minimum=0, maximum=1, step=0.01, scale=1)
+            lora_dropout = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=1)
            lora_target = gr.Textbox(scale=2)
+            resume_lora_training = gr.Checkbox(value=True, scale=1)

    with gr.Row():
        start_btn = gr.Button()
@@ -59,8 +66,12 @@ def create_sft_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[

    with gr.Row():
        with gr.Column(scale=3):
+            with gr.Row():
                output_dir = gr.Textbox()

+            with gr.Row():
+                process_bar = gr.Slider(visible=False, interactive=False)
+
            with gr.Box():
                output_box = gr.Markdown()

@@ -88,21 +99,26 @@ def create_sft_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[
            gradient_accumulation_steps,
            lr_scheduler_type,
            max_grad_norm,
-            dev_ratio,
+            val_size,
            logging_steps,
            save_steps,
            warmup_steps,
            compute_type,
+            padding_side,
            lora_rank,
            lora_dropout,
            lora_target,
+            resume_lora_training,
            output_dir
        ],
-        [output_box]
+        [
+            output_box,
+            process_bar
+        ]
    )
    stop_btn.click(runner.set_abort, queue=False)

-    output_box.change(
+    process_bar.change(
        gen_plot, [top_elems["model_name"], top_elems["finetuning_type"], output_dir], loss_viewer, queue=False
    )

@@ -122,16 +138,18 @@ def create_sft_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[
        gradient_accumulation_steps=gradient_accumulation_steps,
        lr_scheduler_type=lr_scheduler_type,
        max_grad_norm=max_grad_norm,
-        dev_ratio=dev_ratio,
+        val_size=val_size,
        advanced_tab=advanced_tab,
        logging_steps=logging_steps,
        save_steps=save_steps,
        warmup_steps=warmup_steps,
        compute_type=compute_type,
+        padding_side=padding_side,
        lora_tab=lora_tab,
        lora_rank=lora_rank,
        lora_dropout=lora_dropout,
        lora_target=lora_target,
+        resume_lora_training=resume_lora_training,
        start_btn=start_btn,
        stop_btn=stop_btn,
        output_dir=output_dir,
--- a/src/llmtuner/webui/components/top.py
+++ b/src/llmtuner/webui/components/top.py
@@ -43,7 +43,7 @@ def create_top() -> Dict[str, "Component"]:
        can_quantize, [finetuning_type], [quantization_bit]
    )

-    refresh_btn.click(list_checkpoint, [model_name, finetuning_type], [checkpoints])
+    refresh_btn.click(list_checkpoint, [model_name, finetuning_type], [checkpoints], queue=False)

    return dict(
        lang=lang,
--- a/src/llmtuner/webui/interface.py
+++ b/src/llmtuner/webui/interface.py
@@ -6,8 +6,10 @@ from llmtuner.webui.components import (
    create_sft_tab,
    create_eval_tab,
    create_infer_tab,
-    create_export_tab
+    create_export_tab,
+    create_chat_box
 )
+from llmtuner.webui.chat import WebChatModel
 from llmtuner.webui.css import CSS
 from llmtuner.webui.manager import Manager
 from llmtuner.webui.runner import Runner
@@ -47,11 +49,29 @@ def create_ui() -> gr.Blocks:
            manager.gen_label,
            [top_elems["lang"]],
            [elem for elems in elem_list for elem in elems.values()],
+            queue=False
        )

