UTI2_M2_1000steps_1e8rate_05beta_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: 0.6685
• Rewards/chosen: -0.0009
• Rewards/rejected: -0.0566
• Rewards/accuracies: 0.1900
• Rewards/margins: 0.0557
• Logps/rejected: -9.4872
• Logps/chosen: -4.5444
• Logits/rejected: -2.7046
• Logits/chosen: -2.7039

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
0.6931	0.3333	25	0.6941	0.0016	0.0029	0.0700	-0.0013	-9.3682	-4.5393	-2.7069	-2.7061
0.6849	0.6667	50	0.6964	-0.0083	-0.0028	0.1100	-0.0055	-9.3796	-4.5591	-2.7057	-2.7049
0.6934	1.0	75	0.6896	-0.0050	-0.0129	0.1300	0.0079	-9.3998	-4.5524	-2.7063	-2.7056
0.6902	1.3333	100	0.6901	-0.0010	-0.0078	0.1400	0.0068	-9.3896	-4.5445	-2.7066	-2.7058
0.6931	1.6667	125	0.6876	-0.0018	-0.0134	0.1500	0.0117	-9.4008	-4.5460	-2.7054	-2.7047
0.6854	2.0	150	0.6873	0.0005	-0.0127	0.1300	0.0132	-9.3993	-4.5414	-2.7062	-2.7055
0.6833	2.3333	175	0.6804	0.0002	-0.0268	0.1800	0.0269	-9.4275	-4.5421	-2.7052	-2.7044
0.6668	2.6667	200	0.6804	0.0042	-0.0233	0.1700	0.0275	-9.4206	-4.5340	-2.7050	-2.7043
0.6881	3.0	225	0.6735	-0.0007	-0.0445	0.1900	0.0438	-9.4629	-4.5438	-2.7047	-2.7040
0.6721	3.3333	250	0.6742	0.0043	-0.0375	0.1900	0.0418	-9.4490	-4.5339	-2.7051	-2.7044
0.6714	3.6667	275	0.6696	0.0020	-0.0497	0.1900	0.0516	-9.4733	-4.5385	-2.7056	-2.7048
0.6713	4.0	300	0.6671	0.0010	-0.0558	0.2100	0.0567	-9.4855	-4.5405	-2.7042	-2.7035
0.6563	4.3333	325	0.6701	0.0019	-0.0493	0.2000	0.0512	-9.4726	-4.5387	-2.7051	-2.7043
0.6715	4.6667	350	0.6705	0.0015	-0.0473	0.2000	0.0488	-9.4685	-4.5394	-2.7041	-2.7033
0.6551	5.0	375	0.6672	0.0053	-0.0523	0.2000	0.0576	-9.4785	-4.5318	-2.7052	-2.7044
0.6601	5.3333	400	0.6664	0.0025	-0.0562	0.2100	0.0587	-9.4863	-4.5374	-2.7046	-2.7039
0.665	5.6667	425	0.6682	0.0029	-0.0517	0.2000	0.0546	-9.4773	-4.5367	-2.7042	-2.7035
0.6409	6.0	450	0.6668	0.0053	-0.0538	0.2000	0.0591	-9.4816	-4.5319	-2.7047	-2.7039
0.6649	6.3333	475	0.6660	0.0086	-0.0517	0.2000	0.0602	-9.4773	-4.5254	-2.7050	-2.7042
0.6711	6.6667	500	0.6641	0.0013	-0.0654	0.2000	0.0667	-9.5048	-4.5399	-2.7048	-2.7041
0.6583	7.0	525	0.6654	-0.0009	-0.0620	0.2100	0.0611	-9.4979	-4.5442	-2.7041	-2.7033
0.6565	7.3333	550	0.6646	0.0059	-0.0577	0.2000	0.0636	-9.4894	-4.5307	-2.7044	-2.7037
0.6666	7.6667	575	0.6661	0.0028	-0.0590	0.2000	0.0618	-9.4919	-4.5369	-2.7047	-2.7039
0.6817	8.0	600	0.6663	0.0025	-0.0569	0.2000	0.0594	-9.4877	-4.5375	-2.7046	-2.7039
0.6655	8.3333	625	0.6656	0.0029	-0.0593	0.1900	0.0622	-9.4926	-4.5367	-2.7050	-2.7043
0.6344	8.6667	650	0.6700	0.0013	-0.0500	0.1900	0.0513	-9.4740	-4.5399	-2.7051	-2.7044
0.6587	9.0	675	0.6667	-0.0021	-0.0602	0.2000	0.0581	-9.4944	-4.5466	-2.7047	-2.7040
0.6364	9.3333	700	0.6650	0.0021	-0.0606	0.2000	0.0627	-9.4952	-4.5384	-2.7052	-2.7044
0.6623	9.6667	725	0.6685	-0.0010	-0.0580	0.1900	0.0570	-9.4899	-4.5444	-2.7053	-2.7045
0.6824	10.0	750	0.6673	0.0000	-0.0577	0.1900	0.0577	-9.4894	-4.5424	-2.7050	-2.7043
0.6497	10.3333	775	0.6705	-0.0015	-0.0535	0.2000	0.0520	-9.4809	-4.5454	-2.7046	-2.7039
0.6693	10.6667	800	0.6691	-0.0010	-0.0550	0.1900	0.0540	-9.4839	-4.5445	-2.7046	-2.7039
0.6678	11.0	825	0.6670	-0.0003	-0.0609	0.1900	0.0605	-9.4957	-4.5431	-2.7046	-2.7039
0.6551	11.3333	850	0.6685	-0.0009	-0.0566	0.1900	0.0557	-9.4872	-4.5444	-2.7046	-2.7039
0.682	11.6667	875	0.6685	-0.0009	-0.0566	0.1900	0.0557	-9.4872	-4.5444	-2.7046	-2.7039
0.6731	12.0	900	0.6685	-0.0009	-0.0566	0.1900	0.0557	-9.4872	-4.5444	-2.7046	-2.7039
0.6377	12.3333	925	0.6685	-0.0009	-0.0566	0.1900	0.0557	-9.4872	-4.5444	-2.7046	-2.7039
0.6802	12.6667	950	0.6685	-0.0009	-0.0566	0.1900	0.0557	-9.4872	-4.5444	-2.7046	-2.7039
0.6716	13.0	975	0.6685	-0.0009	-0.0566	0.1900	0.0557	-9.4872	-4.5444	-2.7046	-2.7039
0.6571	13.3333	1000	0.6685	-0.0009	-0.0566	0.1900	0.0557	-9.4872	-4.5444	-2.7046	-2.7039

Framework versions

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