release v0.1.2

Former-commit-id: 04aad91b71cc3a1acaf1bcec4304ce6b2098f7dc

README.md

@@ -10,6 +10,8 @@
 
 ## Changelog
 
+[23/07/19] Now we support training the **LLaMA-2** models in this repo. Try `--model_name_or_path meta-llama/Llama-2-7b-hf` argument to use the LLaMA-2 model. Remember to use `--prompt_template llama2` argument when you are using the LLaMA-2-chat model.
+
 [23/07/18] Now we develop an all-in-one Web UI for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development.
 
 [23/07/11] Now we support training the **Baichuan-13B** model in this repo. Please replace the Baichuan-13B model file with `tests/modeling_baichuan.py` and try `--model_name_or_path path_to_baichuan_model` and `--lora_target W_pack` arguments to train the Baichuan-13B model. Remember to use `--prompt_template baichuan` argument when you are using the Baichuan-13B-Chat model.
@@ -20,11 +22,11 @@
 
 [23/07/05] Now we support training the **Falcon-7B/40B** models in this repo. Try `--model_name_or_path tiiuae/falcon-7b` and `--lora_target query_key_value` arguments to use the Falcon model.
 
-[23/06/29] We provide a **reproducible example** of training a chat model using instruction-following datasets, see this [HuggingFace Repo](https://huggingface.co/hiyouga/baichuan-7b-sft) for details.
+[23/06/29] We provide a **reproducible example** of training a chat model using instruction-following datasets, see this [Hugging Face Repo](https://huggingface.co/hiyouga/baichuan-7b-sft) for details.
 
 [23/06/22] Now we align the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in **arbitrary ChatGPT-based applications**.
 
-[23/06/15] Now we support training the **Baichuan-7B** model in this repo. Try `--model_name_or_path baichuan-inc/Baichuan-7B` and `--lora_target W_pack` arguments to use the Baichuan-7B model. If you want to train with RTX3090, use `git checkout baichuan-7b-rtx3090` to switch to the `baichuan-7b-rtx3090` branch and try the `--baichuan_rtx_gpu true` argument. (Other RTX series GPUs can also be tried)
+[23/06/15] Now we support training the **Baichuan-7B** model in this repo. Try `--model_name_or_path baichuan-inc/Baichuan-7B` and `--lora_target W_pack` arguments to use the Baichuan-7B model.
 
 [23/06/03] Now we support quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). Try `--quantization_bit 4/8` argument to work with quantized model. (experimental feature)
 
@@ -33,6 +35,7 @@
 ## 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)
@@ -57,36 +60,36 @@
 ## Provided Datasets
 
 - For pre-training:
-  - [Wiki Demo](data/wiki_demo.txt)
+  - [Wiki Demo (en)](data/wiki_demo.txt)
 - For supervised fine-tuning:
-  - [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
+  - [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
-  - [Stanford Alpaca (Chinese)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
+  - [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
-  - [GPT-4 Generated Data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+  - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
-  - [BELLE 2M](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
+  - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
-  - [BELLE 1M](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
+  - [Self-cognition (zh)](data/self_cognition.json)
-  - [BELLE 0.5M](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
+  - [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
-  - [BELLE Dialogue 0.4M](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
+  - [RefGPT (zh)](https://github.com/sufengniu/RefGPT)
-  - [BELLE School Math 0.25M](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
+  - [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
-  - [BELLE Multiturn Chat 0.8M](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
+  - [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
-  - [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
+  - [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
-  - [Firefly 1.1M](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
+  - [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
-  - [CodeAlpaca 20k](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
+  - [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
-  - [Alpaca CoT](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
+  - [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
-  - [Web QA (Chinese)](https://huggingface.co/datasets/suolyer/webqa)
+  - [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
-  - [UltraChat](https://github.com/thunlp/UltraChat)
+  - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
-  - [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1)
+  - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
-  - [Open Assistant (Chinese)](https://huggingface.co/datasets/OpenAssistant/oasst1)
+  - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
-  - [WebNovel (Chinese)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
+  - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
-- For reward model training:
+  - [UltraChat (en)](https://github.com/thunlp/UltraChat)
-  - [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf)
+  - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
-  - [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1)
+- For reward modelling:
-  - [Open Assistant (Chinese)](https://huggingface.co/datasets/OpenAssistant/oasst1)
+  - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
-  - [GPT-4 Generated Data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+  - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
-  - [GPT-4 Generated Data (Chinese)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+  - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
 
 Please refer to [data/README.md](data/README.md) for details.
 
-Some datasets require confirmation before using them, so we recommend logging in with your HuggingFace account using these commands.
+Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.
 
 ```bash
 pip install --upgrade huggingface_hub
@@ -260,34 +263,55 @@ use_cpu: false
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage pt \
+    --stage sft \
     --model_name_or_path path_to_your_model \
     --do_eval \
     --dataset alpaca_gpt4_en \
+    --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint \
     --output_dir path_to_eval_result \
     --per_device_eval_batch_size 8 \
-    --max_samples 50 \
+    --max_samples 100 \
     --predict_with_generate
 ```
 
 We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit evaluation.
 
+### Predict
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage sft \
+    --model_name_or_path path_to_your_model \
+    --do_predict \
+    --dataset alpaca_gpt4_en \
+    --finetuning_type lora \
+    --checkpoint_dir path_to_checkpoint \
+    --output_dir path_to_predict_result \
+    --per_device_eval_batch_size 8 \
+    --max_samples 100 \
+    --predict_with_generate
+```
+
+If you want to predict the samples with empty responses, please kindly fill the `response` column with **dummy tokens** to ensure the sample will not be discarded throughout the preprocessing phase.
+
 ### API Demo
 
 ```bash
 python src/api_demo.py \
     --model_name_or_path path_to_your_model \
+    --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint
 ```
 
-See `http://localhost:8000/docs` for API documentation.
+Visit `http://localhost:8000/docs` for API documentation.
 
