chaoweihuang/FactAlign-Phi-3-Mini

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

FactAlign-Phi-3-Mini

This model is aligned with our FactAlign framework for improved long-form factuality, from microsoft/Phi-3-mini-4k-instruct.

For more information, please refer to our paper: FactAlign: Long-form Factuality Alignment of Large Language Models.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on the trl-lib/kto-mix-14k and the chaoweihuang/lf-response-phi3-f11000.7-fg0.5 datasets. It achieves the following results on the evaluation set:

• —Loss: 0.4815
• —Rewards/chosen: -0.6601
• —Logps/chosen: -299.7121
• —Rewards/rejected: -2.6435
• —Logps/rejected: -364.3744
• —Rewards/margins: 1.9834
• —Kl: 0.0081
• —Fg Kl: nan
• —Fg Rewards/chosen Sum: 0.0694
• —Fg Logps/policy Chosen: -15.2781
• —Fg Logps/reference Chosen: -14.9295
• —Count/fg Chosen: 16.0137
• —Fg Rewards/rejected Sum: -0.3623
• —Fg Logps/policy Rejected: -19.6552
• —Fg Logps/reference Rejected: -18.7868
• —Count/fg Rejected: 4.0824
• —Fg Logps/policy Kl: -21.1260
• —Fg Logps/reference Kl: -20.2070
• —Fg Loss: 0.7365

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 5e-07
• —trainbatchsize: 1
• —evalbatchsize: 1
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 2
• —gradientaccumulationsteps: 8
• —totaltrainbatch_size: 16
• —totalevalbatch_size: 2
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —lrschedulerwarmup_ratio: 0.1
• —num_epochs: 1.0
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

• —Transformers 4.41.1
• —Pytorch 2.3.0+cu121
• —Datasets 2.19.1
• —Tokenizers 0.19.1