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from huggingface_hub import InferenceClient
import gradio as gr
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def format_prompt(message, history):
prompt = "<|system|>\n</s>\n"
for user_prompt, bot_response in history:
prompt += f"<|user|>\n {user_prompt} </s>\n"
prompt += f"<|assistant|>\n {bot_response} \n</s>\n "
prompt += f"<|user|>\n {message} </s>\n<|assistant|>"
return prompt
def generate(
prompt, history, temperature=0.1, max_new_tokens=256, 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
mychatbot = gr.Chatbot(
avatar_images=["./user.png", "./botz.png"], bubble_full_width=False, show_label=False, show_copy_button=True,)
demo = gr.ChatInterface(fn=generate,
chatbot=mychatbot,
title="Tomoniai Zephyr 7b Chat",
retry_btn=None,
undo_btn=None
)
demo.queue().launch(show_api=False) |