import os from threading import Thread from typing import Iterator import gradio as gr import spaces import torch from transformers import pipeline, AutoTokenizer MAX_MAX_NEW_TOKENS = 2048 DEFAULT_MAX_NEW_TOKENS = 1024 MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) DESCRIPTION = """\ # ZhongJing 2 1.8B Merge This Space demonstrates model [CMLL/ZhongJing-2-1_8b-merge](https://huggingface.co/CMLL/ZhongJing-2-1_8b-merge) for text generation. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints). """ LICENSE = """

--- As a derivative work of [CMLL/ZhongJing-2-1_8b-merge](https://huggingface.co/CMLL/ZhongJing-2-1_8b-merge), this demo is governed by the original [license](https://huggingface.co/CMLL/ZhongJing-2-1_8b-merge/LICENSE). """ 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 = "CMLL/ZhongJing-2-1_8b-merge" pipe = pipeline("text-generation", model=model_id) tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.use_default_system_prompt = False @spaces.GPU def generate( message: str, chat_history: list[tuple[str, str]], system_prompt: str = "You are a helpful TCM medical assistant named 仲景中医大语言模型, created by 医哲未来.", max_new_tokens: int = 1024, temperature: float = 0.6, top_p: float = 0.9, top_k: int = 50, repetition_penalty: float = 1.2, ) -> Iterator[str]: conversation = [{"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_text = "\n".join([f"{entry['role']}: {entry['content']}" for entry in conversation]) generate_kwargs = { "max_new_tokens": max_new_tokens, "do_sample": True, "top_p": top_p, "top_k": top_k, "temperature": temperature, "repetition_penalty": repetition_penalty, } # Function to run the generation def run_generation(): try: results = pipe(input_text, **generate_kwargs) return results except Exception as e: return [f"Error in generation: {e}"] # Run generation in a separate thread and wait for it to finish outputs = [] generation_thread = Thread(target=lambda: outputs.extend(run_generation())) generation_thread.start() generation_thread.join() for output in outputs: yield output['generated_text'] if isinstance(output, dict) else output chat_interface = gr.ChatInterface( fn=generate, additional_inputs=[ gr.Textbox(label="System prompt", lines=6, value="You are a helpful TCM medical assistant named 仲景中医大语言模型, created by 医哲未来."), gr.Slider( label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS, ), gr.Slider( label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6, ), gr.Slider( label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9, ), gr.Slider( label="Top-k", minimum=1, maximum=1000, step=1, value=50, ), gr.Slider( label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2, ), ], stop_btn=None, examples=[ ["Hello there! How are you doing?"], ["Can you explain briefly to me what is the Python programming language?"], ["Explain the plot of Cinderella in a sentence."], ["How many hours does it take a man to eat a Helicopter?"], ["Write a 100-word article on 'Benefits of Open-Source in AI research'"], ], ) with gr.Blocks(css="style.css") as demo: gr.Markdown(DESCRIPTION) gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button") chat_interface.render() gr.Markdown(LICENSE) if __name__ == "__main__": demo.queue(max_size=20).launch()