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zephyr-7b-sft-full-orpo

Text Generation
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mistral
alignment-handbook
trl
orpo
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metadata
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
  - alignment-handbook
  - trl
  - orpo
  - generated_from_trainer
  - trl
  - orpo
  - alignment-handbook
  - generated_from_trainer
datasets:
  - HuggingFaceH4/ultrafeedback_binarized
model-index:
  - name: zephyr-7b-sft-full-orpo
    results: []

Visualize in Weights & Biases

zephyr-7b-sft-full-orpo

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4701
  • Rewards/chosen: -0.0364
  • Rewards/rejected: -0.0499
  • Rewards/accuracies: 0.6587
  • Rewards/margins: 0.0135
  • Logps/rejected: -0.9978
  • Logps/chosen: -0.7282
  • Logits/rejected: -2.9263
  • Logits/chosen: -2.9434
  • Nll Loss: 0.4357
  • Log Odds Ratio: -0.6093
  • Log Odds Chosen: 0.4456

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: 7e-06
  • 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: inverse_sqrt
  • lr_scheduler_warmup_steps: 100
  • 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 Nll Loss Log Odds Ratio Log Odds Chosen
0.5226 0.1049 100 0.5280 -0.0386 -0.0472 0.6329 0.0086 -0.9448 -0.7728 -2.7583 -2.7860 0.4953 -0.6326 0.2873
0.5074 0.2098 200 0.5134 -0.0381 -0.0478 0.6409 0.0098 -0.9566 -0.7612 -2.6736 -2.7002 0.4774 -0.6357 0.3190
0.5265 0.3146 300 0.5012 -0.0379 -0.0479 0.6329 0.0099 -0.9572 -0.7588 -2.7317 -2.7594 0.4653 -0.6374 0.3278
0.5194 0.4195 400 0.4912 -0.0371 -0.0478 0.6429 0.0107 -0.9559 -0.7417 -2.6640 -2.6974 0.4560 -0.6284 0.3607
0.5008 0.5244 500 0.4847 -0.0373 -0.0489 0.6508 0.0117 -0.9786 -0.7455 -2.5957 -2.6294 0.4499 -0.6209 0.3873
0.4725 0.6293 600 0.4794 -0.0362 -0.0470 0.6349 0.0107 -0.9394 -0.7248 -2.6147 -2.6477 0.4435 -0.6320 0.3567
0.4875 0.7341 700 0.4767 -0.0368 -0.0498 0.6409 0.0129 -0.9955 -0.7365 -2.6910 -2.7213 0.4416 -0.6158 0.4180
0.4796 0.8390 800 0.4740 -0.0371 -0.0508 0.6508 0.0137 -1.0162 -0.7416 -2.7913 -2.8114 0.4396 -0.6169 0.4363
0.4851 0.9439 900 0.4714 -0.0357 -0.0466 0.6528 0.0109 -0.9324 -0.7143 -2.9543 -2.9692 0.4361 -0.6245 0.3669

Framework versions

  • Transformers 4.41.0.dev0
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1