This model is a fine-tuned version of tsavage68/IE_L3_1000steps_1e6rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6901
Rewards/chosen: -0.0305
Rewards/rejected: -0.0517
Rewards/accuracies: 0.4200
Rewards/margins: 0.0213
Logps/rejected: -75.7307
Logps/chosen: -82.8587
Logits/rejected: -0.7970
Logits/chosen: -0.7401

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-08
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
0.6994	0.4	50	0.7013	-0.0193	-0.0168	0.375	-0.0025	-75.6609	-82.8363	-0.7968	-0.7397
0.7002	0.8	100	0.7038	-0.0158	-0.0084	0.3450	-0.0074	-75.6441	-82.8293	-0.7971	-0.7401
0.6907	1.2	150	0.7016	-0.0214	-0.0182	0.3800	-0.0033	-75.6636	-82.8406	-0.7968	-0.7396
0.7125	1.6	200	0.6880	-0.0323	-0.0559	0.4100	0.0236	-75.7390	-82.8623	-0.7969	-0.7398
0.6784	2.0	250	0.7048	-0.0506	-0.0419	0.3800	-0.0087	-75.7110	-82.8989	-0.7967	-0.7399
0.7093	2.4	300	0.6873	-0.0310	-0.0578	0.4400	0.0268	-75.7429	-82.8598	-0.7973	-0.7402
0.6769	2.8	350	0.6770	-0.0179	-0.0654	0.4200	0.0475	-75.7580	-82.8335	-0.7972	-0.7402
0.6876	3.2	400	0.6995	-0.0297	-0.0340	0.3500	0.0044	-75.6953	-82.8571	-0.7966	-0.7395
0.6809	3.6	450	0.6703	-0.0395	-0.1022	0.4600	0.0627	-75.8316	-82.8767	-0.7972	-0.7402
0.6812	4.0	500	0.6853	-0.0127	-0.0416	0.3900	0.0289	-75.7105	-82.8232	-0.7972	-0.7404
0.7342	4.4	550	0.6907	-0.0234	-0.0410	0.4150	0.0176	-75.7092	-82.8446	-0.7966	-0.7396
0.6772	4.8	600	0.6824	-0.0324	-0.0676	0.4450	0.0352	-75.7624	-82.8625	-0.7968	-0.7399
0.6918	5.2	650	0.6813	-0.0468	-0.0861	0.3950	0.0393	-75.7994	-82.8913	-0.7973	-0.7402
0.6778	5.6	700	0.6899	-0.0390	-0.0590	0.4250	0.0200	-75.7452	-82.8757	-0.7970	-0.7398
0.6814	6.0	750	0.6861	-0.0310	-0.0623	0.4000	0.0313	-75.7518	-82.8598	-0.7969	-0.7399
0.7158	6.4	800	0.6828	-0.0206	-0.0575	0.4250	0.0370	-75.7423	-82.8389	-0.7970	-0.7400
0.6827	6.8	850	0.6909	-0.0294	-0.0489	0.4200	0.0195	-75.7250	-82.8565	-0.7970	-0.7401
0.7306	7.2	900	0.6901	-0.0305	-0.0517	0.4200	0.0213	-75.7307	-82.8587	-0.7970	-0.7401
0.6964	7.6	950	0.6901	-0.0305	-0.0517	0.4200	0.0213	-75.7307	-82.8587	-0.7970	-0.7401
0.687	8.0	1000	0.6901	-0.0305	-0.0517	0.4200	0.0213	-75.7307	-82.8587	-0.7970	-0.7401

Framework versions

Transformers 4.44.2
Pytorch 2.0.0+cu117
Datasets 3.0.0
Tokenizers 0.19.1

tsavage68/IE_L3_1000steps_1e8rate_05beta_cSFTDPO

IE_L3_1000steps_1e8rate_05beta_cSFTDPO

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tsavage68/
IE_L3_1000steps_1e8rate_05beta_cSFTDPO