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: 0.9939
Rewards/chosen: -3.9532
Rewards/rejected: -5.6547
Rewards/accuracies: 0.6000
Rewards/margins: 1.7015
Logps/rejected: -85.1197
Logps/chosen: -62.9180
Logits/rejected: -2.0229
Logits/chosen: -2.0243

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
0.6073	0.1	50	0.6623	-1.2716	-1.5743	0.5736	0.3026	-44.3150	-36.1020	-2.8014	-2.8019
0.7223	0.2	100	0.7934	-3.0203	-3.2538	0.5077	0.2336	-61.1108	-53.5883	-2.4237	-2.4243
0.8563	0.29	150	0.7580	-1.8675	-2.3470	0.5604	0.4795	-52.0427	-42.0607	-2.5521	-2.5529
0.7701	0.39	200	0.7631	-1.8702	-2.1583	0.5231	0.2882	-50.1556	-42.0875	-2.7052	-2.7056
0.8749	0.49	250	0.7941	-2.4787	-2.6066	0.4879	0.1279	-54.6385	-48.1731	-2.8184	-2.8189
0.6954	0.59	300	0.8039	-1.5721	-1.9872	0.5473	0.4151	-48.4439	-39.1064	-2.8263	-2.8268
0.733	0.68	350	0.7751	-0.5753	-1.0891	0.5253	0.5138	-39.4632	-29.1387	-2.7587	-2.7591
0.8256	0.78	400	0.7376	-1.2950	-1.7911	0.5516	0.4962	-46.4838	-36.3354	-2.9702	-2.9707
0.6485	0.88	450	0.7344	-1.7798	-2.3960	0.5692	0.6162	-52.5322	-41.1838	-2.7167	-2.7174
0.612	0.98	500	0.7051	-1.3500	-2.0968	0.5978	0.7467	-49.5400	-36.8863	-2.5131	-2.5138
0.2108	1.07	550	0.7799	-2.0131	-3.4580	0.6418	1.4449	-63.1524	-43.5171	-2.2469	-2.2482
0.1378	1.17	600	0.9314	-3.4717	-5.1214	0.6198	1.6497	-79.7863	-58.1027	-1.9917	-1.9933
0.188	1.27	650	0.9857	-3.6647	-5.3449	0.6198	1.6803	-82.0219	-60.0328	-1.9585	-1.9601
0.3739	1.37	700	1.0046	-3.6506	-5.3352	0.6176	1.6846	-81.9245	-59.8915	-2.0334	-2.0349
0.0428	1.46	750	0.9881	-3.8094	-5.4955	0.6088	1.6861	-83.5278	-61.4803	-2.0272	-2.0287
0.131	1.56	800	0.9900	-3.9653	-5.6306	0.6022	1.6653	-84.8782	-63.0390	-2.0228	-2.0242
0.1558	1.66	850	0.9943	-3.9735	-5.6628	0.6000	1.6893	-85.2000	-63.1207	-2.0177	-2.0191
0.1876	1.76	900	0.9939	-3.9576	-5.6566	0.6000	1.6989	-85.1381	-62.9622	-2.0227	-2.0241
0.1415	1.86	950	0.9945	-3.9552	-5.6536	0.6022	1.6984	-85.1084	-62.9377	-2.0232	-2.0246
0.1163	1.95	1000	0.9939	-3.9532	-5.6547	0.6000	1.7015	-85.1197	-62.9180	-2.0229	-2.0243

Framework versions

Transformers 4.38.2
Pytorch 2.0.0+cu117
Datasets 2.18.0
Tokenizers 0.15.2

tsavage68/mistralit2_1000_STEPS_1e6_rate_0.1_beta_DPO

mistralit2_1000_STEPS_1e5_rate_0.1_beta_DPO

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tsavage68/
mistralit2_1000_STEPS_1e6_rate_0.1_beta_DPO