epoch=4: train_epoch_loss.item()=5.126230551155686e-09 eval_epoch_loss.item()=7.991040051891218e-10{"valid_regex_ratio": 0.0}
a52b011
| { | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "Salesforce/codegen-350M-mono", | |
| "inference_mode": true, | |
| "num_attention_heads": 16, | |
| "num_layers": 20, | |
| "num_transformer_submodules": 1, | |
| "num_virtual_tokens": 8, | |
| "peft_type": "PROMPT_TUNING", | |
| "prompt_tuning_init": "TEXT", | |
| "prompt_tuning_init_text": "Infer regex that full match the patterns provided. \nNote that:\n1. Each pattern is provided in a new line.\n2. The resulting regex should be wrapped in double quote.\n3. After the resulting regex is presented, the remaining explaination should be provided in a new line.\n\nFor example:\n\nThe patterns are:\n\"1\"\n\"2\"\n\"3\"\nThe regex is:\n\"[1-3]\"\nbecause the patterns are 1~3.\n\nNow start the real inference process:\n ", | |
| "revision": null, | |
| "task_type": "CAUSAL_LM", | |
| "token_dim": 1024, | |
| "tokenizer_name_or_path": "Salesforce/codegen-350M-mono" | |
| } |