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import gradio as gr
from transformers import pipeline
# Загрузка модели Marco-o1 с квантизацией
pipe = pipeline("text-generation", model="AIDC-AI/Marco-o1", device_map="auto", torch_dtype="auto", trust_remote_code=True)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [system_message]
for val in history:
if val[0]:
messages.append(val[0])
if val[1]:
messages.append(val[1])
messages.append(message)
# Объединяем все сообщения в одну строку для передачи в модель
input_text = "\n".join(messages)
response = pipe(
input_text,
max_length=max_tokens + len(input_text),
temperature=temperature,
top_p=top_p,
num_return_sequences=1
)[0]['generated_text']
# Извлекаем новый ответ, исключая входные сообщения
new_response = response[len(input_text):].strip()
yield new_response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch() |