Instructions to use LRJ1981/RITO_Version1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LRJ1981/RITO_Version1.0 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("LRJ1981/RITO_Version1.0") model = AutoModelForSeq2SeqLM.from_pretrained("LRJ1981/RITO_Version1.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "data_path": "autotrain-9t83i-0umcp/autotrain-data", | |
| "model": "LRJ1981/autotrain-redigeretData-version21", | |
| "username": "LRJ1981", | |
| "seed": 42, | |
| "train_split": "train", | |
| "valid_split": "validation", | |
| "project_name": "autotrain-9t83i-0umcp", | |
| "push_to_hub": true, | |
| "text_column": "autotrain_text", | |
| "target_column": "autotrain_label", | |
| "lr": 5e-05, | |
| "epochs": 20, | |
| "max_seq_length": 128, | |
| "max_target_length": 128, | |
| "batch_size": 2, | |
| "warmup_ratio": 0.1, | |
| "gradient_accumulation": 1, | |
| "optimizer": "adamw_torch", | |
| "scheduler": "linear", | |
| "weight_decay": 0.0, | |
| "max_grad_norm": 1.0, | |
| "logging_steps": -1, | |
| "evaluation_strategy": "epoch", | |
| "auto_find_batch_size": false, | |
| "mixed_precision": "fp16", | |
| "save_total_limit": 1, | |
| "peft": false, | |
| "quantization": "int4", | |
| "lora_r": 16, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.05, | |
| "target_modules": "all-linear", | |
| "log": "tensorboard" | |
| } |