    return demo


+def create_web_demo() -> gr.Blocks:
+    chat_model = WebChatModel(lazy_init=False)
+
+    with gr.Blocks(title="Web Demo", css=CSS) as demo:
+        lang = gr.Dropdown(choices=["en", "zh"], value="en")
+
+        _, _, _, chat_elems = create_chat_box(chat_model, visible=True)
+
+        manager = Manager([{"lang": lang}, chat_elems])
+
+        demo.load(manager.gen_label, [lang], [lang] + list(chat_elems.values()))
+
+        lang.change(manager.gen_label, [lang], [lang] + list(chat_elems.values()), queue=False)
+
+    return demo
+
+
 if __name__ == "__main__":
    demo = create_ui()
    demo.queue()
--- a/src/llmtuner/webui/locales.py
+++ b/src/llmtuner/webui/locales.py
@@ -227,9 +227,9 @@ LOCALES = {
            "info": "用于梯度裁剪的范数。"
        }
    },
-    "dev_ratio": {
+    "val_size": {
        "en": {
-            "label": "Dev ratio",
+            "label": "Val size",
            "info": "Proportion of data in the dev set."
        },
        "zh": {
@@ -277,6 +277,16 @@ LOCALES = {
            "info": "是否启用 FP16 或 BF16 混合精度训练。"
        }
    },
+    "padding_side": {
+        "en": {
+            "label": "Padding side",
+            "info": "The side on which the model should have padding applied."
+        },
+        "zh": {
+            "label": "填充位置",
+            "info": "使用左填充或右填充。"
+        }
+    },
    "lora_tab": {
        "en": {
            "label": "LoRA configurations"
@@ -315,6 +325,16 @@ LOCALES = {
            "info": "应用 LoRA 的线性层名称。使用英文逗号分隔多个名称。"
        }
    },
+    "resume_lora_training": {
+        "en": {
+            "label": "Resume LoRA training",
+            "info": "Whether to resume training from the last LoRA weights or create new lora weights."
+        },
+        "zh": {
+            "label": "继续上次的训练",
+            "info": "接着上次的 LoRA 权重训练或创建一个新的 LoRA 权重。"
+        }
+    },
    "start_btn": {
        "en": {
            "value": "Start"
@@ -513,6 +533,10 @@ ALERTS = {
        "en": "Please provide export dir.",
        "zh": "请填写导出目录"
    },
+    "err_failed": {
+        "en": "Failed.",
+        "zh": "训练出错。"
+    },
    "info_aborting": {
        "en": "Aborted, wait for terminating...",
        "zh": "训练中断，正在等待线程结束……"
--- a/src/llmtuner/webui/runner.py
+++ b/src/llmtuner/webui/runner.py
@@ -1,18 +1,20 @@
+import gradio as gr
 import logging
 import os
 import threading
 import time
 import transformers
-from typing import Generator, List, Optional, Tuple
+from transformers.trainer import TRAINING_ARGS_NAME
+from typing import Generator, List, Tuple

 from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.constants import DEFAULT_MODULE
 from llmtuner.extras.logging import LoggerHandler
 from llmtuner.extras.misc import torch_gc
-from llmtuner.tuner import get_train_args, run_sft
+from llmtuner.tuner import run_exp
 from llmtuner.webui.common import get_model_path, get_save_dir
 from llmtuner.webui.locales import ALERTS
-from llmtuner.webui.utils import format_info, get_eval_results
+from llmtuner.webui.utils import get_eval_results, update_process_bar


 class Runner:
@@ -53,14 +55,14 @@ class Runner:
        return model_name_or_path, "", logger_handler, trainer_callback

    def finalize(
-        self, lang: str, finish_info: Optional[str] = None
+        self, lang: str, finish_info: str
    ) -> str:
        self.running = False
        torch_gc()
        if self.aborted:
            return ALERTS["info_aborted"][lang]
        else:
-            return finish_info if finish_info is not None else ALERTS["info_finished"][lang]
+            return finish_info

    def run_train(
        self,
@@ -82,19 +84,21 @@ class Runner:
        gradient_accumulation_steps: int,
        lr_scheduler_type: str,
        max_grad_norm: str,
-        dev_ratio: float,
+        val_size: float,
        logging_steps: int,
        save_steps: int,
        warmup_steps: int,
        compute_type: str,
+        padding_side: str,
        lora_rank: int,
        lora_dropout: float,
        lora_target: str,
+        resume_lora_training: bool,
        output_dir: str
    ) -> Generator[str, None, None]:
        model_name_or_path, error, logger_handler, trainer_callback = self.initialize(lang, model_name, dataset)
        if error:
-            yield error
+            yield error, gr.update(visible=False)
            return

        if checkpoints:
@@ -104,7 +108,10 @@ class Runner:
        else:
            checkpoint_dir = None

