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: 0.6480
Rewards/chosen: -0.1617
Rewards/rejected: -0.2816
Rewards/accuracies: 0.5912
Rewards/margins: 0.1199
Logps/rejected: -17.8183
Logps/chosen: -15.7920
Logits/rejected: -3.3428
Logits/chosen: -3.3429

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
0.6922	0.05	50	0.6921	-0.0001	-0.0024	0.5033	0.0023	-16.8875	-15.2533	-3.3539	-3.3540
0.6805	0.1	100	0.6859	-0.0076	-0.0233	0.5626	0.0156	-16.9571	-15.2784	-3.3527	-3.3527
0.684	0.15	150	0.6780	-0.0207	-0.0549	0.5758	0.0342	-17.0624	-15.3221	-3.3514	-3.3515
0.668	0.2	200	0.6712	-0.0524	-0.1041	0.5736	0.0517	-17.2267	-15.4277	-3.3479	-3.3480
0.6602	0.24	250	0.6656	-0.0787	-0.1439	0.5802	0.0651	-17.3591	-15.5155	-3.3454	-3.3455
0.6512	0.29	300	0.6625	-0.1164	-0.1922	0.5780	0.0758	-17.5202	-15.6409	-3.3452	-3.3453
0.6949	0.34	350	0.6586	-0.1002	-0.1858	0.5956	0.0855	-17.4988	-15.5872	-3.3448	-3.3449
0.6836	0.39	400	0.6558	-0.0983	-0.1934	0.5890	0.0952	-17.5242	-15.5806	-3.3452	-3.3453
0.5895	0.44	450	0.6530	-0.1263	-0.2307	0.5846	0.1044	-17.6486	-15.6741	-3.3440	-3.3441
0.6855	0.49	500	0.6504	-0.1226	-0.2329	0.5890	0.1103	-17.6558	-15.6618	-3.3435	-3.3436
0.5863	0.54	550	0.6497	-0.1490	-0.2631	0.5868	0.1142	-17.7566	-15.7496	-3.3433	-3.3434
0.6496	0.59	600	0.6496	-0.1503	-0.2653	0.5868	0.1150	-17.7639	-15.7542	-3.3431	-3.3432
0.6113	0.64	650	0.6478	-0.1488	-0.2683	0.5934	0.1195	-17.7738	-15.7490	-3.3432	-3.3433
0.6582	0.68	700	0.6482	-0.1563	-0.2757	0.5890	0.1194	-17.7985	-15.7741	-3.3428	-3.3429
0.6477	0.73	750	0.6476	-0.1590	-0.2798	0.5868	0.1208	-17.8123	-15.7831	-3.3428	-3.3430
0.6137	0.78	800	0.6477	-0.1601	-0.2804	0.5912	0.1203	-17.8141	-15.7867	-3.3427	-3.3429
0.6539	0.83	850	0.6475	-0.1611	-0.2818	0.5890	0.1207	-17.8188	-15.7899	-3.3428	-3.3429
0.6508	0.88	900	0.6477	-0.1607	-0.2816	0.5912	0.1209	-17.8182	-15.7887	-3.3428	-3.3430
0.6543	0.93	950	0.6482	-0.1619	-0.2813	0.5934	0.1194	-17.8172	-15.7927	-3.3428	-3.3429
0.6219	0.98	1000	0.6480	-0.1617	-0.2816	0.5912	0.1199	-17.8183	-15.7920	-3.3428	-3.3429

Framework versions

Transformers 4.39.1
Pytorch 2.0.0+cu117
Datasets 2.18.0
Tokenizers 0.15.2

tsavage68/v1_1000_STEPS_1e7_rate_03_beta_DPO

v1_1000_STEPS_1e7_rate_03_beta_DPO

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tsavage68/
v1_1000_STEPS_1e7_rate_03_beta_DPO