Upload folder using huggingface_hub

.gitignore

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+__pycache__/
+*.npy
+*.npz
+*.pyc
+*.pyd
+*.so
+*.ipynb
+.ipynb_checkpoints
+models/base_models/*
+!models/base_models/.gitkeep
+models/lora_weights/*
+!models/lora_weights/.gitkeep
+outputs/*
+!outputs/.gitkeep
+data/*
+!data/.gitkeep
+wandb/
+flagged/
+.DS_Store

LICENSE

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,12 +1,248 @@
 ---
-title: Law Llama
-emoji: 🌍
-colorFrom: red
-colorTo: yellow
+title: Law_llama
+app_file: webui.py
 sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
+sdk_version: 3.39.0
 ---
+# LaWGPT：基于中文法律知识的大语言模型
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<p align="center">
+  <a href="assets/logo/lawgpt.jpeg">
+    <img src="./assets/logo/lawgpt.jpeg" width="80%" >
+  </a>
+</p>
+
+<p align="center">
+    <a href="https://github.com/pengxiao-song/LaWGPT/wiki"><img src="https://img.shields.io/badge/docs-Wiki-brightgreen"></a>
+    <a href="https://huggingface.co/entity303"><img src="https://img.shields.io/badge/Hugging%20Face-entity303-green"></a>
+    <a href=""><img src="https://img.shields.io/badge/version-beta1.1-blue"></a>
+    <a href=""><img src="https://img.shields.io/badge/os-Linux-9cf"></a>
+    <a href=""><img src="https://img.shields.io/github/last-commit/pengxiao-song/lawgpt"></a>
+    <a href="https://star-history.com/#pengxiao-song/LaWGPT&Timeline"><img src="https://img.shields.io/github/stars/pengxiao-song/lawgpt?color=yellow"></a> 
+    <!-- <a href="https://www.lamda.nju.edu.cn/"><img src="https://img.shields.io/badge/support-NJU--LAMDA-9cf.svg"></a> -->
+</p>
+
+LaWGPT 是一系列基于中文法律知识的开源大语言模型。
+
+该系列模型在通用中文基座模型（如 Chinese-LLaMA、ChatGLM 等）的基础上扩充法律领域专有词表、**大规模中文法律语料预训练**，增强了大模型在法律领域的基础语义理解能力。在此基础上，**构造法律领域对话问答数据集、中国司法考试数据集进行指令精调**，提升了模型对法律内容的理解和执行能力。
+
+详细内容请参考[技术报告]()。
+
+---
+
+本项目持续开展，法律领域数据集及系列模型后续相继开源，敬请关注。
+
+## 更新
+
+- 🌟 2023/05/30：公开发布
+  <a href="https://huggingface.co/entity303/lawgpt-lora-7b-v2"><img src="https://img.shields.io/badge/Model-LaWGPT--7B--beta1.1-yellow"></a>
+ 
+  - **LaWGPT-7B-beta1.1**：法律对话模型，构造 35w 高质量法律问答数据集基于 Chinese-alpaca-plus-7B 指令精调
+
+- 📣 2023/05/26：开放 [Discussions 讨论区](https://github.com/pengxiao-song/LaWGPT/discussions)，欢迎朋友们交流探讨、提出意见、分享观点！
+
+- 🛠️ 2023/05/22：项目主分支结构调整，详见[项目结构](https://github.com/pengxiao-song/LaWGPT#项目结构)；支持[命令行批量推理](https://github.com/pengxiao-song/LaWGPT/blob/main/scripts/infer.sh)
+
+- 🪴 2023/05/15：发布 [中文法律数据源汇总（Awesome Chinese Legal Resources）](https://github.com/pengxiao-song/awesome-chinese-legal-resources) 和 [法律领域词表](https://github.com/pengxiao-song/LaWGPT/blob/main/resources/legal_vocab.txt)
+
+- 🌟 2023/05/13：公开发布
+  <a href="https://huggingface.co/entity303/legal-lora-7b"><img src="https://img.shields.io/badge/Model-Legal--Base--7B-blue"></a> 
+  <a href="https://huggingface.co/entity303/lawgpt-legal-lora-7b"><img src="https://img.shields.io/badge/Model-LaWGPT--7B--beta1.0-yellow"></a>
+  
+  - **Legal-Base-7B**：法律基座模型，使用 50w 中文裁判文书数据二次预训练
+  
+  - **LaWGPT-7B-beta1.0**：法律对话模型，构造 30w 高质量法律问答数据集基于 Legal-Base-7B 指令精调
+  
+- 🌟 2023/04/12：内部测试
+  <a href="https://huggingface.co/entity303/lawgpt-lora-7b"><img src="https://img.shields.io/badge/Model-Lawgpt--7B--alpha-yellow"></a>
+  - **LaWGPT-7B-alpha**：在 Chinese-LLaMA-7B 的基础上直接构造 30w 法律问答数据集指令精调
+
+## 快速开始
+
+1. 准备代码，创建环境
+
+   ```bash
+   # 下载代码
+   git clone [email protected]:pengxiao-song/LaWGPT.git
+   cd LaWGPT
+
+   # 创建环境
+   conda create -n lawgpt python=3.10 -y
+   conda activate lawgpt
+   pip install -r requirements.txt
+   ```
+2. **启动 web ui（可选，易于调节参数）**
+
+   - 首先，执行服务启动脚本：`bash scripts/webui.sh`
+
+   - 其次，访问 http://127.0.0.1:7860 ：
+
+   <p align="center">
+      <img style="border-radius: 50%; box-shadow: 0 0 10px rgba(0,0,0,0.5); width: 80%;", src="./assets/demo/example-03.jpeg">
+   </p>
+   
+3. **命令行推理（可选，支持批量测试）**
+
+   - 首先，参考 `resources/example_infer_data.json` 文件内容构造测试样本集；
+   
+   - 其次，执行推理脚本：`bash scripts/infer.sh`。其中 `--infer_data_path` 参数为测试样本集路径，如果为空或者路径出错，则以交互模式运行。
+
+注意，以上步骤的默认模型为 LaWGPT-7B-alpha ，如果您想使用 LaWGPT-7B-beta1.0 模型：
+
+- 由于 [LLaMA](https://github.com/facebookresearch/llama) 和 [Chinese-LLaMA](https://github.com/ymcui/Chinese-LLaMA-Alpaca) 均未开源模型权重。根据相应开源许可，**本项目只能发布 LoRA 权重**，无法发布完整的模型权重，请各位谅��。
+
+- 本项目给出[合并方式](https://github.com/pengxiao-song/LaWGPT/wiki/%E6%A8%A1%E5%9E%8B%E5%90%88%E5%B9%B6)，请各位获取原版权重后自行重构模型。
+
+
+## 项目结构
+
+```bash    
+LaWGPT
+├── assets    # 静态资源
+├── resources # 项目资源
+├── models    # 基座模型及 lora 权重
+│   ├── base_models
+│   └── lora_weights
+├── outputs   # 指令微调的输出权重
+├── data      # 实验数据
+├── scripts   # 脚本目录
+│   ├── finetune.sh # 指令微调脚本
+│   └── webui.sh    # 启动服务脚本
+├── templates # prompt 模板
+├── tools     # 工具包
+├── utils
+├── train_clm.py  # 二次训练
+├── finetune.py   # 指令微调
+├── webui.py      # 启动服务
+├── README.md
+└── requirements.txt
+```
+
+
+## 数据构建
+
+本项目基于中文裁判文书网公开法律文书数据、司法考试数据等数据集展开，详情参考[中文法律数据源汇总（Awesome Chinese Legal Resources）](https://github.com/pengxiao-song/awesome-chinese-legal-resources)。
+
+1. 初级数据生成：根据 [Stanford_alpaca](https://github.com/tatsu-lab/stanford_alpaca#data-generation-process) 和 [self-instruct](https://github.com/yizhongw/self-instruct) 方式生成对话问答数据
+2. 知识引导的数据生成：通过 Knowledge-based Self-Instruct 方式基于中文法律结构化知识生成数据。
+3. 引入 ChatGPT 清洗数据，辅助构造高质量数据集。
+
+## 模型训练
+
+LawGPT 系列模型的训练过程分为两个阶段：
+
+1.  第一阶段：扩充法律领域词表，在大规模法律文书及法典数据上预训练 Chinese-LLaMA
+2.  第二阶段：构造法律领域对话问答数据集，在预训练模型基础上指令精调
+
+### 二次训练流程
+
+1. 参考 `resources/example_instruction_train.json` 构造二次训练数据集
+2. 运行 `scripts/train_clm.sh` 
+
+### 指令精调步骤
+
+1. 参考 `resources/example_instruction_tune.json` 构造指令微调数据集
+2. 运行 `scripts/finetune.sh` 
+
+### 计算资源
+
+8 张 Tesla V100-SXM2-32GB ：二次训练阶段耗时约 24h / epoch，微调阶段耗时约 12h / epoch
+
+## 模型评估
+
+### 输出示例
+
+<details><summary>问题：酒驾撞人怎么判刑？</summary>
+
+![](assets/demo/demo07.jpeg)
+
+</details>
+
+<details><summary>问题：请给出判决意见。</summary>
+
+![](assets/demo/example-05.jpeg)
+
+</details>
+
+<details><summary>问题：请介绍赌博罪的定义。</summary>
+
+![](assets/demo/example-06.jpeg)
+
+</details>
+
+<details><summary>问题：请问加班工资怎么算？</summary>
+
+![](assets/demo/example-04.jpeg)
+
+</details>
+
+<details><summary>问题：民间借贷受国家保护的合法利息是多少?</summary>
+
+![](assets/demo/example-02.jpeg)
+
+</details>
+
+<details><summary>问题：欠了信用卡的钱还不上要坐牢吗？</summary>
+
+![](assets/demo/example-01.jpeg)
+
+</details>
+
+<details><summary>问题：你能否写一段抢劫罪罪名的案情描述？</summary>
+
+![](assets/demo/example-03.jpeg)
+
+</details>
+
+
+### 局限性
+
+由于计算资源、数据规模等因素限制，当前阶段 LawGPT 存在诸多局限性：
+
+1. 数据资源有限、模型容量较小，导致其相对较弱的模型记忆和语言能力。因此，在面对事实性知识任务时，可能会生成不正确的结果。
+2. 该系列模型只进行了初步的人类意图对齐。因此，可能产生不可预测的有害内容以及不符合人类偏好和价值观的内容。
+3. 自我认知能力存在问题，中文理解能力有待增强。
+
+请诸君在使用前了解上述问题，以免造成误解和不必要的麻烦。
+
+
+## 协作者
+
+如下各位合作开展（按字母序排列）：[@cainiao](https://github.com/herobrine19)、[@njuyxw](https://github.com/njuyxw)、[@pengxiao-song](https://github.com/pengxiao-song)
+
+
+## 免责声明
+
+请各位严格遵守如下约定：
+
+1. 本项目任何资源**仅供学术研究使用，严禁任何商业用途**。
+2. 模型输出受多种不确定性因素影响，本项目当前无法保证其准确性，**严禁用于真实法律场景**。
+3. 本项目不承担任何法律责任，亦不对因使用相关资源和输出结果而可能产生的任何损失承担责任。
+
+
+## 问题反馈
+
+如有问题，请在 GitHub Issue 中提交。
+
+- 提交问题之前，建议查阅 FAQ 及以往的 issue 看是否能解决您的问题。
+- 请礼貌讨论，构建和谐社区。
+
+协作者科研之余推进项目进展，由于人力有限难以实时反馈，给诸君带来不便，敬请谅解！
+
+
+## 致谢
+
+本项目基于如下开源项目展开，在此对相关项目和开发人员表示诚挚的感谢：
+
+- Chinese-LLaMA-Alpaca: https://github.com/ymcui/Chinese-LLaMA-Alpaca
+- LLaMA: https://github.com/facebookresearch/llama
+- Alpaca: https://github.com/tatsu-lab/stanford_alpaca
+- alpaca-lora: https://github.com/tloen/alpaca-lora
+- ChatGLM-6B: https://github.com/THUDM/ChatGLM-6B
+
+此外，本项目基于开放数据资源，详见 [Awesome Chinese Legal Resources](https://github.com/pengxiao-song/awesome-chinese-legal-resources)，一并表示感谢。
+
+
+## 引用
+
+如果您觉得我们的工作对您有所帮助，请考虑引用该项目

