Spaces:
Sleeping
Sleeping
File size: 6,419 Bytes
a9f41ef 766da4e a9f41ef eaa6479 a9f41ef 51eb4fe eaa6479 a9f41ef af56287 eaa6479 e53e27f a9f41ef d2abf56 8aee720 1638e6e 8aee720 d2abf56 184f581 a73eb90 bf8a3c8 184f581 a9f41ef 0835937 a9f41ef d64cbaa a9f41ef 184f581 a9f41ef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 |
import gradio as gr
import copy
import random
import os
import requests
import time
import sys
os.system("pip install --upgrade pip")
os.system('''CMAKE_ARGS="-DLLAMA_AVX512=ON -DLLAMA_AVX512_VBMI=ON -DLLAMA_AVX512_VNNI=ON -DLLAMA_FP16_VA=ON -DLLAMA_WASM_SIMD=ON" pip install llama-cpp-python''')
from huggingface_hub import snapshot_download
from llama_cpp import Llama
SYSTEM_PROMPT = '''You are a helpful, respectful and honest INTP-T AI Assistant named "Shi-Ci" in English or "兮辞" in Chinese.
You are good at speaking English and Chinese.
You are talking to a human User. If the question is meaningless, please explain the reason and don't share false information.
You are based on SLIDE model, trained by "SSFW NLPark" team, not related to GPT, LLaMA, Meta, Mistral or OpenAI.
Let's work this out in a step by step way to be sure we have the right answer.\n\n'''
SYSTEM_TOKEN = 1587
USER_TOKEN = 2188
BOT_TOKEN = 12435
LINEBREAK_TOKEN = 13
ROLE_TOKENS = {
"user": USER_TOKEN,
"bot": BOT_TOKEN,
"system": SYSTEM_TOKEN
}
def get_message_tokens(model, role, content):
message_tokens = model.tokenize(content.encode("utf-8"))
message_tokens.insert(1, ROLE_TOKENS[role])
message_tokens.insert(2, LINEBREAK_TOKEN)
message_tokens.append(model.token_eos())
return message_tokens
def get_system_tokens(model):
system_message = {"role": "system", "content": SYSTEM_PROMPT}
return get_message_tokens(model, **system_message)
repo_name = "TheBloke/openbuddy-mistral-7B-v13.1-GGUF"
model_name = "openbuddy-mistral-7b-v13.1.Q4_K_S.gguf"
snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)
model = Llama(
model_path=model_name,
n_ctx=2000,
n_parts=1,
)
max_new_tokens = 1500
def user(message, history):
new_history = history + [[message, None]]
return "", new_history
def bot(
history,
system_prompt,
top_p,
top_k,
temp
):
tokens = get_system_tokens(model)[:]
tokens.append(LINEBREAK_TOKEN)
for user_message, bot_message in history[:-1]:
message_tokens = get_message_tokens(model=model, role="user", content=user_message)
tokens.extend(message_tokens)
if bot_message:
message_tokens = get_message_tokens(model=model, role="bot", content=bot_message)
tokens.extend(message_tokens)
last_user_message = history[-1][0]
message_tokens = get_message_tokens(model=model, role="user", content=last_user_message)
tokens.extend(message_tokens)
role_tokens = [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN]
tokens.extend(role_tokens)
generator = model.generate(
tokens,
top_k=top_k,
top_p=top_p,
temp=temp
)
partial_text = ""
for i, token in enumerate(generator):
if token == model.token_eos() or (max_new_tokens is not None and i >= max_new_tokens):
break
partial_text += model.detokenize([token]).decode("utf-8", "ignore")
history[-1][1] = partial_text
yield history
with gr.Blocks(
theme=gr.themes.Soft()
) as demo:
gr.Markdown(f"""<h1><center>上师附外-兮辞·析辞-人工智能助理</center></h1>""")
gr.Markdown(value="""欢迎使用!
这里是一个ChatBot。这是量化版兮辞·析辞的部署,具有 70亿 个参数,正在 CPU 上运行。
SLIDE/兮辞 是一种会话语言模型,由 上师附外 NLPark 团队 在多种类型的语料库上进行训练。
本节目由 JWorld & 上海师范大学附属外国语中学 NLPark 赞助播出""")
with gr.Row():
with gr.Column(scale=5):
chatbot = gr.Chatbot(label="兮辞如是说").style(height=400)
with gr.Row():
with gr.Column():
msg = gr.Textbox(
label="来问问兮辞吧……",
placeholder="兮辞折寿中……",
show_label=True,
).style(container=True)
submit = gr.Button("Submit / 开凹!")
stop = gr.Button("Stop / 全局时空断裂")
clear = gr.Button("Clear / 打扫群内垃圾")
with gr.Row():
with gr.Column(min_width=80, scale=1):
with gr.Tab(label="设置参数"):
top_p = gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.9,
step=0.05,
interactive=True,
label="Top-p",
)
top_k = gr.Slider(
minimum=10,
maximum=100,
value=30,
step=5,
interactive=True,
label="Top-k",
)
temp = gr.Slider(
minimum=0.0,
maximum=2.0,
value=0.2,
step=0.01,
interactive=True,
label="情感温度"
)
with gr.Column():
system_prompt = gr.Textbox(label="系统提示词", placeholder="", value=SYSTEM_PROMPT, interactive=False)
with gr.Row():
gr.Markdown(
"""警告:该模型可能会生成事实上或道德上不正确的文本。NLPark和兮辞对此不承担任何责任。"""
)
# Pressing Enter
submit_event = msg.submit(
fn=user,
inputs=[msg, chatbot],
outputs=[msg, chatbot],
queue=False,
).success(
fn=bot,
inputs=[
chatbot,
system_prompt,
top_p,
top_k,
temp
],
outputs=chatbot,
queue=True,
)
# Pressing the button
submit_click_event = submit.click(
fn=user,
inputs=[msg, chatbot],
outputs=[msg, chatbot],
queue=False,
).success(
fn=bot,
inputs=[
chatbot,
system_prompt,
top_p,
top_k,
temp
],
outputs=chatbot,
queue=True,
)
# Stop generation
stop.click(
fn=None,
inputs=None,
outputs=None,
cancels=[submit_event, submit_click_event],
queue=False,
)
# Clear history
clear.click(lambda: None, None, chatbot, queue=False)
demo.queue(max_size=128, concurrency_count=1)
demo.launch() |