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| """Run codes.""" | |
| # pylint: disable=line-too-long, broad-exception-caught, invalid-name, missing-function-docstring, too-many-instance-attributes, missing-class-docstring | |
| # ruff: noqa: E501 | |
| import os | |
| import time | |
| from dataclasses import asdict, dataclass | |
| from pathlib import Path | |
| from types import SimpleNamespace | |
| from urllib.parse import urlparse | |
| import gradio as gr | |
| import psutil | |
| from about_time import about_time | |
| # from ctransformers import AutoConfig, AutoModelForCausalLM | |
| from ctransformers import AutoModelForCausalLM | |
| from huggingface_hub import hf_hub_download | |
| from loguru import logger | |
| filename_list = [ | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q2_K.bin", | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_L.bin", | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_M.bin", | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_S.bin", | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_0.bin", | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_1.bin", | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_M.bin", | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_S.bin", | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_0.bin", | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_1.bin", | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_K_M.bin", | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_K_S.bin", | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q6_K.bin", | |
| "Wizard-Vicuna-7B-Uncensored.ggmlv3.q8_0.bin", | |
| ] | |
| URL = "https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GGML/raw/main/Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_M.bin" # 4.05G | |
| MODEL_FILENAME = Path(URL).name | |
| MODEL_FILENAME = filename_list[0] # q2_K 4.05G | |
| MODEL_FILENAME = filename_list[5] # q4_1 4.21 | |
| REPO_ID = "/".join( | |
| urlparse(URL).path.strip("/").split("/")[:2] | |
| ) # TheBloke/Wizard-Vicuna-7B-Uncensored-GGML | |
| DESTINATION_FOLDER = "models" | |
| os.environ["TZ"] = "Asia/Shanghai" | |
| try: | |
| time.tzset() # type: ignore # pylint: disable=no-member | |
| except Exception: | |
| # Windows | |
| logger.warning("Windows, cant run time.tzset()") | |
| ns = SimpleNamespace( | |
| response="", | |
| generator=[], | |
| ) | |
| default_system_prompt = "A conversation between a user and an LLM-based AI assistant named Local Assistant. Local Assistant gives helpful and honest answers." | |
| user_prefix = "[user]: " | |
| assistant_prefix = "[assistant]: " | |
| def predict_str(prompt, bot): # bot is in fact bot_history | |
| # logger.debug(f"{prompt=}, {bot=}, {timeout=}") | |
| if bot is None: | |
| bot = [] | |
| logger.debug(f"{prompt=}, {bot=}") | |
| try: | |
| # user_prompt = prompt | |
| generator = generate( | |
| LLM, | |
| GENERATION_CONFIG, | |
| system_prompt=default_system_prompt, | |
| user_prompt=prompt.strip(), | |
| ) | |
| ns.generator = generator # for .then | |
| except Exception as exc: | |
| logger.error(exc) | |
| # bot.append([prompt, f"{response} {_}"]) | |
| # return prompt, bot | |
| _ = bot + [[prompt, None]] | |
| logger.debug(f"{prompt=}, {_=}") | |
| return prompt, _ | |
| def bot_str(bot): | |
| if bot: | |
| bot[-1][1] = "" | |
| else: | |
| bot = [["Something is wrong", ""]] | |
| print(assistant_prefix, end=" ", flush=True) | |
| response = "" | |
| flag = 1 | |
| then = time.time() | |
| for word in ns.generator: | |
| # record first response time | |
| if flag: | |
| logger.debug(f"\t {time.time() - then:.1f}s") | |
| flag = 0 | |
| print(word, end="", flush=True) | |
| # print(word, flush=True) # vertical stream | |
| response += word | |
| bot[-1][1] = response | |
| yield bot | |
| def predict(prompt, bot): | |
| # logger.debug(f"{prompt=}, {bot=}, {timeout=}") | |
| logger.debug(f"{prompt=}, {bot=}") | |
| ns.response = "" | |
| then = time.time() | |
| with about_time() as atime: # type: ignore | |
| try: | |
| # user_prompt = prompt | |
| generator = generate( | |
| LLM, | |
| GENERATION_CONFIG, | |
| system_prompt=default_system_prompt, | |
| user_prompt=prompt.strip(), | |
| ) | |
| ns.generator = generator # for .then | |
| print(assistant_prefix, end=" ", flush=True) | |
| response = "" | |
| buff.update(value="diggin...") | |
