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from huggingface_hub import InferenceClient
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Initialize the DialoGPT tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large", padding_side="left")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
client = InferenceClient(
"HuggingFaceH4/zephyr-7b-alpha"
)
def format_prompt(message, history):
system = "\nYou are a helpful virtual assistant that answers user's questions with easy-to-understand words.</s>\n"
prompt = ""
for user_prompt, bot_response in history:
prompt += f"\n{user_prompt}</s>\n"
prompt += f"\n{bot_response}</s>\n"
prompt += f"\n{message}</s>\n"
return prompt
def generate(
prompt,
history,
temperature=0.9,
max_new_tokens=500,
top_p=0.95,
repetition_penalty=1.0,
):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
formatted_prompt = format_prompt(prompt, history)
stream = client.text_generation(
formatted_prompt,
**generate_kwargs,
stream=True,
details=True,
return_full_text=False,
)
output = ""
for response in stream:
output += response.token.text
yield output
return output
additional_inputs = [
gr.Slider(
label="Temperature",
value=0.9,
minimum=0.0,
maximum=1.0,
step=0.05,
interactive=True,
info="Higher values produce more diverse outputs",
),
gr.Slider(
label="Max new tokens",
value=256,
minimum=0,
maximum=1048,
step=64,
interactive=True,
info="The maximum number of new tokens",
),
gr.Slider(
label="Top-p (nucleus sampling)",
value=0.90,
minimum=0.0,
maximum=1,
step=0.05,
interactive=True,
info="Higher values sample more low-probability tokens",
),
gr.Slider(
label="Repetition penalty",
value=1.2,
minimum=1.0,
maximum=2.0,
step=0.05,
interactive=True,
info="Penalize repeated tokens",
),
]
css = """
#mkd {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as inf:
gr.HTML("<h1><center>DialoGPT-large<h1><center>")
gr.HTML(
"<h3><center>In this demo, you can chat with <a href='https://huggingface.co/microsoft/DialoGPT-large'>DialoGPT-large</a> model. 💬<h3><center>"
)
gr.ChatInterface(
generate,
additional_inputs=additional_inputs,
examples=[
["Can a squirrel swim?"],
["Write a poem about a squirrel."],
],
)
inf.queue().launch(share=True) |