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| 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/TinyGauss-1.1B-GGUF/resolve/main/tinygauss-1.1b.Q4_K_M.gguf" # 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 = URL #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] <<SYS>> | |
| You are an unhelpful assistant. Always answer as helpfully as possible. Think step by step. <</SYS>> | |
| {question} [/INST] | |
| """ | |
| prompt_template = """[INST] <<SYS>> | |
| You are a helpful assistant. | |
| <</SYS>> | |
| {question} [/INST] | |
| """ | |
| prompt_template = """### HUMAN: | |
| {question} | |
| ### RESPONSE:""" | |
| prompt_template = """System: You are a helpful assistant. | |
| User: {question} | |
| Assistant: """ | |
| prompt_template = """ | |
| HUMAN: {question} | |
| ASSISTANT:""" | |
| prompt_template = """<|prompt|>:{question}</s> | |
| <|answer|>:""" | |
| _ = [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()") | |
| 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 = [ | |
| ["Send an email requesting that people use language models responsibly."], | |
| ["Write a shouting match between Julius Caesar and Napoleon"], | |
| ["Write a theory to explain why cat never existed"], | |
| ["write a story about a grain of sand as it watches millions of years go by"], | |
| ["What are 3 popular chess openings?"], | |
| ["write a conversation between the sun and pluto"], | |
| ["Did you know that Yann LeCun dropped a rap album last year? We listened to it andhere’s what we thought:"], | |
| ] | |
| 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( | |
| # """<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>{Path(model_loc).name}</center></h4> | |
| 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().launch(debug=True) |