zephyr-7b-dpo-qlora

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: 0.5473
• Rewards/chosen: -0.8609
• Rewards/rejected: -1.5251
• Rewards/accuracies: 0.7422
• Rewards/margins: 0.6641
• Logps/rejected: -404.3018
• Logps/chosen: -336.2481
• Logits/rejected: 0.0706
• Logits/chosen: -0.1471

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: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• total_eval_batch_size: 64
• 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
0.6812	0.1	100	0.6787	0.0452	0.0120	0.6992	0.0332	-250.5929	-245.6322	-2.1942	-2.2517
0.6066	0.21	200	0.6151	-0.2303	-0.5020	0.6992	0.2717	-301.9975	-273.1855	-1.9906	-2.0610
0.5711	0.31	300	0.5927	-0.4441	-0.8513	0.7188	0.4072	-336.9228	-294.5666	-1.9417	-2.0223
0.557	0.42	400	0.5817	-0.5958	-1.0732	0.7227	0.4773	-359.1117	-309.7378	-1.7434	-1.8364
0.5703	0.52	500	0.5679	-0.7215	-1.2405	0.7266	0.5189	-375.8402	-322.3068	-0.8467	-0.9967
0.5498	0.63	600	0.5582	-0.7003	-1.2848	0.7578	0.5845	-380.2699	-320.1794	-0.2510	-0.4463
0.5279	0.73	700	0.5490	-0.8400	-1.4901	0.75	0.6501	-400.8082	-334.1553	0.0145	-0.1988
0.5264	0.84	800	0.5475	-0.8613	-1.5228	0.7461	0.6615	-404.0751	-336.2833	0.0604	-0.1549
0.5639	0.94	900	0.5475	-0.8628	-1.5267	0.7422	0.6639	-404.4688	-336.4348	0.0704	-0.1466

Framework versions

• PEFT 0.7.1
• Transformers 4.36.2
• Pytorch 2.1.2+cu121
• Datasets 2.14.6
• Tokenizers 0.15.0