finetune.py

@@ -0,0 +1,283 @@
+import os
+import sys
+from typing import List
+
+import fire
+import torch
+import transformers
+from datasets import load_dataset
+
+"""
+Unused imports:
+import torch.nn as nn
+import bitsandbytes as bnb
+"""
+
+from peft import (
+    LoraConfig,
+    get_peft_model,
+    get_peft_model_state_dict,
+    prepare_model_for_int8_training,
+    set_peft_model_state_dict,
+)
+from transformers import LlamaForCausalLM, LlamaTokenizer
+
+from utils.prompter import Prompter
+
+
+def train(
+    # model/data params
+    base_model: str = "",  # the only required argument
+    data_path: str = "yahma/alpaca-cleaned",
+    output_dir: str = "./lora-alpaca",
+    # training hyperparams
+    batch_size: int = 128,
+    micro_batch_size: int = 4,
+    num_epochs: int = 3,
+    learning_rate: float = 3e-4,
+    cutoff_len: int = 256,
+    val_set_size: int = 2000,
+    # lora hyperparams
+    lora_r: int = 8,
+    lora_alpha: int = 16,
+    lora_dropout: float = 0.05,
+    lora_target_modules: List[str] = [
+        "q_proj",
+        "v_proj",
+    ],
+    # llm hyperparams
+    train_on_inputs: bool = True,  # if False, masks out inputs in loss
+    add_eos_token: bool = True,
+    group_by_length: bool = False,  # faster, but produces an odd training loss curve
+    # wandb params
+    wandb_project: str = "",
+    wandb_run_name: str = "",
+    wandb_watch: str = "",  # options: false | gradients | all
+    wandb_log_model: str = "",  # options: false | true
+    resume_from_checkpoint: str = None,  # either training checkpoint or final adapter
+    prompt_template_name: str = "alpaca",  # The prompt template to use, will default to alpaca.
+):
+    if int(os.environ.get("LOCAL_RANK", 0)) == 0:
+        print(
+            f"Training Alpaca-LoRA model with params:\n"
+            f"base_model: {base_model}\n"
+            f"data_path: {data_path}\n"
+            f"output_dir: {output_dir}\n"
+            f"batch_size: {batch_size}\n"
+            f"micro_batch_size: {micro_batch_size}\n"
+            f"num_epochs: {num_epochs}\n"
+            f"learning_rate: {learning_rate}\n"
+            f"cutoff_len: {cutoff_len}\n"
+            f"val_set_size: {val_set_size}\n"
+            f"lora_r: {lora_r}\n"
+            f"lora_alpha: {lora_alpha}\n"
+            f"lora_dropout: {lora_dropout}\n"
+            f"lora_target_modules: {lora_target_modules}\n"
+            f"train_on_inputs: {train_on_inputs}\n"
+            f"add_eos_token: {add_eos_token}\n"
+            f"group_by_length: {group_by_length}\n"
+            f"wandb_project: {wandb_project}\n"
+            f"wandb_run_name: {wandb_run_name}\n"
+            f"wandb_watch: {wandb_watch}\n"
+            f"wandb_log_model: {wandb_log_model}\n"
+            f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
+            f"prompt template: {prompt_template_name}\n"
+        )
+    assert (
+        base_model
+    ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
+    gradient_accumulation_steps = batch_size // micro_batch_size
+
+    prompter = Prompter(prompt_template_name)
+
+    device_map = "auto"
+    world_size = int(os.environ.get("WORLD_SIZE", 1))
+    ddp = world_size != 1
+    if ddp:
+        device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
+        gradient_accumulation_steps = gradient_accumulation_steps // world_size
+
+    # Check if parameter passed or if set within environ
+    use_wandb = len(wandb_project) > 0 or (
+        "WANDB_PROJECT" in os.environ and len(os.environ["WANDB_PROJECT"]) > 0
+    )
+    # Only overwrite environ if wandb param passed
+    if len(wandb_project) > 0:
+        os.environ["WANDB_PROJECT"] = wandb_project
+    if len(wandb_watch) > 0:
+        os.environ["WANDB_WATCH"] = wandb_watch
+    if len(wandb_log_model) > 0:
+        os.environ["WANDB_LOG_MODEL"] = wandb_log_model
+
+    model = LlamaForCausalLM.from_pretrained(
+        base_model,
+        load_in_8bit=True,
+        torch_dtype=torch.float16,
+        device_map=device_map,
+    )
+
+    tokenizer = LlamaTokenizer.from_pretrained(base_model)
+
+    tokenizer.pad_token_id = (
+        0  # unk. we want this to be different from the eos token
+    )
+    tokenizer.padding_side = "left"  # Allow batched inference
+
+    def tokenize(prompt, add_eos_token=True):
+        # there's probably a way to do this with the tokenizer settings
+        # but again, gotta move fast
+        result = tokenizer(
+            prompt,
+            truncation=True,
+            max_length=cutoff_len,
+            padding=False,
+            return_tensors=None,
+        )
+        if (
+            result["input_ids"][-1] != tokenizer.eos_token_id
+            and len(result["input_ids"]) < cutoff_len
+            and add_eos_token
+        ):
+            result["input_ids"].append(tokenizer.eos_token_id)
+            result["attention_mask"].append(1)
+
+        result["labels"] = result["input_ids"].copy()
+
+        return result
+
+    def generate_and_tokenize_prompt(data_point):
+        full_prompt = prompter.generate_prompt(
+            data_point["instruction"],
+            data_point["input"],
+            data_point["output"],
+        )
+        tokenized_full_prompt = tokenize(full_prompt)
+        if not train_on_inputs:
+            user_prompt = prompter.generate_prompt(
+                data_point["instruction"], data_point["input"]
+            )
+            tokenized_user_prompt = tokenize(
+                user_prompt, add_eos_token=add_eos_token
+            )
+            user_prompt_len = len(tokenized_user_prompt["input_ids"])
+
+            if add_eos_token:
+                user_prompt_len -= 1
+
+            tokenized_full_prompt["labels"] = [
+                -100
+            ] * user_prompt_len + tokenized_full_prompt["labels"][
+                user_prompt_len:
+            ]  # could be sped up, probably
+        return tokenized_full_prompt
+
+    model = prepare_model_for_int8_training(model)
+
+    config = LoraConfig(
+        r=lora_r,
+        lora_alpha=lora_alpha,
+        target_modules=lora_target_modules,
+        lora_dropout=lora_dropout,
+        bias="none",
+        task_type="CAUSAL_LM",
+    )
+    model = get_peft_model(model, config)
+
+    if data_path.endswith(".json") or data_path.endswith(".jsonl"):
+        data = load_dataset("json", data_files=data_path)
+    else:
+        data = load_dataset(data_path)
+
+    if resume_from_checkpoint:
+        # Check the available weights and load them
+        checkpoint_name = os.path.join(
+            resume_from_checkpoint, "pytorch_model.bin"
+        )  # Full checkpoint
+        if not os.path.exists(checkpoint_name):
+            checkpoint_name = os.path.join(
+                resume_from_checkpoint, "adapter_model.bin"
+            )  # only LoRA model - LoRA config above has to fit
+            resume_from_checkpoint = (
+                False  # So the trainer won't try loading its state
+            )
+        # The two files above have a different name depending on how they were saved, but are actually the same.
+        if os.path.exists(checkpoint_name):
+            print(f"Restarting from {checkpoint_name}")
+            adapters_weights = torch.load(checkpoint_name)
+            set_peft_model_state_dict(model, adapters_weights)
+        else:
+            print(f"Checkpoint {checkpoint_name} not found")
+
+    model.print_trainable_parameters()  # Be more transparent about the % of trainable params.
+
+    if val_set_size > 0:
+        train_val = data["train"].train_test_split(
+            test_size=val_set_size, shuffle=True, seed=42
+        )
+        train_data = (
+            train_val["train"].shuffle().map(generate_and_tokenize_prompt)
+        )
+        val_data = (
+            train_val["test"].shuffle().map(generate_and_tokenize_prompt)
+        )
+    else:
+        train_data = data["train"].shuffle().map(generate_and_tokenize_prompt)
+        val_data = None
+
+    if not ddp and torch.cuda.device_count() > 1:
+        # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available
+        model.is_parallelizable = True
+        model.model_parallel = True
+
+    trainer = transformers.Trainer(
+        model=model,
+        train_dataset=train_data,
+        eval_dataset=val_data,
+        args=transformers.TrainingArguments(
+            per_device_train_batch_size=micro_batch_size,
+            gradient_accumulation_steps=gradient_accumulation_steps,
+            warmup_ratio=0.1,
+            num_train_epochs=num_epochs,
+            learning_rate=learning_rate,
+            fp16=True,
+            logging_steps=10,
+            optim="adamw_torch",
+            evaluation_strategy="steps" if val_set_size > 0 else "no",
+            save_strategy="steps",
+            eval_steps=50 if val_set_size > 0 else None,
+            save_steps=50,
+            output_dir=output_dir,
+            save_total_limit=5,
+            load_best_model_at_end=True if val_set_size > 0 else False,
+            ddp_find_unused_parameters=False if ddp else None,
+            group_by_length=group_by_length,
+            report_to="wandb" if use_wandb else None,
+            run_name=wandb_run_name if use_wandb else None,
+        ),
+        data_collator=transformers.DataCollatorForSeq2Seq(
+            tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
+        ),
+    )
+    model.config.use_cache = False
+
+    old_state_dict = model.state_dict
+    model.state_dict = (
+        lambda self, *_, **__: get_peft_model_state_dict(
+            self, old_state_dict()
+        )
+    ).__get__(model, type(model))
+
+    if torch.__version__ >= "2" and sys.platform != "win32":
+        model = torch.compile(model)
+
+    trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+
+    model.save_pretrained(output_dir)
+
+    print(
+        "\n If there's a warning about missing keys above, please disregard :)"
+    )
+
+
+if __name__ == "__main__":
+    fire.Fire(train)

