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binary-husky
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.gitignore
vendored
6
.gitignore
vendored
@ -146,9 +146,9 @@ debug*
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private*
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crazy_functions/test_project/pdf_and_word
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crazy_functions/test_samples
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request_llm/jittorllms
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request_llms/jittorllms
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multi-language
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request_llm/moss
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request_llms/moss
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media
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flagged
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request_llm/ChatGLM-6b-onnx-u8s8
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request_llms/ChatGLM-6b-onnx-u8s8
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@ -126,11 +126,11 @@ python -m pip install -r requirements.txt # 这个步骤和pip安装一样的步
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【可选步骤】如果需要支持清华ChatGLM2/复旦MOSS作为后端，需要额外安装更多依赖（前提条件：熟悉Python + 用过Pytorch + 电脑配置够强）：
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```sh
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# 【可选步骤I】支持清华ChatGLM2。清华ChatGLM备注：如果遇到"Call ChatGLM fail 不能正常加载ChatGLM的参数" 错误，参考如下： 1：以上默认安装的为torch+cpu版，使用cuda需要卸载torch重新安装torch+cuda； 2：如因本机配置不够无法加载模型，可以修改request_llm/bridge_chatglm.py中的模型精度, 将 AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) 都修改为 AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
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python -m pip install -r request_llm/requirements_chatglm.txt
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python -m pip install -r request_llms/requirements_chatglm.txt
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# 【可选步骤II】支持复旦MOSS
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python -m pip install -r request_llm/requirements_moss.txt
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git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llm/moss # 注意执行此行代码时，必须处于项目根路径
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python -m pip install -r request_llms/requirements_moss.txt
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git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # 注意执行此行代码时，必须处于项目根路径
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# 【可选步骤III】支持RWKV Runner
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参考wiki：https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner
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@ -156,7 +156,7 @@ def auto_update(raise_error=False):
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def warm_up_modules():
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print('正在执行一些模块的预热...')
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from toolbox import ProxyNetworkActivate
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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with ProxyNetworkActivate("Warmup_Modules"):
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enc = model_info["gpt-3.5-turbo"]['tokenizer']
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enc.encode("模块预热", disallowed_special=())
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@ -136,7 +136,7 @@ SSL_CERTFILE = ""
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API_ORG = ""
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# 如果需要使用Slack Claude，使用教程详情见 request_llm/README.md
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# 如果需要使用Slack Claude，使用教程详情见 request_llms/README.md
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SLACK_CLAUDE_BOT_ID = ''
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SLACK_CLAUDE_USER_TOKEN = ''
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@ -11,7 +11,7 @@ class PaperFileGroup():
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self.sp_file_tag = []
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# count_token
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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enc = model_info["gpt-3.5-turbo"]['tokenizer']
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
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self.get_token_num = get_token_num
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@ -11,7 +11,7 @@ class PaperFileGroup():
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self.sp_file_tag = []
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# count_token
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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enc = model_info["gpt-3.5-turbo"]['tokenizer']
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
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self.get_token_num = get_token_num
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@ -5,7 +5,7 @@ import logging
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def input_clipping(inputs, history, max_token_limit):
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import numpy as np
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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enc = model_info["gpt-3.5-turbo"]['tokenizer']
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
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@ -63,7 +63,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
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"""
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import time
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from concurrent.futures import ThreadPoolExecutor
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from request_llm.bridge_all import predict_no_ui_long_connection
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from request_llms.bridge_all import predict_no_ui_long_connection
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# 用户反馈
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chatbot.append([inputs_show_user, ""])
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yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
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@ -177,7 +177,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
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"""
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import time, random
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from concurrent.futures import ThreadPoolExecutor
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from request_llm.bridge_all import predict_no_ui_long_connection
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from request_llms.bridge_all import predict_no_ui_long_connection
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assert len(inputs_array) == len(history_array)
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assert len(inputs_array) == len(sys_prompt_array)
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if max_workers == -1: # 读取配置文件
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@ -165,7 +165,7 @@ class LatexPaperFileGroup():
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self.sp_file_tag = []
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# count_token
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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enc = model_info["gpt-3.5-turbo"]['tokenizer']
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
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self.get_token_num = get_token_num
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@ -103,7 +103,7 @@ def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_fi
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inputs_show_user_array = []
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# get_token_num
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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enc = model_info[llm_kwargs['llm_model']]['tokenizer']
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
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@ -1,7 +1,7 @@
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from pydantic import BaseModel, Field
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from typing import List
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from toolbox import update_ui_lastest_msg, disable_auto_promotion
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from request_llm.bridge_all import predict_no_ui_long_connection
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from request_llms.bridge_all import predict_no_ui_long_connection
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from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
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import copy, json, pickle, os, sys, time
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@ -1,7 +1,7 @@
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from pydantic import BaseModel, Field
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from typing import List
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from toolbox import update_ui_lastest_msg, get_conf
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from request_llm.bridge_all import predict_no_ui_long_connection
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from request_llms.bridge_all import predict_no_ui_long_connection
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from crazy_functions.json_fns.pydantic_io import GptJsonIO
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import copy, json, pickle, os, sys
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@ -5,7 +5,7 @@ import datetime
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def gen_image(llm_kwargs, prompt, resolution="256x256"):
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import requests, json, time, os
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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proxies, = get_conf('proxies')
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# Set up OpenAI API key and model
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@ -32,7 +32,7 @@ def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot
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print(file_content)
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# private_upload里面的文件名在解压zip后容易出现乱码（rar和7z格式正常），故可以只分析文章内容，不输入文件名
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from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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max_token = model_info[llm_kwargs['llm_model']]['max_token']
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TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4
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paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
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@ -41,7 +41,7 @@ def split_audio_file(filename, split_duration=1000):
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def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
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import os, requests
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from moviepy.editor import AudioFileClip
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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# 设置OpenAI密钥和模型
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api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
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@ -13,7 +13,7 @@ class PaperFileGroup():
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self.sp_file_tag = []
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# count_token
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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enc = model_info["gpt-3.5-turbo"]['tokenizer']
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
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self.get_token_num = get_token_num
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@ -21,7 +21,7 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
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TOKEN_LIMIT_PER_FRAGMENT = 2500
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from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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enc = model_info["gpt-3.5-turbo"]['tokenizer']
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
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paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
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@ -95,7 +95,7 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
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# 递归地切割PDF文件
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from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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enc = model_info["gpt-3.5-turbo"]['tokenizer']
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
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paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
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@ -19,7 +19,7 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
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TOKEN_LIMIT_PER_FRAGMENT = 2500
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from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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enc = model_info["gpt-3.5-turbo"]['tokenizer']
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
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paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
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@ -2,7 +2,7 @@ from toolbox import CatchException, update_ui
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
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import requests
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from bs4 import BeautifulSoup
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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def google(query, proxies):
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query = query # 在此处替换您要搜索的关键词
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@ -2,7 +2,7 @@ from toolbox import CatchException, update_ui