 ### CLI Demo
 
 ```bash
 python src/cli_demo.py \
     --model_name_or_path path_to_your_model \
+    --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint
 ```
 
@@ -296,6 +320,7 @@ python src/cli_demo.py \
 ```bash
 python src/web_demo.py \
     --model_name_or_path path_to_your_model \
+    --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint
 ```
 
@@ -304,6 +329,7 @@ python src/web_demo.py \
 ```bash
 python src/export_model.py \
     --model_name_or_path path_to_your_model \
+    --finetuning_type lora \
     --checkpoint_dir path_to_checkpoint \
     --output_dir path_to_export
 ```
@@ -315,6 +341,7 @@ This repository is licensed under the [Apache-2.0 License](LICENSE).
 Please follow the model licenses to use the corresponding model weights:
 
 - [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md)
+- [LLaMA-2](https://ai.meta.com/llama/license/)
 - [BLOOM](https://huggingface.co/spaces/bigscience/license)
 - [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)

data/README.md

@@ -1,4 +1,5 @@
-Data format in `dataset_info.json`:
+If you are using a custom dataset, please provide your dataset definition in the following format in `dataset_info.json`.
+
 ```json
 "dataset_name": {
     "hf_hub_url": "the name of the dataset repository on the HuggingFace hub. (if specified, ignore below 3 arguments)",
@@ -14,40 +15,4 @@ Data format in `dataset_info.json`:
 }
 ```
 
-`dataset_info.json` 中的数据集定义格式：
+where the `prompt` and `response` columns should contain non-empty values. The `query` column will be concatenated with the `prompt` column and used as input for the model. The `history` column should contain a list where each element is a string tuple representing a query-response pair.
-```json
-"数据集名称": {
-    "hf_hub_url": "HuggingFace上的项目地址（若指定，则忽略下列三个参数）",
-    "script_url": "包含数据加载脚本的本地文件夹名称（若指定，则忽略下列两个参数）",
-    "file_name": "该目录下数据集文件的名称（若上述参数未指定，则此项必需）",
-    "file_sha1": "数据集文件的SHA-1哈希值（可选）",
-    "columns": {
-        "prompt": "数据集代表提示词的表头名称（默认：instruction）",
-        "query": "数据集代表请求的表头名称（默认：input）",
-        "response": "数据集代表回答的表头名称（默认：output）",
-        "history": "数据集代表历史对话的表头名称（默认：None）"
-    }
-}
-```
-
-部分预置数据集简介：
-
-| 数据集名称 | 规模 | 描述 |
-| --- | --- | --- |
-| [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 52k | 斯坦福大学开源的 Alpaca 数据集，训练了 Alpaca 这类早期基于 LLaMA 的模型 |
-| [Stanford Alpaca (Chinese)](https://github.com/ymcui/Chinese-LLaMA-Alpaca) | 51k | 使用 ChatGPT 翻译的 Alpaca 数据集 |
-| [GPT-4 Generated Data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) | 100k+ | 基于 GPT-4 的 self-instruction 数据集 |
-| [BELLE 2M](https://huggingface.co/datasets/BelleGroup/train_2M_CN) | 2m | 包含约 200 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的中文指令数据 |
-| [BELLE 1M](https://huggingface.co/datasets/BelleGroup/train_1M_CN) | 1m | 包含约 100 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的中文指令数据 |
-| [BELLE 0.5M](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN) | 500k  | 包含约 50 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的中文指令数据 |
-| [BELLE Dialogue 0.4M](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M) | 400k | 包含约 40 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的个性化角色对话数据，包含角色介绍 |
-| [BELLE School Math 0.25M](https://huggingface.co/datasets/BelleGroup/school_math_0.25M) | 250k  | 包含约 25 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的中文数学题数据，包含解题过程 |
-| [BELLE Multiturn Chat 0.8M](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M) | 800k | 包含约 80 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的用户与助手的多轮对话 |
-| [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) | 100k+ | 包含日文、简繁体中文、英文等多类数据，数据集原用于 Guanaco 模型训练 |
-| [Firefly 1.1M](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) | 1.1M  | 中文对话大模型 firefly（流萤）的中文数据集，包含多个 NLP 任务 |
-| [CodeAlpaca 20k](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) | 20k | 英文代码生成任务数据集 |
-| [Alpaca CoT](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT) | 6M | 用于微调的指令数据集集合 |
-| [Web QA](https://huggingface.co/datasets/suolyer/webqa) | 36k | 百度知道汇集的中文问答数据集 |
-| [UltraChat](https://github.com/thunlp/UltraChat) | 1.57M | 清华 NLP 发布的大规模多轮对话数据集 |
-
-注：BELLE 数据集是由 ChatGPT 产生的数据集，不保证数据准确性，所有类 GPT 模型产生的 self-instruction 数据集均不能保证其准确性。

