This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6930
Rewards/chosen: -0.0000
Rewards/rejected: -0.0004
Rewards/accuracies: 0.4571
Rewards/margins: 0.0004
Logps/rejected: -16.8832
Logps/chosen: -15.2532
Logits/rejected: -3.3537
Logits/chosen: -3.3538

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: 1e-08
train_batch_size: 2
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6937	0.05	50	0.6931	-0.0004	-0.0006	0.4813	0.0002	-16.8854	-15.2569	-3.3537	-3.3538
0.6934	0.1	100	0.6930	0.0001	-0.0003	0.4879	0.0004	-16.8825	-15.2519	-3.3538	-3.3539
0.6945	0.15	150	0.6931	-0.0002	-0.0003	0.4725	0.0001	-16.8826	-15.2550	-3.3538	-3.3539
0.6945	0.2	200	0.6933	-0.0003	-0.0000	0.4396	-0.0003	-16.8800	-15.2561	-3.3537	-3.3538
0.693	0.24	250	0.6933	-0.0002	0.0000	0.4352	-0.0002	-16.8791	-15.2548	-3.3538	-3.3539
0.6934	0.29	300	0.6930	0.0003	-0.0001	0.4549	0.0004	-16.8806	-15.2500	-3.3539	-3.3540
0.6945	0.34	350	0.6931	-0.0007	-0.0007	0.4462	0.0001	-16.8867	-15.2596	-3.3539	-3.3539
0.6914	0.39	400	0.6933	-0.0006	-0.0004	0.4593	-0.0002	-16.8834	-15.2590	-3.3538	-3.3538
0.693	0.44	450	0.6932	-0.0001	-0.0001	0.4396	-0.0000	-16.8802	-15.2539	-3.3537	-3.3537
0.6937	0.49	500	0.6931	-0.0002	-0.0003	0.4484	0.0001	-16.8827	-15.2548	-3.3538	-3.3539
0.6918	0.54	550	0.6930	-0.0001	-0.0004	0.4791	0.0003	-16.8834	-15.2541	-3.3537	-3.3538
0.6926	0.59	600	0.6933	-0.0005	-0.0002	0.4505	-0.0002	-16.8820	-15.2580	-3.3537	-3.3537
0.6922	0.64	650	0.6933	-0.0003	-0.0000	0.4549	-0.0003	-16.8796	-15.2563	-3.3538	-3.3539
0.693	0.68	700	0.6931	-0.0006	-0.0007	0.4484	0.0001	-16.8861	-15.2589	-3.3538	-3.3539
0.6926	0.73	750	0.6928	0.0001	-0.0006	0.4879	0.0007	-16.8858	-15.2520	-3.3538	-3.3539
0.693	0.78	800	0.6931	-0.0004	-0.0007	0.4923	0.0002	-16.8861	-15.2574	-3.3538	-3.3539
0.693	0.83	850	0.6930	-0.0002	-0.0005	0.4462	0.0003	-16.8842	-15.2548	-3.3537	-3.3538
0.6926	0.88	900	0.6930	-0.0000	-0.0004	0.4571	0.0004	-16.8832	-15.2532	-3.3537	-3.3538
0.6937	0.93	950	0.6930	-0.0000	-0.0004	0.4571	0.0004	-16.8832	-15.2532	-3.3537	-3.3538
0.6926	0.98	1000	0.6930	-0.0000	-0.0004	0.4571	0.0004	-16.8832	-15.2532	-3.3537	-3.3538

Framework versions

Transformers 4.39.1
Pytorch 2.0.0+cu117
Datasets 2.18.0
Tokenizers 0.15.2

tsavage68/v1_1000_STEPS_1e8_rate_01_beta_DPO

v1_1000_STEPS_1e8_rate_01_beta_DPO

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tsavage68/
v1_1000_STEPS_1e8_rate_01_beta_DPO