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
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import requests
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from bs4 import BeautifulSoup
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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def bing_search(query, proxies=None):
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@ -48,7 +48,7 @@ from pydantic import BaseModel, Field
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from typing import List
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from toolbox import CatchException, update_ui, is_the_upload_folder
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from toolbox import update_ui_lastest_msg, disable_auto_promotion
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from request_llm.bridge_all import predict_no_ui_long_connection
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from request_llms.bridge_all import predict_no_ui_long_connection
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from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
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from crazy_functions.crazy_utils import input_clipping
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from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
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@ -13,7 +13,7 @@ class PaperFileGroup():
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self.sp_file_tag = []
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# count_token
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from request_llm.bridge_all import model_info
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from request_llms.bridge_all import model_info
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enc = model_info["gpt-3.5-turbo"]['tokenizer']
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def get_token_num(txt): return len(
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enc.encode(txt, disallowed_special=()))
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@ -2,7 +2,7 @@ from toolbox import update_ui
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from toolbox import CatchException, get_conf, markdown_convertion
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from crazy_functions.crazy_utils import input_clipping
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from crazy_functions.agent_fns.watchdog import WatchDog
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from request_llm.bridge_all import predict_no_ui_long_connection
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from request_llms.bridge_all import predict_no_ui_long_connection
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import threading, time
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import numpy as np
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from .live_audio.aliyunASR import AliyunASR
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@ -137,7 +137,7 @@ services:
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# P.S. 通过对 command 进行微调，可以便捷地安装额外的依赖
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# command: >
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# bash -c "pip install -r request_llm/requirements_qwen.txt && python3 -u main.py"
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# bash -c "pip install -r request_llms/requirements_qwen.txt && python3 -u main.py"
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### ===================================================
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### 【方案三】 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型
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@ -19,13 +19,13 @@ RUN python3 -m pip install aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad sc
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WORKDIR /gpt
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RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
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WORKDIR /gpt/gpt_academic
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RUN git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llm/moss
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RUN git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss
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RUN python3 -m pip install -r requirements.txt
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RUN python3 -m pip install -r request_llm/requirements_moss.txt
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RUN python3 -m pip install -r request_llm/requirements_qwen.txt
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RUN python3 -m pip install -r request_llm/requirements_chatglm.txt
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RUN python3 -m pip install -r request_llm/requirements_newbing.txt
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RUN python3 -m pip install -r request_llms/requirements_moss.txt
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RUN python3 -m pip install -r request_llms/requirements_qwen.txt
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RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
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RUN python3 -m pip install -r request_llms/requirements_newbing.txt
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RUN python3 -m pip install nougat-ocr
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@ -14,12 +14,12 @@ RUN python3 -m pip install torch --extra-index-url https://download.pytorch.org/
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WORKDIR /gpt
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RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
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WORKDIR /gpt/gpt_academic
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RUN git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss
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RUN git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss
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RUN python3 -m pip install -r requirements.txt
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RUN python3 -m pip install -r request_llm/requirements_moss.txt
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RUN python3 -m pip install -r request_llm/requirements_qwen.txt
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RUN python3 -m pip install -r request_llm/requirements_chatglm.txt
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RUN python3 -m pip install -r request_llm/requirements_newbing.txt
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RUN python3 -m pip install -r request_llms/requirements_moss.txt
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RUN python3 -m pip install -r request_llms/requirements_qwen.txt
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RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
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RUN python3 -m pip install -r request_llms/requirements_newbing.txt
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@ -16,12 +16,12 @@ WORKDIR /gpt
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RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
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WORKDIR /gpt/gpt_academic
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RUN python3 -m pip install -r requirements.txt
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RUN python3 -m pip install -r request_llm/requirements_chatglm.txt
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RUN python3 -m pip install -r request_llm/requirements_newbing.txt
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RUN python3 -m pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I
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RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
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RUN python3 -m pip install -r request_llms/requirements_newbing.txt
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RUN python3 -m pip install -r request_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I
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# 下载JittorLLMs
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RUN git clone https://github.com/binary-husky/JittorLLMs.git --depth 1 request_llm/jittorllms
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RUN git clone https://github.com/binary-husky/JittorLLMs.git --depth 1 request_llms/jittorllms
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# 禁用缓存，确保更新代码
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ADD "https://www.random.org/cgi-bin/randbyte?nbytes=10&format=h" skipcache
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@ -103,12 +103,12 @@ python -m pip install -r requirements.txt # Same step as pip installation
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[Optional Step] If supporting Tsinghua ChatGLM/Fudan MOSS as backend, additional dependencies need to be installed (Prerequisites: Familiar with Python + Used Pytorch + Sufficient computer configuration):
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```sh
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# [Optional Step I] Support Tsinghua ChatGLM. Remark: If encountering "Call ChatGLM fail Cannot load ChatGLM parameters", please refer to the following: 1: The above default installation is torch+cpu version. To use cuda, uninstall torch and reinstall torch+cuda; 2: If the model cannot be loaded due to insufficient machine configuration, you can modify the model precision in `request_llm/bridge_chatglm.py`, and modify all AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
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python -m pip install -r request_llm/requirements_chatglm.txt
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# [Optional Step I] Support Tsinghua ChatGLM. Remark: If encountering "Call ChatGLM fail Cannot load ChatGLM parameters", please refer to the following: 1: The above default installation is torch+cpu version. To use cuda, uninstall torch and reinstall torch+cuda; 2: If the model cannot be loaded due to insufficient machine configuration, you can modify the model precision in `request_llms/bridge_chatglm.py`, and modify all AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
|
||||
python -m pip install -r request_llms/requirements_chatglm.txt
|
||||
|
||||
# [Optional Step II] Support Fudan MOSS
|
||||
python -m pip install -r request_llm/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # When executing this line of code, you must be in the project root path
|
||||
python -m pip install -r request_llms/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss # When executing this line of code, you must be in the project root path
|
||||
|
||||
# [Optional Step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the expected models. Currently supported models are as follows (jittorllms series currently only supports docker solutions):
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
||||
|
||||
@ -109,12 +109,12 @@ python -m pip install -r requirements.txt # questo passaggio funziona allo stess
|
||||
|
||||
【Passaggio facoltativo】 Se si desidera supportare ChatGLM di Tsinghua/MOSS di Fudan come backend, è necessario installare ulteriori dipendenze (prerequisiti: conoscenza di Python, esperienza con Pytorch e computer sufficientemente potente):
|
||||
```sh
|
||||
# 【Passaggio facoltativo I】 Supporto a ChatGLM di Tsinghua. Note su ChatGLM di Tsinghua: in caso di errore "Call ChatGLM fail 不能正常加载ChatGLM的参数" , fare quanto segue: 1. Per impostazione predefinita, viene installata la versione di torch + cpu; per usare CUDA, è necessario disinstallare torch e installare nuovamente torch + cuda; 2. Se non è possibile caricare il modello a causa di una configurazione insufficiente del computer, è possibile modificare la precisione del modello in request_llm/bridge_chatglm.py, cambiando AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) in AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
|
||||
python -m pip install -r request_llm/requirements_chatglm.txt
|
||||
# 【Passaggio facoltativo I】 Supporto a ChatGLM di Tsinghua. Note su ChatGLM di Tsinghua: in caso di errore "Call ChatGLM fail 不能正常加载ChatGLM的参数" , fare quanto segue: 1. Per impostazione predefinita, viene installata la versione di torch + cpu; per usare CUDA, è necessario disinstallare torch e installare nuovamente torch + cuda; 2. Se non è possibile caricare il modello a causa di una configurazione insufficiente del computer, è possibile modificare la precisione del modello in request_llms/bridge_chatglm.py, cambiando AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) in AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
|
||||
python -m pip install -r request_llms/requirements_chatglm.txt
|
||||
|
||||
# 【Passaggio facoltativo II】 Supporto a MOSS di Fudan
|
||||
python -m pip install -r request_llm/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # Si prega di notare che quando si esegue questa riga di codice, si deve essere nella directory radice del progetto
|
||||
python -m pip install -r request_llms/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss # Si prega di notare che quando si esegue questa riga di codice, si deve essere nella directory radice del progetto
|
||||
|
||||
# 【Passaggio facoltativo III】 Assicurati che il file di configurazione config.py includa tutti i modelli desiderati, al momento tutti i modelli supportati sono i seguenti (i modelli della serie jittorllms attualmente supportano solo la soluzione docker):
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
||||
|
||||
@ -104,11 +104,11 @@ python -m pip install -r requirements.txt # 이 단계도 pip install의 단계
|
||||
# 1 : 기본 설치된 것들은 torch + cpu 버전입니다. cuda를 사용하려면 torch를 제거한 다음 torch + cuda를 다시 설치해야합니다.
|
||||
# 2 : 모델을 로드할 수 없는 기계 구성 때문에, AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)를
|
||||
# AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)로 변경합니다.
|
||||
python -m pip install -r request_llm/requirements_chatglm.txt
|
||||
python -m pip install -r request_llms/requirements_chatglm.txt
|
||||
|
||||
# [선택 사항 II] Fudan MOSS 지원
|
||||
python -m pip install -r request_llm/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # 다음 코드 줄을 실행할 때 프로젝트 루트 경로에 있어야합니다.
|
||||
python -m pip install -r request_llms/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss # 다음 코드 줄을 실행할 때 프로젝트 루트 경로에 있어야합니다.
