| | 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)
|
| |
|
| |
|
| | 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)
|
| |
|
| |
|
| | parth = DistilGPT2Model()
|
| |
|