+        output_dir = os.path.join(get_save_dir(model_name), finetuning_type, output_dir)
+
        args = dict(
+            stage="sft",
            model_name_or_path=model_name_or_path,
            do_train=True,
            overwrite_cache=True,
@@ -129,38 +136,37 @@ class Runner:
            warmup_steps=warmup_steps,
            fp16=(compute_type == "fp16"),
            bf16=(compute_type == "bf16"),
+            padding_side=padding_side,
            lora_rank=lora_rank,
            lora_dropout=lora_dropout,
            lora_target=lora_target or DEFAULT_MODULE.get(model_name.split("-")[0], "q_proj,v_proj"),
-            output_dir=os.path.join(get_save_dir(model_name), finetuning_type, output_dir)
+            resume_lora_training=resume_lora_training,
+            output_dir=output_dir
        )

-        if dev_ratio > 1e-6:
-            args["dev_ratio"] = dev_ratio
+        if val_size > 1e-6:
+            args["val_size"] = val_size
            args["evaluation_strategy"] = "steps"
            args["eval_steps"] = save_steps
            args["load_best_model_at_end"] = True

-        model_args, data_args, training_args, finetuning_args, _ = get_train_args(args)
-
-        run_args = dict(
-            model_args=model_args,
-            data_args=data_args,
-            training_args=training_args,
-            finetuning_args=finetuning_args,
-            callbacks=[trainer_callback]
-        )
-        thread = threading.Thread(target=run_sft, kwargs=run_args)
+        run_kwargs = dict(args=args, callbacks=[trainer_callback])
+        thread = threading.Thread(target=run_exp, kwargs=run_kwargs)
        thread.start()

        while thread.is_alive():
-            time.sleep(1)
+            time.sleep(2)
            if self.aborted:
-                yield ALERTS["info_aborting"][lang]
+                yield ALERTS["info_aborting"][lang], gr.update(visible=False)
            else:
-                yield format_info(logger_handler.log, trainer_callback.tracker)
+                yield logger_handler.log, update_process_bar(trainer_callback)

-        yield self.finalize(lang)
+        if os.path.exists(os.path.join(output_dir, TRAINING_ARGS_NAME)):
+            finish_info = ALERTS["info_finished"][lang]
+        else:
+            finish_info = ALERTS["err_failed"][lang]
+
+        yield self.finalize(lang, finish_info), gr.update(visible=False)

    def run_eval(
        self,
@@ -181,7 +187,7 @@ class Runner:
    ) -> Generator[str, None, None]:
        model_name_or_path, error, logger_handler, trainer_callback = self.initialize(lang, model_name, dataset)
        if error:
-            yield error
+            yield error, gr.update(visible=False)
            return

        if checkpoints:
@@ -194,6 +200,7 @@ class Runner:
            output_dir = os.path.join(get_save_dir(model_name), finetuning_type, "eval_base")

        args = dict(
+            stage="sft",
            model_name_or_path=model_name_or_path,
            do_eval=True,
            overwrite_cache=True,
@@ -216,23 +223,20 @@ class Runner:
            args.pop("do_eval", None)
            args["do_predict"] = True

-        model_args, data_args, training_args, finetuning_args, _ = get_train_args(args)
-
-        run_args = dict(
-            model_args=model_args,
-            data_args=data_args,
-            training_args=training_args,
-            finetuning_args=finetuning_args,
-            callbacks=[trainer_callback]
-        )
-        thread = threading.Thread(target=run_sft, kwargs=run_args)
+        run_kwargs = dict(args=args, callbacks=[trainer_callback])
+        thread = threading.Thread(target=run_exp, kwargs=run_kwargs)
        thread.start()

        while thread.is_alive():
-            time.sleep(1)
+            time.sleep(2)
            if self.aborted:
-                yield ALERTS["info_aborting"][lang]
+                yield ALERTS["info_aborting"][lang], gr.update(visible=False)
            else:
-                yield format_info(logger_handler.log, trainer_callback.tracker)
+                yield logger_handler.log, update_process_bar(trainer_callback)

