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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the smaller model and tokenizer
model_name = "distilgpt2" # A smaller model that should work with 16GB of RAM
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Set the device to GPU if available, else use CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
def generate_response(prompt):
# Encode the input prompt
inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
# Generate the output sequence
outputs = model.generate(inputs, max_length=150, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
# Decode the generated sequence
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Set up Gradio interface
iface = gr.Interface(
fn=generate_response,
inputs="text",
outputs="text",
title="Crypto Analysis Model",
description="Enter your prompt related to Bitcoin or cryptocurrency."
)
# Launch the interface
iface.launch()
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