gpt-neo-125m_hh_reward

This model is a fine-tuned version of EleutherAI/gpt-neo-125m on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.7503
• Rewards/chosen: -4.2523
• Rewards/rejected: -4.3731
• Rewards/accuracies: 0.5625
• Rewards/margins: 0.1208
• Logps/rejected: -168.5040
• Logps/chosen: -147.3926
• Logits/rejected: -11.6528
• Logits/chosen: -11.5062

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: 0.0001
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 150
• training_steps: 4050

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.8022	0.2	2000	0.7737	-4.8718	-5.0523	0.5724	0.1805	-175.2956	-153.5872	-11.7730	-11.6673
0.7336	0.4	4000	0.7503	-4.2523	-4.3731	0.5625	0.1208	-168.5040	-147.3926	-11.6528	-11.5062

Framework versions

• Transformers 4.38.2
• Pytorch 2.0.1+cu117
• Datasets 2.17.1
• Tokenizers 0.15.2