"""Credit to https://github.com/THUDM/ChatGLM2-6B/blob/main/web_demo.py while mistakes are mine.""" # pylint: disable=broad-exception-caught, redefined-outer-name, missing-function-docstring, missing-module-docstring, too-many-arguments, line-too-long, invalid-name, redefined-builtin, redefined-argument-from-local # import gradio as gr # model_name = "models/THUDM/chatglm2-6b-int4" # gr.load(model_name).lauch() # %%writefile demo-4bit.py import os import time from textwrap import dedent import gradio as gr import mdtex2html import torch from loguru import logger from transformers import AutoModel, AutoTokenizer # fix timezone in Linux os.environ["TZ"] = "Asia/Shanghai" try: time.tzset() # type: ignore # pylint: disable=no-member except Exception: # Windows logger.warning("Windows, cant run time.tzset()") # model_name = "THUDM/chatglm2-6b" # 7x?G model_name = "THUDM/chatglm2-6b-int4" # 3.9G RETRY_FLAG = False 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() has_cuda = torch.cuda.is_available() # has_cuda = False # force cpu if has_cuda: if model_name.endswith("int4"): model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda() else: model = ( AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda().half() ) else: model = AutoModel.from_pretrained( model_name, trust_remote_code=True ).float() # .half().float(), .float() required for CPU 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'
'
            else:
                lines[i] = "
" else: if i > 0: if count % 2 == 1: line = line.replace("`", r"\`") 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] = "
" + line text = "".join(lines) return text def predict( RETRY_FLAG, input, chatbot, max_length, top_p, temperature, history, past_key_values ): try: chatbot.append((parse_text(input), "")) except Exception as exc: logger.error(exc) logger.debug(f"{chatbot=}") _ = """ if chatbot: chatbot[-1] = (parse_text(input), str(exc)) yield chatbot, history, past_key_values # """ yield chatbot, history, past_key_values 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 < 10: max_length = 4096 if top_p < 0.1 or top_p > 1: top_p = 0.85 if temperature <= 0 or temperature > 1: 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, # type: ignore user_input, chatbot, max_length, top_p, temperature, history, past_key_values, ) #with gr.Blocks(title="ChatGLM2-6B-int4", theme=gr.themes.Soft(text_size="sm")) as demo: with gr.Blocks(title="ChatGLM2-6B-int4", theme='ParityError/Anime') as demo: # gr.HTML("""

ChatGLM2-6B-int4

""") gr.HTML( """
Duplicate SpaceTo avoid the queue and for faster inference Duplicate this Space and upgrade to GPU
""" ) with gr.Accordion("🎈 Info", open=False): _ = f""" ## {model_name} Try to refresh the browser and try again when occasionally an error occurs. With a GPU, a query takes from a few seconds to a few tens of seconds, dependent on the number of words/characters the question and responses contain. The quality of the responses varies quite a bit it seems. Even the same question with the same parameters, asked at different times, can result in quite different responses. * 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, step=1.0, label="Maximum length", interactive=True, ) top_p = gr.Slider( 0, 1, value=0.85, 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, [ RETRY_FLAG, user_input, chatbot, max_length, top_p, temperature, history, past_key_values, ], [chatbot, history, past_key_values], show_progress="full", ) submitBtn.click( predict, [ RETRY_FLAG, user_input, chatbot, max_length, top_p, temperature, history, past_key_values, ], [chatbot, history, past_key_values], show_progress="full", api_name="predict", ) submitBtn.click(reset_user_input, [], [user_input]) emptyBtn.click( reset_state, outputs=[chatbot, history, past_key_values], show_progress="full" ) 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("Example inputs", open=True): etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """ examples = gr.Examples( examples=[ ["What NFL team won the Super Bowl in the year Justin Bieber was born? "], ["What NFL team won the Super Bowl in the year Justin Bieber was born? Think step by step."], ["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"], ["鲁迅和周树人什么关系"], ["从前有一头牛,这头牛后面有什么?"], ["正无穷大加一大于正无穷大吗?"], ["正无穷大加正无穷大大于正无穷大吗?"], ["-2的平方根等于什么"], ["树上有5只鸟,猎人开枪打死了一只。树上还有几只鸟?"], ["树上有11只鸟,猎人开枪打死了一只。树上还有几只鸟?提示:需考虑鸟可能受惊吓飞走。"], ["鲁迅和周树人什么关系 用英文回答"], ["以红楼梦的行文风格写一张委婉的请假条。不少于320字。"], [f"{etext} 翻成中文,列出3个版本"], [f"{etext} \n 翻成中文,保留原意,但使用文学性的语言。不要写解释。列出3个版本"], ["js 判断一个数是不是质数"], ["js 实现python 的 range(10)"], ["js 实现python 的 [*(range(10)]"], ["假定 1 + 2 = 4, 试求 7 + 8"], ["Erkläre die Handlung von Cinderella in einem Satz."], ["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch"], ], inputs=[user_input], examples_per_page=30, ) with gr.Accordion("For Chat/Translation API", open=False, visible=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="full", api_name="tr", ) _ = """ input_text.submit( trans_api, [input_text, max_length, top_p, temperature], out_text, show_progress="full", api_name="tr1", ) # """ # demo.queue().launch(share=False, inbrowser=True) # demo.queue().launch(share=True, inbrowser=True, debug=True) # concurrency_count > 1 requires more memory, max_size: queue size # T4 medium: 30GB, model size: ~4G concurrency_count = 6 # leave one for api access # reduce to 5 if OOM occurs to often demo.queue(concurrency_count=6, max_size=30).launch(debug=True)