This model is a fine-tuned version of YYYYYYibo/nash_dpo_rank4_on_vanilla_iter_1 on the updated and the original datasets. It achieves the following results on the evaluation set:

Loss: 0.6580
Rewards/chosen: -0.1638
Rewards/rejected: -0.2696
Rewards/accuracies: 0.5920
Rewards/margins: 0.1058
Logps/rejected: -289.0013
Logps/chosen: -304.2577
Logits/rejected: -2.3485
Logits/chosen: -2.4282

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
0.5679	0.64	100	0.6580	-0.1638	-0.2696	0.5920	0.1058	-289.0013	-304.2577	-2.3485	-2.4282

Framework versions

PEFT 0.7.1
Transformers 4.36.2
Pytorch 2.3.0+cu121
Datasets 2.14.6
Tokenizers 0.15.2

YYYYYYibo/nash_dpo_rank4_on_vanilla_iter_2

nash_dpo_rank4_on_vanilla_iter_2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

YYYYYYibo/
nash_dpo_rank4_on_vanilla_iter_2