v1_1000_STEPS_1e7_rate_01_beta_DPO

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6730
• Rewards/chosen: -0.0669
• Rewards/rejected: -0.1113
• Rewards/accuracies: 0.5890
• Rewards/margins: 0.0445
• Logps/rejected: -17.9930
• Logps/chosen: -15.9218
• Logits/rejected: -3.3417
• Logits/chosen: -3.3418

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: 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
0.6944	0.05	50	0.6930	-0.0001	-0.0004	0.4791	0.0003	-16.8836	-15.2543	-3.3540	-3.3541
0.6896	0.1	100	0.6907	-0.0026	-0.0076	0.5670	0.0050	-16.9551	-15.2788	-3.3527	-3.3528
0.6879	0.15	150	0.6878	-0.0076	-0.0188	0.5736	0.0112	-17.0680	-15.3294	-3.3516	-3.3517
0.6836	0.2	200	0.6849	-0.0190	-0.0363	0.5670	0.0173	-17.2422	-15.4426	-3.3479	-3.3480
0.6804	0.24	250	0.6825	-0.0285	-0.0510	0.5868	0.0226	-17.3899	-15.5377	-3.3456	-3.3457
0.6753	0.29	300	0.6802	-0.0411	-0.0689	0.5890	0.0277	-17.5681	-15.6645	-3.3452	-3.3453
0.6908	0.34	350	0.6788	-0.0382	-0.0690	0.5956	0.0307	-17.5691	-15.6352	-3.3447	-3.3448
0.6881	0.39	400	0.6773	-0.0391	-0.0735	0.5934	0.0344	-17.6147	-15.6439	-3.3446	-3.3447
0.6519	0.44	450	0.6757	-0.0500	-0.0881	0.5912	0.0381	-17.7606	-15.7528	-3.3434	-3.3435
0.6871	0.49	500	0.6751	-0.0504	-0.0897	0.5978	0.0394	-17.7768	-15.7565	-3.3425	-3.3426
0.6495	0.54	550	0.6737	-0.0598	-0.1025	0.5934	0.0427	-17.9043	-15.8506	-3.3424	-3.3425
0.6756	0.59	600	0.6738	-0.0611	-0.1038	0.5912	0.0427	-17.9179	-15.8641	-3.3420	-3.3421
0.6584	0.64	650	0.6735	-0.0625	-0.1058	0.5890	0.0434	-17.9379	-15.8778	-3.3422	-3.3423
0.6747	0.68	700	0.6734	-0.0652	-0.1089	0.5824	0.0437	-17.9690	-15.9052	-3.3417	-3.3418
0.6735	0.73	750	0.6733	-0.0662	-0.1102	0.5670	0.0440	-17.9819	-15.9150	-3.3417	-3.3418
0.6573	0.78	800	0.6732	-0.0671	-0.1112	0.5868	0.0442	-17.9917	-15.9236	-3.3417	-3.3418
0.6768	0.83	850	0.6732	-0.0671	-0.1112	0.5934	0.0441	-17.9912	-15.9238	-3.3417	-3.3418
0.6745	0.88	900	0.6733	-0.0671	-0.1110	0.5780	0.0439	-17.9897	-15.9243	-3.3416	-3.3418
0.6751	0.93	950	0.6730	-0.0668	-0.1114	0.5868	0.0446	-17.9934	-15.9211	-3.3417	-3.3418
0.6645	0.98	1000	0.6730	-0.0669	-0.1113	0.5890	0.0445	-17.9930	-15.9218	-3.3417	-3.3418

Framework versions

• Transformers 4.39.1
• Pytorch 2.0.0+cu117
• Datasets 2.18.0
• Tokenizers 0.15.2