data/pretrain_data/wiki_long.jsonl

@@ -1,2 +0,0 @@
-{"id": 0,"title": "大卫·亨利","content": "大卫·亨利\n\n大卫·克莱顿·亨利（David Clayton Henrie，），美国演员。近来在迪士尼频道原创电视影集《少年魔法师》（Wizards of Waverly Place）当中演出贾斯汀·鲁索（Justin Russo）一角。\n\n大卫·亨利出生在加州Mission Viejo，在凤凰城长大。他的胞弟劳伦斯·亨利（Lorenzo Henrie）也是演员。大卫·亨利就读夏安传统学校。家中是信奉罗马天主教。 \n\n大卫在2007年拍摄少年魔法师期间认识女演员露西·海尔（Lucy Hale），之后与其交往，于2009年分手。\n\n10岁时，大卫·亨利和SAG在凤凰城签订了合约，并开始走出去试镜。 9岁的时候，在沙加缅度进行商业拍摄，SAG董事建议大卫·亨利搬到洛杉矶。在10岁那年夏天，他和他的家人搬到了好莱坞。他预定他的前2支商业试镜，扮演主要角色为汉堡王和桂格燕麦。他初演电视节目为Providence。 \n\n到了13岁，大卫有了他的第一次重大突破，在福克斯公司的喜剧The Pitts饰演 Petey Pitt一角。大卫下出作品为的Hallmark movie为Monster Maker，和琳达布莱儿、乔治甘迺迪共同演出，并要求回来Hallmark movie公司。 \n\n在18岁时，大卫得到了迪士尼频道原创系列演出机会，该节目2007年10月12日首播。大卫2008年参加了迪士尼频道的游戏节目。他是绿色团队的队长，隔年，为旋风队队长。他在迪士尼原创电影《少年魔法师》之后在《酷爸的疯狂假期》中有饰演一角。\n"}
-{"id": 1,"title": "大卫·亨利","content": "大卫·亨利\n\n大卫·克莱顿·亨利（David Clayton Henrie，），美国演员。近来在迪士尼频道原创电视影集《少年魔法师》（Wizards of Waverly Place）当中演出贾斯汀·鲁索（Justin Russo）一角。\n\n大卫·亨利出生在加州Mission Viejo，在凤凰城长大。他的胞弟劳伦斯·亨利（Lorenzo Henrie）也是演员。大卫·亨利就读夏安传统学校。家中是信奉罗马天主教。 \n\n大卫在2007年拍摄少年魔法师期间认识女演员露西·海尔（Lucy Hale），之后与其交往，于2009年分手。\n\n10岁时，大卫·亨利和SAG在凤凰城签订了合约，并开始走出去试镜。 9岁的时候，在沙加缅度进行商业拍摄，SAG董事建议大卫·亨利搬到洛杉矶。在10岁那年夏天，他和他的家人搬到了好莱坞。他预定他的前2支商业试镜，扮演主要角色为汉堡王和桂格燕麦。他初演电视节目为Providence。 \n\n到了13岁，大卫有了他的第一次重大突破，在福克斯公司的喜剧The Pitts饰演 Petey Pitt一角。大卫下出作品为的Hallmark movie为Monster Maker，和琳达布莱儿、乔治甘迺迪共同演出，并要求回来Hallmark movie公司。 \n\n在18岁时，大卫得到了迪士尼频道原创系列演出机会，该节目2007年10月12日首播。大卫2008年参加了迪士尼频道的游戏节目。他是绿色团队的队长，隔年，为旋风队队长。他在迪士尼原创电影《少年魔法师》之后在《酷爸的疯狂假期》中有饰演一角。\n"}

data/refgpt_zh_50k_p1.json.REMOVED.git-id

@@ -0,0 +1 @@
+56405bb8f52727e52e99693739494b9b7b0d7ba6

data/refgpt_zh_50k_p2.json.REMOVED.git-id

@@ -0,0 +1 @@
+fa935248a5d40d2bdd5649af99a72a754d40ae7a

data/sharegpt_zh_27k.json.REMOVED.git-id

@@ -0,0 +1 @@
+38c89869c6aeca2a3af9ea1e09afe460f9b46810

requirements.txt

@@ -10,7 +10,7 @@ rouge-chinese
 nltk
 gradio>=3.36.0
 uvicorn
-pydantic
+pydantic==1.10.11
-fastapi
+fastapi==0.95.1
 sse-starlette
 matplotlib

src/api_demo.py

@@ -5,9 +5,16 @@
 
 import uvicorn
 
-from llmtuner import create_app
+from llmtuner import ChatModel
+from llmtuner.api.app import create_app
+from llmtuner.tuner import get_infer_args
+
+
+def main():
+    chat_model = ChatModel(*get_infer_args())
+    app = create_app(chat_model)
+    uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
 
 
 if __name__ == "__main__":
-    app = create_app()
+    main()
-    uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)

src/cli_demo.py

@@ -2,7 +2,8 @@
 # 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, get_infer_args
+from llmtuner import ChatModel
+from llmtuner.tuner import get_infer_args
 
 
 def main():

src/export_model.py

@@ -2,7 +2,7 @@
 # Exports the fine-tuned model.
 # Usage: python export_model.py --checkpoint_dir path_to_checkpoint --output_dir path_to_save_model
 
-from llmtuner import get_train_args, load_model_and_tokenizer
+from llmtuner.tuner import get_train_args, load_model_and_tokenizer
 
 
 def main():

src/llmtuner/__init__.py

@@ -1,7 +1,4 @@
-from llmtuner.api import create_app
 from llmtuner.chat import ChatModel
-from llmtuner.tuner import get_train_args, get_infer_args, load_model_and_tokenizer, run_pt, run_sft, run_rm, run_ppo
-from llmtuner.webui import create_ui
 
 
-__version__ = "0.1.0"
+__version__ = "0.1.2"

src/llmtuner/api/__init__.py

@@ -1 +0,0 @@
-from llmtuner.api.app import create_app

src/llmtuner/api/app.py

@@ -1,4 +1,3 @@
-import json
 import uvicorn
 from fastapi import FastAPI, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
@@ -31,9 +30,7 @@ async def lifespan(app: FastAPI): # collects GPU memory
     torch_gc()
 
 
-def create_app():
+def create_app(chat_model: ChatModel) -> FastAPI:
-    chat_model = ChatModel(*get_infer_args())
-
     app = FastAPI(lifespan=lifespan)
 
     app.add_middleware(
@@ -96,7 +93,7 @@ def create_app():
             finish_reason=None
         )
         chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
-        yield json.dumps(chunk, ensure_ascii=False)
+        yield chunk.json(exclude_unset=True, ensure_ascii=False)
 
         for new_text in chat_model.stream_chat(
             query, history, prefix, temperature=request.temperature, top_p=request.top_p, max_new_tokens=request.max_tokens
@@ -110,7 +107,7 @@ def create_app():
                 finish_reason=None
             )
             chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
-            yield json.dumps(chunk, ensure_ascii=False)
+            yield chunk.json(exclude_unset=True, ensure_ascii=False)
 
         choice_data = ChatCompletionResponseStreamChoice(
             index=0,
@@ -118,12 +115,13 @@ def create_app():
             finish_reason=Finish.STOP
         )
         chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data])
-        yield json.dumps(chunk, ensure_ascii=False)
+        yield chunk.json(exclude_unset=True, ensure_ascii=False)
         yield "[DONE]"
 
     return app
 
 
 if __name__ == "__main__":
-    app = create_app()
+    chat_model = ChatModel(*get_infer_args())
+    app = create_app(chat_model)
     uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)

src/llmtuner/chat/stream_chat.py

@@ -4,7 +4,7 @@ from threading import Thread
 from transformers import TextIteratorStreamer
 
 from llmtuner.extras.misc import get_logits_processor
-from llmtuner.extras.template import Template
+from llmtuner.extras.template import get_template
 from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
 from llmtuner.tuner import load_model_and_tokenizer
 
