This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-qlora on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.6813
Rewards/chosen: -0.0009
Rewards/rejected: -0.0252
Rewards/accuracies: 0.2920
Rewards/margins: 0.0243
Logps/rejected: -71.3009
Logps/chosen: -65.4449
Logits/rejected: -2.4428
Logits/chosen: -2.4444

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: 4
total_train_batch_size: 32
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
0.69	0.26	100	0.6897	0.0232	0.0168	0.2680	0.0064	-67.1001	-63.0342	-2.4904	-2.4911
0.6869	0.52	200	0.6849	0.0066	-0.0092	0.3060	0.0159	-69.7060	-64.6950	-2.4556	-2.4573
0.681	0.78	300	0.6815	-0.0026	-0.0264	0.2880	0.0238	-71.4280	-65.6224	-2.4430	-2.4446

Framework versions

PEFT 0.7.1
Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.14.6
Tokenizers 0.15.2

lole25/zephyr-7b-dpo-qlora

zephyr-7b-dpo-qlora

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

lole25/
zephyr-7b-dpo-qlora