Summary_L3_1000steps_1e6rate_03beta_CSFTDPO

This model is a fine-tuned version of tsavage68/Summary_L3_1000steps_1e7rate_SFT2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.5961
• Rewards/chosen: 0.0294
• Rewards/rejected: -2.5656
• Rewards/accuracies: 0.1400
• Rewards/margins: 2.5950
• Logps/rejected: -23.8158
• Logps/chosen: -9.2849
• Logits/rejected: -1.1435
• Logits/chosen: -1.1436

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-06
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 4
• 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
0.5553	0.2004	50	0.5962	0.0778	-1.2696	0.1400	1.3473	-19.4956	-9.1236	-1.1038	-1.1053
0.6585	0.4008	100	0.5962	0.0854	-1.4439	0.1400	1.5292	-20.0766	-9.0982	-1.1078	-1.1092
0.6238	0.6012	150	0.5961	0.0687	-2.1556	0.1400	2.2243	-22.4490	-9.1538	-1.1298	-1.1306
0.6065	0.8016	200	0.5961	0.0322	-2.5726	0.1400	2.6048	-23.8390	-9.2754	-1.1437	-1.1438
0.6238	1.0020	250	0.5961	0.0294	-2.5678	0.1400	2.5971	-23.8230	-9.2849	-1.1438	-1.1440
0.6238	1.2024	300	0.5961	0.0279	-2.5674	0.1400	2.5953	-23.8219	-9.2899	-1.1439	-1.1440
0.6238	1.4028	350	0.5961	0.0304	-2.5648	0.1400	2.5952	-23.8131	-9.2814	-1.1438	-1.1439
0.5718	1.6032	400	0.5961	0.0304	-2.5648	0.1400	2.5952	-23.8131	-9.2814	-1.1438	-1.1439
0.5892	1.8036	450	0.5961	0.0338	-2.5715	0.1400	2.6052	-23.8353	-9.2702	-1.1435	-1.1436
0.5718	2.0040	500	0.5961	0.0279	-2.5720	0.1400	2.5999	-23.8372	-9.2897	-1.1434	-1.1435
0.5718	2.2044	550	0.5961	0.0266	-2.5750	0.1400	2.6016	-23.8472	-9.2942	-1.1438	-1.1440
0.5545	2.4048	600	0.5961	0.0271	-2.5761	0.1400	2.6032	-23.8507	-9.2925	-1.1438	-1.1440
0.5199	2.6052	650	0.5961	0.0271	-2.5761	0.1400	2.6032	-23.8507	-9.2925	-1.1438	-1.1440
0.6238	2.8056	700	0.5961	0.0270	-2.5764	0.1400	2.6035	-23.8519	-9.2928	-1.1438	-1.1440
0.6065	3.0060	750	0.5961	0.0315	-2.5674	0.1400	2.5989	-23.8216	-9.2777	-1.1434	-1.1436
0.6412	3.2064	800	0.5961	0.0276	-2.5662	0.1400	2.5937	-23.8176	-9.2909	-1.1434	-1.1436
0.6585	3.4068	850	0.5961	0.0277	-2.5666	0.1400	2.5943	-23.8191	-9.2903	-1.1434	-1.1436
0.6238	3.6072	900	0.5961	0.0281	-2.5670	0.1400	2.5952	-23.8205	-9.2891	-1.1434	-1.1436
0.5372	3.8076	950	0.5961	0.0310	-2.5656	0.1400	2.5966	-23.8159	-9.2795	-1.1435	-1.1436
0.6238	4.0080	1000	0.5961	0.0294	-2.5656	0.1400	2.5950	-23.8158	-9.2849	-1.1435	-1.1436

Framework versions

• Transformers 4.41.2
• Pytorch 2.0.0+cu117
• Datasets 2.20.0
• Tokenizers 0.19.1