This model is a fine-tuned version of tsavage68/IE_M2_1000steps_1e7rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3743
Rewards/chosen: -0.6213
Rewards/rejected: -9.0827
Rewards/accuracies: 0.4600
Rewards/margins: 8.4614
Logps/rejected: -59.1872
Logps/chosen: -43.4481
Logits/rejected: -2.8827
Logits/chosen: -2.8204

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: 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
0.4505	0.4	50	0.3743	-0.5495	-8.1396	0.4600	7.5901	-57.3010	-43.3045	-2.8855	-2.8234
0.3812	0.8	100	0.3743	-0.5954	-8.5105	0.4600	7.9152	-58.0429	-43.3963	-2.8832	-2.8209
0.3119	1.2	150	0.3743	-0.6132	-8.8178	0.4600	8.2046	-58.6574	-43.4318	-2.8827	-2.8203
0.3639	1.6	200	0.3743	-0.6125	-8.8526	0.4600	8.2401	-58.7270	-43.4304	-2.8828	-2.8204
0.4332	2.0	250	0.3743	-0.6117	-8.9373	0.4600	8.3256	-58.8965	-43.4290	-2.8831	-2.8207
0.3986	2.4	300	0.3743	-0.6002	-8.9298	0.4600	8.3296	-58.8814	-43.4059	-2.8831	-2.8207
0.3986	2.8	350	0.3743	-0.6140	-8.9769	0.4600	8.3630	-58.9757	-43.4335	-2.8828	-2.8204
0.4505	3.2	400	0.3743	-0.6256	-8.9903	0.4600	8.3647	-59.0024	-43.4567	-2.8829	-2.8205
0.4505	3.6	450	0.3743	-0.6066	-8.9960	0.4600	8.3894	-59.0138	-43.4187	-2.8831	-2.8207
0.4332	4.0	500	0.3743	-0.6183	-9.0594	0.4600	8.4410	-59.1406	-43.4422	-2.8830	-2.8206
0.3292	4.4	550	0.3743	-0.6163	-9.0734	0.4600	8.4571	-59.1686	-43.4381	-2.8830	-2.8206
0.3639	4.8	600	0.3743	-0.6171	-9.0601	0.4600	8.4430	-59.1421	-43.4397	-2.8828	-2.8204
0.4505	5.2	650	0.3743	-0.6207	-9.0642	0.4600	8.4435	-59.1503	-43.4470	-2.8830	-2.8207
0.4505	5.6	700	0.3743	-0.6061	-9.0651	0.4600	8.4589	-59.1519	-43.4178	-2.8831	-2.8206
0.3639	6.0	750	0.3743	-0.6217	-9.0731	0.4600	8.4514	-59.1681	-43.4490	-2.8829	-2.8206
0.2426	6.4	800	0.3743	-0.6241	-9.0805	0.4600	8.4564	-59.1827	-43.4537	-2.8829	-2.8205
0.5025	6.8	850	0.3743	-0.6248	-9.0702	0.4600	8.4454	-59.1623	-43.4552	-2.8827	-2.8204
0.3119	7.2	900	0.3743	-0.6258	-9.0760	0.4600	8.4502	-59.1739	-43.4571	-2.8826	-2.8203
0.3466	7.6	950	0.3743	-0.6208	-9.0821	0.4600	8.4613	-59.1860	-43.4471	-2.8827	-2.8204
0.3812	8.0	1000	0.3743	-0.6213	-9.0827	0.4600	8.4614	-59.1872	-43.4481	-2.8827	-2.8204

Framework versions

Transformers 4.44.2
Pytorch 2.0.0+cu117
Datasets 3.0.0
Tokenizers 0.19.1

tsavage68/IE_M2_1000steps_1e6rate_05beta_cSFTDPO

IE_M2_1000steps_1e6rate_05beta_cSFTDPO

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tsavage68/
IE_M2_1000steps_1e6rate_05beta_cSFTDPO