| import torch | |
| import torch.nn as nn | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| class NATO1000Cortex(nn.Module): | |
| def __init__(self, model_name="codellama/CodeLlama-7b-hf", max_length=1000000): | |
| super().__init__() | |
| self.tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| self.model = AutoModelForCausalLM.from_pretrained(model_name) | |
| self.max_length = max_length | |
| def forward(self, prompt): | |
| inputs = self.tokenizer(prompt, return_tensors="pt", max_length=self.max_length, truncation=True) | |
| generate_ids = self.model.generate(inputs.input_ids, max_length=self.max_length) | |
| return self.tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] | |
| # Example usage: | |
| # model = NATO1000Cortex() | |
| # code_prompt = "def fibonacci(n):\n if n <= 0:\n return []\n elif n == 1:\n return [0]\n else:\n list_fib = [0, 1]\n while len(list_fib) < n:\n next_fib = list_fib[-1] + list_fib[-2]\n list_fib.append(next_fib)\n return list_fib\n\n# Write a unit test for the fibonacci function" | |
| # generated_code = model(code_prompt) | |
| # print(generated_code) | |