infer.py

@@ -0,0 +1,136 @@
+import sys
+import json
+
+import fire
+import torch
+from peft import PeftModel
+from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
+
+from utils.prompter import Prompter
+
+if torch.cuda.is_available():
+    device = "cuda"
+
+
+class Infer():
+    def __init__(
+        self,
+        load_8bit: bool = False,
+        base_model: str = "",
+        lora_weights: str = "",
+        prompt_template: str = "",  # The prompt template to use, will default to alpaca.
+    ):
+        prompter = Prompter(prompt_template)
+        tokenizer = LlamaTokenizer.from_pretrained(base_model)
+        model = LlamaForCausalLM.from_pretrained(
+            base_model,
+            load_in_8bit=load_8bit,
+            torch_dtype=torch.float16,
+            device_map="auto",
+        )
+        
+        try:
+            print(f"Using lora {lora_weights}")
+            model = PeftModel.from_pretrained(
+                model,
+                lora_weights,
+                torch_dtype=torch.float16,
+            )
+        except:
+            print("*"*50, "\n Attention! No Lora Weights \n", "*"*50)
+            
+        # unwind broken decapoda-research config
+        model.config.pad_token_id = tokenizer.pad_token_id = 0  # unk
+        model.config.bos_token_id = 1
+        model.config.eos_token_id = 2
+        if not load_8bit:
+            model.half()  # seems to fix bugs for some users.
+
+        model.eval()
+
+        if torch.__version__ >= "2" and sys.platform != "win32":
+            model = torch.compile(model)
+        
+        self.base_model = base_model
+        self.lora_weights = lora_weights
+        self.model = model
+        self.prompter = prompter
+        self.tokenizer = tokenizer
+
+    def generate_output(
+        self,
+        instruction,
+        input=None,
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=1,
+        max_new_tokens=256,
+        **kwargs,
+    ):
+        prompt = self.prompter.generate_prompt(instruction, input)
+        inputs = self.tokenizer(prompt, return_tensors="pt")
+        input_ids = inputs["input_ids"].to(device)
+        generation_config = GenerationConfig(
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            num_beams=num_beams,
+            # repetition_penalty=10.0,
+            **kwargs,
+        )
+        with torch.no_grad():
+            generation_output = self.model.generate(
+                input_ids=input_ids,
+                generation_config=generation_config,
+                return_dict_in_generate=True,
+                output_scores=True,
+                max_new_tokens=max_new_tokens,
+            )
+        s = generation_output.sequences[0]
+        output = self.tokenizer.decode(s)
+        return self.prompter.get_response(output)
+
+    def infer_from_file(self, infer_data_path):
+        with open(infer_data_path) as f:
+            for line in f:
+                data = json.loads(line)
+                instruction = data["instruction"]
+                output = data["output"]
+                print('=' * 100)
+                print(f"Base Model: {self.base_model}    Lora Weights: {self.lora_weights}")
+                print("Instruction:\n", instruction)
+                model_output = self.generate_output(instruction)
+                print("Model Output:\n", model_output)
+                print("Ground Truth:\n", output)
+                print('=' * 100)
+
+
+def main(
+    load_8bit: bool = False,
+    base_model: str = "",
+    lora_weights: str = "",
+    prompt_template: str = "",  # The prompt template to use, will default to alpaca.
+    infer_data_path: str = "",
+):
+    infer = Infer(
+        load_8bit=load_8bit,
+        base_model=base_model,
+        lora_weights=lora_weights,
+        prompt_template=prompt_template
+    )
+    
+    try:
+        infer.infer_from_file(infer_data_path)
+    except Exception as e:
+        print(e, "Read infer_data_path Failed! Now Interactive Mode: ")
+        while True:
+            print('=' * 100)
+            instruction = input("请输入您的问题: ")
+            print("LaWGPT:")
+            print(infer.generate_output(instruction))
+            print('=' * 100)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

