File size: 5,601 Bytes
7f9f96d
 
 
 
 
e4f695b
7f9f96d
e4f695b
7f9f96d
 
 
 
 
 
 
e4f695b
7f9f96d
 
e4f695b
7f9f96d
 
 
e4f695b
7f9f96d
e4f695b
 
 
 
 
 
7f9f96d
 
e4f695b
 
 
 
 
 
 
7f9f96d
 
e4f695b
 
 
 
 
 
7f9f96d
e4f695b
 
 
 
3360664
e4f695b
 
7f9f96d
 
3360664
e4f695b
 
 
 
7f9f96d
e4f695b
 
 
 
 
 
 
 
7f9f96d
e4f695b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7f9f96d
e4f695b
 
 
 
 
 
7f9f96d
e4f695b
 
 
 
 
 
7f9f96d
e4f695b
 
 
 
 
 
7f9f96d
 
e4f695b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7f9f96d
 
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
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
from typing import Iterator

import gradio as gr
import torch

from model import run

DEFAULT_SYSTEM_PROMPT = ""
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = 4000
DESCRIPTION = """
# Baichuan2-13B-Chat
Baichuan 2 is the new generation of open-source large language models launched by Baichuan Intelligent Technology. It was trained on a high-quality corpus with 2.6 trillion tokens.
"""
LICENSE = ""

if not torch.cuda.is_available():
  DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>'


def clear_and_save_textbox(message: str) -> tuple[str, str]:
  return '', message

def display_input(
  message: str,
  history: list[tuple[str, str]]
) -> list[tuple[str, str]]:
  history.append((message, ''))
  return history

def delete_prev_fn(
  history: list[tuple[str, str]]
) -> tuple[list[tuple[str, str]], str]:
  try:
    message, _ = history.pop()
  except IndexError:
    message = ''
  return history, message or ''

def generate(
  message: str,
  history_with_input: list[tuple[str, str]],
  max_new_tokens: int,
  temperature: float,
  top_p: float,
  top_k: int,
) -> Iterator[list[tuple[str, str]]]:
  if max_new_tokens > MAX_MAX_NEW_TOKENS:
    raise ValueError

  history = history_with_input[:-1]
  generator = run(message, history, max_new_tokens, temperature, top_p, top_k)
  for response in generator:
    yield history + [(message, response)]

def process_example(message: str) -> tuple[str, list[tuple[str, str]]]:
  generator = generate(message, [], DEFAULT_MAX_NEW_TOKENS, 1, 0.95, 5)
  for x in generator:
    pass
  return '', x

with gr.Blocks(css='style.css') as demo:
  gr.Markdown(DESCRIPTION)
  gr.DuplicateButton(
    value='Duplicate Space for private use',
    elem_id='duplicate-button'
  )

  with gr.Group():
    chatbot = gr.Chatbot(label='Chatbot')
    with gr.Row():
      textbox = gr.Textbox(
        container=False,
        show_label=False,
        placeholder='Type a message...',
        scale=10,
      )
      submit_button = gr.Button(
        'Submit',
        variant='primary',
        scale=1,
        min_width=0
      )

  with gr.Row():
    retry_button = gr.Button('🔄  Retry', variant='secondary')
    undo_button = gr.Button('↩️ Undo', variant='secondary')
    clear_button = gr.Button('🗑️  Clear', variant='secondary')

  saved_input = gr.State()

  with gr.Accordion(label='Advanced options', open=False):
    max_new_tokens = gr.Slider(
      label='Max new tokens',
      minimum=1,
      maximum=MAX_MAX_NEW_TOKENS,
      step=1,
      value=DEFAULT_MAX_NEW_TOKENS,
    )
    temperature = gr.Slider(
      label='Temperature',
      minimum=0.1,
      maximum=4.0,
      step=0.1,
      value=1.0,
    )
    top_p = gr.Slider(
      label='Top-p (nucleus sampling)',
      minimum=0.05,
      maximum=1.0,
      step=0.05,
      value=0.95,
    )
    top_k = gr.Slider(
      label='Top-k',
      minimum=1,
      maximum=1000,
      step=1,
      value=5,
    )

  gr.Examples(
    examples=[
      '介绍下你自己',
      '找到下列数组的中位数[3.1,6.2,1.3,8.4,10.5,11.6,2.1],请用python代码完成以上功能',
      '鸡和兔在一个笼子里,共有26个头,68只脚,那么鸡有多少只,兔有多少只?',
      '以下物理常识题目,哪一个是错误的?A.在自然环境下,声音在固体中传播速度最快。B.牛顿第一定律:一个物体如果不受力作用,将保持静止或匀速直线运动的状态。C.牛顿第三定律:对于每个作用力,都有一个相等而反向的反作用力。D.声音在空气中的传播速度为1000m/s。',
    ],
    inputs=textbox,
    outputs=[textbox, chatbot],
    fn=process_example,
    cache_examples=True,
  )

  gr.Markdown(LICENSE)

  textbox.submit(
    fn=clear_and_save_textbox,
    inputs=textbox,
    outputs=[textbox, saved_input],
    api_name=False,
    queue=False,
  ).then(
    fn=display_input,
    inputs=[saved_input, chatbot],
    outputs=chatbot,
    api_name=False,
    queue=False,
  ).then(
    fn=generate,
    inputs=[
      saved_input,
      chatbot,
      max_new_tokens,
      temperature,
      top_p,
      top_k,
    ],
    outputs=chatbot,
    api_name=False,
  )

  button_event_preprocess = submit_button.click(
    fn=clear_and_save_textbox,
    inputs=textbox,
    outputs=[textbox, saved_input],
    api_name=False,
    queue=False,
  ).then(
    fn=display_input,
    inputs=[saved_input, chatbot],
    outputs=chatbot,
    api_name=False,
    queue=False,
  ).then(
    fn=generate,
    inputs=[
      saved_input,
      chatbot,
      max_new_tokens,
      temperature,
      top_p,
      top_k,
    ],
    outputs=chatbot,
    api_name=False,
  )

  retry_button.click(
    fn=delete_prev_fn,
    inputs=chatbot,
    outputs=[chatbot, saved_input],
    api_name=False,
    queue=False,
  ).then(
    fn=display_input,
    inputs=[saved_input, chatbot],
    outputs=chatbot,
    api_name=False,
    queue=False,
  ).then(
    fn=generate,
    inputs=[
      saved_input,
      chatbot,
      max_new_tokens,
      temperature,
      top_p,
      top_k,
    ],
    outputs=chatbot,
    api_name=False,
  )

  undo_button.click(
    fn=delete_prev_fn,
    inputs=chatbot,
    outputs=[chatbot, saved_input],
    api_name=False,
    queue=False,
  ).then(
    fn=lambda x: x,
    inputs=[saved_input],
    outputs=textbox,
    api_name=False,
    queue=False,
  )

  clear_button.click(
    fn=lambda: ([], ''),
    outputs=[chatbot, saved_input],
    queue=False,
    api_name=False,
  )

demo.queue(max_size=20).launch()