phi-2-gpo-ultrafeedback-lora

This model is a fine-tuned version of lole25/phi-2-sft-ultrachat-lora on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

• Loss: 0.0004
• Rewards/chosen: -0.0084
• Rewards/rejected: -0.0177
• Rewards/accuracies: 0.6700
• Rewards/margins: 0.0093
• Logps/rejected: -233.2047
• Logps/chosen: -261.0818
• Logits/rejected: 0.8824
• Logits/chosen: 0.7796

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: 5e-06
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• total_eval_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Logits/chosen	Logits/rejected	Logps/chosen	Logps/rejected	Validation Loss	Rewards/accuracies	Rewards/chosen	Rewards/margins	Rewards/rejected
0.0026	0.21	100	0.8151	0.9175	-260.2373	-231.4896	0.0025	0.5080	0.0001	0.0006	-0.0005
0.0023	0.42	200	0.8092	0.9120	-260.3932	-232.1152	0.0023	0.6560	-0.0015	0.0053	-0.0068
0.0022	0.63	300	0.7992	0.9022	-260.9179	-232.8447	0.0022	0.6700	-0.0067	0.0073	-0.0141
0.0021	0.84	400	0.7884	0.8914	-261.1620	-233.2157	0.0022	0.6640	-0.0092	0.0086	-0.0178
0.0022	1.05	500	0.7821	0.8853	-261.1852	-233.3614	0.0021	0.7100	-0.0094	0.0098	-0.0193
0.002	1.26	600	0.7815	0.8840	-261.1207	-233.2843	0.0021	0.6940	-0.0088	0.0097	-0.0185
0.0021	1.47	700	0.7790	0.8816	-261.0788	-233.2560	0.0021	0.7000	-0.0083	0.0099	-0.0182
0.0021	1.67	800	0.7781	0.8811	-261.0643	-233.2740	0.0021	0.6940	-0.0082	0.0102	-0.0184
0.0021	1.88	900	0.7806	0.8833	-261.0922	-233.2118	0.0021	0.6900	-0.0085	0.0093	-0.0178

Framework versions

• PEFT 0.7.1
• Transformers 4.36.2
• Pytorch 2.1.2+cu118
• Datasets 2.14.6
• Tokenizers 0.15.2