merge.py

@@ -0,0 +1,74 @@
+import os
+
+import torch
+import transformers
+from peft import PeftModel
+from transformers import LlamaForCausalLM, LlamaTokenizer  # noqa: F402
+
+
+import argparse
+parser = argparse.ArgumentParser(description='Merge Base Model and Lora')
+parser.add_argument('--base_model', type=str, default="minlik/chinese-llama-7b-merged", help='base model path')
+parser.add_argument('--lora_model', type=str, default="entity303/legal-lora-7b", help='lora model path')
+parser.add_argument('--output_dir', type=str, default="./models/base_models/llama-7b-legal-lora-merged", help='output model path')
+args = parser.parse_args()
+
+BASE_MODEL = args.base_model
+LORA_MODEL = args.lora_model
+OUTPUT_DIR = args.output_dir
+
+
+assert (
+    BASE_MODEL
+), "Please specify a value for BASE_MODEL environment variable, e.g. `export BASE_MODEL=huggyllama/llama-7b`"  # noqa: E501
+
+
+print(f"{'*'*20} Using base model: {BASE_MODEL} {'*'*20}")
+print(f"{'*'*20} Using lora model: {LORA_MODEL} {'*'*20}")
+print(f"{'*'*20} Saving to: {OUTPUT_DIR} {'*'*20}")
+
+tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)
+
+base_model = LlamaForCausalLM.from_pretrained(
+    BASE_MODEL,
+    load_in_8bit=False,
+    torch_dtype=torch.float16,
+    device_map={"": "cpu"},
+)
+
+first_weight = base_model.model.layers[0].self_attn.q_proj.weight
+first_weight_old = first_weight.clone()
+
+lora_model = PeftModel.from_pretrained(
+    base_model,
+    LORA_MODEL,
+    device_map={"": "cpu"},
+    torch_dtype=torch.float16,
+)
+
+lora_weight = lora_model.base_model.model.model.layers[
+    0
+].self_attn.q_proj.weight
+
+assert torch.allclose(first_weight_old, first_weight)
+
+# merge weights - new merging method from peft
+lora_model = lora_model.merge_and_unload()
+
+lora_model.train(False)
+
+# did we do anything?
+assert not torch.allclose(first_weight_old, first_weight)
+
+lora_model_sd = lora_model.state_dict()
+deloreanized_sd = {
+    k.replace("base_model.model.", ""): v
+    for k, v in lora_model_sd.items()
+    if "lora" not in k
+}
+
+LlamaForCausalLM.save_pretrained(
+    base_model, OUTPUT_DIR, state_dict=deloreanized_sd, max_shard_size="2048MB"
+)
+
+LlamaTokenizer.save_pretrained(tokenizer, OUTPUT_DIR)

requirements.txt

@@ -0,0 +1,14 @@
+accelerate
+appdirs
+bitsandbytes
+black
+black[jupyter]
+datasets
+fire
+git+https://github.com/huggingface/peft.git@e536616888d51b453ed354a6f1e243fecb02ea08
+git+https://github.com/huggingface/transformers.git
+gradio
+sentencepiece
+wandb
+scipy
+socksio

resources/example_infer_data.json

@@ -0,0 +1,5 @@
+{"instruction":"请介绍赌博罪的定义。","input":"","output":"无"}
+{"instruction":"请问加班工资怎么算？","input":"","output":"无"}
+{"instruction":"民间借贷受国家保护的合法利息是多少?","input":"","output":"无"}
+{"instruction":"欠了信用卡的钱还不上要坐牢吗？","input":"","output":"无"}
+{"instruction":"你能否写一段抢劫罪罪名的案情描述？","input":"","output":"无"}

resources/example_instruction_train.json

@@ -0,0 +1,8 @@
+[
+    {
+      "content": "中华人民共和国最高人民法院 再 审 决 定 书（2022）最高法刑申136号 原审被告人张某某犯挪用资金罪和伪造、变造国家机关公文罪一案，山西省运城市盐湖区人民法院于2012年5月2日以（2012）运盐刑初字第69号刑事判决，认定张克云犯贪污罪，判处有期徒刑十二年，犯伪造、变造国家机关公文罪，判处有期徒刑三年，决定执行有期徒刑十三年。宣判后，张克云不服，提出上诉。山西省运城市中级人民法院于2012年11月12日以（2012）运中刑二终字第125号刑事裁定，驳回上诉，维持原判。裁判生效后，张克云不服，提出申诉。运城市中级人民法院于2013年1月7日以（2013）运中刑申字第3号驳回申诉通知，驳回其申诉。山西省高级人民法院于2017年7月13日以（2013）晋刑监字第8号再审决定，提审本案，并于2019年12月24日以（2017）晋刑再第2号刑事判决，认定张克云犯挪用资金罪，判处有期徒刑七年六个月，与原判伪造、变造国家机关公文罪被判处的有期徒刑三年数罪并罚，决定执行有期徒刑十年。张克云仍不服，以原审认定事实错误，其作为学校董事长、全资投资人有权决定学校相关款项用途，学校仍欠其债务，个人账户用于学校经费开支，没有挪用资金的动机和行为，不构成挪用资金罪等为由，向本院提出申诉。本院经审查认为，原审生效裁判对挪用资金罪定罪量刑的证据不确实、不充分，依法应当予以排除。依照《中华人民共和国刑事诉讼法》第二百五十三条第二项、第二百五十四条第二款、第二百五十五条的规定，决定如下：指令河南省高级人民法院对本案进行再审。二〇二二年十二月二十九日"
+    },
+    {
+      "content":"中华人民共和国最高人民法院 驳 回 申 诉 通 知 书（2022）最高法刑申122号 袁某银、袁某财：你们因原审被告人袁德银故意伤害一案，对江苏省南京市溧水区人民法院（2014）溧刑初字第268号刑事判决、南京市中级人民法院（2015）宁刑终字第433号刑事裁定不服，以被害人朱宽荣住院期间的ＣＴ（136678号）报告并未显示其左侧4、5、6、7、8肋骨骨折，出院记录及137470号、143006号ＣＴ报告均系伪造，江苏省高级人民法院（2019）苏刑申172号驳回申诉通知书对137470号ＣＴ报告的形成时间认定错误为由，向本院提出申诉，请求撤销原判，依法重新审理本案。本院依法组成合议庭认真审查后认为，原审认定原审被告人袁德银因邻里纠纷，殴打被害人朱宽荣致其左胸多发肋骨骨折，构成轻伤二级，其行为构成故意伤害罪，并无不当。关于你们提出的原审认定被害人朱宽荣轻伤二级的证据系伪造的申诉理由。首先，根据你们提供的136678号ＣＴ报告，朱宽荣于2015年2月12日入院时经ＣＴ检查被诊断为左侧多发肋骨骨折，该份ＣＴ报告虽未明确骨折的具体位置，但与出院记录记载的“左侧多发肋骨骨折（4、5、6、7、8）”并不矛盾。根据《人体损伤程度鉴定标准》的规定，肋骨骨折2处以上即构成轻伤二级，故鉴定意见认定朱宽荣的损伤构成轻伤二级并无不当。其次，你们主张出院记录、137470号和143006号ＣＴ报告系伪造，但并未提供证据证实。即使排除这两份ＣＴ报告，也不影响对本案事实的认定。再次，我院对申诉进行复查的对象是已经发生法律效力的判决、裁定，本案的生效裁判是（2014）溧刑初字第268号刑事判决和（2015）宁刑终字第433号刑事裁定，江苏省高级人民法院作出的驳回申诉通知书不属于我院复查的范围。综上，本院认为，你们的申诉不符合《中华人民共和国刑事诉讼法》第二百五十三条规定的应当重新审判的情形，予以驳回。望你们尊重人民法院的生效裁判，自觉服判息诉。特此通知。二〇二二年十二月十四日"
+    }
+]

resources/example_instruction_tune.json

@@ -0,0 +1,12 @@
+[
+  {
+    "instruction": "酒驾撞人要判多久？",
+    "input": "",
+    "output": "《刑法》第一百三十三条规定:违反交通运输管理法规，因而发生重大事故，致人重伤、死广或者使公私财产遭受重大损失的，处三年以下有期徒刑或者拘役;交通运输肇事后逃逸，致人重伤的，处三年以上七年以下有期徒刑。交通肇事后逃逸致人死亡的，处七年以上有期徒刑。"
+  },
+  {
+    "instruction": "偷狗被派出所抓到，会怎么处理？",
+    "input": "",
+    "output": "取决于偷盗的狗的价值，可能按盗窃罪的罪名处理。如果价值达到犯罪标准，就构成盗窃罪，要承担刑事责任；如果不到犯罪标准，就是治安处罚、罚款或者拘留治安处罚这会涉嫌构成盗窃。如果不到一千元，则不会构成犯罪。如果超过一千元，则可能会是构成犯罪的。"
+  }
+]

scripts/finetune.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+export WANDB_MODE=disabled # 禁用wandb
+
+# 使用chinese-alpaca-plus-7b-merged模型在law_data.json数据集上finetune
+experiment_name="chinese-alpaca-plus-7b-law-e1"
+
+# 单卡或者模型并行
+python finetune.py \
+    --base_model "minlik/chinese-alpaca-plus-7b-merged" \
+    --data_path "./data/finetune_law_data.json" \
+    --output_dir "./outputs/"${experiment_name} \
+    --batch_size 64 \
+    --micro_batch_size 8 \
+    --num_epochs 20 \
+    --learning_rate 3e-4 \
+    --cutoff_len 256 \
+    --val_set_size 0 \
+    --lora_r 8 \
+    --lora_alpha 16 \
+    --lora_dropout 0.05 \
+    --lora_target_modules "[q_proj,v_proj]" \
+    --train_on_inputs False \
+    --add_eos_token True \
+    --group_by_length False \
+    --wandb_project "" \
+    --wandb_run_name "" \
+    --wandb_watch "" \
+    --wandb_log_model "" \
+    --resume_from_checkpoint "./outputs/"${experiment_name} \
+    --prompt_template_name "alpaca" \
+
+
+# 多卡数据并行
+# WORLD_SIZE=8 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nproc_per_node=8 --master_port=1234 finetune.py \
+#     --base_model "minlik/chinese-alpaca-plus-7b-merged" \
+#     --data_path "./data/finetune_law_data.json" \
+#     --output_dir "./outputs/"${experiment_name} \
+#     --batch_size 64 \
+#     --micro_batch_size 8 \
+#     --num_epochs 20 \
+#     --learning_rate 3e-4 \
+#     --cutoff_len 256 \
+#     --val_set_size 0 \
+#     --lora_r 8 \
+#     --lora_alpha 16 \
+#     --lora_dropout 0.05 \
+#     --lora_target_modules "[q_proj,v_proj]" \
+#     --train_on_inputs True \
+#     --add_eos_token True \
+#     --group_by_length False \
+#     --wandb_project \
+#     --wandb_run_name \
+#     --wandb_watch \
+#     --wandb_log_model \
+#     --resume_from_checkpoint "./outputs/"${experiment_name} \
+#     --prompt_template_name "alpaca" \

