UTI2_L3_300steps_1e7rate_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: 0.5211
• Rewards/chosen: 0.1947
• Rewards/rejected: -0.2183
• Rewards/accuracies: 0.6500
• Rewards/margins: 0.4131
• Logps/rejected: -30.6679
• Logps/chosen: -17.1558
• Logits/rejected: -1.1555
• Logits/chosen: -1.1504

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-07
• 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: 300

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6928	0.3333	25	0.6924	0.0009	-0.0007	0.3600	0.0016	-28.4922	-19.0947	-1.1524	-1.1488
0.6893	0.6667	50	0.6863	0.0103	-0.0035	0.6100	0.0138	-28.5194	-19.0000	-1.1524	-1.1488
0.6736	1.0	75	0.6701	0.0321	-0.0151	0.6300	0.0471	-28.6352	-18.7825	-1.1527	-1.1490
0.622	1.3333	100	0.6366	0.0753	-0.0439	0.6400	0.1192	-28.9234	-18.3503	-1.1534	-1.1493
0.5799	1.6667	125	0.5944	0.1218	-0.0954	0.6400	0.2172	-29.4390	-17.8854	-1.1535	-1.1491
0.5812	2.0	150	0.5630	0.1556	-0.1409	0.6500	0.2965	-29.8935	-17.5476	-1.1544	-1.1497
0.5284	2.3333	175	0.5418	0.1752	-0.1786	0.6500	0.3538	-30.2706	-17.3511	-1.1548	-1.1499
0.4992	2.6667	200	0.5285	0.1875	-0.2039	0.6500	0.3913	-30.5232	-17.2286	-1.1552	-1.1502
0.4892	3.0	225	0.5235	0.1916	-0.2145	0.6500	0.4061	-30.6293	-17.1869	-1.1554	-1.1503
0.4895	3.3333	250	0.5212	0.1956	-0.2171	0.6500	0.4127	-30.6554	-17.1470	-1.1554	-1.1503
0.4676	3.6667	275	0.5216	0.1945	-0.2170	0.6500	0.4115	-30.6547	-17.1581	-1.1553	-1.1502
0.5106	4.0	300	0.5211	0.1947	-0.2183	0.6500	0.4131	-30.6679	-17.1558	-1.1555	-1.1504

Framework versions

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