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generate.py

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+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)