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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
# Инициализация модели и токенизатора
MODEL_NAME = "yandex/YandexGPT-5-Lite-8B-pretrain"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, legacy=False)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
device_map="cuda" if torch.cuda.is_available() else "cpu",
torch_dtype="auto",
)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Формируем контекст из истории
full_prompt = f"{system_message}\n\n"
for user_msg, assistant_msg in history:
if user_msg:
full_prompt += f"User: {user_msg}\n"
if assistant_msg:
full_prompt += f"Assistant: {assistant_msg}\n"
full_prompt += f"User: {message}\nAssistant:"
# Токенизация и генерация
inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
stream=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Убираем начальный промпт из ответа
response = response[len(full_prompt):]
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docsx/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()
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