This model is a fine-tuned version of DUAL-GPO/phi-2-gpo-test-longest-iter-v1-3 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.0015
Rewards/chosen: 0.0002
Rewards/rejected: -0.0007
Rewards/accuracies: 0.5360
Rewards/margins: 0.0010
Logps/rejected: -278.6109
Logps/chosen: -306.2527
Logits/rejected: 0.0934
Logits/chosen: -0.0054

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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.001	1.6	100	0.0015	0.0000	-0.0003	0.5040	0.0004	-278.5704	-306.2733	0.0926	-0.0051
0.001	3.2	200	0.0015	-0.0000	-0.0004	0.4885	0.0004	-278.5775	-306.2765	0.0977	-0.0020

Framework versions

PEFT 0.7.1
Transformers 4.36.2
Pytorch 2.2.1+cu121
Datasets 2.14.6
Tokenizers 0.15.2

DUAL-GPO/phi-2-gpo-test-longest-iter-v1-4

phi-2-gpo-test-longest-iter-v1-4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DUAL-GPO/
phi-2-gpo-test-longest-iter-v1-4