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import gradio as gr
import torch # Add this import
from transformers import pipeline
model_name = "EleutherAI/gpt-neo-1.3B"
generator = pipeline("text-generation", model=model_name)
def generate_text(prompt):
return generator(
prompt,
max_length=50, # Reduced max length
temperature=0.5, # Lower temperature for predictable output
top_k=20, # Smaller token pool for quicker responses
top_p=0.8, # Adjusted nucleus sampling
repetition_penalty=1.4, # Increased to reduce redundancy
do_sample=True
)[0]["generated_text"]
interface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
interface.launch()
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
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()
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