v1_1000_STEPS_1e8_rate_03_beta_DPO

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: 0.6933
• Rewards/chosen: -0.0011
• Rewards/rejected: -0.0010
• Rewards/accuracies: 0.4615
• Rewards/margins: -0.0001
• Logps/rejected: -16.8827
• Logps/chosen: -15.2567
• Logits/rejected: -3.3538
• Logits/chosen: -3.3539

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
0.6951	0.05	50	0.6937	-0.0020	-0.0011	0.4681	-0.0009	-16.8832	-15.2599	-3.3538	-3.3539
0.6947	0.1	100	0.6933	0.0007	0.0008	0.4484	-0.0001	-16.8769	-15.2508	-3.3538	-3.3538
0.6922	0.15	150	0.6932	0.0005	0.0004	0.4615	0.0001	-16.8783	-15.2513	-3.3537	-3.3538
0.6927	0.2	200	0.6937	-0.0002	0.0007	0.4527	-0.0009	-16.8771	-15.2536	-3.3538	-3.3538
0.6924	0.24	250	0.6927	0.0006	-0.0005	0.4593	0.0011	-16.8811	-15.2510	-3.3537	-3.3538
0.6916	0.29	300	0.6934	-0.0007	-0.0004	0.4418	-0.0003	-16.8810	-15.2554	-3.3538	-3.3538
0.6948	0.34	350	0.6932	0.0004	0.0003	0.4637	0.0001	-16.8785	-15.2516	-3.3538	-3.3539
0.6929	0.39	400	0.6925	-0.0004	-0.0018	0.4637	0.0014	-16.8855	-15.2543	-3.3538	-3.3538
0.69	0.44	450	0.6936	-0.0007	-0.0000	0.4374	-0.0007	-16.8796	-15.2555	-3.3536	-3.3537
0.694	0.49	500	0.6930	-0.0004	-0.0008	0.4505	0.0005	-16.8823	-15.2542	-3.3538	-3.3538
0.6895	0.54	550	0.6932	-0.0009	-0.0012	0.4703	0.0002	-16.8834	-15.2562	-3.3537	-3.3537
0.6955	0.59	600	0.6930	0.0007	0.0002	0.4747	0.0004	-16.8788	-15.2509	-3.3538	-3.3539
0.6903	0.64	650	0.6934	-0.0005	-0.0003	0.4593	-0.0002	-16.8804	-15.2548	-3.3537	-3.3538
0.6904	0.68	700	0.6934	-0.0004	-0.0001	0.4549	-0.0003	-16.8800	-15.2544	-3.3538	-3.3538
0.6921	0.73	750	0.6930	-0.0008	-0.0013	0.4703	0.0004	-16.8838	-15.2558	-3.3538	-3.3539
0.6945	0.78	800	0.6930	-0.0003	-0.0008	0.4813	0.0005	-16.8823	-15.2540	-3.3538	-3.3539
0.6915	0.83	850	0.6939	-0.0016	-0.0003	0.4484	-0.0014	-16.8804	-15.2585	-3.3538	-3.3539
0.6903	0.88	900	0.6933	-0.0011	-0.0010	0.4615	-0.0001	-16.8827	-15.2567	-3.3538	-3.3539
0.6971	0.93	950	0.6933	-0.0011	-0.0010	0.4615	-0.0001	-16.8827	-15.2567	-3.3538	-3.3539
0.6939	0.98	1000	0.6933	-0.0011	-0.0010	0.4615	-0.0001	-16.8827	-15.2567	-3.3538	-3.3539

Framework versions

• Transformers 4.39.1
• Pytorch 2.0.0+cu117
• Datasets 2.18.0
• Tokenizers 0.15.2