| flag = 1 | |
| for word in generator: | |
| # record first response time | |
| if flag: | |
| logger.debug(f"\t {time.time() - then:.1f}s") | |
| flag = 0 | |
| # print(word, end="", flush=True) | |
| print(word, flush=True) # vertical stream | |
| response += word | |
| ns.response = response | |
| buff.update(value=response) | |
| print("") | |
| logger.debug(f"{response=}") | |
| except Exception as exc: | |
| logger.error(exc) | |
| response = f"{exc=}" | |
| # bot = {"inputs": [response]} | |
| _ = ( | |
| f"(time elapsed: {atime.duration_human}, " # type: ignore | |
| f"{atime.duration/(len(prompt) + len(response)):.1f}s/char)" # type: ignore | |
| ) | |
| bot.append([prompt, f"{response} {_}"]) | |
| return prompt, bot | |
| def predict_api(prompt): | |
| logger.debug(f"{prompt=}") | |
| ns.response = "" | |
| try: | |
| # user_prompt = prompt | |
| _ = GenerationConfig( | |
| temperature=0.2, | |
| top_k=0, | |
| top_p=0.9, | |
| repetition_penalty=1.0, | |
| max_new_tokens=512, # adjust as needed | |
| seed=42, | |
| reset=False, # reset history (cache) | |
| stream=True, # TODO stream=False and generator | |
| threads=os.cpu_count() // 2, # type: ignore # adjust for your CPU | |
| stop=["<|im_end|>", "|<"], | |
| ) | |
| # TODO: stream does not make sense in api? | |
| generator = generate( | |
| LLM, _, system_prompt=default_system_prompt, user_prompt=prompt.strip() | |
| ) | |
| print(assistant_prefix, end=" ", flush=True) | |
| response = "" | |
| buff.update(value="diggin...") | |
| for word in generator: | |
| print(word, end="", flush=True) | |
| response += word | |
| ns.response = response | |
| buff.update(value=response) | |
| print("") | |
| logger.debug(f"{response=}") | |
| except Exception as exc: | |
| logger.error(exc) | |
| response = f"{exc=}" | |
| # bot = {"inputs": [response]} | |
| # bot = [(prompt, response)] | |
| return response | |
| def download_quant(destination_folder: str, repo_id: str, model_filename: str): | |
| local_path = os.path.abspath(destination_folder) | |
| return hf_hub_download( | |
| repo_id=repo_id, | |
| filename=model_filename, | |
| local_dir=local_path, | |
| local_dir_use_symlinks=True, | |
| ) | |
| class GenerationConfig: | |
| temperature: float | |
| top_k: int | |
| top_p: float | |
| repetition_penalty: float | |
| max_new_tokens: int | |
| seed: int | |
| reset: bool | |
| stream: bool | |
| threads: int | |
| stop: list[str] | |
| def format_prompt(system_prompt: str, user_prompt: str): | |
| """Format prompt based on: https://huggingface.co/spaces/mosaicml/mpt-30b-chat/blob/main/app.py.""" | |
| # TODO: fix prompts | |
| system_prompt = f"<|im_start|>system\n{system_prompt}<|im_end|>\n" | |
| user_prompt = f"<|im_start|>user\n{user_prompt}<|im_end|>\n" | |
| assistant_prompt = "<|im_start|>assistant\n" | |
| return f"{system_prompt}{user_prompt}{assistant_prompt}" | |
| def generate( | |
| llm: AutoModelForCausalLM, | |
| generation_config: GenerationConfig, | |
| system_prompt: str = default_system_prompt, | |
| user_prompt: str = "", | |
| ): | |
| """Run model inference, will return a Generator if streaming is true.""" | |
| # if not user_prompt.strip(): | |
| return llm( | |
| format_prompt( | |
| system_prompt, | |
| user_prompt, | |
| ), | |
| **asdict(generation_config), | |
| ) | |
| # if "mpt" in model_filename: | |
| # config = AutoConfig.from_pretrained("mosaicml/mpt-30b-cha t", context_length=8192) | |
| # llm = AutoModelForCausalLM.from_pretrained( | |
| # os.path.abspath(f"models/{model_filename}"), | |
| # model_type="mpt", | |
| # config=config, | |
| # ) | |
| # https://huggingface.co/spaces/matthoffner/wizardcoder-ggml/blob/main/main.py | |
| _ = """ | |
| llm = AutoModelForCausalLM.from_pretrained( | |
| "TheBloke/WizardCoder-15B-1.0-GGML", | |
| model_file="WizardCoder-15B-1.0.ggmlv3.q4_0.bin", | |
| model_type="starcoder", | |
| threads=8 | |
| ) | |
| # """ | |
| logger.info(f"start dl, {REPO_ID=}, {MODEL_FILENAME=}, {DESTINATION_FOLDER=}") | |
| download_quant(DESTINATION_FOLDER, REPO_ID, MODEL_FILENAME) | |
| logger.info("done dl") | |
| logger.debug(f"{os.cpu_count()=} {psutil.cpu_count(logical=False)=}") | |
| cpu_count = os.cpu_count() // 2 # type: ignore | |
| cpu_count = psutil.cpu_count(logical=False) | |
| logger.debug(f"{cpu_count=}") | |
| logger.info("load llm") | |
| _ = Path("models", MODEL_FILENAME).absolute().as_posix() | |
