zephyr-7b / README.md
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metadata
license: apache-2.0
library_name: peft
tags:
  - alignment-handbook
  - trl
  - dpo
  - generated_from_trainer
  - trl
  - dpo
  - generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
datasets:
  - HuggingFaceH4/ultrafeedback_binarized
model-index:
  - name: zephyr-7b
    results: []

zephyr-7b

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

  • Loss: 0.6171
  • Rewards/chosen: -0.4648
  • Rewards/rejected: -0.8388
  • Rewards/accuracies: 0.3711
  • Rewards/margins: 0.3740
  • Logps/rejected: -161.0705
  • Logps/chosen: -110.3948
  • Logits/rejected: 1.0411
  • Logits/chosen: 0.9868
  • Use Label: 0.0
  • Pred Label: 0.0

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-06
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • total_eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • 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 Use Label Pred Label
0.6553 0.21 100 0.6557 -0.1267 -0.2685 0.3633 0.1419 -104.0477 -76.5787 -2.0726 -2.0833 0.0 0.0
0.6446 0.42 200 0.6343 -0.2873 -0.5376 0.3828 0.2503 -130.9503 -92.6377 -0.6864 -0.7124 0.0 0.0
0.6273 0.63 300 0.6204 -0.4623 -0.7994 0.3672 0.3371 -157.1332 -110.1469 0.6726 0.6280 0.0 0.0
0.6165 0.84 400 0.6182 -0.4457 -0.8122 0.3672 0.3666 -158.4149 -108.4784 0.9580 0.9035 0.0 0.0

Framework versions

  • PEFT 0.7.1
  • Transformers 4.38.2
  • Pytorch 2.1.1+cu121
  • Datasets 2.14.6
  • Tokenizers 0.15.2