@@ -19,14 +19,14 @@ class ChatModel:
         generating_args: GeneratingArguments
     ) -> None:
         self.model, self.tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
-        self.template = Template(data_args.prompt_template)
+        self.template = get_template(data_args.prompt_template)
-        self.source_prefix = data_args.source_prefix if data_args.source_prefix else ""
+        self.source_prefix = data_args.source_prefix or ""
         self.generating_args = generating_args
 
     def process_args(
         self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = None, **input_kwargs
     ) -> Tuple[Dict[str, Any], int]:
-        prefix = prefix if prefix else self.source_prefix
+        prefix = prefix or self.source_prefix
 
         inputs = self.tokenizer([self.template.get_prompt(query, history, prefix)], return_tensors="pt")
         inputs = inputs.to(self.model.device)
@@ -81,5 +81,4 @@ class ChatModel:
         thread = Thread(target=self.model.generate, kwargs=gen_kwargs)
         thread.start()
 
-        for new_text in streamer:
+        yield from streamer
-            yield new_text

src/llmtuner/dsets/__init__.py

@@ -1,2 +1,3 @@
 from llmtuner.dsets.loader import get_dataset
 from llmtuner.dsets.preprocess import preprocess_dataset
+from llmtuner.dsets.utils import split_dataset

src/llmtuner/dsets/callbacks.py

@@ -1,63 +0,0 @@
-import os
-import json
-import time
-from datetime import timedelta
-
-from transformers import (
-    TrainerCallback,
-    TrainerControl,
-    TrainerState,
-    TrainingArguments
-)
-
-
-class LogCallback(TrainerCallback):
-
-    def __init__(self, runner=None):
-        self.runner = runner
-        self.start_time = time.time()
-        self.tracker = {}
-
-    def on_step_begin(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.
-        """
-        if self.runner is not None and self.runner.aborted:
-            control.should_epoch_stop = True
-            control.should_training_stop = True
-
-    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 self.runner is not None and self.runner.aborted:
-            control.should_epoch_stop = True
-            control.should_training_stop = True
-
-    def on_log(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs) -> None:
-        r"""
-        Event called after logging the last logs.
-        """
-        if "loss" not in state.log_history[-1]:
-            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),
-            "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)))
-        }
-        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")

src/llmtuner/dsets/preprocess.py

@@ -6,7 +6,7 @@ from transformers.tokenization_utils import PreTrainedTokenizer
 from datasets import Dataset
 
 from llmtuner.extras.constants import IGNORE_INDEX
-from llmtuner.extras.template import Template
+from llmtuner.extras.template import get_template
 from llmtuner.hparams import DataArguments
 
 
@@ -19,7 +19,7 @@ def preprocess_dataset(
 ) -> Dataset:
 
     column_names = list(dataset.column_names)
-    prompt_template = Template(data_args.prompt_template)
+    prompt_template = get_template(data_args.prompt_template)
 
     # support question with a single answer or multiple answers
     def get_dialog(examples):

src/llmtuner/dsets/utils.py

@@ -0,0 +1,16 @@
+from typing import Dict
+from datasets import Dataset
+
+
+def split_dataset(
+    dataset: Dataset, dev_ratio: float, do_train: bool
+) -> Dict[str, Dataset]:
+    # Split the dataset
+    if do_train:
+        if dev_ratio > 1e-6:
+            dataset = dataset.train_test_split(test_size=dev_ratio)
+            return {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
+        else:
+            return {"train_dataset": dataset}
+    else: # do_eval or do_predict
+        return {"eval_dataset": dataset}

src/llmtuner/extras/constants.py

@@ -13,6 +13,12 @@ SUPPORTED_MODELS = {
     "LLaMA-13B": "huggyllama/llama-13b",
     "LLaMA-30B": "huggyllama/llama-30b",
     "LLaMA-65B": "huggyllama/llama-65b",
+    "LLaMA2-7B": "meta-llama/Llama-2-7b-hf",
+    "LLaMA2-13B": "meta-llama/Llama-2-13b-hf",
+    "LLaMA2-70B": "meta-llama/Llama-2-70b-hf",
+    "LLaMA2-7B-Chat": "meta-llama/Llama-2-7b-chat-hf",
+    "LLaMA2-13B-Chat": "meta-llama/Llama-2-13b-chat-hf",
+    "LLaMA2-70B-Chat": "meta-llama/Llama-2-70b-chat-hf",
     "BLOOM-560M": "bigscience/bloom-560m",
     "BLOOM-3B": "bigscience/bloom-3b",
     "BLOOM-7B1": "bigscience/bloom-7b1",
@@ -30,8 +36,9 @@ SUPPORTED_MODELS = {
     "InternLM-7B-Chat": "internlm/internlm-chat-7b"
 }
 
-DEFAULT_MODULE = { # will be deprecated
+DEFAULT_MODULE = {
     "LLaMA": "q_proj,v_proj",
+    "LLaMA2": "q_proj,v_proj",
     "BLOOM": "query_key_value",
     "BLOOMZ": "query_key_value",
     "Falcon": "query_key_value",

src/llmtuner/extras/template.py

@@ -3,30 +3,13 @@ from dataclasses import dataclass
 
 
 @dataclass
-class Format:
+class Template:
+
     prefix: str
     prompt: str
     sep: str
     use_history: bool
 
-
-templates: Dict[str, Format] = {}
-
-
-@dataclass
-class Template:
-
-    name: str
-
-    def __post_init__(self):
-        if self.name in templates:
-            self.prefix = templates[self.name].prefix
-            self.prompt = templates[self.name].prompt
-            self.sep = templates[self.name].sep
-            self.use_history = templates[self.name].use_history
-        else:
-            raise ValueError("Template {} does not exist.".format(self.name))
-
     def get_prompt(
         self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = ""
     ) -> str:
@@ -46,7 +29,7 @@ class Template:
     def _format_example(
         self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = ""
     ) -> List[str]:
-        prefix = prefix if prefix else self.prefix # use prefix if provided
+        prefix = prefix or self.prefix # use prefix if provided
         prefix = prefix + self.sep if prefix else "" # add separator for non-empty prefix
         history = history if (history and self.use_history) else []
         history = history + [(query, "<dummy>")]
@@ -61,8 +44,11 @@ class Template:
         return convs[:-1] # drop last
 