scripts/infer.sh

@@ -0,0 +1,7 @@
+
+python infer.py \
+    --load_8bit True \
+    --base_model 'minlik/chinese-llama-7b-merged' \
+    --lora_weights 'entity303/lawgpt-lora-7b' \
+    --prompt_template 'law_template' \
+    --infer_data_path './resources/example_infer_data.json'

scripts/merge.sh

@@ -0,0 +1,4 @@
+python merge.py \
+    --base_model 'minlik/chinese-llama-7b-merged' \
+    --lora_model 'entity303/legal-lora-7b' \
+    --output_dir './models/base_models/legal_base-7b' \

scripts/train_clm.sh

@@ -0,0 +1,20 @@
+#!/bin/bash
+
+WORLD_SIZE=8 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nproc_per_node=8 --master_port=1235 train_clm.py \
+    --base_model './models/base_models/chinese_llama_7b' \
+    --data_path './data/train_clm_data.json' \
+    --output_dir './outputs/train-clm' \
+    --batch_size 128 \
+    --micro_batch_size 8 \
+    --num_epochs 1 \
+    --learning_rate 0.0003 \
+    --cutoff_len 1024 \
+    --val_set_size 0 \
+    --lora_r 16 \
+    --lora_alpha 32 \
+    --lora_dropout 0.05 \
+    --lora_target_modules '[q_proj, v_proj, k_proj, o_proj]' \
+    --train_on_inputs True \
+    --add_eos_token True \
+    --group_by_length True \
+    --resume_from_checkpoint './outputs/train-clm'

scripts/webui.sh

@@ -0,0 +1,28 @@
+#!/bin/bash
+###
+ # Copyright (C) Baidu Ltd. All rights reserved.
+ # @FilePath: /Law_llama/scripts/webui.sh
+ # @Autor: [email protected]
+ # @Date: 2023-08-23 10:25:20
+ # @Description: 
+### 
+
+
+# 使用huggingface上已经训练好的模型
+XPU_VISIBLE_DEVICES=4 python3 -m xacc webui.py \
+    --load_8bit False \
+    --base_model 'ziqingyang/chinese-alpaca-2-7b' \
+    --lora_weights 'entity303/lawgpt-lora-7b-v' \
+    --prompt_template "law_template" \
+    --server_name "0.0.0.0" \
+    --share_gradio True \
+
+
+# 使用自己finetune的lora, 把自己的模型放到对应目录即可
+# python webui.py \
+#     --load_8bit True \
+#     --base_model 'minlik/chinese-alpaca-plus-7b-merged' \
+#     --lora_weights './outputs/chinese-alpaca-plus-7b-law-e1' \
+#     --prompt_template "alpaca" \
+#     --server_name "0.0.0.0" \
+#     --share_gradio True \

templates/alpaca.json

@@ -0,0 +1,6 @@
+{
+    "description": "Template used by Alpaca-LoRA.",
+    "prompt_input": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
+    "prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n",
+    "response_split": "### Response:"    
+}

templates/law_template.json

@@ -0,0 +1,6 @@
+{
+    "description": "Template used by Law Instruction Tuning",
+    "prompt_input": "你是中国顶尖智能法律顾问 LaWGPT，具备强大的中文法律基础语义理解能力，能够出色地理解和执行与法律问题和指令。你只能回答与中国法律领域相关的问题，其余领域的问题请礼貌地拒绝回答。接下来，请依据中国法律来回答下面这个问题。\n### 问题:\n{instruction}\n### 回答:\n",
+    "prompt_no_input": "你是中国顶尖智能法律顾问 LaWGPT，具备强大的中文法律基础语义理解能力，能够出色地理解和执行与法律问题和指令。你只能回答与中国法律领域相关的问题，其余领域的问题请礼貌地拒绝回答。接下来，请依据中国法律来回答下面这个问题。\n### 问题:\n{instruction}\n### 回答:\n",
+    "response_split": "### 回答:"
+}

tools/clear_law.py

@@ -0,0 +1,78 @@
+import re
+import json
+
+
+class read_lawfile:
+    def __init__(self, chapter_moder=r"第[零一二三四五六七八九十百千万]+章 .+\b", entry_mode=r"第[零一二三四五六七八九十百千万]+条\b"):
+        # 识别章和节
+        self.chapter_mode = chapter_moder
+        self.entry_mode = entry_mode
+
+    def read_file(self, file_path):
+        # 读取文件
+        self.law = {}
+        f = open(file_path, encoding='utf-8')
+        content = f.read()
+        content = content.replace("\n\n", "\n")
+        content = content.replace("##", "")
+        # print(content)
+        chapter_p = re.search(self.chapter_mode, content)
+        while chapter_p is not None:
+            c_start = chapter_p.start()
+            c_end = chapter_p.end()
+            key = content[c_start:c_end]
+            content = content[c_end:]
+
+            chapter_p = re.search(self.chapter_mode, content)
+            if chapter_p is not None:
+                end = chapter_p.start()
+                c_content = content[:end]
+                self.law[key] = self.read_entrys(c_content)
+            # print(content[c_start:c_end])
+            else:
+                self.law[key] = self.read_entrys(content)
+        f.close()
+        return self.law
+
+    def read_entrys(self, content):
+        entrys = {}
+        entry_p = re.search(self.entry_mode, content)
+        while entry_p is not None:
+            e_start = entry_p.start()
+            e_end = entry_p.end()
+            key = content[e_start:e_end]
+            content = content[e_end+1:]
+
+            entry_p = re.search(self.entry_mode, content)
+            if entry_p is not None:
+                end = entry_p.start()
+                e_content = content[:end]
+                entrys[key] = e_content
+            else:
+                entrys[key] = content
+        return entrys
+    # entry_p = re.search(entry_mode, content)
+    # while entry_p is not None:
+    #     start = entry_p.start()
+    #     end = entry_p.end()
+    #     # print(content[start:end])
+    #     content = content[end:]
+    #     law[content[start:end]] = read_entrys(content)
+    #     chapter_p = re.search(chapter_mode, content)
+
+    def show(self):
+        for key in self.law:
+            print(key, '\n')
+            for item in self.law[key]:
+                print(item, ' ', self.law[key][item])
+
+
+if __name__ == '__main__':
+    file_path = "D:/11496/Documents/project/Laws-master/经济法/价格法(1997-12-29).md"
+    r = read_lawfile()
+    dict = r.read_file(file_path)
+    r.show()
+    print(dict)
+    with open('./a.json', 'w') as f:
+        # json.dumps(dict, f, ensure_ascii=False)
+        json.dump(dict, f, ensure_ascii=False)

tools/merge_vocabulary.py

@@ -0,0 +1,63 @@
+from transformers import LlamaTokenizer
+from sentencepiece import sentencepiece_model_pb2 as model
+import sentencepiece as sp
+import argparse
+import os
+
+if __name__ == '__main__':
+    # Load arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--load_path', default='../src/models/base_model/chinese_llama_7b/tokenizer_chinese.model', type=str)
+    parser.add_argument('--save_dir', default='../src/models/base_model/save_chinese', type=str)
+    parser.add_argument('--voc_path', default='../data/vocabulary/legal_vocab_processed.txt', type=str)
+    args = parser.parse_args()
+
+    LOAD_PATH = args.load_path
+    SAVE_DIR = args.save_dir
+    VOC_PATH = args.voc_path
+    
+    # Load pre-trained llama tokenizer and sentencepiece model
+    llama_spm = model.ModelProto()
+    llama_spm.ParseFromString(open(LOAD_PATH, "rb").read())
+
+    # show size of llama's vocabulary
+    llama_spm_tokens_set = set(p.piece for p in llama_spm.pieces)
+    print(f"Size of initial llama's vocabulary: {len(llama_spm_tokens_set)}")
+    
+    # Load custom vocabulary
+    new_tokens = open(VOC_PATH, "r").read().split("\n")    
+    for token in new_tokens:
+        if token not in llama_spm_tokens_set:
+            new_token = model.ModelProto().SentencePiece()
+            new_token.piece = token
+            new_token.score = 0
+            llama_spm.pieces.append(new_token)
+    print(f"Size of merged llama's vocabulary: {len(llama_spm.pieces)}")
+
+    # save
+    os.makedirs(SAVE_DIR, exist_ok=True)
+    SAVE_MODEL_PATH = os.path.join(SAVE_DIR, 'tokenizer.model')
+    SAVE_VOCAB_PATH = os.path.join(SAVE_DIR, 'tokenizer.vocab')
+    with open(SAVE_MODEL_PATH, 'wb') as f:
+        f.write(llama_spm.SerializeToString())
+    with open(SAVE_VOCAB_PATH, 'w')  as f:
+        f.writelines([f'{token.piece} {token.score}\n' for token in llama_spm.pieces])
+    tokenizer = LlamaTokenizer(SAVE_MODEL_PATH)
+    tokenizer.save_pretrained(SAVE_DIR)
+    print(f'New llama tokenizer and spm has been saved to {SAVE_DIR}')
+
+    # test
+    llama_tokenizer_old = LlamaTokenizer.from_pretrained(LOAD_PATH)
+    llama_tokenizer_new = LlamaTokenizer.from_pretrained(SAVE_DIR)
+    text = '''登记错误赔偿责任登记等手续登记等手续生效登记机构和登记办法登记机构赔偿后登记机构应当提供登记收费问题'''
+    
+    print(f'Size of old vocabulary: {llama_tokenizer_old.vocab_size}')
+    print(f'Size of new vocabulary: {llama_tokenizer_new.vocab_size}')
+    print('All special tokens and ids in new llama:')
+    print(llama_tokenizer_new.all_special_tokens)
+    print(llama_tokenizer_new.all_special_ids)
+    print(llama_tokenizer_new.special_tokens_map)
+
+    print(f'Text:\n{text}')
+    print(f'Tokenized by LLaMA tokenizer:\n {llama_tokenizer_old.tokenize(text)}')
+    print(f'Tokenized by NEW LLaMA tokenizer:\n {llama_tokenizer_new.tokenize(text)}')

