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6a75ab7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | import argparse
from pathlib import Path
import torch
from train import TinyTransformerLM
@torch.no_grad()
def generate(model, idx, max_new_tokens, temperature, itos):
model.eval()
for _ in range(max_new_tokens):
idx_cond = idx[:, -model.block_size :]
logits, _ = model(idx_cond)
logits = logits[:, -1, :] / temperature
probs = torch.softmax(logits, dim=-1)
next_id = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, next_id), dim=1)
return "".join(itos[int(i)] for i in idx[0])
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="runs/tiny-char-model.pt")
parser.add_argument("--prompt", default="hello")
parser.add_argument("--tokens", type=int, default=400)
parser.add_argument("--temperature", type=float, default=0.8)
args = parser.parse_args()
checkpoint = torch.load(Path(args.model), map_location="cpu")
config = checkpoint["config"]
stoi = checkpoint["stoi"]
itos = {int(k): v for k, v in checkpoint["itos"].items()}
model = TinyTransformerLM(**config)
model.load_state_dict(checkpoint["model"])
fallback = next(iter(stoi.values()))
encoded = [stoi.get(ch, fallback) for ch in args.prompt]
idx = torch.tensor([encoded], dtype=torch.long)
print(generate(model, idx, args.tokens, args.temperature, itos))
if __name__ == "__main__":
main()
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