import os from threading import Thread from typing import Iterator import gradio as gr import spaces import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer import time MAX_MAX_NEW_TOKENS = 2048 DEFAULT_MAX_NEW_TOKENS = 1024 MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) DESCRIPTION = """\ # Dorna-Llama3-8B-Instruct Chat """ PLACEHOLDER = """

Dorna-Llama3-8B-Instruct

""" custom_css = """ @import url('https://fonts.googleapis.com/css2?family=Vazirmatn&display=swap'); body, .gradio-container, .gr-button, .gr-input, .gr-slider, .gr-dropdown, .gr-markdown { font-family: 'Vazirmatn', sans-serif !important; } ._button { font-size: 20px; } pre, code { direction: ltr !important; unicode-bidi: plaintext !important; } """ system_prompt = str(os.getenv("SYSTEM_PROMPT")) def execution_time_calculator(start_time, log=True): delta = time.time() - start_time if log: print("--- %s seconds ---" % (delta)) return delta def token_per_second_calculator(tokens_count, time_delta): return tokens_count/time_delta if not torch.cuda.is_available(): DESCRIPTION = "\n

Running on CPU 🥶 This demo does not work on CPU.

" if torch.cuda.is_available(): model_id = "PartAI/Dorna-Llama3-8B-Instruct" model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_id) generation_speed = 0 def get_generation_speed(): global generation_speed return generation_speed @spaces.GPU def generate( message: str, chat_history: list[tuple[str, str]], max_new_tokens: int = 1024, temperature: float = 0.6, top_p: float = 0.9, top_k: int = 50, repetition_penalty: float = 1.2, do_sample: bool =True, ) -> Iterator[str]: global generation_speed global system_prompt conversation = [] if system_prompt: conversation.append({"role": "system", "content": system_prompt}) for user, assistant in chat_history: conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) conversation.append({"role": "user", "content": message}) input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt") if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") input_ids = input_ids.to(model.device) streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( {"input_ids": input_ids}, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=do_sample, top_p=top_p, top_k=top_k, temperature=temperature, num_beams=1, repetition_penalty=repetition_penalty, ) start_time = time.time() t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() outputs = [] sum_tokens = 0 for text in streamer: num_tokens = len(tokenizer.tokenize(text)) sum_tokens += num_tokens outputs.append(text) yield "".join(outputs) time_delta = execution_time_calculator(start_time, log=False) generation_speed = token_per_second_calculator(sum_tokens, time_delta) print(f"generation_speed: {generation_speed}") chatbot = gr.Chatbot(placeholder=PLACEHOLDER, scale=1, show_copy_button=True, height="68%", rtl=True) #, elem_classes=["chatbot"]) chat_input = gr.Textbox(show_label=False, lines=2, rtl=True, placeholder="ورودی", show_copy_button=True, scale=4) submit_btn = gr.Button(variant="primary", value="ارسال", size="sm", scale=1, elem_classes=["_button"]) chat_interface = gr.ChatInterface( fn=generate, additional_inputs_accordion=gr.Accordion(label="ورودی‌های اضافی", open=False), additional_inputs=[ gr.Slider( label="حداکثر تعداد توکن ها", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS, ), gr.Slider( label="Temperature", minimum=0.01, maximum=4.0, step=0.01, value=0.6, ), gr.Slider( label="Top-p", minimum=0.05, maximum=1.0, step=0.01, value=0.65, ), gr.Slider( label="Top-k", minimum=1, maximum=1000, step=1, value=40, ), gr.Slider( label="جریمه تکرار", minimum=1.0, maximum=2.0, step=0.05, value=1.2, ), gr.Dropdown( label="نمونه‌گیری", choices=[False, True], value=True) ], stop_btn="توقف", chatbot=chatbot, textbox=chat_input, submit_btn=submit_btn, retry_btn="🔄 تلاش مجدد", undo_btn="↩️ بازگشت", clear_btn="🗑️ پاک کردن", title="درنا، محصول مرکز تحقیقات هوش مصنوعی پارت" ) with gr.Blocks(css=custom_css, fill_height=False) as demo: gr.Markdown(DESCRIPTION) chat_interface.render() if __name__ == "__main__": demo.queue(max_size=20).launch()