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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load tokenizer and model (simulating EvoTransformer with GPT-2-like architecture)
model_name = "username/evo_finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.eval()
# Mock EvoTransformer architecture traits
architecture = {
"layers": 6,
"heads": 8,
"ffn_dim": 2048,
"parameters": "58M"
}
def generate_response(user_input, max_length=100):
# Tokenize input with a conversational prompt
prompt = f"User: {user_input} Assistant: "
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
input_ids = inputs["input_ids"]
# Generate response
with torch.no_grad():
outputs = model.generate(
input_ids,
max_length=max_length,
num_return_sequences=1,
do_sample=True,
top_p=0.9,
temperature=0.7,
pad_token_id=tokenizer.eos_token_id
)
# Decode response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
response = response[len(prompt):].strip()
# Format output with architecture details
arch_info = (
f"Model Architecture:\n"
f"- Layers: {architecture['layers']}\n"
f"- Attention Heads: {architecture['heads']}\n"
f"- FFN Dimension: {architecture['ffn_dim']}\n"
f"- Parameters: {architecture['parameters']}"
)
return f"**Response**: {response}\n\n**{arch_info}**"
# Gradio interface
iface = gr.Interface(
fn=generate_response,
inputs=gr.Textbox(lines=2, placeholder="Type your message here..."),
outputs="markdown",
title="EvoTransformer Chat Demo",
description="Chat with a simplified EvoTransformer model, designed to evolve Transformer architectures. Enter a message to get a response and view model details."
)
if __name__ == "__main__":
iface.launch() |