MPT_1000_STEPS_1e8_rate_01_beta_DPO

This model is a fine-tuned version of mosaicml/mpt-7b-instruct on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6939
• Rewards/chosen: -0.0008
• Rewards/rejected: 0.0005
• Rewards/accuracies: 0.4747
• Rewards/margins: -0.0013
• Logps/rejected: -21.5525
• Logps/chosen: -20.8004
• Logits/rejected: 14.2517
• Logits/chosen: 14.2543

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
0.6939	0.05	50	0.6936	-0.0008	-0.0000	0.4791	-0.0007	-21.5578	-20.7998	14.2549	14.2575
0.6946	0.1	100	0.6930	-0.0001	-0.0005	0.4923	0.0004	-21.5626	-20.7929	14.2554	14.2579
0.6931	0.15	150	0.6935	-0.0014	-0.0009	0.4967	-0.0005	-21.5666	-20.8066	14.2572	14.2598
0.6917	0.2	200	0.6929	0.0003	-0.0004	0.4813	0.0006	-21.5611	-20.7895	14.2562	14.2588
0.6954	0.24	250	0.6940	-0.0021	-0.0006	0.4857	-0.0015	-21.5632	-20.8129	14.2623	14.2649
0.6932	0.29	300	0.6931	-0.0008	-0.0009	0.4967	0.0001	-21.5667	-20.8001	14.2610	14.2636
0.6954	0.34	350	0.6934	-0.0012	-0.0009	0.5011	-0.0003	-21.5662	-20.8041	14.2641	14.2667
0.6891	0.39	400	0.6945	-0.0025	0.0001	0.4725	-0.0026	-21.5566	-20.8174	14.2546	14.2572
0.6917	0.44	450	0.6935	-0.0011	-0.0005	0.4593	-0.0006	-21.5622	-20.8030	14.2562	14.2588
0.6908	0.49	500	0.6936	-0.0018	-0.0010	0.4813	-0.0007	-21.5679	-20.8101	14.2507	14.2533
0.6927	0.54	550	0.6941	-0.0016	0.0001	0.4681	-0.0017	-21.5560	-20.8079	14.2549	14.2575
0.6923	0.59	600	0.6939	-0.0011	0.0003	0.4527	-0.0014	-21.5542	-20.8035	14.2539	14.2565
0.6946	0.64	650	0.6944	-0.0013	0.0012	0.4593	-0.0024	-21.5459	-20.8048	14.2527	14.2553
0.6918	0.68	700	0.6934	-0.0002	0.0002	0.4747	-0.0004	-21.5558	-20.7942	14.2531	14.2557
0.6923	0.73	750	0.6939	-0.0011	0.0002	0.4967	-0.0013	-21.5551	-20.8028	14.2522	14.2547
0.6895	0.78	800	0.6937	-0.0006	0.0004	0.4945	-0.0010	-21.5532	-20.7977	14.2513	14.2539
0.6936	0.83	850	0.6937	-0.0007	0.0003	0.4945	-0.0010	-21.5541	-20.7990	14.2516	14.2542
0.6904	0.88	900	0.6939	-0.0008	0.0005	0.4747	-0.0013	-21.5525	-20.8004	14.2517	14.2543
0.6926	0.93	950	0.6939	-0.0008	0.0005	0.4747	-0.0013	-21.5525	-20.8004	14.2517	14.2543
0.691	0.98	1000	0.6939	-0.0008	0.0005	0.4747	-0.0013	-21.5525	-20.8004	14.2517	14.2543

Framework versions

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