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import gradio as gr
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
# {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
def chat_response(message, history):
msg = messages.copy()
for m in history:
q, a = m
msg.append({"role": "user", "content": q})
msg.append({"role": "assistant", "content": a})
msg.append({"role": "user", "content": message})
prompt = pipe.tokenizer.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
output = outputs[0]["generated_text"]
messages.append({"role": "assistant", "content": output})
response_start = output.rfind('<|assistant|>')
return output[response_start + len('<|assistant|>'):]
demo = gr.ChatInterface(chat_response)
demo.launch()
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