| import os |
|
|
| import torch |
| from transformers import AutoTokenizer |
|
|
| from modeling_reward import load_finetuned_model |
|
|
|
|
| def main(): |
| repo_root = os.path.dirname(os.path.abspath(__file__)) |
| tokenizer = AutoTokenizer.from_pretrained(repo_root) |
| model = load_finetuned_model(repo_root) |
|
|
| sql = "SELECT COUNT(*) FROM orders WHERE status = 'complete';" |
| reasoning = "think: Count rows in orders filtered by status 'complete'." |
| nl = "How many completed orders exist?" |
| text = f"SQL: {sql}\nReasoning: {reasoning}\nNL: {nl}" |
|
|
| inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=2048) |
| with torch.no_grad(): |
| score = model(**inputs)["scores"].item() |
|
|
| print(f"Reward score: {score:.3f}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
|
|
