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import os
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Define models as None to delay loading
model, model_instruct = None, None
tokenizer, tokenizer_instruct = None, None
# Define the response function with lazy loading
def generate_response(input_text, max_new_tokens, temperature, top_k, top_p, repetition_penalty, num_beams, length_penalty, model_choice):
global model, model_instruct, tokenizer, tokenizer_instruct
# Lazy loading of the selected model
if model_choice == "Zamba2-7B":
if model is None: # Load only if not already loaded
tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-7B")
model = AutoModelForCausalLM.from_pretrained(
"Zyphra/Zamba2-7B", device_map="cuda", torch_dtype=torch.bfloat16
)
selected_model = model
selected_tokenizer = tokenizer
else:
if model_instruct is None: # Load only if not already loaded
tokenizer_instruct = AutoTokenizer.from_pretrained("Zyphra/Zamba2-7B-instruct")
model_instruct = AutoModelForCausalLM.from_pretrained(
"Zyphra/Zamba2-7B-instruct", device_map="cuda", torch_dtype=torch.bfloat16
)
selected_model = model_instruct
selected_tokenizer = tokenizer_instruct
# Tokenize and generate response
input_ids = selected_tokenizer(input_text, return_tensors="pt").input_ids.to(selected_model.device)
outputs = selected_model.generate(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
num_beams=num_beams,
length_penalty=length_penalty,
num_return_sequences=1
)
response = selected_tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Gradio interface with model selection
demo = gr.Interface(
fn=generate_response,
inputs=[
gr.Textbox(lines=1, placeholder="Enter your input text...", label="Input Text"),
gr.Slider(50, 1000, step=50, value=500, label="Max New Tokens"),
gr.Slider(0.1, 1.5, step=0.1, value=0.7, label="Temperature"),
gr.Slider(1, 100, step=1, value=50, label="Top K"),
gr.Slider(0.1, 1.0, step=0.1, value=0.9, label="Top P"),
gr.Slider(1.0, 2.0, step=0.1, value=1.2, label="Repetition Penalty"),
gr.Slider(1, 10, step=1, value=5, label="Number of Beams"),
gr.Slider(0.0, 2.0, step=0.1, value=1.0, label="Length Penalty"),
gr.Dropdown(["Zamba2-7B", "Zamba2-7B-instruct"], label="Model Choice")
],
outputs=gr.Textbox(label="Generated Response"),
title="Zamba2-7B Model Selector",
description="Choose a model and ask a question with customizable parameters."
)
if __name__ == "__main__":
demo.launch()
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