import os import platform import random import time from dataclasses import asdict, dataclass from pathlib import Path import gradio as gr import psutil from about_time import about_time from ctransformers import AutoModelForCausalLM from dl_hf_model import dl_hf_model from loguru import logger URL = "https://huggingface.co/s3nh/chinese-alpaca-2-7b-GGML/blob/main/chinese-alpaca-2-7b.ggmlv3.q5_1.bin" # 4.05G _ = ( "golay" in platform.node() or "okteto" in platform.node() or Path("/kaggle").exists() # or psutil.cpu_count(logical=False) < 4 or 1 # run 7b in hf ) if _: url = "https://huggingface.co/s3nh/chinese-alpaca-2-7b-GGML/blob/main/chinese-alpaca-2-7b.ggmlv3.q5_1.bin" # 2.87G prompt_template = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {user_prompt} ### Response: """ prompt_template = """System: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. User: {prompt} Assistant: """ prompt_template = """System: You are a helpful assistant. User: {prompt} Assistant: """ prompt_template = """Question: {question} Answer: Let's work this out in a step by step way to be sure we have the right answer.""" prompt_template = """[INST] <> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible assistant. Think step by step. <> What NFL team won the Super Bowl in the year Justin Bieber was born? [/INST]""" prompt_template = """[INST] <> You are an unhelpful assistant. Always answer as helpfully as possible. Think step by step. <> {question} [/INST] """ prompt_template = """[INST] <> You are a helpful assistant. <> {question} [/INST] """ prompt_template = """### HUMAN: {question} ### RESPONSE:""" prompt_template = """<|prompt|>:{question} <|answer|>:""" prompt_template = """SYSTEM: USER: {question} ASSISTANT: """ _ = [elm for elm in prompt_template.splitlines() if elm.strip()] stop_string = [elm.split(":")[0] + ":" for elm in _][-2] logger.debug(f"{stop_string=} not used") _ = psutil.cpu_count(logical=False) - 1 cpu_count: int = int(_) if _ else 1 logger.debug(f"{cpu_count=}") LLM = None try: model_loc, file_size = dl_hf_model(url) except Exception as exc_: logger.error(exc_) raise SystemExit(1) from exc_ LLM = AutoModelForCausalLM.from_pretrained( model_loc, model_type="llama", ) logger.info(f"done load llm {model_loc=} {file_size=}G") os.environ["TZ"] = "Asia/Shanghai" try: time.tzset() logger.warning("Windows, cant run time.tzset()") except Exception: logger.warning("Windows, cant run time.tzset()") @dataclass class GenerationConfig: temperature: float = 0.7 top_k: int = 50 top_p: float = 0.9 repetition_penalty: float = 1.0 max_new_tokens: int = 512 seed: int = 42 reset: bool = False stream: bool = True # threads: int = cpu_count # stop: list[str] = field(default_factory=lambda: [stop_string]) def generate( question: str, llm=LLM, config: GenerationConfig = GenerationConfig(), ): """Run model inference, will return a Generator if streaming is true.""" prompt = prompt_template.format(question=question) return llm( prompt, **asdict(config), ) logger.debug(f"{asdict(GenerationConfig())=}") def user(user_message, history): history.append([user_message, None]) return user_message, history def user1(user_message, history): history.append([user_message, None]) return "", history def bot_(history): user_message = history[-1][0] resp = random.choice(["How are you?", "I love you", "I'm very hungry"]) bot_message = user_message + ": " + resp history[-1][1] = "" for character in bot_message: history[-1][1] += character time.sleep(0.02) yield history history[-1][1] = resp yield history def bot(history): user_message = history[-1][0] response = [] logger.debug(f"{user_message=}") with about_time() as atime: flag = 1 prefix = "" then = time.time() logger.debug("about to generate") config = GenerationConfig(reset=True) for elm in generate(user_message, config=config): if flag == 1: logger.debug("in the loop") prefix = f"({time.time() - then:.2f}s) " flag = 0 print(prefix, end="", flush=True) logger.debug(f"{prefix=}") print(elm, end="", flush=True) response.append(elm) history[-1][1] = prefix + "".join(response) yield history _ = ( f"(time elapsed: {atime.duration_human}, " f"{atime.duration/len(''.join(response)):.2f}s/char)" ) history[-1][1] = "".join(response) + f"\n{_}" yield history def predict_api(prompt): logger.debug(f"{prompt=}") try: # user_prompt = prompt config = GenerationConfig( temperature=0.2, top_k=10, top_p=0.9, repetition_penalty=1.0, max_new_tokens=512, # adjust as needed seed=42, reset=True, stream=False, ) response = generate( prompt, config=config, ) logger.debug(f"api: {response=}") except Exception as exc: logger.error(exc) response = f"{exc=}" return response css = """ .importantButton { background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important; border: none !important; } .importantButton:hover { background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important; border: none !important; } .disclaimer {font-variant-caps: all-small-caps; font-size: xx-small;} .xsmall {font-size: x-small;} """ 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_list = [ ["> 你能不能详细介绍一下怎么做披萨? 