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Running
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Running
on
Zero
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 = """ | |
<p/> | |
--- | |
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<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
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 | |
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() | |