train_clm.py

@@ -0,0 +1,259 @@
+import os
+import sys
+from typing import List
+
+import fire
+import torch
+import transformers
+from datasets import load_dataset
+
+from peft import (
+    LoraConfig,
+    get_peft_model,
+    get_peft_model_state_dict,
+    prepare_model_for_int8_training,
+    set_peft_model_state_dict,
+)
+from transformers import LlamaForCausalLM, LlamaTokenizer
+from utils.prompter import Prompter
+
+
+def train(
+    # model/data params
+    base_model: str = "./models/base_models/your_base_model_dir",
+    data_path: str = "./data/your_data.json",
+    output_dir: str = "./outputs/your_version_dir",
+
+    # training hyperparams
+    batch_size: int = 128,
+    micro_batch_size: int = 4,
+    num_epochs: int = 10,
+    learning_rate: float = 3e-4,
+    cutoff_len: int = 512,
+    val_set_size: int = 2000,
+
+    # lora hyperparams
+    lora_r: int = 8,
+    lora_alpha: int = 16,
+    lora_dropout: float = 0.05,
+    lora_target_modules: List[str] = ["q_proj", "v_proj",],
+
+    # llm hyperparams
+    train_on_inputs: bool = True,  # if False, masks out inputs in loss
+    add_eos_token: bool = True,
+    group_by_length: bool = False,  # faster, but produces an odd training loss curve
+
+    # wandb params
+    wandb_project: str = "",
+    wandb_run_name: str = "",
+    wandb_watch: str = "",  # options: false | gradients | all
+    wandb_log_model: str = "",  # options: false | true
+
+    # either training checkpoint or final adapter
+    resume_from_checkpoint: str = None,
+
+    # The prompt template to use, will default to alpaca.
+    prompt_template_name: str = "alpaca",
+):
+    if int(os.environ.get("LOCAL_RANK", 0)) == 0:
+        print(
+            f"Training Alpaca-LoRA model with params:\n"
+            f"base_model: {base_model}\n"
+            f"data_path: {data_path}\n"
+            f"output_dir: {output_dir}\n"
+            f"batch_size: {batch_size}\n"
+            f"micro_batch_size: {micro_batch_size}\n"
+            f"num_epochs: {num_epochs}\n"
+            f"learning_rate: {learning_rate}\n"
+            f"cutoff_len: {cutoff_len}\n"
+            f"val_set_size: {val_set_size}\n"
+            f"lora_r: {lora_r}\n"
+            f"lora_alpha: {lora_alpha}\n"
+            f"lora_dropout: {lora_dropout}\n"
+            f"lora_target_modules: {lora_target_modules}\n"
+            f"train_on_inputs: {train_on_inputs}\n"
+            f"add_eos_token: {add_eos_token}\n"
+            f"group_by_length: {group_by_length}\n"
+            f"wandb_project: {wandb_project}\n"
+            f"wandb_run_name: {wandb_run_name}\n"
+            f"wandb_watch: {wandb_watch}\n"
+            f"wandb_log_model: {wandb_log_model}\n"
+            f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
+            f"prompt template: {prompt_template_name}\n"
+        )
+    gradient_accumulation_steps = batch_size // micro_batch_size
+
+    prompter = Prompter(prompt_template_name)
+
+    # Configure device and distributed training
+    device_map = "auto"
+    world_size = int(os.environ.get("WORLD_SIZE", 1))
+    ddp = world_size != 1
+    if ddp:
+        device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
+        gradient_accumulation_steps = gradient_accumulation_steps // world_size
+
+    # Check if parameter passed or if set within environ
+    use_wandb = len(wandb_project) > 0 or (
+        "WANDB_PROJECT" in os.environ and len(os.environ["WANDB_PROJECT"]) > 0)
+
+    # Only overwrite environ if wandb param passed
+    if len(wandb_project) > 0:
+        os.environ["WANDB_PROJECT"] = wandb_project
+    if len(wandb_watch) > 0:
+        os.environ["WANDB_WATCH"] = wandb_watch
+    if len(wandb_log_model) > 0:
+        os.environ["WANDB_LOG_MODEL"] = wandb_log_model
+
+    model = LlamaForCausalLM.from_pretrained(
+        base_model,
+        load_in_8bit=True,
+        torch_dtype=torch.float16,
+        device_map=device_map,
+    )
+
+    tokenizer = LlamaTokenizer.from_pretrained(base_model)
+    tokenizer.bos_token_id = 1
+    tokenizer.eos_token_id = 2
+    bos = tokenizer.bos_token_id
+    eos = tokenizer.eos_token_id
+    pad = tokenizer.pad_token_id
+
+    print("pre-trained model's BOS EOS and PAD token id:",
+          bos, eos, pad, " => It should be 1,2,none")
+
+    tokenizer.pad_token_id = (
+        0  # unk. we want this to be different from the eos token
+    )
+    tokenizer.padding_side = "left"  # Allow batched inference
+
+    def tokenize(prompt, add_eos_token=True):
+        # there's probably a way to do this with the tokenizer settings
+        # but again, gotta move fast
+        result = tokenizer(
+            prompt,
+            truncation=True,
+            max_length=cutoff_len,
+            padding=False,
+            return_tensors=None,
+        )
+        if (
+            result["input_ids"][-1] != tokenizer.eos_token_id
+            and len(result["input_ids"]) < cutoff_len
+            and add_eos_token
+        ):
+            result["input_ids"].append(tokenizer.eos_token_id)
+            result["attention_mask"].append(1)
+
+        result["labels"] = result["input_ids"].copy()
+
+        return result
+    
+    def generate_and_tokenize_prompt(data_point):
+        text = data_point['content']
+        tokenized_full_prompt = tokenize(text)
+        return tokenized_full_prompt
+
+    model = prepare_model_for_int8_training(model)
+
+    config = LoraConfig(
+        r=lora_r,
+        lora_alpha=lora_alpha,
+        target_modules=lora_target_modules,
+        lora_dropout=lora_dropout,
+        bias="none",
+        task_type="CAUSAL_LM",
+    )
+    model = get_peft_model(model, config)
+
+    if data_path.endswith(".json") or data_path.endswith(".jsonl"):
+        data = load_dataset("json", data_files=data_path)
+    else:
+        data = load_dataset(data_path)
+
+    if resume_from_checkpoint:
+        # Check the available weights and load them
+        checkpoint_name = os.path.join(
+            resume_from_checkpoint, "pytorch_model.bin"
+        )  # Full checkpoint
+        if not os.path.exists(checkpoint_name):
+            checkpoint_name = os.path.join(
+                resume_from_checkpoint, "adapter_model.bin"
+            )  # only LoRA model - LoRA config above has to fit
+            resume_from_checkpoint = (
+                False  # So the trainer won't try loading its state
+            )
+        # The two files above have a different name depending on how they were saved, but are actually the same.
+        if os.path.exists(checkpoint_name):
+            print(f"Restarting from {checkpoint_name}")
+            adapters_weights = torch.load(checkpoint_name)
+            set_peft_model_state_dict(model, adapters_weights)
+        else:
+            print(f"Checkpoint {checkpoint_name} not found")
+
+    # Be more transparent about the % of trainable params.
+    model.print_trainable_parameters()
+
+    if val_set_size > 0:
+        train_val = data["train"].train_test_split(test_size=val_set_size, shuffle=True, seed=42)
+        train_data = (train_val["train"].shuffle().map(generate_and_tokenize_prompt))
+        val_data = (train_val["test"].shuffle().map(generate_and_tokenize_prompt))
+    else:
+        train_data = data["train"].shuffle().map(generate_and_tokenize_prompt)
+        val_data = None
+
+    if not ddp and torch.cuda.device_count() > 1:
+        # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available
+        model.is_parallelizable = True
+        model.model_parallel = True
+
+    trainer = transformers.Trainer(
+        model=model,
+        train_dataset=train_data,
+        eval_dataset=val_data,
+        args=transformers.TrainingArguments(
+            per_device_train_batch_size=micro_batch_size,
+            gradient_accumulation_steps=gradient_accumulation_steps,
+            warmup_steps=100,
+            num_train_epochs=num_epochs,
+            learning_rate=learning_rate,
+            fp16=True,
+            logging_steps=10,
+            optim="adamw_torch",
+            evaluation_strategy="steps" if val_set_size > 0 else "no",
+            save_strategy="steps",
+            eval_steps=100 if val_set_size > 0 else None,
+            save_steps=100,
+            output_dir=output_dir,
+            save_total_limit=3,
+            load_best_model_at_end=True if val_set_size > 0 else False,
+            ddp_find_unused_parameters=False if ddp else None,
+            group_by_length=group_by_length,
+            report_to="wandb" if use_wandb else None,
+            run_name=wandb_run_name if use_wandb else None,
+        ),
+        data_collator=transformers.DataCollatorForSeq2Seq(
+            tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
+        ),
+    )
+    model.config.use_cache = False
+
+    old_state_dict = model.state_dict
+    model.state_dict = (
+        lambda self, *_, **__: get_peft_model_state_dict(
+            self, old_state_dict()
+        )
+    ).__get__(model, type(model))
+
+    if torch.__version__ >= "2" and sys.platform != "win32":
+        model = torch.compile(model)
+
+    trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+
+    model.save_pretrained(output_dir)
+
+    print("\n If there's a warning about missing keys above, please disregard :)")
+
+
+if __name__ == "__main__":
+    fire.Fire(train)