 
+templates: Dict[str, Template] = {}
+
+
 def register_template(name: str, prefix: str, prompt: str, sep: str, use_history: bool) -> None:
-    templates[name] = Format(
+    templates[name] = Template(
         prefix=prefix,
         prompt=prompt,
         sep=sep,
@@ -70,6 +56,12 @@ def register_template(name: str, prefix: str, prompt: str, sep: str, use_history
     )
 
 
+def get_template(name: str) -> Template:
+    template = templates.get(name, None)
+    assert template is not None, "Template {} does not exist.".format(name)
+    return template
+
+
 r"""
 Supports language model inference without histories.
 """
@@ -95,6 +87,27 @@ register_template(
 )
 
 
+r"""
+Supports: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
+          https://huggingface.co/meta-llama/Llama-2-13b-chat-hf
+          https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
+"""
+register_template(
+    name="llama2",
+    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>",
+    use_history=True
+)
+
+
 r"""
 Supports: https://huggingface.co/tatsu-lab/alpaca-7b-wdiff
           https://github.com/ymcui/Chinese-LLaMA-Alpaca
@@ -202,7 +215,7 @@ Supports: https://huggingface.co/baichuan-inc/Baichuan-13B-Chat
 register_template(
     name="baichuan",
     prefix="",
-    prompt=" <reserved_102> {query} <reserved_103> ",
+    prompt="<reserved_102>{query}<reserved_103>",
     sep="</s>",
     use_history=True
 )

src/llmtuner/hparams/finetuning_args.py

@@ -16,9 +16,10 @@ class FinetuningArguments:
         default=32,
         metadata={"help": "Number of decoder blocks in the model. \
                   LLaMA choices: [\"32\", \"40\", \"60\", \"80\"], \
+                  LLaMA-2 choices: [\"32\", \"40\", \"80\"], \
                   BLOOM choices: [\"24\", \"30\", \"70\"], \
                   Falcon choices: [\"32\", \"60\"], \
-                  Baichuan choices: [\"32\"]"}
+                  Baichuan choices: [\"32\", \"40\"]"}
     )
     num_layer_trainable: Optional[int] = field(
         default=3,
@@ -27,7 +28,7 @@ class FinetuningArguments:
     name_module_trainable: Optional[Literal["mlp", "self_attn", "self_attention"]] = field(
         default="mlp",
         metadata={"help": "Name of trainable modules for Freeze fine-tuning. \
-                  LLaMA choices: [\"mlp\", \"self_attn\"], \
+                  LLaMA & LLaMA-2 choices: [\"mlp\", \"self_attn\"], \
                   BLOOM & Falcon choices: [\"mlp\", \"self_attention\"], \
                   Baichuan choices: [\"mlp\", \"self_attn\"]"}
     )
@@ -46,7 +47,7 @@ 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 choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
+                  LLaMA & LLaMA-2 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\"]"}
     )

src/llmtuner/tuner/core/loader.py

@@ -10,6 +10,7 @@ from transformers import (
 )
 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
@@ -108,7 +109,7 @@ def load_model_and_tokenizer(
         model_to_load,
         config=config,
         torch_dtype=torch.bfloat16 if model_args.compute_dtype == torch.bfloat16 else torch.float16,
-        low_cpu_mem_usage=True,
+        low_cpu_mem_usage=(not is_deepspeed_zero3_enabled()),
         **config_kwargs
     )
 

src/llmtuner/tuner/ppo/trainer.py

@@ -107,7 +107,11 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
             # Compute rewards
             replace_model(unwrapped_model, target="reward")
             with torch.no_grad():
-                _, _, values = self.model(**self.prepare_model_inputs(queries, responses))
+                _, _, 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")
 

src/llmtuner/tuner/pt/workflow.py

@@ -4,7 +4,7 @@ import math
 from typing import Optional, List
 from transformers import Seq2SeqTrainingArguments, DataCollatorForSeq2Seq, TrainerCallback
 
-from llmtuner.dsets import get_dataset, preprocess_dataset
+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
@@ -28,16 +28,6 @@ def run_pt(
         label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
     )
 
-    # Split the dataset
-    if training_args.do_train:
-        if data_args.dev_ratio > 1e-6:
-            dataset = dataset.train_test_split(test_size=data_args.dev_ratio)
-            trainer_kwargs = {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
-        else:
-            trainer_kwargs = {"train_dataset": dataset}
-    else: # do_eval or do_predict
-        trainer_kwargs = {"eval_dataset": dataset}
-
     # Initialize our Trainer
     trainer = PeftTrainer(
         finetuning_args=finetuning_args,
@@ -46,7 +36,7 @@ def run_pt(
         tokenizer=tokenizer,
         data_collator=data_collator,
         callbacks=callbacks,
-        **trainer_kwargs
+        **split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
     )
 
     # Training

src/llmtuner/tuner/rm/trainer.py

@@ -32,7 +32,7 @@ class PairwisePeftTrainer(PeftTrainer):
         See: https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/trainer.py#L3509
         """
         batch_size = inputs["input_ids"].size(0) // 2
-        _, _, values = model(**inputs)
+        _, _, values = model(**inputs, output_hidden_states=True, return_dict=True)
         r_accept, r_reject = values[:, -1].split(batch_size, dim=0)
         loss = -torch.log(torch.sigmoid(r_accept - r_reject)).mean()
         return (loss, [loss, r_accept, r_reject]) if return_outputs else loss

src/llmtuner/tuner/rm/workflow.py

@@ -5,7 +5,7 @@
 from typing import Optional, List
 from transformers import Seq2SeqTrainingArguments, TrainerCallback
 
-from llmtuner.dsets import get_dataset, preprocess_dataset
+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.hparams import ModelArguments, DataArguments, FinetuningArguments
@@ -29,16 +29,6 @@ def run_rm(
 
     training_args.remove_unused_columns = False # important for pairwise dataset
 
-    # Split the dataset
-    if training_args.do_train:
-        if data_args.dev_ratio > 1e-6:
-            dataset = dataset.train_test_split(test_size=data_args.dev_ratio)
-            trainer_kwargs = {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
-        else:
-            trainer_kwargs = {"train_dataset": dataset}
-    else: # do_eval or do_predict
-        trainer_kwargs = {"eval_dataset": dataset}
-
     # Initialize our Trainer
     trainer = PairwisePeftTrainer(
         finetuning_args=finetuning_args,
@@ -48,7 +38,7 @@ def run_rm(
         data_collator=data_collator,
         callbacks=callbacks,
         compute_metrics=compute_accuracy,
-        **trainer_kwargs
+        **split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
     )
 