-        yield self.finalize(lang, get_eval_results(os.path.join(output_dir, "all_results.json")))
+        if os.path.exists(os.path.join(output_dir, "all_results.json")):
+            finish_info = get_eval_results(os.path.join(output_dir, "all_results.json"))
+        else:
+            finish_info = ALERTS["err_failed"][lang]
+
+        yield self.finalize(lang, finish_info), gr.update(visible=False)
--- a/src/llmtuner/webui/utils.py
+++ b/src/llmtuner/webui/utils.py
@@ -3,22 +3,30 @@ import json
 import gradio as gr
 import matplotlib.figure
 import matplotlib.pyplot as plt
-from typing import Any, Dict, Generator, List, Tuple
+from typing import TYPE_CHECKING, Any, Dict, Generator, List, Tuple
 from datetime import datetime

 from llmtuner.extras.ploting import smooth
-from llmtuner.tuner import get_infer_args, load_model_and_tokenizer
+from llmtuner.tuner import export_model
 from llmtuner.webui.common import get_model_path, get_save_dir, DATA_CONFIG
 from llmtuner.webui.locales import ALERTS

+if TYPE_CHECKING:
+    from llmtuner.extras.callbacks import LogCallback

-def format_info(log: str, tracker: dict) -> str:
-    info = log
-    if "current_steps" in tracker:
-        info += "Running **{:d}/{:d}**: {} < {}\n".format(
-            tracker["current_steps"], tracker["total_steps"], tracker["elapsed_time"], tracker["remaining_time"]
+
+def update_process_bar(callback: "LogCallback") -> Dict[str, Any]:
+    if not callback.max_steps:
+        return gr.update(visible=False)
+
+    percentage = round(100 * callback.cur_steps / callback.max_steps, 0) if callback.max_steps != 0 else 100.0
+    label = "Running {:d}/{:d}: {} < {}".format(
+        callback.cur_steps,
+        callback.max_steps,
+        callback.elapsed_time,
+        callback.remaining_time
    )
-    return info
+    return gr.update(label=label, value=percentage, visible=True)


 def get_time() -> str:
@@ -87,8 +95,14 @@ def gen_plot(base_model: str, finetuning_type: str, output_dir: str) -> matplotl
    return fig


-def export_model(
-    lang: str, model_name: str, checkpoints: List[str], finetuning_type: str, max_shard_size: int, save_dir: str
+def save_model(
+    lang: str,
+    model_name: str,
+    checkpoints: List[str],
+    finetuning_type: str,
+    template: str,
+    max_shard_size: int,
+    save_dir: str
 ) -> Generator[str, None, None]:
    if not model_name:
        yield ALERTS["err_no_model"][lang]
@@ -114,12 +128,11 @@ def export_model(
    args = dict(
        model_name_or_path=model_name_or_path,
        checkpoint_dir=checkpoint_dir,
-        finetuning_type=finetuning_type
+        finetuning_type=finetuning_type,
+        template=template,
+        output_dir=save_dir
    )

    yield ALERTS["info_exporting"][lang]
-    model_args, _, finetuning_args, _ = get_infer_args(args)
-    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
-    model.save_pretrained(save_dir, max_shard_size=str(max_shard_size)+"GB")
-    tokenizer.save_pretrained(save_dir)
+    export_model(args, max_shard_size="{}GB".format(max_shard_size))
    yield ALERTS["info_exported"][lang]
--- a/src/train_bash.py
+++ b/src/train_bash.py
@@ -1,17 +1,8 @@
-from llmtuner.tuner import get_train_args, run_pt, run_sft, run_rm, run_ppo
+from llmtuner import run_exp


 def main():
-    model_args, data_args, training_args, finetuning_args, general_args = get_train_args()
-
-    if general_args.stage == "pt":
-        run_pt(model_args, data_args, training_args, finetuning_args)
-    elif general_args.stage == "sft":
-        run_sft(model_args, data_args, training_args, finetuning_args)
-    elif general_args.stage == "rm":
-        run_rm(model_args, data_args, training_args, finetuning_args)
-    elif general_args.stage == "ppo":
-        run_ppo(model_args, data_args, training_args, finetuning_args)
+    run_exp()


