Create example_run.py

example_run.py

@@ -0,0 +1,39 @@
+# example_run.py
+from i3_model import i3Model, ChunkTokenizer
+from modeling_i3 import I3ForCausalLM, I3Config
+from tokenizer_i3 import I3Tokenizer
+import torch
+
+# Path to local model files (current folder)
+model_path = "."
+
+# Load tokenizer
+tokenizer = I3Tokenizer(vocab_file=f"{model_path}/chunk_vocab_combined.json")
+
+# Load HF-style model
+model = I3ForCausalLM.from_pretrained(model_path)
+model.eval()
+
+# Example prompt
+prompt = "hello, how are you"
+
+# Encode text
+input_ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long)
+
+# Optional: move to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+input_ids = input_ids.to(device)
+
+# Generate tokens
+with torch.no_grad():
+    generated_ids = model.i3.generate(
+        input_ids,
+        max_new_tokens=50,
+        temperature=0.8,
+        top_k=40
+    )
+
+# Decode generated text
+generated_text = tokenizer.decode(generated_ids[0].cpu().tolist())
+print("Generated text:", generated_text)