UTI2_M2_300steps_1e8rate_03beta_CSFTDPO

This model is a fine-tuned version of tsavage68/UTI_M2_1000steps_1e7rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6666
• Rewards/chosen: 0.0060
• Rewards/rejected: -0.0495
• Rewards/accuracies: 0.7100
• Rewards/margins: 0.0555
• Logps/rejected: -39.5212
• Logps/chosen: -19.9016
• Logits/rejected: -2.6824
• Logits/chosen: -2.6798

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: 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
0.6931	0.3333	25	0.6924	0.0019	0.0000	0.1900	0.0018	-39.3560	-19.9153	-2.6832	-2.6806
0.6873	0.6667	50	0.6859	0.0055	-0.0103	0.5400	0.0158	-39.3904	-19.9033	-2.6823	-2.6798
0.6944	1.0	75	0.6937	-0.0057	-0.0058	0.4500	0.0001	-39.3756	-19.9405	-2.6824	-2.6798
0.6899	1.3333	100	0.6855	-0.0113	-0.0281	0.5600	0.0168	-39.4498	-19.9591	-2.6820	-2.6794
0.6858	1.6667	125	0.6752	0.0071	-0.0308	0.6400	0.0379	-39.4588	-19.8979	-2.6822	-2.6796
0.6767	2.0	150	0.6734	0.0063	-0.0351	0.6600	0.0415	-39.4733	-19.9004	-2.6827	-2.6802
0.6625	2.3333	175	0.6598	0.0094	-0.0599	0.7600	0.0693	-39.5558	-19.8902	-2.6809	-2.6783
0.6658	2.6667	200	0.6644	0.0077	-0.0525	0.6900	0.0602	-39.5310	-19.8958	-2.6823	-2.6797
0.6793	3.0	225	0.6654	0.0092	-0.0489	0.7200	0.0581	-39.5192	-19.8907	-2.6819	-2.6793
0.6836	3.3333	250	0.6662	0.0062	-0.0499	0.7300	0.0561	-39.5225	-19.9009	-2.6824	-2.6798
0.6704	3.6667	275	0.6666	0.0060	-0.0495	0.7100	0.0555	-39.5212	-19.9016	-2.6824	-2.6798
0.6726	4.0	300	0.6666	0.0060	-0.0495	0.7100	0.0555	-39.5212	-19.9016	-2.6824	-2.6798

Framework versions

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