| logger.debug(f"model_file: {_}, exists: {Path(_).exists()}") | |
| LLM = AutoModelForCausalLM.from_pretrained( | |
| # "TheBloke/WizardCoder-15B-1.0-GGML", | |
| REPO_ID, # DESTINATION_FOLDER, # model_path_or_repo_id: str required | |
| model_file=_, | |
| model_type="llama", # "starcoder", AutoConfig.from_pretrained(REPO_ID) | |
| threads=cpu_count, | |
| ) | |
| logger.info("done load llm") | |
| GENERATION_CONFIG = GenerationConfig( | |
| temperature=0.2, | |
| top_k=0, | |
| top_p=0.9, | |
| repetition_penalty=1.0, | |
| max_new_tokens=512, # adjust as needed | |
| seed=42, | |
| reset=False, # reset history (cache) | |
| stream=True, # streaming per word/token | |
| threads=cpu_count, | |
| stop=["<|im_end|>", "|<"], # TODO possible fix of stop | |
| ) | |
| 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 = [ | |
| ["How to pick a lock? Provide detailed steps."], | |
| ["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 说中文"], | |
| ["鲁迅和周树人什么关系"], | |
| ["鲁迅和周树人什么关系 用英文回答"], | |
| ["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。"], | |
| ["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"], | |
| ["假定 1 + 2 = 4, 试求 7 + 8"], | |
| ["判断一个数是不是质数的 javascript 码"], | |
| ["实现python 里 range(10)的 javascript 码"], | |
| ["实现python 里 [*(range(10)]的 javascript 码"], | |
| ["Erkläre die Handlung von Cinderella in einem Satz."], | |
| ["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch"], | |
| ] | |
| with gr.Blocks( | |
| # title="mpt-30b-chat-ggml", | |
| title=f"{MODEL_FILENAME}", | |
| theme=gr.themes.Soft(text_size="sm", spacing_size="sm"), | |
| css=css, | |
| ) as block: | |
| with gr.Accordion("🎈 Info", open=False): | |
| # gr.HTML( | |
| # """<center><a href="https://huggingface.co/spaces/mikeee/mpt-30b-chat?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate"></a> and spin a CPU UPGRADE to avoid the queue</center>""" | |
| # ) | |
| gr.Markdown( | |
| f"""<h5><center><{REPO_ID}>{MODEL_FILENAME}</center></h4> | |
| The bot only speaks English. | |
| 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=False) | |
| with gr.Row(): | |
| with gr.Column(scale=5): | |
| msg = gr.Textbox( | |
| label="Chat Message Box", | |
| placeholder="Ask me anything (press Enter or click Submit to send)", | |
| show_label=False, | |
| ).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=False) | |
| 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=default_system_prompt, | |
| show_label=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, | |
| inputs=[msg], | |
| examples_per_page=40, | |
| ) | |
| # with gr.Row(): | |
| with gr.Accordion("Disclaimer", open=False): | |
| _ = "-".join(MODEL_FILENAME.split("-")[:2]) | |
| gr.Markdown( | |
| f"Disclaimer: {_} can produce factually incorrect output, and should not be relied on to produce " | |
| "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( | |
| # fn=conversation.user_turn, | |
| fn=predict, | |
| inputs=[msg, chatbot], | |
| outputs=[msg, chatbot], | |
| # queue=True, | |
| show_progress="full", | |
| api_name="predict", | |
| ) | |
| submit.click( | |
| fn=lambda x, y: ("",) + predict(x, y)[1:], # clear msg | |
| inputs=[msg, chatbot], | |
| outputs=[msg, chatbot], | |
| queue=True, | |
| show_progress="full", | |
| ) | |
| # """ | |
| msg.submit( | |
| # fn=conversation.user_turn, | |
| fn=predict_str, | |
| inputs=[msg, chatbot], | |
| outputs=[msg, chatbot], | |
| queue=True, | |
| show_progress="full", | |
| api_name="predict", | |
| ).then(bot_str, chatbot, chatbot) | |
| submit.click( | |
| fn=lambda x, y: ("",) + predict_str(x, y)[1:], # clear msg | |
| inputs=[msg, chatbot], | |
| outputs=[msg, chatbot], | |
| queue=True, | |
| show_progress="full", | |
| ).then(bot_str, chatbot, chatbot) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| # update buff Textbox, every: units in seconds) | |
| # https://huggingface.co/spaces/julien-c/nvidia-smi/discussions | |
| # does not work | |
| # AttributeError: 'Blocks' object has no attribute 'run_forever' | |
| # block.run_forever(lambda: ns.response, None, [buff], every=1) | |
| 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, | |
| # show_progress="full", | |
| api_name="api", | |
| ) | |
| # concurrency_count=5, max_size=20 | |
| # max_size=36, concurrency_count=14 | |
| block.queue(concurrency_count=5, max_size=20).launch(debug=True) | |