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: 0.1802
Rewards/chosen: -0.8216
Rewards/rejected: -13.7782
Rewards/accuracies: 0.7400
Rewards/margins: 12.9566
Logps/rejected: -213.4093
Logps/chosen: -91.0134
Logits/rejected: -0.8670
Logits/chosen: -0.7142

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
0.1913	0.4	50	0.1803	-0.5046	-8.7772	0.7400	8.2726	-163.3993	-87.8437	-0.8451	-0.7284
0.1386	0.8	100	0.1802	-1.0228	-11.9098	0.7400	10.8870	-194.7255	-93.0261	-0.8546	-0.7152
0.1386	1.2	150	0.1802	-0.6732	-12.7363	0.7400	12.0631	-202.9905	-89.5298	-0.8582	-0.7093
0.1733	1.6	200	0.1802	-0.6775	-12.8705	0.7400	12.1930	-204.3321	-89.5723	-0.8611	-0.7114
0.2253	2.0	250	0.1802	-0.7149	-13.0474	0.7400	12.3326	-206.1017	-89.9464	-0.8603	-0.7104
0.1386	2.4	300	0.1802	-0.7327	-13.0995	0.7400	12.3668	-206.6222	-90.1248	-0.8593	-0.7091
0.1213	2.8	350	0.1802	-0.7598	-13.2905	0.7400	12.5307	-208.5327	-90.3961	-0.8621	-0.7116
0.1906	3.2	400	0.1802	-0.7893	-13.4540	0.7400	12.6647	-210.1669	-90.6907	-0.8653	-0.7135
0.1906	3.6	450	0.1802	-0.7880	-13.4497	0.7400	12.6617	-210.1245	-90.6778	-0.8657	-0.7141
0.2079	4.0	500	0.1802	-0.8075	-13.6024	0.7400	12.7949	-211.6511	-90.8724	-0.8653	-0.7127
0.156	4.4	550	0.1802	-0.8042	-13.6207	0.7400	12.8165	-211.8345	-90.8401	-0.8658	-0.7138
0.1213	4.8	600	0.1802	-0.8154	-13.6478	0.7400	12.8323	-212.1049	-90.9520	-0.8661	-0.7139
0.1906	5.2	650	0.1802	-0.8263	-13.7419	0.7400	12.9156	-213.0464	-91.0612	-0.8667	-0.7144
0.2426	5.6	700	0.1802	-0.8316	-13.7569	0.7400	12.9253	-213.1964	-91.1135	-0.8668	-0.7144
0.2599	6.0	750	0.1802	-0.8155	-13.7626	0.7400	12.9471	-213.2537	-90.9532	-0.8669	-0.7141
0.1213	6.4	800	0.1802	-0.8348	-13.7975	0.7400	12.9627	-213.6019	-91.1453	-0.8666	-0.7139
0.2426	6.8	850	0.1802	-0.8359	-13.7784	0.7400	12.9425	-213.4111	-91.1564	-0.8664	-0.7143
0.1733	7.2	900	0.1802	-0.8274	-13.7943	0.7400	12.9670	-213.5706	-91.0716	-0.8673	-0.7144
0.1386	7.6	950	0.1802	-0.8173	-13.7791	0.7400	12.9618	-213.4180	-90.9708	-0.8670	-0.7140
0.156	8.0	1000	0.1802	-0.8216	-13.7782	0.7400	12.9566	-213.4093	-91.0134	-0.8670	-0.7142

Framework versions

Transformers 4.44.2
Pytorch 2.0.0+cu117
Datasets 3.0.0
Tokenizers 0.19.1

tsavage68/IE_L3_1000steps_1e6rate_01beta_cSFTDPO

IE_L3_1000steps_1e6rate_01beta_cSFTDPO

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tsavage68/
IE_L3_1000steps_1e6rate_01beta_cSFTDPO