mistralit2_1000_STEPS_1e6_05_beta_DPO

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.7261
• Rewards/chosen: -2.7031
• Rewards/rejected: -5.5561
• Rewards/accuracies: 0.5890
• Rewards/margins: 2.8530
• Logps/rejected: -39.6846
• Logps/chosen: -28.7920
• Logits/rejected: -2.5943
• Logits/chosen: -2.5947

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: 4
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• 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
0.7251	0.1	50	0.8837	0.1755	-0.1037	0.4901	0.2792	-28.7799	-23.0348	-2.8359	-2.8362
0.9163	0.2	100	1.7788	-4.6432	-6.2118	0.5231	1.5686	-40.9959	-32.6723	-2.6192	-2.6196
2.5499	0.29	150	1.9611	-3.8807	-4.8711	0.5033	0.9904	-38.3145	-31.1472	-2.8718	-2.8723
1.6289	0.39	200	2.1262	-4.2615	-4.3039	0.4462	0.0423	-37.1802	-31.9089	-2.5439	-2.5442
2.3907	0.49	250	2.1527	-2.9174	-2.6939	0.4527	-0.2235	-33.9602	-29.2207	-2.7643	-2.7646
1.4887	0.59	300	2.2144	-2.7649	-3.3119	0.4725	0.5470	-35.1962	-28.9157	-2.7607	-2.7611
1.9594	0.68	350	2.1934	-0.0315	0.0006	0.4593	-0.0322	-28.5711	-23.4489	-2.6191	-2.6193
2.1399	0.78	400	1.9044	-4.4917	-5.1288	0.4989	0.6371	-38.8300	-32.3693	-2.8491	-2.8494
1.1937	0.88	450	1.9658	-2.8086	-3.5888	0.4989	0.7802	-35.7500	-29.0030	-2.8330	-2.8333
1.6222	0.98	500	1.8626	-2.3058	-3.5222	0.5363	1.2164	-35.6167	-27.9974	-2.7302	-2.7305
0.5066	1.07	550	1.8660	-2.9490	-5.0994	0.5758	2.1504	-38.7712	-29.2838	-2.7083	-2.7087
0.4413	1.17	600	1.7645	-4.3370	-6.8789	0.5868	2.5419	-42.3302	-32.0597	-2.6355	-2.6360
0.2726	1.27	650	1.7971	-1.8488	-4.1281	0.5780	2.2793	-36.8285	-27.0834	-2.6083	-2.6085
0.2803	1.37	700	1.7498	-2.2886	-4.8524	0.5802	2.5639	-38.2772	-27.9629	-2.6089	-2.6092
0.199	1.46	750	1.7383	-2.5467	-5.2810	0.5868	2.7343	-39.1343	-28.4792	-2.5998	-2.6002
0.2405	1.56	800	1.7280	-2.4873	-5.2804	0.5890	2.7931	-39.1332	-28.3604	-2.5980	-2.5984
0.2125	1.66	850	1.7269	-2.6426	-5.4648	0.5846	2.8223	-39.5021	-28.6710	-2.5949	-2.5953
0.3193	1.76	900	1.7253	-2.6905	-5.5366	0.5912	2.8461	-39.6456	-28.7668	-2.5945	-2.5949
0.3209	1.86	950	1.7242	-2.6996	-5.5548	0.5912	2.8552	-39.6820	-28.7851	-2.5942	-2.5946
0.278	1.95	1000	1.7261	-2.7031	-5.5561	0.5890	2.8530	-39.6846	-28.7920	-2.5943	-2.5947

Framework versions

• Transformers 4.38.2
• Pytorch 2.0.0+cu117
• Datasets 2.18.0
• Tokenizers 0.15.2