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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

Model 1st Turn Score 2nd Turn Score Average Score
zephyr-7b-sft-full 6.6625 6.0250 6.34375
zephyr-7b-spin-iter0-v0 6.64375 6.1750 6.409375
zephyr-7b-spin-iter1-v0 6.90625 6.3000 6.603125
zephyr-7b-spin-iter2-v0 7.1375 6.3125 6.725000
zephyr-7b-spin-iter3-v0 7.09375 6.4500 6.771875

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
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • total_eval_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 2.0

Training results

Training Loss Epoch Step Validation Loss Rewards/real Rewards/generated Rewards/accuracies Rewards/margins Logps/generated Logps/real Logits/generated Logits/real
0.1827 0.49 25 0.1651 0.1714 -3.3650 0.9688 3.5364 -524.2469 -283.6962 -2.7482 -2.7944
0.0462 0.97 50 0.0835 1.4823 -4.4998 1.0 5.9821 -535.5947 -270.5871 -2.6963 -2.7356
0.0047 1.46 75 0.0837 1.3725 -5.2500 0.9896 6.6225 -543.0965 -271.6846 -2.6847 -2.7211
0.0034 1.94 100 0.0831 1.3037 -5.4434 0.9792 6.7471 -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
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