This model is a fine-tuned version of tsavage68/UTI_L3_1000steps_1e5rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0069
Rewards/chosen: 2.5286
Rewards/rejected: -48.6639
Rewards/accuracies: 0.9900
Rewards/margins: 51.1926
Logps/rejected: -160.5225
Logps/chosen: -27.4217
Logits/rejected: -1.3535
Logits/chosen: -1.3136

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-05
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
0.0	0.6667	50	0.0071	1.6538	-15.9005	0.9900	17.5543	-94.9957	-29.1714	-1.3772	-1.3510
0.0173	1.3333	100	0.1641	5.8391	-14.1581	0.9100	19.9972	-91.5108	-20.8008	-1.4149	-1.3894
0.0347	2.0	150	0.0069	2.5321	-48.6719	0.9900	51.2040	-160.5385	-27.4149	-1.3535	-1.3136
0.0	2.6667	200	0.0069	2.5321	-48.6719	0.9900	51.2040	-160.5385	-27.4149	-1.3535	-1.3136
0.0173	3.3333	250	0.0069	2.5321	-48.6719	0.9900	51.2040	-160.5385	-27.4149	-1.3535	-1.3136
0.0347	4.0	300	0.0069	2.5321	-48.6719	0.9900	51.2040	-160.5385	-27.4149	-1.3535	-1.3136
0.0173	4.6667	350	0.0069	2.5321	-48.6719	0.9900	51.2040	-160.5385	-27.4149	-1.3535	-1.3136
0.0173	5.3333	400	0.0069	2.5321	-48.6719	0.9900	51.2040	-160.5385	-27.4148	-1.3535	-1.3136
0.0173	6.0	450	0.0069	2.5321	-48.6719	0.9900	51.2040	-160.5385	-27.4148	-1.3535	-1.3136
0.0347	6.6667	500	0.0069	2.5321	-48.6719	0.9900	51.2040	-160.5385	-27.4148	-1.3535	-1.3136
0.0	7.3333	550	0.0069	2.5319	-48.6721	0.9900	51.2040	-160.5388	-27.4152	-1.3535	-1.3136
0.0347	8.0	600	0.0069	2.5286	-48.6639	0.9900	51.1926	-160.5225	-27.4217	-1.3535	-1.3136
0.0	8.6667	650	0.0069	2.5286	-48.6639	0.9900	51.1926	-160.5225	-27.4217	-1.3535	-1.3136
0.0173	9.3333	700	0.0069	2.5286	-48.6639	0.9900	51.1926	-160.5225	-27.4217	-1.3535	-1.3136
0.0	10.0	750	0.0069	2.5286	-48.6639	0.9900	51.1926	-160.5225	-27.4217	-1.3535	-1.3136
0.0173	10.6667	800	0.0069	2.5286	-48.6639	0.9900	51.1926	-160.5225	-27.4217	-1.3535	-1.3136
0.0	11.3333	850	0.0069	2.5286	-48.6639	0.9900	51.1926	-160.5225	-27.4217	-1.3535	-1.3136
0.0	12.0	900	0.0069	2.5286	-48.6639	0.9900	51.1926	-160.5225	-27.4217	-1.3535	-1.3136
0.0173	12.6667	950	0.0069	2.5286	-48.6639	0.9900	51.1926	-160.5225	-27.4217	-1.3535	-1.3136
0.0	13.3333	1000	0.0069	2.5286	-48.6639	0.9900	51.1926	-160.5225	-27.4217	-1.3535	-1.3136

Framework versions

Transformers 4.41.2
Pytorch 2.0.0+cu117
Datasets 2.19.2
Tokenizers 0.19.1

tsavage68/UTI_L3_1000steps_1e5rate_05beta_CSFTDPO

UTI_L3_1000steps_1e5rate_05beta_CSFTDPO

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tsavage68/
UTI_L3_1000steps_1e5rate_05beta_CSFTDPO