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

Loss: 0.4980
Rewards/chosen: -2.1242
Rewards/rejected: -3.0843
Rewards/accuracies: 0.7380
Rewards/margins: 0.9601
Logps/rejected: -497.6194
Logps/chosen: -397.4371
Logits/rejected: -0.2690
Logits/chosen: -0.8689

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-07
train_batch_size: 1
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
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
0.5294	0.2617	500	0.5470	-1.7358	-2.3925	0.6980	0.6567	-428.4361	-358.6011	-0.4724	-0.8639
0.5232	0.5234	1000	0.5099	-1.9184	-2.7566	0.7160	0.8382	-464.8497	-376.8646	-0.0573	-0.6162
0.4707	0.7851	1500	0.5000	-2.1875	-3.1436	0.7320	0.9561	-503.5489	-403.7713	-0.0548	-0.6702

Framework versions

Transformers 4.40.1
Pytorch 2.1.2
Datasets 2.19.0
Tokenizers 0.19.1

DaYin/zephyr-7b-dpo-full

zephyr-7b-dpo-full

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DaYin/
zephyr-7b-dpo-full