 def _mp_fn(index):
--- a/src/train_web.py
+++ b/src/train_web.py
@@ -1,4 +1,4 @@
-from llmtuner.webui.interface import create_ui
+from llmtuner import create_ui


 def main():
--- a/src/web_demo.py
+++ b/src/web_demo.py
@@ -1,33 +1,8 @@
-# coding=utf-8
-# Implements user interface in browser for fine-tuned models.
-# Usage: python web_demo.py --model_name_or_path path_to_model --checkpoint_dir path_to_checkpoint
-
-import gradio as gr
-from transformers.utils.versions import require_version
-
-from llmtuner.tuner import get_infer_args
-from llmtuner.webui.chat import WebChatModel
-from llmtuner.webui.components.chatbot import create_chat_box
-from llmtuner.webui.manager import Manager
-
-
-require_version("gradio>=3.36.0", "To fix: pip install gradio>=3.36.0")
+from llmtuner import create_web_demo


 def main():
-    chat_model = WebChatModel(*get_infer_args())
-
-    with gr.Blocks(title="Web Demo") as demo:
-        lang = gr.Dropdown(choices=["en", "zh"], value="en")
-
-        _, _, _, chat_elems = create_chat_box(chat_model, visible=True)
-
-        manager = Manager([{"lang": lang}, chat_elems])
-
-        demo.load(manager.gen_label, [lang], [lang] + list(chat_elems.values()))
-
-        lang.change(manager.gen_label, [lang], [lang] + list(chat_elems.values()))
-
+    demo = create_web_demo()
    demo.queue()
    demo.launch(server_name="0.0.0.0", server_port=7860, share=False, inbrowser=True)
Author	SHA1	Message	Date
hiyouga	d5f1b99ac4	Release v0.1.6 Former-commit-id: 43c8b3c3c8bfb2e32d17fb3e8b194938e37d54bd	2023-08-11 23:25:57 +08:00
hiyouga	2144bb0e27	Update README_zh.md Former-commit-id: 4fc154bcf039ba3f9240213158df757881cf3579	2023-08-11 14:06:02 +08:00
hiyouga	bc665bacc7	add defaults Former-commit-id: 4636d3bbe6b984ca93e3a80ae5239f3ddda461bd	2023-08-11 13:56:26 +08:00
hiyouga	52bfcf4883	fix stop word in baichuan template Former-commit-id: cba5ac9cfc81f11b97831998ea15def5e0b487c2	2023-08-11 13:51:46 +08:00
hiyouga	06df3d6fb6	fix baichuan template Former-commit-id: b1681fe35346381cda613297f1cbb710f0a6daa6	2023-08-11 13:45:47 +08:00
hiyouga	ca719a8697	support DPO training (2305.18290) Former-commit-id: 6d98de148e4af63a7028dfaeb6cf86eb56a4488f	2023-08-11 03:02:53 +08:00
hoshi-hiyouga	72dfd74005	Merge pull request #451 from jovialchen/main huggingface login for projects must login while running Former-commit-id: 246ac241277908909b81cdf85fec1f24449dbae9	2023-08-10 17:25:38 +08:00
hiyouga	69302c4420	fix webui val size Former-commit-id: 490c067d4e0828832e0ebdb704a9207dc974b15b	2023-08-10 15:20:44 +08:00
jiongxuc	42d7019b2e	huggingface login for projects must login while running Former-commit-id: 0a4a2a1d3e0ff1f57215512d294d782080bd383c	2023-08-10 14:57:12 +08:00
hiyouga	5f0d0d6b9b	fix template Former-commit-id: e3967eb1cdd8d19e8afee9ba52e7eb7d6cd86129	2023-08-09 23:14:27 +08:00