     # Training

src/llmtuner/tuner/sft/trainer.py

@@ -36,31 +36,44 @@ class Seq2SeqPeftTrainer(PeftTrainer):
             inputs["labels"] = self._pad_tensors_to_target_len(inputs["labels"], inputs["input_ids"])
         if label_len > prompt_len:
             inputs["input_ids"] = self._pad_tensors_to_target_len(inputs["input_ids"], inputs["labels"])
+            if "attention_mask" in inputs:
+                inputs["attention_mask"] = self._pad_tensors_to_target_len(
+                    inputs["attention_mask"], inputs["labels"], pad_token_id=0
+                )
+            if "position_ids" in inputs:
+                inputs["position_ids"] = self._pad_tensors_to_target_len(
+                    inputs["position_ids"], inputs["labels"], pad_token_id=0
+                )
 
         loss, generated_tokens, labels = super().prediction_step(
             model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
         )
-        generated_tokens = generated_tokens[:, max(prompt_len, label_len):] if generated_tokens is not None else None
+        generated_tokens = (
+            generated_tokens[:, max(prompt_len, label_len):] if generated_tokens is not None else None
+        )
 
         return (loss, generated_tokens, labels)
 
-    def _pad_tensors_to_target_len(self, src_tensor: torch.Tensor, tgt_tensor: torch.Tensor) -> torch.Tensor:
+    def _pad_tensors_to_target_len(
+        self,
+        src_tensor: torch.Tensor,
+        tgt_tensor: torch.Tensor,
+        pad_token_id: Optional[int] = None
+    ) -> torch.Tensor:
         r"""
         Pads the tensor to the same length as the target tensor.
 
         Should only be called when predict_with_generate=True.
         """
-        if self.tokenizer is not None and hasattr(self.tokenizer, "pad_token_id"):
+        if pad_token_id is None:
-            assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor."
+            if self.tokenizer is not None and hasattr(self.tokenizer, "pad_token_id"):
-            # If PAD token is not defined at least EOS token has to be defined
+                assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor."
-            pad_token_id = (
+                pad_token_id = self.tokenizer.pad_token_id
-                self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
-            )
-        else:
-            if self.model.config.pad_token_id is not None:
-                pad_token_id = self.model.config.pad_token_id
             else:
-                raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors")
+                if self.model.config.pad_token_id is not None:
+                    pad_token_id = self.model.config.pad_token_id
+                else:
+                    raise ValueError("Pad_token_id must be set in the configuration of the model.")
 
         padded_tensor = pad_token_id * torch.ones_like(tgt_tensor)
         padded_tensor[:, -src_tensor.shape[-1]:] = src_tensor # adopt left-padding

src/llmtuner/tuner/sft/workflow.py

@@ -3,7 +3,7 @@
 from typing import Optional, List
 from transformers import Seq2SeqTrainingArguments, DataCollatorForSeq2Seq, TrainerCallback
 
-from llmtuner.dsets import get_dataset, preprocess_dataset
+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
@@ -35,16 +35,6 @@ def run_sft(
     training_args.generation_num_beams = data_args.eval_num_beams if \
                 data_args.eval_num_beams is not None else training_args.generation_num_beams
 
-    # Split the dataset
-    if training_args.do_train:
-        if data_args.dev_ratio > 1e-6:
-            dataset = dataset.train_test_split(test_size=data_args.dev_ratio)
-            trainer_kwargs = {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
-        else:
-            trainer_kwargs = {"train_dataset": dataset}
-    else: # do_eval or do_predict
-        trainer_kwargs = {"eval_dataset": dataset}
-
     # Initialize our Trainer
     trainer = Seq2SeqPeftTrainer(
         finetuning_args=finetuning_args,
@@ -54,7 +44,7 @@ def run_sft(
         data_collator=data_collator,
         callbacks=callbacks,
         compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None,
-        **trainer_kwargs
+        **split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
     )
 
     # Keyword arguments for `model.generate`

src/llmtuner/webui/__init__.py

@@ -1 +0,0 @@
-from llmtuner.webui.interface import create_ui

src/llmtuner/webui/chat.py

@@ -73,6 +73,7 @@ class WebChatModel(ChatModel):
         chatbot: List[Tuple[str, str]],
         query: str,
         history: List[Tuple[str, str]],
+        prefix: str,
         max_new_tokens: int,
         top_p: float,
         temperature: float
@@ -80,7 +81,7 @@ class WebChatModel(ChatModel):
         chatbot.append([query, ""])
         response = ""
         for new_text in self.stream_chat(
-            query, history, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
+            query, history, prefix, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
         ):
             response += new_text
             new_history = history + [(query, response)]

src/llmtuner/webui/components/chatbot.py

@@ -16,11 +16,9 @@ def create_chat_box(
 
         with gr.Row():
             with gr.Column(scale=4):
-                with gr.Column(scale=12):
+                prefix = gr.Textbox(show_label=False)
-                    query = gr.Textbox(show_label=False, lines=8)
+                query = gr.Textbox(show_label=False, lines=8)
-
+                submit_btn = gr.Button(variant="primary")
-                with gr.Column(min_width=32, scale=1):
-                    submit_btn = gr.Button(variant="primary")
 
             with gr.Column(scale=1):
                 clear_btn = gr.Button()
@@ -36,7 +34,7 @@ def create_chat_box(
 
     submit_btn.click(
         chat_model.predict,
-        [chatbot, query, history, max_new_tokens, top_p, temperature],
+        [chatbot, query, history, prefix, max_new_tokens, top_p, temperature],
         [chatbot, history],
         show_progress=True
     ).then(
@@ -46,6 +44,7 @@ def create_chat_box(
     clear_btn.click(lambda: ([], []), outputs=[chatbot, history], show_progress=True)
 
     return chat_box, chatbot, history, dict(
+        prefix=prefix,
         query=query,
         submit_btn=submit_btn,
         clear_btn=clear_btn,

src/llmtuner/webui/components/eval.py

@@ -31,7 +31,8 @@ def create_eval_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str
         start_btn = gr.Button()
         stop_btn = gr.Button()
 
