Upload generate.py with huggingface_hub

generate.py

@@ -0,0 +1,45 @@
+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()