MedQA_L3_350steps_1e7rate_03beta_CSFTDPO

This model is a fine-tuned version of tsavage68/MedQA_L3_1000steps_1e6rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6516
• Rewards/chosen: 0.2738
• Rewards/rejected: 0.1790
• Rewards/accuracies: 0.7099
• Rewards/margins: 0.0948
• Logps/rejected: -33.2582
• Logps/chosen: -30.4158
• Logits/rejected: -0.7313
• Logits/chosen: -0.7305

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-07
• 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: 350

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6925	0.0489	50	0.6930	-0.0016	-0.0023	0.5011	0.0007	-33.8624	-31.3338	-0.7320	-0.7314
0.6841	0.0977	100	0.6807	0.2459	0.2195	0.6549	0.0264	-33.1233	-30.5088	-0.7330	-0.7323
0.6524	0.1466	150	0.6658	0.3522	0.2898	0.6703	0.0624	-32.8887	-30.1544	-0.7315	-0.7308
0.631	0.1954	200	0.6545	0.1829	0.0948	0.6923	0.0881	-33.5389	-30.7188	-0.7310	-0.7303
0.6675	0.2443	250	0.6520	0.2481	0.1544	0.7121	0.0938	-33.3403	-30.5014	-0.7309	-0.7301
0.6479	0.2931	300	0.6509	0.2738	0.1773	0.7099	0.0966	-33.2640	-30.4157	-0.7310	-0.7303
0.6583	0.3420	350	0.6516	0.2738	0.1790	0.7099	0.0948	-33.2582	-30.4158	-0.7313	-0.7305

Framework versions

• Transformers 4.41.1
• Pytorch 2.0.0+cu117
• Datasets 2.19.1
• Tokenizers 0.19.1