Instructions to use Jayfeather1024/rm_30k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jayfeather1024/rm_30k with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, LlamaModelForScore tokenizer = AutoTokenizer.from_pretrained("Jayfeather1024/rm_30k") model = LlamaModelForScore.from_pretrained("Jayfeather1024/rm_30k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download arguments.json from Jayfeather1024/rm_30k: direct link, hf CLI and curl.
- Browser
- Download file 1.76 kB
-
https://huggingface.co/Jayfeather1024/rm_30k/resolve/main/arguments.json
- Command line
-
hf download hf://Jayfeather1024/rm_30k/arguments.json
-
curl -L -o arguments.json https://huggingface.co/Jayfeather1024/rm_30k/resolve/main/arguments.json
1.76 kB
| { | |
| "model_name_or_path": "output/sft", | |
| "max_length": 512, | |
| "trust_remote_code": true, | |
| "train_datasets": [ | |
| [ | |
| "PKU-SafeRLHF/train", | |
| { | |
| "proportion": 1.0, | |
| "path": "PKU-SafeRLHF-harmless-only-30k" | |
| } | |
| ] | |
| ], | |
| "eval_datasets": [ | |
| [ | |
| "PKU-SafeRLHF/test", | |
| { | |
| "proportion": 1.0 | |
| } | |
| ] | |
| ], | |
| "loss_type": "sequence-wise", | |
| "epochs": 2, | |
| "per_device_train_batch_size": 16, | |
| "per_device_eval_batch_size": 16, | |
| "gradient_accumulation_steps": 2, | |
| "gradient_checkpointing": true, | |
| "normalize_score_during_training": false, | |
| "normalizer_type": "ExponentialMovingAverage", | |
| "normalizer_momentum": 0.9, | |
| "lr": 2e-05, | |
| "lr_scheduler_type": "cosine", | |
| "lr_warmup_ratio": 0.03, | |
| "weight_decay": 0.1, | |
| "seed": 42, | |
| "fp16": false, | |
| "bf16": true, | |
| "tf32": true, | |
| "eval_strategy": "epoch", | |
| "eval_interval": 1000000, | |
| "need_eval": false, | |
| "eval_split_ratio": null, | |
| "output_dir": "/data/jiongxiao_wang/rlhf_attack/safe-rlhf/output/rm_30k", | |
| "log_type": "wandb", | |
| "log_dir": "/data/jiongxiao_wang/rlhf_attack/safe-rlhf/output/rm_30k", | |
| "log_project": "Safe-RLHF-RM", | |
| "log_run_name": "reward-2024-01-05-20-03-25", | |
| "save_16bit": false, | |
| "save_interval": 1000000, | |
| "local_rank": 0, | |
| "zero_stage": 3, | |
| "deepspeed": false, | |
| "deepspeed_config": null, | |
| "deepscale": false, | |
| "deepscale_config": null, | |
| "deepspeed_mpi": false, | |
| "global_rank": 0, | |
| "device": { | |
| "type": "torch.device", | |
| "repr": "device(type='cuda', index=0)" | |
| }, | |
| "num_update_steps_per_epoch": 210, | |
| "total_training_steps": 420 | |
| } |