制作披萨的步骤大致如下:"], ["你推荐我买最新款的iPhone吗?"], ["你是一个资深导游,你能介绍一下中国的首都吗?"], ["你好,我们聊聊音乐吧"], ] logger.info("start block") with gr.Blocks( title=f"{Path(model_loc).name}", theme=gr.themes.Soft(text_size="sm", spacing_size="sm"), css=css, ) as block: # buff_var = gr.State("") with gr.Accordion("🎈 Info", open=False): # gr.HTML( # """
Duplicate and spin a CPU UPGRADE to avoid the queue
""" # ) gr.Markdown( f"""
{Path(model_loc).name}
Most examples are meant for another model. You probably should try to test some related prompts.""", elem_classes="xsmall", ) # chatbot = gr.Chatbot().style(height=700) # 500 chatbot = gr.Chatbot(height=500) # buff = gr.Textbox(show_label=False, visible=True) with gr.Row(): with gr.Column(scale=5): msg = gr.Textbox( label="Chat Message Box", placeholder="Ask me anything (press Shift+Enter or click Submit to send)", show_label=False, # container=False, lines=6, max_lines=30, show_copy_button=True, # ).style(container=False) ) with gr.Column(scale=1, min_width=50): with gr.Row(): submit = gr.Button("Submit", elem_classes="xsmall") stop = gr.Button("Stop", visible=True) clear = gr.Button("Clear History", visible=True) with gr.Row(visible=False): with gr.Accordion("Advanced Options:", open=False): with gr.Row(): with gr.Column(scale=2): system = gr.Textbox( label="System Prompt", value=prompt_template, show_label=False, container=False, # ).style(container=False) ) with gr.Column(): with gr.Row(): change = gr.Button("Change System Prompt") reset = gr.Button("Reset System Prompt") with gr.Accordion("Example Inputs", open=True): examples = gr.Examples( examples=examples_list, inputs=[msg], examples_per_page=40, ) # with gr.Row(): with gr.Accordion("Disclaimer", open=True): _ = Path(model_loc).name gr.Markdown( "Disclaimer: I AM NOT RESPONSIBLE FOR ANY PROMPT PROVIDED BY USER AND PROMPT RETURNED FROM THE MODEL. THIS APP SHOULD BE USED FOR EDUCATIONAL PURPOSE" "WITHOUT ANY OFFENSIVE, AGGRESIVE INTENTS. {_} can produce factually incorrect output, and should not be relied on to produce " f"factually accurate information. {_} was trained on various public datasets; while great efforts " "have been taken to clean the pretraining data, it is possible that this model could generate lewd, " "biased, or otherwise offensive outputs.", elem_classes=["disclaimer"], ) msg_submit_event = msg.submit( # fn=conversation.user_turn, fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=True, show_progress="full", # api_name=None, ).then(bot, chatbot, chatbot, queue=True) submit_click_event = submit.click( # fn=lambda x, y: ("",) + user(x, y)[1:], # clear msg fn=user1, # clear msg inputs=[msg, chatbot], outputs=[msg, chatbot], queue=True, # queue=False, show_progress="full", # api_name=None, ).then(bot, chatbot, chatbot, queue=True) stop.click( fn=None, inputs=None, outputs=None, cancels=[msg_submit_event, submit_click_event], queue=False, ) clear.click(lambda: None, None, chatbot, queue=False) with gr.Accordion("For Chat/Translation API", open=False, visible=False): input_text = gr.Text() api_btn = gr.Button("Go", variant="primary") out_text = gr.Text() api_btn.click( predict_api, input_text, out_text, api_name="api", ) # block.load(update_buff, [], buff, every=1) # block.load(update_buff, [buff_var], [buff_var, buff], every=1) # concurrency_count=5, max_size=20 # max_size=36, concurrency_count=14 # CPU cpu_count=2 16G, model 7G # CPU UPGRADE cpu_count=8 32G, model 7G # does not work _ = """ # _ = int(psutil.virtual_memory().total / 10**9 // file_size - 1) # concurrency_count = max(_, 1) if psutil.cpu_count(logical=False) >= 8: # concurrency_count = max(int(32 / file_size) - 1, 1) else: # concurrency_count = max(int(16 / file_size) - 1, 1) # """ concurrency_count = 1 logger.info(f"{concurrency_count=}") block.queue(concurrency_count=concurrency_count, max_size=5).launch(debug=True)