utils/callbacks.py

@@ -0,0 +1,75 @@
+"""
+Helpers to support streaming generate output.
+Borrowed from https://github.com/oobabooga/text-generation-webui/blob/ad37f396fc8bcbab90e11ecf17c56c97bfbd4a9c/modules/callbacks.py
+"""
+
+import gc
+import traceback
+from queue import Queue
+from threading import Thread
+
+import torch
+import transformers
+
+
+class Stream(transformers.StoppingCriteria):
+    def __init__(self, callback_func=None):
+        self.callback_func = callback_func
+
+    def __call__(self, input_ids, scores) -> bool:
+        if self.callback_func is not None:
+            self.callback_func(input_ids[0])
+        return False
+
+
+class Iteratorize:
+
+    """
+    Transforms a function that takes a callback
+    into a lazy iterator (generator).
+    """
+
+    def __init__(self, func, kwargs={}, callback=None):
+        self.mfunc = func
+        self.c_callback = callback
+        self.q = Queue()
+        self.sentinel = object()
+        self.kwargs = kwargs
+        self.stop_now = False
+
+        def _callback(val):
+            if self.stop_now:
+                raise ValueError
+            self.q.put(val)
+
+        def gentask():
+            try:
+                ret = self.mfunc(callback=_callback, **self.kwargs)
+            except ValueError:
+                pass
+            except:
+                traceback.print_exc()
+                pass
+
+            self.q.put(self.sentinel)
+            if self.c_callback:
+                self.c_callback(ret)
+
+        self.thread = Thread(target=gentask)
+        self.thread.start()
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        obj = self.q.get(True, None)
+        if obj is self.sentinel:
+            raise StopIteration
+        else:
+            return obj
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.stop_now = True

utils/evaluate.py

@@ -0,0 +1,196 @@
+import math
+import os
+import sys
+
+import fire
+from tqdm import tqdm
+import pandas as pd
+import torch
+import transformers
+from peft import PeftModel
+import datasets
+from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
+
+from utils.callbacks import Iteratorize, Stream
+from utils.prompter import Prompter
+
+device = "cuda"
+
+
+def main(
+    load_8bit: bool = True,
+    base_model: str = "decapoda-research/llama-7b-hf",
+    lora_weights: str = "./lora-alpaca",
+    data_path: str = "./data",
+    output_path: str = "./output",
+    eval_rate: float = 0.1,
+    batch_size: int = 32,
+    # The prompt template to use, will default to alpaca.
+    prompt_template: str = "alpaca",
+):
+    base_model = base_model or os.environ.get("BASE_MODEL", "")
+    assert (base_model), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
+
+    prompter = Prompter(prompt_template)
+    tokenizer = LlamaTokenizer.from_pretrained(base_model)
+    if device == "cuda":
+        model = LlamaForCausalLM.from_pretrained(
+            base_model,
+            load_in_8bit=load_8bit,
+            torch_dtype=torch.float16,
+            device_map="auto",
+        )
+        model = PeftModel.from_pretrained(
+            model,
+            lora_weights,
+            torch_dtype=torch.float16,
+        )
+
+    # unwind broken decapoda-research config
+    model.config.pad_token_id = tokenizer.pad_token_id = 0  # unk
+    model.config.bos_token_id = 1
+    model.config.eos_token_id = 2
+
+    if not load_8bit:
+        model.half()  # seems to fix bugs for some users.
+
+    model.eval()
+    if torch.__version__ >= "2" and sys.platform != "win32":
+        model = torch.compile(model)
+
+    def evaluate_one(
+        instruction,
+        input=None,
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=2,
+        max_new_tokens=128,
+        **kwargs,
+    ):
+        prompt = prompter.generate_prompt(instruction, input)
+        inputs = tokenizer(prompt, return_tensors="pt")
+        input_ids = inputs["input_ids"].to(device)
+        generation_config = GenerationConfig(
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            num_beams=num_beams,
+            **kwargs,
+        )
+
+        # Without streaming
+        with torch.no_grad():
+            generation_output = model.generate(
+                input_ids=input_ids,
+                generation_config=generation_config,
+                return_dict_in_generate=True,
+                output_scores=True,
+                max_new_tokens=max_new_tokens,
+            )
+        s = generation_output.sequences[0]
+        output = tokenizer.decode(s, skip_special_tokens=True)
+        return prompter.get_response(output)
+
+    def evaluate_all():
+        # data = datasets.load_dataset("json", data_files=data_path)
+        # data = data["train"]
+        # df = data.to_pandas()
+        df = pd.read_json(data_path, orient='records')
+        print(df.info())
+        # 计算准确率
+        correct = 0
+        total = 0
+        total_step = len(df)
+        pbar = tqdm(total=total_step, unit='batch')
+        error = []
+        for i in range(total_step):
+            instruction = df['instruction'].iloc[i]
+            input = df['input'].iloc[i]
+            label = df['output'].iloc[i]
+            pred = evaluate_one(instruction=instruction, input=input)
+            if pred == label:
+                correct += 1
+            else:
+                error.append((label, pred))
+            total += 1
+            acc = correct / total
+            # 更新进度条
+            # Update the progress bar
+            pbar.set_description(
+                f"Testing: Sample [{total}/{total_step}] Acc: {acc :.4f}")
+            pbar.update(1)
+
+        for e in error:
+            print(e)
+
+    def evaluate_by_batch(
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=1,
+        max_new_tokens=32
+    ):
+        df = pd.read_json(data_path, orient='records')
+        # df = df.sample(frac=eval_rate).reset_index(drop=True)
+        df['prompt'] = df.apply(lambda x: prompter.generate_prompt(
+            x['instruction'], x['input']), axis=1)
+        tokenizer.padding_side = "left"  # Allow batched inference
+
+        generation_config = GenerationConfig(
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            num_beams=num_beams
+        )
+
+        outputs = []
+        total = 0
+        total_step = math.ceil(len(df) / batch_size)
+        pbar = tqdm(total=total_step, unit='batch')
+        # 计算准确率
+        with torch.no_grad():
+            for i in range(total_step):
+                batch = df.iloc[i*batch_size:(i+1)*batch_size]
+                inputs = tokenizer(batch['prompt'].tolist(), return_tensors="pt", padding=True)[
+                    'input_ids'].to(device)
+
+                generation_outputs = model.generate(
+                    input_ids=inputs,
+                    generation_config=generation_config,
+                    max_new_tokens=max_new_tokens,
+                    pad_token_id=tokenizer.pad_token_id
+                )
+
+                for g in generation_outputs:
+                    decoded_item = tokenizer.decode(
+                        g, skip_special_tokens=True)
+                    try:
+                        output = prompter.get_response(decoded_item)
+                    except:
+                        output = decoded_item
+                    outputs.append(output)
+                    total += 1
+
+                # 更新进度条
+                pbar.set_description(f"Testing: Sample [{total}/{len(df)}] ")
+                pbar.update(1)
+        df['pred'] = outputs
+        df['pred'].to_csv(output_path, index=False)
+
+    evaluate_by_batch()
+
+
+if __name__ == "__main__":
+    # fire.Fire(main)
+    import yaml
+    dataset_param = sys.argv[1]
+    with open("./configs/evaluate_params.yaml", "r") as stream:
+        # try:
+        params = yaml.safe_load(stream)
+        print('=' * 80)
+        print(params[dataset_param])
+        print('=' * 80)
+
+    # fire.Fire(train)
+    main(**params[dataset_param])

