lambda-llama-3-8b-ipo-test

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the HuggingFaceH4/ultrafeedback_binarized and the tanliboy/orca_dpo_pairs datasets. It achieves the following results on the evaluation set:

  • Loss: 0.8931
  • Rewards/chosen: -0.3610
  • Rewards/rejected: -0.5883
  • Rewards/accuracies: 0.7922
  • Rewards/margins: 0.2272
  • Logps/rejected: -3.1373
  • Logps/chosen: -2.5334
  • Logits/rejected: -2.9939
  • Logits/chosen: -2.9244

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: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • total_eval_batch_size: 32
  • 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
1.1749 0.1744 100 1.0763 -0.1732 -0.3120 0.7892 0.1388 -2.4465 -2.0638 -2.5676 -2.5133
0.9802 0.3489 200 0.9501 -0.3184 -0.5302 0.8012 0.2118 -2.9922 -2.4269 -2.7873 -2.7230
0.9548 0.5233 300 0.9136 -0.3761 -0.6028 0.8163 0.2267 -3.1736 -2.5710 -2.8788 -2.8087
0.9834 0.6978 400 0.9041 -0.3384 -0.5537 0.8042 0.2153 -3.0509 -2.4770 -2.9371 -2.8667
0.9967 0.8722 500 0.8938 -0.3750 -0.6076 0.7892 0.2326 -3.1855 -2.5684 -3.0293 -2.9592

Framework versions

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