-    output_box = gr.Markdown()
+    with gr.Box():
+        output_box = gr.Markdown()
 
     start_btn.click(
         runner.run_eval,

src/llmtuner/webui/components/infer.py

@@ -12,7 +12,7 @@ def create_infer_tab(top_elems: Dict[str, Component]) -> Dict[str, Component]:
         load_btn = gr.Button()
         unload_btn = gr.Button()
 
-    info_box = gr.Markdown()
+    info_box = gr.Textbox(show_label=False, interactive=False)
 
     chat_model = WebChatModel()
     chat_box, chatbot, history, chat_elems = create_chat_box(chat_model)

src/llmtuner/webui/components/sft.py

@@ -35,12 +35,21 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
         lr_scheduler_type = gr.Dropdown(
             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)
-        fp16 = gr.Checkbox(value=True)
 
-    with gr.Row():
+    with gr.Accordion(label="Advanced config", open=False) as advanced_tab:
-        logging_steps = gr.Slider(value=5, minimum=5, maximum=1000, step=5)
+        with gr.Row():
-        save_steps = gr.Slider(value=100, minimum=10, maximum=5000, step=10)
+            logging_steps = gr.Slider(value=5, minimum=5, maximum=1000, step=5)
+            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")
+
+    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_target = gr.Textbox(scale=2)
 
     with gr.Row():
         start_btn = gr.Button()
@@ -49,7 +58,9 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
     with gr.Row():
         with gr.Column(scale=4):
             output_dir = gr.Textbox(interactive=True)
-            output_box = gr.Markdown()
+
+            with gr.Box():
+                output_box = gr.Markdown()
 
         with gr.Column(scale=1):
             loss_viewer = gr.Plot()
@@ -74,10 +85,15 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
             batch_size,
             gradient_accumulation_steps,
             lr_scheduler_type,
+            max_grad_norm,
             dev_ratio,
-            fp16,
             logging_steps,
             save_steps,
+            warmup_steps,
+            compute_type,
+            lora_rank,
+            lora_dropout,
+            lora_target,
             output_dir
         ],
         [output_box]
@@ -103,10 +119,17 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
         batch_size=batch_size,
         gradient_accumulation_steps=gradient_accumulation_steps,
         lr_scheduler_type=lr_scheduler_type,
+        max_grad_norm=max_grad_norm,
         dev_ratio=dev_ratio,
-        fp16=fp16,
+        advanced_tab=advanced_tab,
         logging_steps=logging_steps,
         save_steps=save_steps,
+        warmup_steps=warmup_steps,
+        compute_type=compute_type,
+        lora_tab=lora_tab,
+        lora_rank=lora_rank,
+        lora_dropout=lora_dropout,
+        lora_target=lora_target,
         start_btn=start_btn,
         stop_btn=stop_btn,
         output_dir=output_dir,

src/llmtuner/webui/components/top.py

@@ -22,10 +22,11 @@ def create_top() -> Dict[str, Component]:
         checkpoints = gr.Dropdown(multiselect=True, scale=5)
         refresh_btn = gr.Button(scale=1)
 
-    with gr.Row():
+    with gr.Accordion(label="Advanced config", open=False) as advanced_tab:
-        quantization_bit = gr.Dropdown([8, 4], scale=1)
+        with gr.Row():
-        template = gr.Dropdown(value="default", choices=list(templates.keys()), scale=2)
+            quantization_bit = gr.Dropdown([8, 4], scale=1)
-        source_prefix = gr.Textbox(scale=4)
+            template = gr.Dropdown(value="default", choices=list(templates.keys()), scale=1)
+            source_prefix = gr.Textbox(scale=2)
 
     model_name.change(
         list_checkpoint, [model_name, finetuning_type], [checkpoints]
@@ -47,9 +48,10 @@ def create_top() -> Dict[str, Component]:
         model_name=model_name,
         model_path=model_path,
         finetuning_type=finetuning_type,
-        template=template,
         checkpoints=checkpoints,
         refresh_btn=refresh_btn,
+        advanced_tab=advanced_tab,
         quantization_bit=quantization_bit,
+        template=template,
         source_prefix=source_prefix
     )

src/llmtuner/webui/interface.py

@@ -27,7 +27,7 @@ def create_ui() -> gr.Blocks:
         with gr.Tab("Evaluate"):
             eval_elems = create_eval_tab(top_elems, runner)
 
-        with gr.Tab("Inference"):
+        with gr.Tab("Chat"):
             infer_elems = create_infer_tab(top_elems)
 
         elem_list = [top_elems, sft_elems, eval_elems, infer_elems]

