selfbiorag-7b-dpo-full-sft-wo-kqa_golden

This model is a fine-tuned version of Minbyul/selfbiorag-7b-wo-kqa_golden-sft on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

• Loss: 0.2401
• Rewards/chosen: -1.0928
• Rewards/rejected: -13.1704
• Rewards/accuracies: 0.8942
• Rewards/margins: 12.0777
• Logps/rejected: -2031.5652
• Logps/chosen: -567.3484
• Logits/rejected: -0.2100
• Logits/chosen: -0.3532

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: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• total_eval_batch_size: 32
• 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
0.249	0.31	100	0.3604	-0.7724	-7.8952	0.8942	7.1228	-1504.0413	-535.3107	-0.2666	-0.2359
0.1374	0.62	200	0.2389	-0.9231	-8.0656	0.9038	7.1425	-1521.0862	-550.3824	-0.1753	-0.2822
0.0982	0.92	300	0.2413	-1.0961	-13.1849	0.8942	12.0888	-2033.0142	-567.6829	-0.2111	-0.3569

Framework versions

• Transformers 4.39.0.dev0
• Pytorch 2.1.2
• Datasets 2.14.6
• Tokenizers 0.15.2