ChatGLM 黑盒微调插件

This commit is contained in:
binary-husky 2023-07-09 03:37:47 +08:00
parent 56c0e4d575
commit fcc5534e66
3 changed files with 107 additions and 14 deletions

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@ -365,6 +365,20 @@ def get_crazy_functions():
except: except:
print('Load function plugin failed') print('Load function plugin failed')
# try:
# from crazy_functions.chatglm微调工具 import 微调数据集生成
# function_plugins.update({
# "黑盒模型学习: 微调数据集生成 (先上传数据集)": {
# "Color": "stop",
# "AsButton": False,
# "AdvancedArgs": True,
# "ArgsReminder": "针对数据集输入(如 绿帽子*深蓝色衬衫*黑色运动裤)给出指令,例如您可以将以下命令复制到下方: --llm_to_learn=azure-gpt-3.5 --prompt_prefix='根据下面的服装类型提示想象一个穿着者对这个人外貌、身处的环境、内心世界、过去经历进行描写。要求100字以内用第二人称。' --system_prompt=''",
# "Function": HotReload(微调数据集生成)
# }
# })
# except:
# print('Load function plugin failed')
try: try:
from crazy_functions.Latex输出PDF结果 import Latex英文纠错加PDF对比 from crazy_functions.Latex输出PDF结果 import Latex英文纠错加PDF对比
function_plugins.update({ function_plugins.update({

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@ -0,0 +1,71 @@
from toolbox import CatchException, update_ui, promote_file_to_downloadzone
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
import datetime, json
def fetch_items(list_of_items, batch_size):
for i in range(0, len(list_of_items), batch_size):
yield list_of_items[i:i + batch_size]
def string_to_options(arguments):
import argparse
import shlex
# Create an argparse.ArgumentParser instance
parser = argparse.ArgumentParser()
# Add command-line arguments
parser.add_argument("--llm_to_learn", type=str, help="LLM model to learn", default="gpt-3.5-turbo")
parser.add_argument("--prompt_prefix", type=str, help="Prompt prefix", default='')
parser.add_argument("--system_prompt", type=str, help="System prompt", default='')
parser.add_argument("--batch", type=int, help="System prompt", default=50)
# Parse the arguments
args = parser.parse_args(shlex.split(arguments))
return args
@CatchException
def 微调数据集生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
"""
txt 输入栏用户输入的文本例如需要翻译的一段话再例如一个包含了待处理文件的路径
llm_kwargs gpt模型参数如温度和top_p等一般原样传递下去就行
plugin_kwargs 插件模型的参数
chatbot 聊天显示框的句柄用于显示给用户
history 聊天历史前情提要
system_prompt 给gpt的静默提醒
web_port 当前软件运行的端口号
"""
history = [] # 清空历史,以免输入溢出
chatbot.append(("这是什么功能?", "[Local Message] 微调数据集生成"))
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
args = plugin_kwargs.get("advanced_arg", None)
if args is None:
chatbot.append(("没给定指令", "退出"))
yield from update_ui(chatbot=chatbot, history=history); return
else:
arguments = string_to_options(arguments=args)
dat = []
with open(txt, 'r', encoding='utf8') as f:
for line in f.readlines():
json_dat = json.loads(line)
dat.append(json_dat["content"])
llm_kwargs['llm_model'] = arguments.llm_to_learn
for batch in fetch_items(dat, arguments.batch):
res = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
inputs_array=[f"{arguments.prompt_prefix}\n\n{b}" for b in (batch)],
inputs_show_user_array=[f"Show Nothing" for _ in (batch)],
llm_kwargs=llm_kwargs,
chatbot=chatbot,
history_array=[[] for _ in (batch)],
sys_prompt_array=[arguments.system_prompt for _ in (batch)],
max_workers=10 # OpenAI所允许的最大并行过载
)
with open(txt+'.generated.json', 'a+', encoding='utf8') as f:
for b, r in zip(batch, res[1::2]):
f.write(json.dumps({"content":b, "summary":r}, ensure_ascii=False)+'\n')
promote_file_to_downloadzone(txt+'.generated.json', rename_file='generated.json', chatbot=chatbot)
return

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@ -211,22 +211,30 @@ def test_Latex():
# # for cookies, cb, hist, msg in silence_stdout(编译Latex)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): # # for cookies, cb, hist, msg in silence_stdout(编译Latex)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
# cli_printer.print(cb) # print(cb) # cli_printer.print(cb) # print(cb)
def test_chatglm_finetune():
from crazy_functions.chatglm微调工具 import 微调数据集生成
txt = 'build/dev.json'
plugin_kwargs = {"advanced_arg":"--llm_to_learn=gpt-3.5-turbo --prompt_prefix='根据下面的服装类型提示想象一个穿着者对这个人外貌、身处的环境、内心世界、人设进行描写。要求100字以内用第二人称。' --system_prompt=''" }
for cookies, cb, hist, msg in (微调数据集生成)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
cli_printer.print(cb)
# test_解析一个Python项目()
# test_Latex英文润色()
# test_Markdown中译英()
# test_批量翻译PDF文档()
# test_谷歌检索小助手()
# test_总结word文档()
# test_下载arxiv论文并翻译摘要()
# test_解析一个Cpp项目()
# test_联网回答问题()
# test_解析ipynb文件()
# test_数学动画生成manim()
# test_Langchain知识库()
# test_Langchain知识库读取()
if __name__ == "__main__": if __name__ == "__main__":
test_Latex() # test_解析一个Python项目()
# test_Latex英文润色()
# test_Markdown中译英()
# test_批量翻译PDF文档()
# test_谷歌检索小助手()
# test_总结word文档()
# test_下载arxiv论文并翻译摘要()
# test_解析一个Cpp项目()
# test_联网回答问题()
# test_解析ipynb文件()
# test_数学动画生成manim()
# test_Langchain知识库()
# test_Langchain知识库读取()
# test_Latex()
test_chatglm_finetune()
input("程序完成,回车退出。") input("程序完成,回车退出。")
print("退出。") print("退出。")