|
||||
|
||||
# [선택 사항III] AVAIL_LLM_MODELS config.py 구성 파일에 기대하는 모델이 포함되어 있는지 확인하십시오.
|
||||
# 현재 지원되는 전체 모델 :
|
||||
|
||||
@ -119,12 +119,12 @@ python -m pip install -r requirements.txt # This step is the same as the pip ins
|
||||
|
||||
[Optional Step] If you need to support Tsinghua ChatGLM / Fudan MOSS as the backend, you need to install more dependencies (prerequisite: familiar with Python + used Pytorch + computer configuration is strong):
|
||||
```sh
|
||||
# 【Optional Step I】support Tsinghua ChatGLM。Tsinghua ChatGLM Note: If you encounter a "Call ChatGLM fails cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installed is torch+cpu version, and using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient computer configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
|
||||
python -m pip install -r request_llm/requirements_chatglm.txt
|
||||
# 【Optional Step I】support Tsinghua ChatGLM。Tsinghua ChatGLM Note: If you encounter a "Call ChatGLM fails cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installed is torch+cpu version, and using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient computer configuration, you can modify the model accuracy in request_llms/bridge_chatglm.py and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
|
||||
python -m pip install -r request_llms/requirements_chatglm.txt
|
||||
|
||||
# 【Optional Step II】support Fudan MOSS
|
||||
python -m pip install -r request_llm/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # Note: When executing this line of code, you must be in the project root path
|
||||
python -m pip install -r request_llms/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss # Note: When executing this line of code, you must be in the project root path
|
||||
|
||||
# 【Optional Step III】Make sure that the AVAIL_LLM_MODELS in the config.py configuration file contains the expected model. Currently, all supported models are as follows (jittorllms series currently only supports docker solutions):
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
||||
|
||||
@ -106,12 +106,12 @@ python -m pip install -r requirements.txt # this step is the same as pip install
|
||||
|
||||
[Optional step] If you need to support Tsinghua ChatGLM/Fudan MOSS as a backend, you need to install more dependencies (prerequisites: familiar with Python + used Pytorch + computer configuration is strong enough):
|
||||
```sh
|
||||
# [Optional Step I] Support Tsinghua ChatGLM. Tsinghua ChatGLM remarks: if you encounter the "Call ChatGLM fail cannot load ChatGLM parameters" error, refer to this: 1: The default installation above is torch + cpu version, to use cuda, you need to uninstall torch and reinstall torch + cuda; 2: If the model cannot be loaded due to insufficient local configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py, and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code = True)
|
||||
python -m pip install -r request_llm/requirements_chatglm.txt
|
||||
# [Optional Step I] Support Tsinghua ChatGLM. Tsinghua ChatGLM remarks: if you encounter the "Call ChatGLM fail cannot load ChatGLM parameters" error, refer to this: 1: The default installation above is torch + cpu version, to use cuda, you need to uninstall torch and reinstall torch + cuda; 2: If the model cannot be loaded due to insufficient local configuration, you can modify the model accuracy in request_llms/bridge_chatglm.py, and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code = True)
|
||||
python -m pip install -r request_llms/requirements_chatglm.txt
|
||||
|
||||
# [Optional Step II] Support Fudan MOSS
|
||||
python -m pip install -r request_llm/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # When executing this line of code, you must be in the root directory of the project
|
||||
python -m pip install -r request_llms/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss # When executing this line of code, you must be in the root directory of the project
|
||||
|
||||
# [Optional Step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file includes the expected models. Currently supported models are as follows (the jittorllms series only supports the docker solution for the time being):
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
||||
|
||||
@ -111,12 +111,12 @@ python -m pip install -r requirements.txt # Same step as pip instalation
|
||||
|
||||
【Optional】 Si vous souhaitez prendre en charge THU ChatGLM/FDU MOSS en tant que backend, des dépendances supplémentaires doivent être installées (prérequis: compétent en Python + utilisez Pytorch + configuration suffisante de l'ordinateur):
|
||||
```sh
|
||||
# 【Optional Step I】 Support THU ChatGLM. Remarque sur THU ChatGLM: Si vous rencontrez l'erreur "Appel à ChatGLM échoué, les paramètres ChatGLM ne peuvent pas être chargés normalement", reportez-vous à ce qui suit: 1: La version par défaut installée est torch+cpu, si vous souhaitez utiliser cuda, vous devez désinstaller torch et réinstaller torch+cuda; 2: Si le modèle ne peut pas être chargé en raison d'une configuration insuffisante de l'ordinateur local, vous pouvez modifier la précision du modèle dans request_llm/bridge_chatglm.py, modifier AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) par AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
|
||||
python -m pip install -r request_llm/requirements_chatglm.txt
|
||||
# 【Optional Step I】 Support THU ChatGLM. Remarque sur THU ChatGLM: Si vous rencontrez l'erreur "Appel à ChatGLM échoué, les paramètres ChatGLM ne peuvent pas être chargés normalement", reportez-vous à ce qui suit: 1: La version par défaut installée est torch+cpu, si vous souhaitez utiliser cuda, vous devez désinstaller torch et réinstaller torch+cuda; 2: Si le modèle ne peut pas être chargé en raison d'une configuration insuffisante de l'ordinateur local, vous pouvez modifier la précision du modèle dans request_llms/bridge_chatglm.py, modifier AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) par AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
|
||||
python -m pip install -r request_llms/requirements_chatglm.txt
|
||||
|
||||
# 【Optional Step II】 Support FDU MOSS
|
||||
python -m pip install -r request_llm/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # Note: When running this line of code, you must be in the project root path.
|
||||
python -m pip install -r request_llms/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss # Note: When running this line of code, you must be in the project root path.
|
||||
|
||||
# 【Optional Step III】Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the desired model. Currently, all models supported are as follows (the jittorllms series currently only supports the docker scheme):
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
||||
|
||||
@ -120,12 +120,12 @@ python -m pip install -r requirements.txt # This step is the same as the pip ins
|
||||
[Optional Steps] If you need to support Tsinghua ChatGLM/Fudan MOSS as a backend, you need to install more dependencies (precondition: familiar with Python + used Pytorch + computer configuration). Strong enough):
|
||||
|
||||
```sh
|
||||
# Optional step I: support Tsinghua ChatGLM. Tsinghua ChatGLM remarks: If you encounter the error "Call ChatGLM fail cannot load ChatGLM parameters normally", refer to the following: 1: The version installed above is torch+cpu version, using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient local configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py, and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).
|
||||
python -m pip install -r request_llm/requirements_chatglm.txt
|
||||
# Optional step I: support Tsinghua ChatGLM. Tsinghua ChatGLM remarks: If you encounter the error "Call ChatGLM fail cannot load ChatGLM parameters normally", refer to the following: 1: The version installed above is torch+cpu version, using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient local configuration, you can modify the model accuracy in request_llms/bridge_chatglm.py, and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).
|
||||
python -m pip install -r request_llms/requirements_chatglm.txt
|
||||
|
||||
# Optional Step II: Support Fudan MOSS.
|
||||
python -m pip install -r request_llm/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # Note that when executing this line of code, it must be in the project root.
|
||||
python -m pip install -r request_llms/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss # Note that when executing this line of code, it must be in the project root.
