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

Loss: 0.3027
Rewards/chosen: -1.2987
Rewards/rejected: -5.2535
Rewards/accuracies: 0.8697
Rewards/margins: 3.9549
Logps/rejected: -1323.0533
Logps/chosen: -663.1464
Logits/rejected: -0.3508
Logits/chosen: -0.2498

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
0.2357	0.35	100	0.3364	-0.8114	-3.4524	0.8579	2.6410	-1142.9419	-614.4251	-0.0367	0.0065
0.154	0.71	200	0.3009	-0.9868	-4.1905	0.8632	3.2037	-1216.7521	-631.9589	-0.3156	-0.2396

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.1.2
Datasets 2.14.6
Tokenizers 0.15.2

RichardErkhov/Minbyul_-_selfbiorag-7b-dpo-full-sft-wo-kqa_silver_wogold-4bits

Original model description:

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

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
Minbyul_-_selfbiorag-7b-dpo-full-sft-wo-kqa_silver_wogold-4bits