MedQA_L3_250steps_1e7rate_05beta_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: 0.6492
• Rewards/chosen: 0.3403
• Rewards/rejected: 0.2334
• Rewards/accuracies: 0.6857
• Rewards/margins: 0.1070
• Logps/rejected: -33.3881
• Logps/chosen: -30.6478
• Logits/rejected: -0.7314
• Logits/chosen: -0.7307

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: 250

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6857	0.0489	50	0.6947	-0.0249	-0.0232	0.4879	-0.0018	-33.9011	-31.3784	-0.7318	-0.7312
0.6799	0.0977	100	0.6734	0.3881	0.3450	0.6681	0.0432	-33.1649	-30.5522	-0.7330	-0.7323
0.6286	0.1466	150	0.6528	0.4844	0.3866	0.6813	0.0978	-33.0816	-30.3598	-0.7312	-0.7306
0.6183	0.1954	200	0.6449	0.3270	0.2107	0.7143	0.1163	-33.4334	-30.6745	-0.7312	-0.7305
0.6593	0.2443	250	0.6492	0.3403	0.2334	0.6857	0.1070	-33.3881	-30.6478	-0.7314	-0.7307

Framework versions

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