|
||||
|
||||
# 【Optional Step III】Ensure that the AVAIL_LLM_MODELS in the config.py configuration file contains the expected model. Currently, all supported models are as follows (jittorllms series currently only supports the docker solution):
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
||||
|
||||
@ -108,12 +108,12 @@ python -m pip install -r requirements.txt # This step is the same as the pip ins
|
||||
|
||||
[Optional step] If you need to support Tsinghua ChatGLM/Fudan MOSS as backend, you need to install more dependencies (prerequisites: familiar with Python + have used Pytorch + computer configuration is strong):
|
||||
```sh
|
||||
# [Optional step I] Support Tsinghua ChatGLM. Tsinghua ChatGLM note: If you encounter the "Call ChatGLM fail cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installation above is torch+cpu version, and cuda is used Need to uninstall torch and reinstall torch+cuda; 2: If you cannot load the model due to insufficient local configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py, AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) Modify to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
|
||||
python -m pip install -r request_llm/requirements_chatglm.txt
|
||||
# [Optional step I] Support Tsinghua ChatGLM. Tsinghua ChatGLM note: If you encounter the "Call ChatGLM fail cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installation above is torch+cpu version, and cuda is used Need to uninstall torch and reinstall torch+cuda; 2: If you cannot load the model due to insufficient local configuration, you can modify the model accuracy in request_llms/bridge_chatglm.py, AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) Modify to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
|
||||
python -m pip install -r request_llms/requirements_chatglm.txt
|
||||
|
||||
# [Optional step II] Support Fudan MOSS
|
||||
python -m pip install -r request_llm/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # Note that when executing this line of code, you must be in the project root path
|
||||
python -m pip install -r request_llms/requirements_moss.txt
|
||||
git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss # Note that when executing this line of code, you must be in the project root path
|
||||
|
||||
# [Optional step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the expected models. Currently, all supported models are as follows (the jittorllms series currently only supports the docker solution):
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
||||
|
||||
@ -38,20 +38,20 @@
|
||||
| crazy_functions\读文章写摘要.py | 对论文进行解析和全文摘要生成 |
|
||||
| crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 |
|
||||
| crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 |
|
||||
| request_llm\bridge_all.py | 基于不同LLM模型进行对话。 |
|
||||
| request_llm\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
|
||||
| request_llm\bridge_chatgpt.py | 基于GPT模型完成对话。 |
|
||||
| request_llm\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
|
||||
| request_llm\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
|
||||
| request_llm\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
|
||||
| request_llm\bridge_moss.py | 加载Moss模型完成对话功能。 |
|
||||
| request_llm\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
|
||||
| request_llm\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
|
||||
| request_llm\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
|
||||
| request_llm\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
|
||||
| request_llm\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
|
||||
| request_llm\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
|
||||
| request_llm\test_llms.py | 对llm模型进行单元测试。 |
|
||||
| request_llms\bridge_all.py | 基于不同LLM模型进行对话。 |
|
||||
| request_llms\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
|
||||
| request_llms\bridge_chatgpt.py | 基于GPT模型完成对话。 |
|
||||
| request_llms\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
|
||||
| request_llms\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
|
||||
| request_llms\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
|
||||
| request_llms\bridge_moss.py | 加载Moss模型完成对话功能。 |
|
||||
| request_llms\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
|
||||
| request_llms\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
|
||||
| request_llms\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
|
||||
| request_llms\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
|
||||
| request_llms\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
|
||||
| request_llms\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
|
||||
| request_llms\test_llms.py | 对llm模型进行单元测试。 |
|
||||
|
||||
## 接下来请你逐文件分析下面的工程[0/48] 请对下面的程序文件做一个概述: check_proxy.py
|
||||
|
||||
@ -129,7 +129,7 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
|
||||
1. `input_clipping`: 该函数用于裁剪输入文本长度，使其不超过一定的限制。
|
||||
2. `request_gpt_model_in_new_thread_with_ui_alive`: 该函数用于请求 GPT 模型并保持用户界面的响应，支持多线程和实时更新用户界面。
|
||||
|
||||
这两个函数都依赖于从 `toolbox` 和 `request_llm` 中导入的一些工具函数。函数的输入和输出有详细的描述文档。
|
||||
这两个函数都依赖于从 `toolbox` 和 `request_llms` 中导入的一些工具函数。函数的输入和输出有详细的描述文档。
|
||||
|
||||
## [12/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文润色.py
|
||||
|
||||
@ -137,7 +137,7 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
|
||||
|
||||
## [13/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文翻译.py
|
||||
|
||||
这个文件包含两个函数 `Latex英译中` 和 `Latex中译英`，它们都会对整个Latex项目进行翻译。这个文件还包含一个类 `PaperFileGroup`，它拥有一个方法 `run_file_split`，用于把长文本文件分成多个短文件。其中使用了工具库 `toolbox` 中的一些函数和从 `request_llm` 中导入了 `model_info`。接下来的函数把文件读取进来，把它们的注释删除，进行分割，并进行翻译。这个文件还包括了一些异常处理和界面更新的操作。
|
||||
这个文件包含两个函数 `Latex英译中` 和 `Latex中译英`，它们都会对整个Latex项目进行翻译。这个文件还包含一个类 `PaperFileGroup`，它拥有一个方法 `run_file_split`，用于把长文本文件分成多个短文件。其中使用了工具库 `toolbox` 中的一些函数和从 `request_llms` 中导入了 `model_info`。接下来的函数把文件读取进来，把它们的注释删除，进行分割，并进行翻译。这个文件还包括了一些异常处理和界面更新的操作。
|
||||
|
||||
## [14/48] 请对下面的程序文件做一个概述: crazy_functions\__init__.py
|
||||
|
||||
@ -227,19 +227,19 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
|
||||
|
||||
该程序文件定义了一个名为高阶功能模板函数的函数，该函数接受多个参数，包括输入的文本、gpt模型参数、插件模型参数、聊天显示框的句柄、聊天历史等，并利用送出请求，使用 Unsplash API 发送相关图片。其中，为了避免输入溢出，函数会在开始时清空历史。函数也有一些 UI 更新的语句。该程序文件还依赖于其他两个模块：CatchException 和 update_ui，以及一个名为 request_gpt_model_in_new_thread_with_ui_alive 的来自 crazy_utils 模块（应该是自定义的工具包）的函数。
|
||||
|
||||
## [34/48] 请对下面的程序文件做一个概述: request_llm\bridge_all.py
|
