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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import spaces
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2-0.5B-Instruct",
torch_dtype="auto",
device_map="auto"
).to(device)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
@spaces.GPU
def chatbot(user_input, history):
system_message = {"role": "system", "content": "You are a helpful assistant."}
messages = history + [{"role": "user", "content": user_input}]
if len(history) == 0:
messages.insert(0, system_message)
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
attention_mask = torch.ones(model_inputs.input_ids.shape, device=device)
generated_ids = model.generate(
model_inputs.input_ids,
attention_mask=attention_mask,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
history.append({"role": "user", "content": user_input})
history.append({"role": "assistant", "content": response})
gradio_history = [[msg["role"], msg["content"]] for msg in history]
return gradio_history, history
with gr.Blocks() as demo:
chatbot_interface = gr.Chatbot()
state = gr.State([])
with gr.Row():
txt = gr.Textbox(show_label=False, placeholder="Ask anything")
txt.submit(chatbot, [txt, state], [chatbot_interface, state])
demo.launch()
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