src/llmtuner/webui/locales.py

@@ -49,6 +49,14 @@ LOCALES = {
             "value": "刷新断点"
         }
     },
+    "advanced_tab": {
+        "en": {
+            "label": "Advanced configurations"
+        },
+        "zh": {
+            "label": "高级设置"
+        }
+    },
     "quantization_bit": {
         "en": {
             "label": "Quantization bit (optional)",
@@ -71,12 +79,12 @@ LOCALES = {
     },
     "source_prefix": {
         "en": {
-            "label": "Source prefix (optional)",
+            "label": "System prompt (optional)",
-            "info": "A sequence used as the prefix of each samples."
+            "info": "A sequence used as the default system prompt."
         },
         "zh": {
-            "label": "前缀序列（非必填）",
+            "label": "系统提示词（非必填）",
-            "info": "作为每个输入样本前缀的序列"
+            "info": "默认使用的系统提示词"
         }
     },
     "dataset_dir": {
@@ -209,6 +217,16 @@ LOCALES = {
             "info": "采用的学习率调节器名称。"
         }
     },
+    "max_grad_norm": {
+        "en": {
+            "label": "Maximum gradient norm",
+            "info": "Norm for gradient clipping.."
+        },
+        "zh": {
+            "label": "最大梯度范数",
+            "info": "用于梯度裁剪的范数。"
+        }
+    },
     "dev_ratio": {
         "en": {
             "label": "Dev ratio",
@@ -219,20 +237,10 @@ LOCALES = {
             "info": "验证集占全部样本的百分比。"
         }
     },
-    "fp16": {
-        "en": {
-            "label": "fp16",
-            "info": "Whether to use fp16 mixed precision training."
-        },
-        "zh": {
-            "label": "fp16",
-            "info": "是否启用 FP16 混合精度训练。"
-        }
-    },
     "logging_steps": {
         "en": {
             "label": "Logging steps",
-            "info": "Number of update steps between two logs."
+            "info": "Number of steps between two logs."
         },
         "zh": {
             "label": "日志间隔",
@@ -242,13 +250,71 @@ LOCALES = {
     "save_steps": {
         "en": {
             "label": "Save steps",
-            "info": "Number of updates steps between two checkpoints."
+            "info": "Number of steps between two checkpoints."
         },
         "zh": {
             "label": "保存间隔",
             "info": "每两次断点保存间的更新步数。"
         }
     },
+    "warmup_steps": {
+        "en": {
+            "label": "Warmup steps",
+            "info": "Number of steps used for warmup."
+        },
+        "zh": {
+            "label": "预热步数",
+            "info": "学习率预热采用的步数。"
+        }
+    },
+    "compute_type": {
+        "en": {
+            "label": "Compute type",
+            "info": "Whether to use fp16 or bf16 mixed precision training."
+        },
+        "zh": {
+            "label": "计算类型",
+            "info": "是否启用 FP16 或 BF16 混合精度训练。"
+        }
+    },
+    "lora_tab": {
+        "en": {
+            "label": "LoRA configurations"
+        },
+        "zh": {
+            "label": "LoRA 参数设置"
+        }
+    },
+    "lora_rank": {
+        "en": {
+            "label": "LoRA rank",
+            "info": "The rank of LoRA matrices."
+        },
+        "zh": {
+            "label": "LoRA 秩",
+            "info": "LoRA 矩阵的秩。"
+        }
+    },
+    "lora_dropout": {
+        "en": {
+            "label": "LoRA Dropout",
+            "info": "Dropout ratio of LoRA weights."
+        },
+        "zh": {
+            "label": "LoRA 随机丢弃",
+            "info": "LoRA 权重随机丢弃的概率。"
+        }
+    },
+    "lora_target": {
+        "en": {
+            "label": "LoRA modules (optional)",
+            "info": "The name(s) of target modules to apply LoRA. Use commas to separate multiple modules."
+        },
+        "zh": {
+            "label": "LoRA 作用层（非必填）",
+            "info": "应用 LoRA 的线性层名称。使用英文逗号分隔多个名称。"
+        }
+    },
     "start_btn": {
         "en": {
             "value": "Start"
@@ -323,6 +389,14 @@ LOCALES = {
             "value": "模型未加载，请先加载模型。"
         }
     },
+    "prefix": {
+        "en": {
+            "placeholder": "System prompt (optional)"
+        },
+        "zh": {
+            "placeholder": "系统提示词（非必填）"
+        }
+    },
     "query": {
         "en": {
             "placeholder": "Input..."

src/llmtuner/webui/runner.py

@@ -6,7 +6,7 @@ import transformers
 from typing import List, Optional, Tuple
 
 from llmtuner.extras.callbacks import LogCallback
-from llmtuner.extras.constants import DEFAULT_MODULE # will be deprecated
+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
@@ -77,10 +77,15 @@ class Runner:
         batch_size: int,
         gradient_accumulation_steps: int,
         lr_scheduler_type: str,
+        max_grad_norm: str,
         dev_ratio: float,
-        fp16: bool,
         logging_steps: int,
         save_steps: int,
+        warmup_steps: int,
+        compute_type: str,
+        lora_rank: int,
+        lora_dropout: float,
+        lora_target: str,
         output_dir: str
     ):
         model_name_or_path, error, logger_handler, trainer_callback = self.initialize(lang, model_name, dataset)
@@ -99,7 +104,6 @@ class Runner:
             model_name_or_path=model_name_or_path,
             do_train=True,
             overwrite_cache=True,
-            lora_target=DEFAULT_MODULE.get(model_name.split("-")[0], None) or "q_proj,v_proj",
             checkpoint_dir=checkpoint_dir,
             finetuning_type=finetuning_type,
             quantization_bit=int(quantization_bit) if quantization_bit else None,
@@ -115,9 +119,15 @@ class Runner:
             per_device_train_batch_size=batch_size,
             gradient_accumulation_steps=gradient_accumulation_steps,
             lr_scheduler_type=lr_scheduler_type,
-            fp16=fp16,
+            max_grad_norm=float(max_grad_norm),
             logging_steps=logging_steps,
             save_steps=save_steps,
+            warmup_steps=warmup_steps,
+            fp16=(compute_type == "fp16"),
+            bf16=(compute_type == "bf16"),
+            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)
         )
 

src/train_bash.py

@@ -1,4 +1,4 @@
-from llmtuner import get_train_args, run_pt, run_sft, run_rm, run_ppo
+from llmtuner.tuner import get_train_args, run_pt, run_sft, run_rm, run_ppo
 
 
 def main():

src/train_web.py

@@ -1,4 +1,4 @@
-from llmtuner import create_ui
+from llmtuner.webui.interface import create_ui
 
 
 def main():

src/web_demo.py

@@ -5,7 +5,7 @@
 import gradio as gr
 from transformers.utils.versions import require_version
 
-from llmtuner import get_infer_args
+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
@@ -24,17 +24,9 @@ def main():
 
         manager = Manager([{"lang": lang}, chat_elems])
 
-        demo.load(
+        demo.load(manager.gen_label, [lang], [lang] + list(chat_elems.values()))
-            manager.gen_label,
-            [lang],
-            [lang] + [elem for elem in chat_elems.values()],
-        )
 
-        lang.change(
+        lang.change(manager.gen_label, [lang], [lang] + list(chat_elems.values()))
-            manager.gen_label,
-            [lang],
-            [lang] + [elem for elem in chat_elems.values()],
-        )
 
     demo.queue()
     demo.launch(server_name="0.0.0.0", share=False, inbrowser=True)