||||
## [34/48] 请对下面的程序文件做一个概述: request_llms\bridge_all.py
|
||||
|
||||
该文件包含两个函数：predict和predict_no_ui_long_connection，用于基于不同的LLM模型进行对话。该文件还包含一个lazyloadTiktoken类和一个LLM_CATCH_EXCEPTION修饰器函数。其中lazyloadTiktoken类用于懒加载模型的tokenizer，LLM_CATCH_EXCEPTION用于错误处理。整个文件还定义了一些全局变量和模型信息字典，用于引用和配置LLM模型。
|
||||
|
||||
## [35/48] 请对下面的程序文件做一个概述: request_llm\bridge_chatglm.py
|
||||
## [35/48] 请对下面的程序文件做一个概述: request_llms\bridge_chatglm.py
|
||||
|
||||
这是一个Python程序文件，名为`bridge_chatglm.py`，其中定义了一个名为`GetGLMHandle`的类和三个方法：`predict_no_ui_long_connection`、 `predict`和 `stream_chat`。该文件依赖于多个Python库，如`transformers`和`sentencepiece`。该文件实现了一个聊天机器人，使用ChatGLM模型来生成回复，支持单线程和多线程方式。程序启动时需要加载ChatGLM的模型和tokenizer，需要一段时间。在配置文件`config.py`中设置参数会影响模型的内存和显存使用，因此程序可能会导致低配计算机卡死。
|
||||
|
||||
## [36/48] 请对下面的程序文件做一个概述: request_llm\bridge_chatgpt.py
|
||||
## [36/48] 请对下面的程序文件做一个概述: request_llms\bridge_chatgpt.py
|
||||
|
||||
该文件为 Python 代码文件，文件名为 request_llm\bridge_chatgpt.py。该代码文件主要提供三个函数：predict、predict_no_ui和 predict_no_ui_long_connection，用于发送至 chatGPT 并等待回复，获取输出。该代码文件还包含一些辅助函数，用于处理连接异常、生成 HTTP 请求等。该文件的代码架构清晰，使用了多个自定义函数和模块。
|
||||
该文件为 Python 代码文件，文件名为 request_llms\bridge_chatgpt.py。该代码文件主要提供三个函数：predict、predict_no_ui和 predict_no_ui_long_connection，用于发送至 chatGPT 并等待回复，获取输出。该代码文件还包含一些辅助函数，用于处理连接异常、生成 HTTP 请求等。该文件的代码架构清晰，使用了多个自定义函数和模块。
|
||||
|
||||
## [37/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_llama.py
|
||||
## [37/48] 请对下面的程序文件做一个概述: request_llms\bridge_jittorllms_llama.py
|
||||
|
||||
该代码文件实现了一个聊天机器人，其中使用了 JittorLLMs 模型。主要包括以下几个部分：
|
||||
1. GetGLMHandle 类：一个进程类，用于加载 JittorLLMs 模型并接收并处理请求。
|
||||
@ -248,17 +248,17 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
|
||||
|
||||
这个文件中还有一些辅助函数和全局变量，例如 importlib、time、threading 等。
|
||||
|
||||
## [38/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_pangualpha.py
|
||||
## [38/48] 请对下面的程序文件做一个概述: request_llms\bridge_jittorllms_pangualpha.py
|
||||
|
||||
这个文件是为了实现使用jittorllms（一种机器学习模型）来进行聊天功能的代码。其中包括了模型加载、模型的参数加载、消息的收发等相关操作。其中使用了多进程和多线程来提高性能和效率。代码中还包括了处理依赖关系的函数和预处理函数等。
|
||||
|
||||
## [39/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_rwkv.py
|
||||
## [39/48] 请对下面的程序文件做一个概述: request_llms\bridge_jittorllms_rwkv.py
|
||||
|
||||
这个文件是一个Python程序，文件名为request_llm\bridge_jittorllms_rwkv.py。它依赖transformers、time、threading、importlib、multiprocessing等库。在文件中，通过定义GetGLMHandle类加载jittorllms模型参数和定义stream_chat方法来实现与jittorllms模型的交互。同时，该文件还定义了predict_no_ui_long_connection和predict方法来处理历史信息、调用jittorllms模型、接收回复信息并输出结果。
|
||||
|
||||
## [40/48] 请对下面的程序文件做一个概述: request_llm\bridge_moss.py
|
||||
## [40/48] 请对下面的程序文件做一个概述: request_llms\bridge_moss.py
|
||||
|
||||
该文件为一个Python源代码文件，文件名为 request_llm\bridge_moss.py。代码定义了一个 GetGLMHandle 类和两个函数 predict_no_ui_long_connection 和 predict。
|
||||
该文件为一个Python源代码文件，文件名为 request_llms\bridge_moss.py。代码定义了一个 GetGLMHandle 类和两个函数 predict_no_ui_long_connection 和 predict。
|
||||
|
||||
GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个子进程并加载 MOSS 模型参数，通过 Pipe 进行主子进程的通信。该类还定义了 check_dependency、moss_init、run 和 stream_chat 等方法，其中 check_dependency 和 moss_init 是子进程的初始化方法，run 是子进程运行方法，stream_chat 实现了主进程和子进程的交互过程。
|
||||
|
||||
@ -266,7 +266,7 @@ GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个
|
||||
|
||||
函数 predict 是单线程方法，通过调用 update_ui 将交互过程中 MOSS 的回复实时更新到UI（User Interface）中，并执行一个 named function（additional_fn）指定的函数对输入进行预处理。
|
||||
|
||||
## [41/48] 请对下面的程序文件做一个概述: request_llm\bridge_newbing.py
|
||||
## [41/48] 请对下面的程序文件做一个概述: request_llms\bridge_newbing.py
|
||||
|
||||
这是一个名为`bridge_newbing.py`的程序文件，包含三个部分：
|
||||
|
||||
@ -276,11 +276,11 @@ GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个
|
||||
|
||||
第三部分定义了一个名为`newbing_handle`的全局变量，并导出了`predict_no_ui_long_connection`和`predict`这两个方法，以供其他程序可以调用。
|
||||
|
||||
## [42/48] 请对下面的程序文件做一个概述: request_llm\bridge_newbingfree.py
|
||||
## [42/48] 请对下面的程序文件做一个概述: request_llms\bridge_newbingfree.py
|
||||
|
||||
这个Python文件包含了三部分内容。第一部分是来自edge_gpt_free.py文件的聊天机器人程序。第二部分是子进程Worker，用于调用主体。第三部分提供了两个函数：predict_no_ui_long_connection和predict用于调用NewBing聊天机器人和返回响应。其中predict函数还提供了一些参数用于控制聊天机器人的回复和更新UI界面。
|
||||
|
||||
## [43/48] 请对下面的程序文件做一个概述: request_llm\bridge_stackclaude.py
|
||||
## [43/48] 请对下面的程序文件做一个概述: request_llms\bridge_stackclaude.py
|
||||
|
||||
这是一个Python源代码文件，文件名为request_llm\bridge_stackclaude.py。代码分为三个主要部分：
|
||||
|
||||
@ -290,21 +290,21 @@ GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个
|
||||
|
||||
第三部分定义了predict_no_ui_long_connection和predict两个函数，主要用于通过调用ClaudeHandle对象的stream_chat方法来获取Claude的回复，并更新ui以显示相关信息。其中predict函数采用单线程方法，而predict_no_ui_long_connection函数使用多线程方法。
|
||||
|
||||
## [44/48] 请对下面的程序文件做一个概述: request_llm\bridge_tgui.py
|
||||
## [44/48] 请对下面的程序文件做一个概述: request_llms\bridge_tgui.py
|
||||
|
||||
该文件是一个Python代码文件，名为request_llm\bridge_tgui.py。它包含了一些函数用于与chatbot UI交互，并通过WebSocket协议与远程LLM模型通信完成文本生成任务，其中最重要的函数是predict()和predict_no_ui_long_connection()。这个程序还有其他的辅助函数，如random_hash()。整个代码文件在协作的基础上完成了一次修改。
|
||||
|
||||
## [45/48] 请对下面的程序文件做一个概述: request_llm\edge_gpt.py
|
||||
## [45/48] 请对下面的程序文件做一个概述: request_llms\edge_gpt.py
|
||||
|
||||
该文件是一个用于调用Bing chatbot API的Python程序，它由多个类和辅助函数构成，可以根据给定的对话连接在对话中提出问题，使用websocket与远程服务通信。程序实现了一个聊天机器人，可以为用户提供人工智能聊天。
|
||||
|
||||
## [46/48] 请对下面的程序文件做一个概述: request_llm\edge_gpt_free.py
|
||||
## [46/48] 请对下面的程序文件做一个概述: request_llms\edge_gpt_free.py
|
||||
|
||||
该代码文件为一个会话API，可通过Chathub发送消息以返回响应。其中使用了 aiohttp 和 httpx 库进行网络请求并发送。代码中包含了一些函数和常量，多数用于生成请求数据或是请求头信息等。同时该代码文件还包含了一个 Conversation 类，调用该类可实现对话交互。
|
||||
|
||||
## [47/48] 请对下面的程序文件做一个概述: request_llm\test_llms.py
|
||||
## [47/48] 请对下面的程序文件做一个概述: request_llms\test_llms.py
|
||||
|
||||
这个文件是用于对llm模型进行单元测试的Python程序。程序导入一个名为"request_llm.bridge_newbingfree"的模块，然后三次使用该模块中的predict_no_ui_long_connection()函数进行预测，并输出结果。此外，还有一些注释掉的代码段，这些代码段也是关于模型预测的。
|
||||
这个文件是用于对llm模型进行单元测试的Python程序。程序导入一个名为"request_llms.bridge_newbingfree"的模块，然后三次使用该模块中的predict_no_ui_long_connection()函数进行预测，并输出结果。此外，还有一些注释掉的代码段，这些代码段也是关于模型预测的。
|
||||
|
||||
## 用一张Markdown表格简要描述以下文件的功能：
|
||||
check_proxy.py, colorful.py, config.py, config_private.py, core_functional.py, crazy_functional.py, main.py, multi_language.py, theme.py, toolbox.py, crazy_functions\crazy_functions_test.py, crazy_functions\crazy_utils.py, crazy_functions\Latex全文润色.py, crazy_functions\Latex全文翻译.py, crazy_functions\__init__.py, crazy_functions\下载arxiv论文翻译摘要.py。根据以上分析，用一句话概括程序的整体功能。
|
||||
@ -355,24 +355,24 @@ crazy_functions\代码重写为全英文_多线程.py, crazy_functions\图片生
|
||||
