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# import gradio as gr | |
# model_name = "models/THUDM/chatglm2-6b-int4" | |
# gr.load(model_name).lauch() | |
# %%writefile demo-4bit.py | |
from textwrap import dedent | |
# credit to https://github.com/THUDM/ChatGLM2-6B/blob/main/web_demo.py | |
# while mistakes are mine | |
from transformers import AutoModel, AutoTokenizer | |
import gradio as gr | |
import mdtex2html | |
from loguru import logger | |
model_name = "THUDM/chatglm2-6b" | |
model_name = "THUDM/chatglm2-6b-int4" | |
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) | |
# model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda() | |
# 4/8 bit | |
# model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).quantize(4).cuda() | |
import torch | |
has_cuda = torch.cuda.is_available() | |
# has_cuda = False # force cpu | |
if has_cuda: | |
model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda() # 3.92G | |
else: | |
model = AutoModel.from_pretrained(model_name, trust_remote_code=True).half() # .float() .half().float() | |
model = model.eval() | |
_ = """Override Chatbot.postprocess""" | |
def postprocess(self, y): | |
if y is None: | |
return [] | |
for i, (message, response) in enumerate(y): | |
y[i] = ( | |
None if message is None else mdtex2html.convert((message)), | |
None if response is None else mdtex2html.convert(response), | |
) | |
return y | |
gr.Chatbot.postprocess = postprocess | |
def parse_text(text): | |
"""copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/""" | |
lines = text.split("\n") | |
lines = [line for line in lines if line != ""] | |
count = 0 | |
for i, line in enumerate(lines): | |
if "```" in line: | |
count += 1 | |
items = line.split('`') | |
if count % 2 == 1: | |
lines[i] = f'<pre><code class="language-{items[-1]}">' | |
else: | |
lines[i] = f'<br></code></pre>' | |
else: | |
if i > 0: | |
if count % 2 == 1: | |
line = line.replace("`", "\`") | |
line = line.replace("<", "<") | |
line = line.replace(">", ">") | |
line = line.replace(" ", " ") | |
line = line.replace("*", "*") | |
line = line.replace("_", "_") | |
line = line.replace("-", "-") | |
line = line.replace(".", ".") | |
line = line.replace("!", "!") | |
line = line.replace("(", "(") | |
line = line.replace(")", ")") | |
line = line.replace("$", "$") | |
lines[i] = "<br>"+line | |
text = "".join(lines) | |
return text | |
def predict(RETRY_FLAG, input, chatbot, max_length, top_p, temperature, history, past_key_values): | |
chatbot.append((parse_text(input), "")) | |
for response, history, past_key_values in model.stream_chat(tokenizer, input, history, past_key_values=past_key_values, | |
return_past_key_values=True, | |
max_length=max_length, top_p=top_p, | |
temperature=temperature): | |
chatbot[-1] = (parse_text(input), parse_text(response)) | |
yield chatbot, history, past_key_values | |
def trans_api(input, max_length=4096, top_p=0.8, temperature=0.2): | |
if max_length < 100: | |
max_length = 4096 | |
if top_p < 0.1: | |
top_p = 0.8 | |
if temperature <= 0: | |
temperature = 0.01 | |
try: | |
res, _ = model.chat( | |
tokenizer, | |
input, | |
history=[], | |
past_key_values=None, | |
max_length=max_length, | |
top_p=top_p, | |
temperature=temperature, | |
) | |
# logger.debug(f"{res=} \n{_=}") | |
except Exception as exc: | |
logger.error(f"{exc=}") | |
res = str(exc) | |
return res | |
def reset_user_input(): | |
return gr.update(value='') | |
def reset_state(): | |
return [], [], None | |
# Delete last turn | |
def delete_last_turn(chat, history): | |
if chat and history: | |
chat.pop(-1) | |
history.pop(-1) | |
return chat, history | |
# Regenerate response | |
def retry_last_answer( | |
user_input, | |
chatbot, | |
max_length, | |
top_p, | |
temperature, | |
history, | |
past_key_values | |
): | |
if chatbot and history: | |
# Removing the previous conversation from chat | |
chatbot.pop(-1) | |
# Setting up a flag to capture a retry | |
RETRY_FLAG = True | |
# Getting last message from user | |
user_input = history[-1][0] | |
# Removing bot response from the history | |
history.pop(-1) | |
yield from predict( | |
RETRY_FLAG, | |
user_input, | |
chatbot, | |
max_length, | |
top_p, | |
temperature, | |
history, | |
past_key_values | |
) | |
with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
