This model is a fine-tuned version of DUAL-GPO/phi-2-gpo-renew2-b0.001-i0 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.0538
Rewards/chosen: 0.0010
Rewards/rejected: 0.0012
Rewards/accuracies: 0.4290
Rewards/margins: -0.0002
Logps/rejected: -366.0280
Logps/chosen: -395.2844
Logits/rejected: -0.7463
Logits/chosen: -0.8436

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: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 4
total_train_batch_size: 16
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	Logits/chosen	Logits/rejected	Logps/chosen	Logps/rejected	Validation Loss	Rewards/accuracies	Rewards/chosen	Rewards/margins	Rewards/rejected
0.1204	0.32	100	-0.8372	-0.7448	-396.1279	-367.0282	0.0537	0.4495	0.0002	-0.0000	0.0002
0.1673	0.64	200	0.0538	0.0013	0.0015	0.4305	-0.0002	-365.7495	-395.0410	-0.7569	-0.8518
0.1395	0.96	300	0.0538	0.0010	0.0012	0.4395	-0.0002	-365.9886	-395.3006	-0.7587	-0.8541

Framework versions

PEFT 0.7.1
Transformers 4.36.2
Pytorch 2.1.2
Datasets 2.14.6
Tokenizers 0.15.2

DUAL-GPO/phi-2-gpo-renew2-b0.001-i1

phi-2-gpo-renew2-b0.001-i1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DUAL-GPO/
phi-2-gpo-renew2-b0.001-i1