概括程序的整体功能：提供了一系列处理文本、文件和代码的功能，使用了各类语言模型、多线程、网络请求和数据解析技术来提高效率和精度。
|
||||
|
||||
## 用一张Markdown表格简要描述以下文件的功能：
|
||||
crazy_functions\谷歌检索小助手.py, crazy_functions\高级功能函数模板.py, request_llm\bridge_all.py, request_llm\bridge_chatglm.py, request_llm\bridge_chatgpt.py, request_llm\bridge_jittorllms_llama.py, request_llm\bridge_jittorllms_pangualpha.py, request_llm\bridge_jittorllms_rwkv.py, request_llm\bridge_moss.py, request_llm\bridge_newbing.py, request_llm\bridge_newbingfree.py, request_llm\bridge_stackclaude.py, request_llm\bridge_tgui.py, request_llm\edge_gpt.py, request_llm\edge_gpt_free.py, request_llm\test_llms.py。根据以上分析，用一句话概括程序的整体功能。
|
||||
crazy_functions\谷歌检索小助手.py, crazy_functions\高级功能函数模板.py, request_llms\bridge_all.py, request_llms\bridge_chatglm.py, request_llms\bridge_chatgpt.py, request_llms\bridge_jittorllms_llama.py, request_llms\bridge_jittorllms_pangualpha.py, request_llms\bridge_jittorllms_rwkv.py, request_llms\bridge_moss.py, request_llms\bridge_newbing.py, request_llms\bridge_newbingfree.py, request_llms\bridge_stackclaude.py, request_llms\bridge_tgui.py, request_llms\edge_gpt.py, request_llms\edge_gpt_free.py, request_llms\test_llms.py。根据以上分析，用一句话概括程序的整体功能。
|
||||
|
||||
| 文件名 | 功能描述 |
|
||||
| --- | --- |
|
||||
| crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 |
|
||||
| crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 |
|
||||
| request_llm\bridge_all.py | 基于不同LLM模型进行对话。 |
|
||||
| request_llm\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
|
||||
| request_llm\bridge_chatgpt.py | 基于GPT模型完成对话。 |
|
||||
| request_llm\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
|
||||
| request_llm\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
|
||||
| request_llm\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
|
||||
| request_llm\bridge_moss.py | 加载Moss模型完成对话功能。 |
|
||||
| request_llm\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
|
||||
| request_llm\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
|
||||
| request_llm\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
|
||||
| request_llm\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
|
||||
| request_llm\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
|
||||
| request_llm\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
|
||||
| request_llm\test_llms.py | 对llm模型进行单元测试。 |
|
||||
| request_llms\bridge_all.py | 基于不同LLM模型进行对话。 |
|
||||
| request_llms\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
|
||||
| request_llms\bridge_chatgpt.py | 基于GPT模型完成对话。 |
|
||||
| request_llms\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
|
||||
| request_llms\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
|
||||
| request_llms\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
|
||||
| request_llms\bridge_moss.py | 加载Moss模型完成对话功能。 |
|
||||
| request_llms\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
|
||||
| request_llms\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
|
||||
| request_llms\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
|
||||
| request_llms\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
|
||||
| request_llms\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
|
||||
| request_llms\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
|
||||
| request_llms\test_llms.py | 对llm模型进行单元测试。 |
|
||||
| 程序整体功能 | 实现不同种类的聊天机器人，可以根据输入进行文本生成。 |
|
||||
|
||||
@ -1184,7 +1184,7 @@
|
||||
"Call ChatGLM fail 不能正常加载ChatGLM的参数": "Call ChatGLM fail, unable to load parameters for ChatGLM",
|
||||
"不能正常加载ChatGLM的参数！": "Unable to load parameters for ChatGLM!",
|
||||
"多线程方法": "Multithreading method",
|
||||
"函数的说明请见 request_llm/bridge_all.py": "For function details, please see request_llm/bridge_all.py",
|
||||
"函数的说明请见 request_llms/bridge_all.py": "For function details, please see request_llms/bridge_all.py",
|
||||
"程序终止": "Program terminated",
|
||||
"单线程方法": "Single-threaded method",
|
||||
"等待ChatGLM响应中": "Waiting for response from ChatGLM",
|
||||
@ -1543,7 +1543,7 @@
|
||||
"str类型": "str type",
|
||||
"所有音频都总结完成了吗": "Are all audio summaries completed?",
|
||||
"SummaryAudioVideo内容": "SummaryAudioVideo content",
|
||||
"使用教程详情见 request_llm/README.md": "See request_llm/README.md for detailed usage instructions",
|
||||
"使用教程详情见 request_llms/README.md": "See request_llms/README.md for detailed usage instructions",
|
||||
"删除中间文件夹": "Delete intermediate folder",
|
||||
"Claude组件初始化成功": "Claude component initialized successfully",
|
||||
"$c$ 是光速": "$c$ is the speed of light",
|
||||
|
||||
@ -782,7 +782,7 @@
|
||||
"主进程统一调用函数接口": "メインプロセスが関数インターフェースを統一的に呼び出します",
|
||||
"再例如一个包含了待处理文件的路径": "処理待ちのファイルを含むパスの例",
|
||||
"负责把学术论文准确翻译成中文": "学術論文を正確に中国語に翻訳する責任があります",
|
||||
"函数的说明请见 request_llm/bridge_all.py": "関数の説明については、request_llm/bridge_all.pyを参照してください",
|
||||
"函数的说明请见 request_llms/bridge_all.py": "関数の説明については、request_llms/bridge_all.pyを参照してください",
|
||||
"然后回车提交": "そしてEnterを押して提出してください",
|
||||
"防止爆token": "トークンの爆発を防止する",
|
||||
"Latex项目全文中译英": "LaTeXプロジェクト全文の中国語から英語への翻訳",
|
||||
@ -1616,7 +1616,7 @@
|
||||
"正在重试": "再試行中",
|
||||
"从而更全面地理解项目的整体功能": "プロジェクトの全体的な機能をより理解するために",
|
||||
"正在等您说完问题": "質問が完了するのをお待ちしています",
|
||||
"使用教程详情见 request_llm/README.md": "使用方法の詳細については、request_llm/README.mdを参照してください",
|
||||
"使用教程详情见 request_llms/README.md": "使用方法の詳細については、request_llms/README.mdを参照してください",
|
||||
"6.25 加入判定latex模板的代码": "6.25 テンプレートの判定コードを追加",
|
||||
"找不到任何音频或视频文件": "音声またはビデオファイルが見つかりません",
|
||||
"请求GPT模型的": "GPTモデルのリクエスト",
|
||||
|
||||
@ -123,7 +123,7 @@
|
||||
"的第": "的第",
|
||||
"减少重复": "減少重複",
|
||||
"如果超过期限没有喂狗": "如果超過期限沒有餵狗",
|
||||
"函数的说明请见 request_llm/bridge_all.py": "函數的說明請見 request_llm/bridge_all.py",
|
||||
"函数的说明请见 request_llms/bridge_all.py": "函數的說明請見 request_llms/bridge_all.py",
|
||||
"第7步": "第7步",
|
||||
"说": "說",
|
||||
"中途接收可能的终止指令": "中途接收可能的終止指令",
|
||||
@ -1887,7 +1887,7 @@
|
||||
"请继续分析其他源代码": "請繼續分析其他源代碼",
|
||||
"质能方程式": "質能方程式",
|
||||
"功能尚不稳定": "功能尚不穩定",
|
||||
"使用教程详情见 request_llm/README.md": "使用教程詳情見 request_llm/README.md",
|
||||
"使用教程详情见 request_llms/README.md": "使用教程詳情見 request_llms/README.md",
|
||||
"从以上搜索结果中抽取信息": "從以上搜索結果中抽取信息",
|
||||
"虽然PDF生成失败了": "雖然PDF生成失敗了",
|
||||
"找图片": "尋找圖片",
|
||||
|
||||
2
main.py
2
main.py
@ -7,7 +7,7 @@ def main():
|
||||
import gradio as gr
|
||||
if gr.__version__ not in ['3.32.6']:
|
||||
raise ModuleNotFoundError("使用项目内置Gradio获取最优体验! 请运行 `pip install -r requirements.txt` 指令安装内置Gradio及其他依赖, 详情信息见requirements.txt.")
