Running on CPU 🥶 This demo does not work on CPU.
" if torch.cuda.is_available(): model_id = "PartAI/Dorna1.1-Llama3.1-8B-Instruct" model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16) tokenizer = AutoTokenizer.from_pretrained(model_id) generation_speed = 0 def get_generation_speed(): global generation_speed return generation_speed @observe() def log_to_langfuse(message, chat_history, max_new_tokens, temperature, top_p, top_k, repetition_penalty, do_sample, generation_speed, model_outputs): print(f"generation_speed: {generation_speed}") return "".join(model_outputs) @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, bad_words_ids=REJECTED_VOCAB, ) 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) log_function = log_to_langfuse( message=message, chat_history=chat_history, max_new_tokens=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty, do_sample=do_sample, generation_speed=generation_speed, model_outputs=outputs, ) 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=0.5, ), gr.Slider( label="Top-p", minimum=0.05, maximum=1.0, step=0.01, value=0.9, ), gr.Slider( label="Top-k", minimum=1, maximum=1000, step=1, value=20, ), 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()