hiyouga	76cb63e4f6	fix template Former-commit-id: 907e8cd86fbd4cdfa26dad21ceaf6e01d8fe37e4	2023-08-09 23:10:20 +08:00
hiyouga	467d571206	support val set in streaming mode Former-commit-id: faed15b58ed00b1e09bb091e7eee48f5ef7c508b	2023-08-09 23:00:26 +08:00
hiyouga	972bfa700a	fix tokenizer Former-commit-id: 7849587cd4e149291d08edef9a528a1bad796c7e	2023-08-09 17:52:15 +08:00
hiyouga	990eeccf45	fix sft trainer Former-commit-id: 08cc888b1569572d0cd20bcf3f07e20072a0311a	2023-08-09 16:35:03 +08:00
hiyouga	a3a7465f00	fix rm #420 , fix template #426 , fix #423 Former-commit-id: 70ea3caaa7a7695c77179cd1bb18707a80a373d7	2023-08-09 16:23:31 +08:00
hoshi-hiyouga	031a819257	fix llama2 template Former-commit-id: 6c74f726d4e672f5a1a57df201c27c1f697384f0	2023-08-09 00:58:27 +08:00
hoshi-hiyouga	eb4b4e3c8c	fix tokenizer Former-commit-id: fa463ef279b596d5d53cc169831f51b42031fc05	2023-08-09 00:54:54 +08:00
hiyouga	d2e1fe9b1d	update webui Former-commit-id: 343a4cd82b07a40f96ba413d1d991419ff07a24a	2023-08-09 00:26:11 +08:00
hiyouga	6e27a9e39a	fix tokenizer #417 Former-commit-id: 01aa678311bfd213a4b410a4e0ff09f48a0d40a1	2023-08-08 23:59:41 +08:00
hiyouga	805478c911	fix bug Former-commit-id: 0dff1d951f1a9fe05a74d334bf477b55c7c64199	2023-08-08 21:28:28 +08:00
hiyouga	a281cdeb89	fix bug Former-commit-id: c13ce66021b21e015871b84489eeafa127a424a4	2023-08-08 17:55:55 +08:00
hiyouga	cda698a67f	fix chatml template #408 Former-commit-id: 21e0cc3f44c35ae689b00b274391492f413725ac	2023-08-08 17:44:39 +08:00
hiyouga	15acd17716	update args spec Former-commit-id: a006068346edda6e2851b23d2005fdb218a7287d	2023-08-07 15:23:35 +08:00
hiyouga	34a2bddfcd	update readme Former-commit-id: 06bcbb901f69265632892a5fcbc956b8be1153da	2023-08-07 15:02:02 +08:00
hiyouga	370f817549	Merge branch 'main' of https://github.com/hiyouga/LLaMA-Efficient-Tuning Former-commit-id: 5c5657227db285048e3850631badb040eea9b6ca	2023-08-07 13:59:16 +08:00
hiyouga	041390c37e	fix #376 Former-commit-id: a5b01257ba3323bcb2dd0217fb89a387e39ddbec	2023-08-07 13:58:59 +08:00
hoshi-hiyouga	d9fe4bf500	Merge pull request #382 from hiyouga/feature-updateReadme add detailed model configs Former-commit-id: 371c50cf3fd4e3f5e8fb390508c27cb5f18fa531	2023-08-07 13:43:38 +08:00
hiyouga	e0c7e944fc	update trainer Former-commit-id: 0d39b53a5164e34d22fe0a492eaa0d7ac63102fe	2023-08-07 13:34:35 +08:00
codemayq	0845fe67db	add detailed model configs Former-commit-id: 438c43f820e39738eaa1c296aadcf6d141c3289f	2023-08-07 09:30:23 +08:00
hiyouga	fe3b12d900	fix qwen eos token Former-commit-id: 770830c67886f5872b39b9608949ec62d4616b27	2023-08-06 13:31:17 +08:00
hiyouga	a70d56864e	fix qwen tokenizer #361 Former-commit-id: 78a2fa95c8ab669254a6c8fce8138c4395fb0a09	2023-08-05 17:06:05 +08:00
hiyouga	fdbb2c5378	fix template for tiktoken Former-commit-id: 8328447f81eb5b90310df08cf2928c83ef6355fe	2023-08-05 13:42:42 +08:00