|
||||
from request_llm.bridge_all import predict
|
||||
from request_llms.bridge_all import predict
|
||||
from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, DummyWith
|
||||
# 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
|
||||
proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION = get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION')
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
|
||||
## ChatGLM
|
||||
|
||||
- 安装依赖 `pip install -r request_llm/requirements_chatglm.txt`
|
||||
- 安装依赖 `pip install -r request_llms/requirements_chatglm.txt`
|
||||
- 修改配置，在config.py中将LLM_MODEL的值改为"chatglm"
|
||||
|
||||
``` sh
|
||||
@ -27,7 +27,7 @@ class GetGLMHandle(Process):
|
||||
self.info = "依赖检测通过"
|
||||
self.success = True
|
||||
except:
|
||||
self.info = "缺少ChatGLM的依赖，如果要使用ChatGLM，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_chatglm.txt`安装ChatGLM的依赖。"
|
||||
self.info = "缺少ChatGLM的依赖，如果要使用ChatGLM，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_chatglm.txt`安装ChatGLM的依赖。"
|
||||
self.success = False
|
||||
|
||||
def ready(self):
|
||||
@ -100,7 +100,7 @@ glm_handle = None
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
|
||||
"""
|
||||
多线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
global glm_handle
|
||||
if glm_handle is None:
|
||||
@ -131,7 +131,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
单线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
chatbot.append((inputs, ""))
|
||||
|
||||
@ -44,7 +44,7 @@ class GetGLMFTHandle(Process):
|
||||
self.info = "依赖检测通过"
|
||||
self.success = True
|
||||
except:
|
||||
self.info = "缺少ChatGLMFT的依赖，如果要使用ChatGLMFT，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_chatglm.txt`安装ChatGLM的依赖。"
|
||||
self.info = "缺少ChatGLMFT的依赖，如果要使用ChatGLMFT，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_chatglm.txt`安装ChatGLM的依赖。"
|
||||
self.success = False
|
||||
|
||||
def ready(self):
|
||||
@ -59,7 +59,7 @@ class GetGLMFTHandle(Process):
|
||||
if self.chatglmft_model is None:
|
||||
from transformers import AutoConfig
|
||||
import torch
|
||||
# conf = 'request_llm/current_ptune_model.json'
|
||||
# conf = 'request_llms/current_ptune_model.json'
|
||||
# if not os.path.exists(conf): raise RuntimeError('找不到微调模型信息')
|
||||
# with open(conf, 'r', encoding='utf8') as f:
|
||||
# model_args = json.loads(f.read())
|
||||
@ -140,7 +140,7 @@ glmft_handle = None
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
|
||||
"""
|
||||
多线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
global glmft_handle
|
||||
if glmft_handle is None:
|
||||
@ -171,7 +171,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
单线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
chatbot.append((inputs, ""))
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
model_name = "ChatGLM-ONNX"
|
||||
cmd_to_install = "`pip install -r request_llm/requirements_chatglm_onnx.txt`"
|
||||
cmd_to_install = "`pip install -r request_llms/requirements_chatglm_onnx.txt`"
|
||||
|
||||
|
||||
from transformers import AutoModel, AutoTokenizer
|
||||
@ -28,13 +28,13 @@ class GetONNXGLMHandle(LocalLLMHandle):
|
||||
def load_model_and_tokenizer(self):
|
||||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
||||
import os, glob
|
||||
if not len(glob.glob("./request_llm/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/*.bin")) >= 7: # 该模型有七个 bin 文件
|
||||
if not len(glob.glob("./request_llms/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/*.bin")) >= 7: # 该模型有七个 bin 文件
|
||||
from huggingface_hub import snapshot_download
|
||||
snapshot_download(repo_id="K024/ChatGLM-6b-onnx-u8s8", local_dir="./request_llm/ChatGLM-6b-onnx-u8s8")
|
||||
snapshot_download(repo_id="K024/ChatGLM-6b-onnx-u8s8", local_dir="./request_llms/ChatGLM-6b-onnx-u8s8")
|
||||
def create_model():
|
||||
return ChatGLMModel(
|
||||
tokenizer_path = "./request_llm/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/sentencepiece.model",
|
||||
onnx_model_path = "./request_llm/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/chatglm-6b-int8.onnx"
|
||||
tokenizer_path = "./request_llms/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/sentencepiece.model",
|
||||
onnx_model_path = "./request_llms/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/chatglm-6b-int8.onnx"
|
||||
)
|
||||
self._model = create_model()
|
||||
return self._model, None
|
||||
@ -1,5 +1,5 @@
|
||||
model_name = "InternLM"
|
||||
cmd_to_install = "`pip install -r request_llm/requirements_chatglm.txt`"
|
||||
cmd_to_install = "`pip install -r request_llms/requirements_chatglm.txt`"
|
||||
|
||||
from transformers import AutoModel, AutoTokenizer
|
||||
import time
|
||||
@ -28,8 +28,8 @@ class GetGLMHandle(Process):
|
||||
self.success = True
|
||||
except:
|
||||
from toolbox import trimmed_format_exc
|
||||
self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
|
||||
r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
|
||||
self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
|
||||
r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llms/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
|
||||
r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc()
|
||||
self.success = False
|
||||
|
||||
@ -45,8 +45,8 @@ class GetGLMHandle(Process):
|
||||
env = os.environ.get("PATH", "")
|
||||
os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin')
|
||||
root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..')
|
||||
os.chdir(root_dir_assume + '/request_llm/jittorllms')
|
||||
sys.path.append(root_dir_assume + '/request_llm/jittorllms')
|
||||
os.chdir(root_dir_assume + '/request_llms/jittorllms')
|
||||
sys.path.append(root_dir_assume + '/request_llms/jittorllms')
|
||||
validate_path() # validate path so you can run from base directory
|
||||
|
||||
def load_model():
|
||||
@ -109,7 +109,7 @@ llama_glm_handle = None
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
|
||||
"""
|
||||
多线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
global llama_glm_handle
|
||||
if llama_glm_handle is None:
|
||||
@ -140,7 +140,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
单线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
chatbot.append((inputs, ""))
|
||||
|
||||
@ -28,8 +28,8 @@ class GetGLMHandle(Process):
|
||||
self.success = True
|
||||
except:
|
||||
from toolbox import trimmed_format_exc
|
||||
self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
|
||||
r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
|
||||
self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
|
||||
r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llms/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
|
||||
r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc()
|
||||
self.success = False
|
||||
|
||||
@ -45,8 +45,8 @@ class GetGLMHandle(Process):
|
||||
env = os.environ.get("PATH", "")
|
||||
os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin')
|
||||
root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..')
|
||||
os.chdir(root_dir_assume + '/request_llm/jittorllms')
|
||||
sys.path.append(root_dir_assume + '/request_llm/jittorllms')
|
||||
os.chdir(root_dir_assume + '/request_llms/jittorllms')
|
||||
sys.path.append(root_dir_assume + '/request_llms/jittorllms')
|
||||
validate_path() # validate path so you can run from base directory
|
||||
|
||||
def load_model():
|
||||
@ -109,7 +109,7 @@ pangu_glm_handle = None
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
|
||||
"""
|
||||
多线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
global pangu_glm_handle
|
||||
if pangu_glm_handle is None:
|
||||
@ -140,7 +140,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
单线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
chatbot.append((inputs, ""))
|
||||
|
||||
@ -28,8 +28,8 @@ class GetGLMHandle(Process):
|
||||
self.success = True
|
||||
except:
|
||||
from toolbox import trimmed_format_exc
|
||||
self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
|
||||
r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
|
||||
self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
|
||||
r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llms/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
|
||||
r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc()
|
||||
self.success = False
|
||||
|
||||
@ -45,8 +45,8 @@ class GetGLMHandle(Process):
|
||||
env = os.environ.get("PATH", "")
|
||||
os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin')
|
||||
root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..')
