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: 0.0539
Rewards/chosen: 0.0016
Rewards/rejected: 0.0019
Rewards/accuracies: 0.4295
Rewards/margins: -0.0003
Logps/rejected: -365.2908
Logps/chosen: -394.6620
Logits/rejected: -0.7531
Logits/chosen: -0.8487

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	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.1204	0.32	100	0.0538	0.0003	0.0005	0.4400	-0.0002	-366.7078	-395.9741	-0.7404	-0.8344
0.1673	0.64	200	0.0538	0.0014	0.0014	0.4465	-0.0001	-365.8175	-394.9525	-0.7452	-0.8413
0.1395	0.96	300	0.0538	0.0016	0.0018	0.4410	-0.0002	-365.4079	-394.7079	-0.7549	-0.8511

Framework versions

PEFT 0.7.1
Transformers 4.36.2
Pytorch 2.1.2
Datasets 2.14.6
Tokenizers 0.15.2

DUAL-GPO-2/phi-2-gpo-renew2-b0.001-v2-i1

phi-2-gpo-renew2-b0.001-v2-i1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DUAL-GPO-2/
phi-2-gpo-renew2-b0.001-v2-i1