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argilla/zephyr-7b-spin-iter1-v0

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face
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Model Card

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zephyr-7b-spin-iter1-v0

This model is a fine-tuned version of argilla/zephyr-7b-spin-iter0-v0 on the argilla/10k_prompts_SPIN_iter1_zephyr_top and argilla/10k_prompts_SPIN_iter0_zephyr_top dataset.

It achieves the following results on the evaluation set:

  • —Loss: 0.0831
  • —Rewards/real: 1.3037
  • —Rewards/generated: -5.4434
  • —Rewards/accuracies: 0.9792
  • —Rewards/margins: 6.7471
  • —Logps/generated: -545.0309
  • —Logps/real: -272.3726
  • —Logits/generated: -2.6844
  • —Logits/real: -2.7197

MT-Bench results

Model1st Turn Score2nd Turn ScoreAverage Score
zephyr-7b-sft-full6.66256.02506.34375
zephyr-7b-spin-iter0-v06.643756.17506.409375
zephyr-7b-spin-iter1-v06.906256.30006.603125
zephyr-7b-spin-iter2-v07.13756.31256.725000
zephyr-7b-spin-iter3-v07.093756.45006.771875

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-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: linear
  • —lrschedulerwarmup_ratio: 0.1
  • —num_epochs: 2.0

Training results

Training LossEpochStepValidation LossRewards/realRewards/generatedRewards/accuraciesRewards/marginsLogps/generatedLogps/realLogits/generatedLogits/real
0.18270.49250.16510.1714-3.36500.96883.5364-524.2469-283.6962-2.7482-2.7944
0.04620.97500.08351.4823-4.49981.05.9821-535.5947-270.5871-2.6963-2.7356
0.00471.46750.08371.3725-5.25000.98966.6225-543.0965-271.6846-2.6847-2.7211
0.00341.941000.08311.3037-5.44340.97926.7471-545.0309-272.3726-2.6844-2.7197

Framework versions

  • —Transformers 4.37.0
  • —Pytorch 2.1.2+cu121
  • —Datasets 2.14.6
  • —Tokenizers 0.15.2