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Sleeping
| """ | |
| Simple test script for the trained model | |
| """ | |
| import os | |
| import torch | |
| import tiktoken | |
| from model import GPTConfig, GPT | |
| def test_model(): | |
| # Load model | |
| ckpt_path = "out-srs/ckpt_000600.pt" | |
| print(f"Loading {ckpt_path}...") | |
| checkpoint = torch.load(ckpt_path, map_location="mps") | |
| gptconf = GPTConfig(**checkpoint['model_args']) | |
| model = GPT(gptconf) | |
| # Load weights | |
| state_dict = checkpoint['model'] | |
| unwanted_prefix = '_orig_mod.' | |
| for k, v in list(state_dict.items()): | |
| if k.startswith(unwanted_prefix): | |
| state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) | |
| model.load_state_dict(state_dict) | |
| model.eval() | |
| model.to("mps") | |
| print(f"Model loaded! (iteration {checkpoint['iter_num']})") | |
| # Test generation | |
| enc = tiktoken.get_encoding("gpt2") | |
| encode = lambda s: enc.encode(s, allowed_special={"<|endoftext|>"}) | |
| decode = lambda l: enc.decode(l) | |
| prompt = "Hello, how are you?" | |
| print(f"\nPrompt: {prompt}") | |
| start_ids = encode(prompt) | |
| x = torch.tensor(start_ids, dtype=torch.long, device="mps")[None, ...] | |
| with torch.no_grad(): | |
| y = model.generate(x, 50, temperature=0.8, top_k=200) | |
| result = decode(y[0].tolist()) | |
| print(f"Generated: {result}") | |
| return True | |
| if __name__ == "__main__": | |
| test_model() |