gr.HTML("""<h1 align="center">ChatGLM2-6B-int4</h1>""") | |
gr.HTML("""<center><a href="https://huggingface.co/spaces/mikeee/chatglm2-6b-4bit?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>To avoid the queue and for faster inference Duplicate this Space and upgrade to GPU</center>""") | |
with gr.Accordion("Info", open=False): | |
_ = """ | |
A query takes from 30 seconds to a few tens of seconds, dependent on the number of words/characters | |
the question and answer contain. | |
* Low temperature: responses will be more deterministic and focused; High temperature: responses more creative. | |
* Suggested temperatures -- translation: up to 0.3; chatting: > 0.4 | |
* Top P controls dynamic vocabulary selection based on context. | |
For a table of example values for different scenarios, refer to [this](https://community.openai.com/t/cheat-sheet-mastering-temperature-and-top-p-in-chatgpt-api-a-few-tips-and-tricks-on-controlling-the-creativity-deterministic-output-of-prompt-responses/172683) | |
If the instance is not on a GPU (T4), it will be very slow. You can try to run the colab notebook [chatglm2-6b-4bit colab notebook](https://colab.research.google.com/drive/1WkF7kOjVCcBBatDHjaGkuJHnPdMWNtbW?usp=sharing) for a spin. | |
The T4 GPU is sponsored by a community GPU grant from Huggingface. Thanks a lot! | |
""" | |
gr.Markdown(dedent(_)) | |
chatbot = gr.Chatbot() | |
with gr.Row(): | |
with gr.Column(scale=4): | |
with gr.Column(scale=12): | |
user_input = gr.Textbox(show_label=False, placeholder="Input...", ).style( | |
container=False) | |
RETRY_FLAG = gr.Checkbox(value=False, visible=False) | |
with gr.Column(min_width=32, scale=1): | |
with gr.Row(): | |
submitBtn = gr.Button("Submit", variant="primary") | |
deleteBtn = gr.Button("Delete last turn", variant="secondary") | |
retryBtn = gr.Button("Regenerate", variant="secondary") | |
with gr.Column(scale=1): | |
emptyBtn = gr.Button("Clear History") | |
max_length = gr.Slider(0, 32768, value=8192/2, step=1.0, label="Maximum length", interactive=True) | |
top_p = gr.Slider(0, 1, value=0.8, step=0.01, label="Top P", interactive=True) | |
temperature = gr.Slider(0.01, 1, value=0.95, step=0.01, label="Temperature", interactive=True) | |
history = gr.State([]) | |
past_key_values = gr.State(None) | |
user_input.submit(predict, [user_input, chatbot, max_length, top_p, temperature, history, past_key_values], | |
[chatbot, history, past_key_values], show_progress=True) | |
submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history, past_key_values], | |
[chatbot, history, past_key_values], show_progress=True, api_name="predict") | |
submitBtn.click(reset_user_input, [], [user_input]) | |
emptyBtn.click(reset_state, outputs=[chatbot, history, past_key_values], show_progress=True) | |
retryBtn.click( | |
retry_last_answer, | |
inputs = [user_input, chatbot, max_length, top_p, temperature, history, past_key_values], | |
#outputs = [chatbot, history, last_user_message, user_message] | |
outputs=[chatbot, history, past_key_values] | |
) | |
deleteBtn.click(delete_last_turn, [chatbot, history], [chatbot, history]) | |
with gr.Accordion("For Translation API", open=False): | |
input_text = gr.Text() | |
tr_btn = gr.Button("Go", variant="primary") | |
out_text = gr.Text() | |
tr_btn.click(trans_api, [input_text, max_length, top_p, temperature], out_text, show_progress=True, api_name="tr") | |
input_text.submit(trans_api, [input_text, max_length, top_p, temperature], out_text, show_progress=True, api_name="tr") | |
with gr.Accordion("Example inputs", open=True): | |
examples = gr.Examples( | |
examples=[["Explain the plot of Cinderella in a sentence."], | |
["How long does it take to become proficient in French, and what are the best methods for retaining information?"], | |
["What are some common mistakes to avoid when writing code?"], | |
["Build a prompt to generate a beautiful portrait of a horse"], | |
["Suggest four metaphors to describe the benefits of AI"], | |
["Write a pop song about leaving home for the sandy beaches."], | |
["Write a summary demonstrating my ability to tame lions"]], | |
inputs = [user_input], | |
) | |
# demo.queue().launch(share=False, inbrowser=True) | |
# demo.queue().launch(share=True, inbrowser=True, debug=True) | |
demo.queue().launch(debug=True) |