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9dafea7 | 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 47 48 49 50 | import torch
import sys
import argparse
from gpt import GPT
from tokenization.character import vocab_size, decode, encode
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def generate_text(prompt="Once upon a time", max_new_tokens=300, temperature=0.8, top_k=40, top_p=0.9):
model = GPT(
vocab_size=vocab_size,
d_model=512,
num_heads=8,
hidden_dim=2048,
num_layers=4,
attention_type="mha",
normalization_type="rms",
feedforward_type="swiglu",
position_encoding="sinusoidal"
).to(device)
model.load_state_dict(torch.load("checkpoints/gpt_character.pth", map_location=device))
model.eval()
context = torch.tensor([encode(prompt)], dtype=torch.long, device=device)
generated = model.generate(context, max_new_tokens=max_new_tokens, temperature=temperature, top_k=top_k, top_p=top_p)
text = decode(generated[0].tolist())
return text
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate text from trained GPT model")
parser.add_argument("--prompt", type=str, default="Once upon a time", help="Initial prompt text")
parser.add_argument("--max_tokens", type=int, default=300, help="Number of tokens to generate")
parser.add_argument("--temp", type=float, default=0.8, help="Sampling temperature")
parser.add_argument("--top_k", type=int, default=40, help="Top-k filtering")
parser.add_argument("--top_p", type=float, default=0.9, help="Top-p (nucleus) filtering")
args = parser.parse_args()
print(f"\nPrompt: '{args.prompt}'")
print(f"Sampling Parameters: Temp={args.temp}, Top-k={args.top_k}, Top-p={args.top_p}")
print("=" * 60)
result = generate_text(
prompt=args.prompt,
max_new_tokens=args.max_tokens,
temperature=args.temp,
top_k=args.top_k,
top_p=args.top_p
)
print(result)
print("=" * 60)
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