|
||||
os.chdir(root_dir_assume + '/request_llm/jittorllms')
|
||||
sys.path.append(root_dir_assume + '/request_llm/jittorllms')
|
||||
os.chdir(root_dir_assume + '/request_llms/jittorllms')
|
||||
sys.path.append(root_dir_assume + '/request_llms/jittorllms')
|
||||
validate_path() # validate path so you can run from base directory
|
||||
|
||||
def load_model():
|
||||
@ -109,7 +109,7 @@ rwkv_glm_handle = None
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
|
||||
"""
|
||||
多线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
global rwkv_glm_handle
|
||||
if rwkv_glm_handle is None:
|
||||
@ -140,7 +140,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
单线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
chatbot.append((inputs, ""))
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
model_name = "LLaMA"
|
||||
cmd_to_install = "`pip install -r request_llm/requirements_chatglm.txt`"
|
||||
cmd_to_install = "`pip install -r request_llms/requirements_chatglm.txt`"
|
||||
|
||||
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
|
||||
@ -24,12 +24,12 @@ class GetGLMHandle(Process):
|
||||
def check_dependency(self): # 主进程执行
|
||||
try:
|
||||
import datasets, os
|
||||
assert os.path.exists('request_llm/moss/models')
|
||||
assert os.path.exists('request_llms/moss/models')
|
||||
self.info = "依赖检测通过"
|
||||
self.success = True
|
||||
except:
|
||||
self.info = """
|
||||
缺少MOSS的依赖，如果要使用MOSS，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_moss.txt`和`git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss`安装MOSS的依赖。
|
||||
缺少MOSS的依赖，如果要使用MOSS，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_moss.txt`和`git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss`安装MOSS的依赖。
|
||||
"""
|
||||
self.success = False
|
||||
return self.success
|
||||
@ -110,8 +110,8 @@ class GetGLMHandle(Process):
|
||||
def validate_path():
|
||||
import os, sys
|
||||
root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..')
|
||||
os.chdir(root_dir_assume + '/request_llm/moss')
|
||||
sys.path.append(root_dir_assume + '/request_llm/moss')
|
||||
os.chdir(root_dir_assume + '/request_llms/moss')
|
||||
sys.path.append(root_dir_assume + '/request_llms/moss')
|
||||
validate_path() # validate path so you can run from base directory
|
||||
|
||||
try:
|
||||
@ -176,7 +176,7 @@ moss_handle = None
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
|
||||
"""
|
||||
多线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
global moss_handle
|
||||
if moss_handle is None:
|
||||
@ -206,7 +206,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
单线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
chatbot.append((inputs, ""))
|
||||
|
||||
@ -54,7 +54,7 @@ class NewBingHandle(Process):
|
||||
self.info = "依赖检测通过，等待NewBing响应。注意目前不能多人同时调用NewBing接口（有线程锁），否则将导致每个人的NewBing问询历史互相渗透。调用NewBing时，会自动使用已配置的代理。"
|
||||
self.success = True
|
||||
except:
|
||||
self.info = "缺少的依赖，如果要使用Newbing，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_newbing.txt`安装Newbing的依赖。"
|
||||
self.info = "缺少的依赖，如果要使用Newbing，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_newbing.txt`安装Newbing的依赖。"
|
||||
self.success = False
|
||||
|
||||
def ready(self):
|
||||
@ -63,7 +63,7 @@ class NewBingHandle(Process):
|
||||
async def async_run(self):
|
||||
# 读取配置
|
||||
NEWBING_STYLE, = get_conf('NEWBING_STYLE')
|
||||
from request_llm.bridge_all import model_info
|
||||
from request_llms.bridge_all import model_info
|
||||
endpoint = model_info['newbing']['endpoint']
|
||||
while True:
|
||||
# 等待
|
||||
@ -181,7 +181,7 @@ newbingfree_handle = None
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
|
||||
"""
|
||||
多线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
global newbingfree_handle
|
||||
if (newbingfree_handle is None) or (not newbingfree_handle.success):
|
||||
@ -210,7 +210,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
单线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
chatbot.append((inputs, "[Local Message]: 等待NewBing响应中 ..."))
|
||||
|
||||
@ -119,7 +119,7 @@ def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
|
||||
"""
|
||||
⭐多线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
watch_dog_patience = 5
|
||||
response = ""
|
||||
@ -134,7 +134,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
⭐单线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
chatbot.append((inputs, ""))
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
model_name = "Qwen"
|
||||
cmd_to_install = "`pip install -r request_llm/requirements_qwen.txt`"
|
||||
cmd_to_install = "`pip install -r request_llms/requirements_qwen.txt`"
|
||||
|
||||
|
||||
from transformers import AutoModel, AutoTokenizer
|
||||
@ -16,7 +16,7 @@ def validate_key():
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
|
||||
"""
|
||||
⭐多线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
watch_dog_patience = 5
|
||||
response = ""
|
||||
@ -36,7 +36,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
⭐单线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
chatbot.append((inputs, ""))
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
@ -99,7 +99,7 @@ class ClaudeHandle(Process):
|
||||
self.info = "依赖检测通过，等待Claude响应。注意目前不能多人同时调用Claude接口（有线程锁），否则将导致每个人的Claude问询历史互相渗透。调用Claude时，会自动使用已配置的代理。"
|
||||
self.success = True
|
||||
except:
|
||||
self.info = "缺少的依赖，如果要使用Claude，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_slackclaude.txt`安装Claude的依赖，然后重启程序。"
|
||||
self.info = "缺少的依赖，如果要使用Claude，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_slackclaude.txt`安装Claude的依赖，然后重启程序。"
|
||||
self.success = False
|
||||
|
||||
def ready(self):
|
||||
@ -204,7 +204,7 @@ claude_handle = None
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
|
||||
"""
|
||||
多线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
global claude_handle
|
||||
if (claude_handle is None) or (not claude_handle.success):
|
||||
@ -234,7 +234,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
|
||||
"""
|
||||
单线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
chatbot.append((inputs, "[Local Message]: 等待Claude响应中 ..."))
|
||||
|
||||
@ -120,7 +120,7 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name):
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
|
||||
"""
|
||||
⭐多线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
_llm_handle = LLMSingletonClass()
|
||||
if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + _llm_handle.info
|
||||
@ -146,7 +146,7 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name):
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
⭐单线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
函数的说明请见 request_llms/bridge_all.py
|
||||
"""
|
||||
chatbot.append((inputs, ""))
|
||||
|
||||
@ -10,14 +10,14 @@ def validate_path():
|
||||
|
||||
validate_path() # validate path so you can run from base directory
|
||||
if __name__ == "__main__":
|
||||
# from request_llm.bridge_newbingfree import predict_no_ui_long_connection
|
||||
# from request_llm.bridge_moss import predict_no_ui_long_connection
|
||||
# from request_llm.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
|
||||
# from request_llm.bridge_jittorllms_llama import predict_no_ui_long_connection
|
||||
# from request_llm.bridge_claude import predict_no_ui_long_connection
|
||||
# from request_llm.bridge_internlm import predict_no_ui_long_connection
|
||||
# from request_llm.bridge_qwen import predict_no_ui_long_connection
|
||||
from request_llm.bridge_spark import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_newbingfree import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_moss import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_jittorllms_llama import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_claude import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_internlm import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_qwen import predict_no_ui_long_connection
|
||||
from request_llms.bridge_spark import predict_no_ui_long_connection
|
||||
|
||||
llm_kwargs = {
|
||||
'max_length': 4096,
|
||||
|
||||
@ -878,7 +878,7 @@ def clip_history(inputs, history, tokenizer, max_token_limit):
|
||||
直到历史记录的标记数量降低到阈值以下。
|
||||
"""
|
||||
import numpy as np
|
||||
from request_llm.bridge_all import model_info
|
||||
from request_llms.bridge_all import model_info
|
||||
def get_token_num(txt):
|
||||
return len(tokenizer.encode(txt, disallowed_special=()))
|
||||
input_token_num = get_token_num(inputs)
|
||||
@ -1069,7 +1069,7 @@ def get_plugin_handle(plugin_name):
|
||||
def get_chat_handle():
|
||||
"""
|
||||
"""
|
||||
from request_llm.bridge_all import predict_no_ui_long_connection
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
return predict_no_ui_long_connection
|
||||
|
||||
def get_plugin_default_kwargs():
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user