utils/merge.py

@@ -0,0 +1,51 @@
+import os
+
+import torch
+import transformers
+from peft import PeftModel
+from transformers import LlamaForCausalLM, LlamaTokenizer  # noqa: F402
+
+BASE_MODEL = os.environ.get("BASE_MODEL", None)
+assert (
+    BASE_MODEL
+), "Please specify a value for BASE_MODEL environment variable, e.g. `export BASE_MODEL=huggyllama/llama-7b`"  # noqa: E501
+
+tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)
+
+base_model = LlamaForCausalLM.from_pretrained(
+    BASE_MODEL,
+    load_in_8bit=False,
+    torch_dtype=torch.float16,
+    device_map={"": "cpu"},
+)
+
+first_weight = base_model.model.layers[0].self_attn.q_proj.weight
+first_weight_old = first_weight.clone()
+
+lora_model = PeftModel.from_pretrained(
+    base_model,
+    "../outputs/lora-llama-clm-e2",
+    device_map={"": "cpu"},
+    torch_dtype=torch.float16,
+)
+
+lora_weight = lora_model.base_model.model.model.layers[0].self_attn.q_proj.weight
+
+assert torch.allclose(first_weight_old, first_weight)
+
+# merge weights - new merging method from peft
+lora_model = lora_model.merge_and_unload()
+
+lora_model.train(False)
+
+# did we do anything?
+assert not torch.allclose(first_weight_old, first_weight)
+
+lora_model_sd = lora_model.state_dict()
+deloreanized_sd = {
+    k.replace("base_model.model.", ""): v
+    for k, v in lora_model_sd.items()
+    if "lora" not in k
+}
+
+LlamaForCausalLM.save_pretrained(base_model, '../models/legal-base-7b', state_dict=deloreanized_sd, max_shard_size="400MB")

utils/prompter.py

@@ -0,0 +1,51 @@
+"""
+A dedicated helper to manage templates and prompt building.
+"""
+
+import json
+import os.path as osp
+from typing import Union
+
+
+class Prompter(object):
+    __slots__ = ("template", "_verbose")
+
+    def __init__(self, template_name: str = "", verbose: bool = False):
+        self._verbose = verbose
+        if not template_name:
+            # Enforce the default here, so the constructor can be called with '' and will not break.
+            template_name = "alpaca"
+        file_name = osp.join("templates", f"{template_name}.json")
+        if not osp.exists(file_name):
+            raise ValueError(f"Can't read {file_name}")
+        with open(file_name) as fp:
+            self.template = json.load(fp)
+        if self._verbose:
+            print(
+                f"Using prompt template {template_name}: {self.template['description']}"
+            )
+
+    def generate_prompt(
+        self,
+        instruction: str,
+        input: Union[None, str] = None,
+        label: Union[None, str] = None,
+    ) -> str:
+        # returns the full prompt from instruction and optional input
+        # if a label (=response, =output) is provided, it's also appended.
+        if input:
+            res = self.template["prompt_input"].format(
+                instruction=instruction, input=input
+            )
+        else:
+            res = self.template["prompt_no_input"].format(
+                instruction=instruction
+            )
+        if label:
+            res = f"{res}{label}"
+        if self._verbose:
+            print(res)
+        return res
+
+    def get_response(self, output: str) -> str:
+        return output.split(self.template["response_split"])[1].strip()

webui.py

@@ -0,0 +1,191 @@
+import os
+import sys
+
+import fire
+import gradio as gr
+import torch
+import transformers
+from peft import PeftModel
+from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer, AutoModel, AutoTokenizer, AutoModelForCausalLM,AutoConfig
+
+from utils.callbacks import Iteratorize, Stream
+from utils.prompter import Prompter
+
+# if torch.cuda.is_available():
+#     device = "cuda"
+# else:
+#     device = "cpu"
+
+# try:
+#     if torch.backends.mps.is_available():
+#         device = "mps"
+# except: 
+#     pass
+
+device = "xpu"
+
+
+def main(
+    load_8bit: bool = False,
+    base_model: str = "",
+    lora_weights: str = "",
+    prompt_template: str = "",  # The prompt template to use, will default to alpaca.
+    server_name: str = "0.0.0.0",  # Allows to listen on all interfaces by providing '0.
+    share_gradio: bool = False,
+):
+    base_model = base_model or os.environ.get("BASE_MODEL", "")
+    assert (
+        base_model
+    ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
+
+    prompter = Prompter(prompt_template)
+    tokenizer = LlamaTokenizer.from_pretrained(base_model)
+    prompter = Prompter(prompt_template)
+    tokenizer = LlamaTokenizer.from_pretrained(base_model)
+    config = AutoConfig.from_pretrained(base_model)
+    model = AutoModelForCausalLM.from_pretrained(
+        base_model,
+        config=config,
+        load_in_8bit=load_8bit,
+        torch_dtype=torch.float16,
+        device_map="auto",
+    )
+        
+    try:
+        print(f"Using lora {lora_weights}")
+        model = PeftModel.from_pretrained(
+            model,
+            lora_weights,
+            torch_dtype=torch.float16,
+        )
+    except:
+        print("*"*50, "\n Attention! No Lora Weights \n", "*"*50)
+
+    # unwind broken decapoda-research config
+    model.config.pad_token_id = tokenizer.pad_token_id = 0  # unk
+    model.config.bos_token_id = 1
+    model.config.eos_token_id = 2
+
+    # if not load_8bit:
+    #     model.half()  # seems to fix bugs for some users.
+
+    model.eval()
+    if torch.__version__ >= "2" and sys.platform != "win32":
+        model = torch.compile(model)
+
+    def evaluate(
+        instruction,
+        # input=None,
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=4,
+        max_new_tokens=128,
+        stream_output=False,
+        **kwargs,
+    ):
+        input=None
+        prompt = prompter.generate_prompt(instruction, input)
+        inputs = tokenizer(prompt, return_tensors="pt")
+        input_ids = inputs["input_ids"].to(device)
+        generation_config = GenerationConfig(
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            num_beams=num_beams,
+            **kwargs,
+        )
+
+        generate_params = {
+            "input_ids": input_ids,
+            "generation_config": generation_config,
+            "return_dict_in_generate": True,
+            "output_scores": True,
+            "max_new_tokens": max_new_tokens,
+        }
+
+        if stream_output:
+            # Stream the reply 1 token at a time.
+            # This is based on the trick of using 'stopping_criteria' to create an iterator,
+            # from https://github.com/oobabooga/text-generation-webui/blob/ad37f396fc8bcbab90e11ecf17c56c97bfbd4a9c/modules/text_generation.py#L216-L243.
+
+            def generate_with_callback(callback=None, **kwargs):
+                kwargs.setdefault(
+                    "stopping_criteria", transformers.StoppingCriteriaList()
+                )
+                kwargs["stopping_criteria"].append(
+                    Stream(callback_func=callback)
+                )
+                with torch.no_grad():
+                    model.generate(**kwargs)
+
+            def generate_with_streaming(**kwargs):
+                return Iteratorize(
+                    generate_with_callback, kwargs, callback=None
+                )
+
+            with generate_with_streaming(**generate_params) as generator:
+                for output in generator:
+                    # new_tokens = len(output) - len(input_ids[0])
+                    decoded_output = tokenizer.decode(output)
+
+                    if output[-1] in [tokenizer.eos_token_id]:
+                        break
+
+                    yield prompter.get_response(decoded_output)
+            print(decoded_output)
+            return  # early return for stream_output
+
+        # Without streaming
+        with torch.no_grad():
+            generation_output = model.generate(
+                input_ids=input_ids,
+                generation_config=generation_config,
+                return_dict_in_generate=True,
+                output_scores=True,
+                max_new_tokens=max_new_tokens,
+            )
+        s = generation_output.sequences[0]
+        output = tokenizer.decode(s)
+        print(output)
+        yield prompter.get_response(output)
+
+    gr.Interface(
+        fn=evaluate,
+        inputs=[
+            gr.components.Textbox(
+                lines=2,
+                label="Instruction",
+                placeholder="此处输入法律相关问题",
+            ),
+            # gr.components.Textbox(lines=2, label="Input", placeholder="none"),
+            gr.components.Slider(
+                minimum=0, maximum=1, value=0.1, label="Temperature"
+            ),
+            gr.components.Slider(
+                minimum=0, maximum=1, value=0.75, label="Top p"
+            ),
+            gr.components.Slider(
+                minimum=0, maximum=100, step=1, value=40, label="Top k"
+            ),
+            gr.components.Slider(
+                minimum=1, maximum=4, step=1, value=1, label="Beams"
+            ),
+            gr.components.Slider(
+                minimum=1, maximum=2000, step=1, value=256, label="Max tokens"
+            ),
+            gr.components.Checkbox(label="Stream output",  value=True),
+        ],
+        outputs=[
+            gr.inputs.Textbox(
+                lines=8,
+                label="Output",
+            )
+        ],
+        title="🦙🌲 LaWGPT",
+        description="",
+    ).queue().launch(server_name="0.0.0.0", share=share_gradio)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)