This model is a fine-tuned version of YYYYYYibo/vanilla_dpo_iter_2 on the updated and the original datasets. It achieves the following results on the evaluation set:

Loss: 0.5922
Rewards/chosen: -0.2212
Rewards/rejected: -0.5103
Rewards/accuracies: 0.6820
Rewards/margins: 0.2891
Logps/rejected: -335.8713
Logps/chosen: -327.4886
Logits/rejected: -2.3692
Logits/chosen: -2.4667

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 16
total_train_batch_size: 128
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.5785	0.63	100	0.5922	-0.2212	-0.5103	0.6820	0.2891	-335.8713	-327.4886	-2.3692	-2.4667

Framework versions

PEFT 0.7.1
Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.14.6
Tokenizers 0.15.2

YYYYYYibo/vanilla_dpo_iter_3

vanilla_dpo_iter_3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

YYYYYYibo/
vanilla_dpo_iter_3