UTI_L3_1000steps_1e5rate_01beta_CSFTDPO

This model is a fine-tuned version of tsavage68/UTI_L3_1000steps_1e5rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0069
• Rewards/chosen: -2.8206
• Rewards/rejected: -21.2510
• Rewards/accuracies: 0.9900
• Rewards/margins: 18.4304
• Logps/rejected: -275.7048
• Logps/chosen: -60.6847
• Logits/rejected: -2.0149
• Logits/chosen: -1.9810

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-05
• 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
0.0002	0.6667	50	0.0072	0.7766	-13.0912	0.9900	13.8678	-194.1069	-24.7133	-1.5529	-1.4952
0.0173	1.3333	100	0.0086	-1.2523	-14.7999	0.9900	13.5476	-211.1941	-45.0020	-1.7169	-1.6585
0.0371	2.0	150	0.0069	-2.9050	-20.9463	0.9900	18.0414	-272.6581	-61.5287	-2.0084	-1.9771
0.0	2.6667	200	0.0069	-2.8291	-21.1059	0.9900	18.2768	-274.2534	-60.7697	-2.0121	-1.9789
0.0173	3.3333	250	0.0069	-2.8268	-21.1156	0.9900	18.2889	-274.3510	-60.7466	-2.0124	-1.9791
0.0347	4.0	300	0.0069	-2.8254	-21.1309	0.9900	18.3055	-274.5038	-60.7333	-2.0126	-1.9792
0.0173	4.6667	350	0.0069	-2.8156	-21.1516	0.9900	18.3360	-274.7103	-60.6348	-2.0131	-1.9796
0.0173	5.3333	400	0.0069	-2.8155	-21.1665	0.9900	18.3511	-274.8600	-60.6336	-2.0133	-1.9797
0.0173	6.0	450	0.0069	-2.8146	-21.1758	0.9900	18.3612	-274.9522	-60.6250	-2.0136	-1.9799
0.0347	6.6667	500	0.0069	-2.8128	-21.1899	0.9900	18.3771	-275.0935	-60.6071	-2.0140	-1.9802
0.0	7.3333	550	0.0069	-2.8143	-21.2087	0.9900	18.3944	-275.2815	-60.6221	-2.0143	-1.9804
0.0347	8.0	600	0.0069	-2.8161	-21.2215	0.9900	18.4054	-275.4096	-60.6400	-2.0144	-1.9805
0.0	8.6667	650	0.0069	-2.8197	-21.2301	0.9900	18.4104	-275.4954	-60.6758	-2.0147	-1.9807
0.0173	9.3333	700	0.0069	-2.8217	-21.2410	0.9900	18.4193	-275.6051	-60.6962	-2.0148	-1.9809
0.0	10.0	750	0.0069	-2.8204	-21.2414	0.9900	18.4210	-275.6092	-60.6834	-2.0148	-1.9809
0.0173	10.6667	800	0.0069	-2.8221	-21.2513	0.9900	18.4292	-275.7073	-60.7001	-2.0148	-1.9808
0.0	11.3333	850	0.0069	-2.8219	-21.2497	0.9900	18.4278	-275.6921	-60.6985	-2.0148	-1.9808
0.0	12.0	900	0.0069	-2.8223	-21.2528	0.9900	18.4305	-275.7229	-60.7022	-2.0151	-1.9811
0.0173	12.6667	950	0.0069	-2.8218	-21.2512	0.9900	18.4295	-275.7072	-60.6970	-2.0149	-1.9810
0.0	13.3333	1000	0.0069	-2.8206	-21.2510	0.9900	18.4304	-275.7048	-60.6847	-2.0149	-1.9810

Framework versions

• Transformers 4.41.2
• Pytorch 2.0.0+cu117
• Datasets 2.19.2
• Tokenizers 0.19.1