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taicheng/zephyr-7b-align-scan-3e-07-0.62-polynomial-3.0

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face
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Model Card

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zephyr-7b-align-scan-3e-07-0.62-polynomial-3.0

This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.8726
  • —Rewards/chosen: -0.1874
  • —Rewards/rejected: -1.8304
  • —Rewards/accuracies: 0.375
  • —Rewards/margins: 1.6430
  • —Logps/rejected: -84.0806
  • —Logps/chosen: -74.7935
  • —Logits/rejected: -2.6285
  • —Logits/chosen: -2.6453

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: 3e-07
  • —trainbatchsize: 8
  • —evalbatchsize: 8
  • —seed: 42
  • —distributed_type: multi-GPU
  • —num_devices: 4
  • —gradientaccumulationsteps: 2
  • —totaltrainbatch_size: 64
  • —totalevalbatch_size: 32
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: polynomial
  • —lrschedulerwarmup_ratio: 0.1
  • —num_epochs: 3

Training results

Training LossEpochStepValidation LossRewards/chosenRewards/rejectedRewards/accuraciesRewards/marginsLogps/rejectedLogps/chosenLogits/rejectedLogits/chosen
0.66290.34841000.63740.75860.39970.34520.3589-80.4837-73.2678-2.5455-2.5615
0.70440.69692000.67850.61150.11870.33530.4927-80.9369-73.5050-2.5325-2.5487
0.39451.04533000.69750.76670.10710.35520.6597-80.9557-73.2546-2.5596-2.5753
0.38591.39374000.73961.46710.56580.35710.9013-80.2158-72.1250-2.5834-2.5995
0.38931.74225000.7904-0.4771-1.40600.34920.9290-83.3962-75.2607-2.6499-2.6659
0.37492.09066000.81250.5611-0.48470.36311.0458-81.9100-73.5862-2.6159-2.6321
0.36622.43907000.8412-0.6104-2.08690.36511.4765-84.4944-75.4757-2.5941-2.6112
0.36152.78758000.8766-0.9523-2.56660.36111.6143-85.2680-76.0272-2.6367-2.6538

Framework versions

  • —Transformers 4.44.2
  • —Pytorch 2.4.0
  • —Datasets 2.21.0
  • —Tokenizers 0.19.1