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metadata
library_name: transformers
license: apache-2.0
base_model: alignment-handbook/zephyr-7b-sft-full
tags:
  - alignment-handbook
  - trl
  - dpo
  - generated_from_trainer
  - trl
  - dpo
  - alignment-handbook
  - generated_from_trainer
datasets:
  - data/rlced_conifer
model-index:
  - name: rlced-conifer-zephyr-7b-dpo-2e
    results: []

rlced-conifer-zephyr-7b-dpo-2e

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

  • Loss: 0.1578
  • Rewards/chosen: -8.8806
  • Rewards/rejected: -21.7173
  • Rewards/accuracies: 0.9216
  • Rewards/margins: 12.8367
  • Logps/rejected: -2617.6685
  • Logps/chosen: -1311.4659
  • Logits/rejected: 3.0195
  • Logits/chosen: -0.1677

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: 8
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • 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: 2

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.1586 0.4982 240 0.1689 -2.6868 -8.8615 0.9252 6.1746 -1332.0890 -692.0928 1.5975 -0.1975
0.1408 0.9964 480 0.1482 -6.5979 -15.9265 0.9326 9.3286 -2038.5934 -1083.2030 3.3424 0.5015
0.0852 1.4946 720 0.1644 -10.0141 -23.7065 0.9228 13.6924 -2816.5886 -1424.8193 3.4873 0.0636
0.0743 1.9927 960 0.1578 -8.8688 -21.7065 0.9203 12.8376 -2616.5852 -1310.2896 3.0165 -0.1724

Framework versions

  • Transformers 4.44.1
  • Pytorch 2.1.2+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1