lambda-qwen2.5-32b-dpo-test

This model is a fine-tuned version of Qwen/Qwen2.5-32B-Instruct on the tanliboy/orca_dpo_pairs dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0003
  • Rewards/chosen: -10.1618
  • Rewards/rejected: -26.1150
  • Rewards/accuracies: 1.0
  • Rewards/margins: 15.9532
  • Logps/rejected: -3049.5271
  • Logps/chosen: -1372.8903
  • Logits/rejected: -0.3154
  • Logits/chosen: -0.0600

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: 2
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • total_eval_batch_size: 16
  • 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
0.0103 0.2618 100 0.0060 -8.5159 -18.7731 1.0 10.2572 -2315.3333 -1208.2968 -0.5485 -0.2481
0.0005 0.5236 200 0.0005 -9.9255 -24.9117 1.0 14.9862 -2929.1948 -1349.2588 -0.3723 -0.0661
0.0005 0.7853 300 0.0004 -10.0873 -25.9319 1.0 15.8446 -3031.2175 -1365.4342 -0.2882 -0.0014

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1
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