phi3-offline-dpo-lora-noise-0.0-5e-6-42 – AI Model by Wenboz | AlphaNeural AI

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Wenboz/
phi3-offline-dpo-lora-noise-0.0-5e-6-42

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alignment-handbook

generated_from_trainer

microsoft/Phi-3-mini-4k-instruct

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phi3-offline-dpo-lora-noise-0.0-5e-6-42

This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6639
Rewards/chosen: -0.1248
Rewards/rejected: -0.1933
Rewards/accuracies: 0.7421
Rewards/margins: 0.0685
Logps/rejected: -403.0595
Logps/chosen: -421.0499
Logits/rejected: 12.1072
Logits/chosen: 13.9087

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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6931	0.1778	100	0.6835	-0.0511	-0.0728	0.6905	0.0218	-391.0186	-413.6780	12.3764	14.1803
0.689	0.3556	200	0.6682	-0.1441	-0.2014	0.7460	0.0573	-403.8743	-422.9761	12.1803	13.9841
0.6923	0.5333	300	0.6673	-0.1140	-0.1749	0.7897	0.0609	-401.2295	-419.9747	12.1769	13.9748
0.6914	0.7111	400	0.6655	-0.1195	-0.1839	0.7698	0.0644	-402.1236	-420.5240	12.1267	13.9317
0.696	0.8889	500	0.6633	-0.1262	-0.1959	0.7540	0.0697	-403.3280	-421.1901	12.0952	13.8997

Framework versions

PEFT 0.7.1
Transformers 4.42.3
Pytorch 2.3.0+cu121
Datasets 2.14.6
Tokenizers 0.19.1