| |
| """Test di inferenza locale per Transformer Baseline 45M (Vocab 1.2k pezzi).""" |
| from pathlib import Path |
| from modeling_transformer import TransformerModel, TransformerTokenizer |
|
|
| ROOT = Path(__file__).resolve().parent |
|
|
| def main(): |
| print("1. Caricamento Tokenizer (1.2k pezzi)...") |
| tokenizer = TransformerTokenizer.from_pretrained(ROOT) |
|
|
| print("2. Caricamento Transformer Baseline (Midtrain 10k)...") |
| model = TransformerModel.from_pretrained(ROOT, weights_file="model.safetensors") |
|
|
| prompt = "La capitale d'Italia è " |
| print(f"\n--- Test Prompt: '{prompt}' ---") |
| gen = model.generate(prompt=prompt, tokenizer=tokenizer, max_new_tokens=30, temperature=0.0) |
| print(f"Risposta Midtrain (greedy): {prompt}{gen}") |
|
|
| print("\n3. Caricamento Transformer Baseline (Pretrain 185k)...") |
| model_pre = TransformerModel.from_pretrained(ROOT, weights_file="model_pretrain_185k.safetensors") |
| gen_pre = model_pre.generate(prompt=prompt, tokenizer=tokenizer, max_new_tokens=30, temperature=0.0) |
| print(f"Risposta Pretrain 185k (greedy): {prompt}{gen_pre}") |
|
|
| if __name__ == "__main__": |
| main() |
|
|
