zephyr-7b-UFB-ref / README.md
weijie210's picture
Model save
daca4e7 verified
metadata
license: apache-2.0
base_model: alignment-handbook/zephyr-7b-sft-full
tags:
  - trl
  - dpo
  - generated_from_trainer
model-index:
  - name: zephyr-7b-UFB-ref
    results: []

zephyr-7b-UFB-ref

This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5067
  • Rewards/chosen: -1.1023
  • Rewards/rejected: -2.3762
  • Rewards/accuracies: 0.7098
  • Rewards/margins: 1.2739
  • Logps/rejected: -120.8096
  • Logps/chosen: -110.8900
  • Logits/rejected: -2.1865
  • Logits/chosen: -2.2284

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: 4
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • total_train_batch_size: 16
  • 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: 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
0.5598 0.15 500 0.6348 -0.4646 -1.5732 0.7121 1.1087 -112.7802 -104.5124 -2.2989 -2.3398
0.6708 0.3 1000 0.5807 -1.9508 -2.8042 0.6830 0.8534 -125.0895 -119.3747 -2.2155 -2.2623
0.5984 0.45 1500 0.5244 -1.4451 -2.6765 0.7188 1.2313 -123.8126 -114.3180 -2.1383 -2.1824
0.5508 0.6 2000 0.5644 -1.7905 -2.8869 0.6786 1.0964 -125.9164 -117.7717 -2.0760 -2.1208
0.5218 0.74 2500 0.5183 -1.3228 -2.5470 0.7031 1.2242 -122.5180 -113.0946 -2.2172 -2.2616
0.4914 0.89 3000 0.5079 -1.0825 -2.3551 0.7121 1.2725 -120.5985 -110.6918 -2.2149 -2.2567

Framework versions

  • Transformers 4.36.1
  • Pytorch 2.0.1+cu117
  • Datasets 2.16.1
  • Tokenizers 0.15.0