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import gradio as gr
from transformers import pipeline
import torch
# Initialize the pipeline for text generation
# pipe = pipeline("text-generation", model="cognitivecomputations/dolphin-2.9.4-llama3.1-8b")
pipe = pipeline("text-generation", model="cognitivecomputations/dolphin-2.9.4-llama3.1-8b", torch_dtype=torch.float16)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Prepare conversation history with system message
conversation_history = system_message + "\n"
for user_message, assistant_message in history:
if user_message:
conversation_history += f"User: {user_message}\n"
if assistant_message:
conversation_history += f"Assistant: {assistant_message}\n"
conversation_history += f"User: {message}\n"
# Generate response
response = ""
result = pipe(
conversation_history,
max_length=max_tokens,
do_sample=True,
temperature=temperature,
top_p=top_p
)[0]["generated_text"]
# Extract only the new assistant response
new_response = result.split(conversation_history)[-1].strip()
for token in new_response:
response += token
yield response
# Define Gradio interface
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() |