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from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import os
class DistilGPT2Model:
def __init__(self, model_name="distilgpt2", model_path="models"):
self.model_path = model_path
self.model_name = model_name
os.makedirs(model_path, exist_ok=True)
if os.path.exists(os.path.join(model_path, "model")):
print("Loading model from local storage...")
self.tokenizer = AutoTokenizer.from_pretrained(os.path.join(model_path, "model"))
self.model = AutoModelForCausalLM.from_pretrained(os.path.join(model_path, "model"))
else:
print("Downloading model from Hugging Face...")
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
# Save model locally
print("Saving model to local storage...")
self.model.save_pretrained(os.path.join(model_path, "model"))
self.tokenizer.save_pretrained(os.path.join(model_path, "model"))
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
def generate_text(self, prompt: str, max_length: int = 50):
inputs = self.tokenizer.encode(prompt, return_tensors="pt").to(self.device)
outputs = self.model.generate(
inputs,
max_length=max_length,
do_sample=True,
top_k=50,
pad_token_id=self.tokenizer.eos_token_id,
)
return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
# Singleton instance for reuse
parth = DistilGPT2Model()
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