hiyouga	3c0aaf42af	remove redundant code Former-commit-id: dcec1717592107ba9d26eb2ac520309da19d1805	2023-08-05 00:27:27 +08:00
hiyouga	438e19160a	fix template Former-commit-id: b88200a88ea112e043dc44058606805c60e32844	2023-08-05 00:25:00 +08:00
hiyouga	f2b2ff6950	fix llama2 template Former-commit-id: 08f37145e0bca5f1a8fd7bad01c64dc69b07361b	2023-08-05 00:07:54 +08:00
hoshi-hiyouga	86cef96305	Support safe ChatML template, fix qwen tok #351 #354 https://github.com/openai/openai-python/blob/main/chatml.md Former-commit-id: 94bfc9d85f7cef3a5eb15085e0124a424373814f	2023-08-05 00:00:23 +08:00
hiyouga	5f50944baf	fix bos and eos token Former-commit-id: ab386f4c0fb5eaac24264a5bbef4c03deeb92158	2023-08-04 23:55:57 +08:00
hiyouga	0804fd2353	fix encode Former-commit-id: ec382abd906d93cf78c7fbaec753ce6bcf8cfebd	2023-08-04 23:27:55 +08:00
hiyouga	86419eb457	support chatml safe encoding Former-commit-id: ea52bb135bf9d07738091006ec7ada8df14cf15e	2023-08-04 23:14:28 +08:00
hiyouga	76f3ae7bf3	support interleave probs Former-commit-id: 168d99816f9bdc746c587f7f09753ba7e0a4b19d	2023-08-04 21:27:35 +08:00
hiyouga	aaa85190eb	fix webui export model Former-commit-id: c34469c05e681239db23e2e666b5ac6a4e38aba9	2023-08-04 14:20:27 +08:00
hiyouga	e2a4e926b9	fix mtloader Former-commit-id: ca48c2c02c3cfa9afb99971b50daeda9cf14e7cb	2023-08-03 19:29:02 +08:00
hiyouga	d6e922dc1c	tiny fix Former-commit-id: 81ef7017a4c96441951adeff0276cc5ab76a3544	2023-08-03 17:42:28 +08:00
hiyouga	27f4317ec6	fix qwen inference Former-commit-id: 823f0de0ca0a92b6f48a90e5ffe57a48dc018f1d	2023-08-03 16:31:55 +08:00
hiyouga	e434348216	fix qwen inference Former-commit-id: 2c5fe45ce1405124f12ecd20e263b5538af97972	2023-08-03 16:15:38 +08:00
hiyouga	2e19afedb8	support Qwen-7B, fix InternLM-7B inference Former-commit-id: 25d2ca29ecb70cbfd5206333c667042a0c4d2e5a	2023-08-03 15:53:32 +08:00
hiyouga	da08fa7c63	update web demo Former-commit-id: 5b6ad9adb665096bfb36dc90789a1d4a16345122	2023-08-03 13:28:28 +08:00
hiyouga	9c96b97dc7	fix webui Former-commit-id: e87630ef77977b2879f1199b9a421acbbbb32a51	2023-08-03 12:43:12 +08:00
hiyouga	28a51b622b	modify code structure Former-commit-id: 6369f9b1751e6f9bb709ba76a85f69cbe0823e5d	2023-08-02 23:17:36 +08:00
hiyouga	8bd1da7144	fix PPO trainer Former-commit-id: 21982a7d4dd9b7c3a1145b481f02b9990e32dc00	2023-08-02 19:10:23 +08:00
hiyouga	e4d0b8ee6e	update ppo trainer Former-commit-id: c27136a83e167465d3f825e40f10c7b9fcfbf97a	2023-08-02 18:46:41 +08:00
hiyouga	1dfb28b362	fix memory leak of PPO trainer Former-commit-id: 38410894a5ebf0b043b55a6bd5cca3cd0a44b27d	2023-08-02 17:41:34 +08:00
				`@@ -0,0 +1 @@`
				`from llmtuner.tuner.dpo.workflow import run_dpo`
				`@@ -0,0 +1 @@`
				`from llmtuner.webui.interface import create_ui, create_web_demo`