"""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 = "wangrongsheng/IvyGPT-35" model_name = "wangrongsheng/Med-InternLM" RETRY_FLAG = False tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) #model = AutoModel.from_pretrained(model_name, trust_remote_code=True).quantize(4).half().cuda() model = AutoModel.from_pretrained(model_name, trust_remote_code=True).half().cuda() 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): 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, ): """ for response, history in model.stream_chat(tokenizer, input, history, 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=40960, top_p=0.7, temperature=0.95): if max_length < 10: max_length = 40960 if top_p < 0.1 or top_p > 1: top_p = 0.7 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="Med-InternLM", theme=gr.themes.Soft(text_size="sm")) as demo: # gr.HTML("""

ChatGLM2-6B-int4

""") gr.HTML( """

Med-InternLM医疗对话大模型

""" ) with gr.Accordion("🎈 Info", open=False): _ = f""" ## 欢迎体验Med-InternLM 近期在通用领域中出现的大语言模型（LLMs），例如ChatGPT，在遵循指令和产生类人响应方面表现出了显著的成功。然而，这样的大型语言模型并没有被广泛应用于医学领域，导致响应的准确性较差，无法提供关于医学诊断、药物等合理的建议。Med-InternLM是一个医疗大语言模型，它在高质量的医学问答数据上进行了监督微调，并使用人类反馈的强化学习进行了训练。 [模型下载地址](https://huggingface.co/wangrongsheng/Med-InternLM) """ 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("删除最后一条对话", variant="secondary") retryBtn = gr.Button("重新生成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): examples = gr.Examples( examples=[ ["熬夜对身体有什么危害? "], ["新冠肺炎怎么预防"], ["系统性红斑狼疮的危害和治疗方法是什么？"], ], inputs=[user_input], examples_per_page=50, ) 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)