llama-7b-sft-DPO

This model is a fine-tuned version of argsearch/llama-7b-sft-float32 on the Dahoas/full-hh-rlhf dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6525
  • Rewards/chosen: 0.3315
  • Rewards/rejected: 0.1953
  • Rewards/accuracies: 0.6080
  • Rewards/margins: 0.1362
  • Logps/rejected: -633.3815
  • Logps/chosen: -690.5654
  • Logits/rejected: -1.9212
  • Logits/chosen: -1.9766

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: 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: 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
0.6884 0.06 100 0.6886 0.0879 0.0774 0.5647 0.0105 -645.1731 -714.9250 -2.7786 -2.8754
0.6769 0.11 200 0.6809 0.2546 0.2194 0.5747 0.0352 -630.9728 -698.2556 -2.6094 -2.6971
0.6734 0.17 300 0.6755 0.2980 0.2471 0.5833 0.0508 -628.1946 -693.9142 -2.5226 -2.6062
0.6684 0.23 400 0.6713 0.3480 0.2822 0.5888 0.0658 -624.6848 -688.9108 -2.4007 -2.4782
0.6647 0.29 500 0.6671 0.3495 0.2706 0.6048 0.0789 -625.8477 -688.7593 -2.3026 -2.3749
0.6598 0.34 600 0.6636 0.3311 0.2429 0.6058 0.0882 -628.6143 -690.6030 -2.1694 -2.2345
0.6598 0.4 700 0.6606 0.2824 0.1853 0.6106 0.0971 -634.3779 -695.4718 -1.9252 -1.9781
0.6563 0.46 800 0.6585 0.3476 0.2374 0.6071 0.1102 -629.1707 -688.9521 -2.0030 -2.0599
0.6636 0.51 900 0.6572 0.3569 0.2427 0.6119 0.1142 -628.6379 -688.0209 -1.9872 -2.0440
0.6436 0.57 1000 0.6558 0.2921 0.1732 0.6096 0.1190 -635.5912 -694.4999 -1.9618 -2.0181
0.6759 0.63 1100 0.6548 0.3436 0.2165 0.6071 0.1272 -631.2626 -689.3489 -1.9627 -2.0198
0.6679 0.69 1200 0.6542 0.3533 0.2212 0.6077 0.1321 -630.7878 -688.3820 -1.9058 -1.9598
0.6358 0.74 1300 0.6533 0.3363 0.2036 0.6074 0.1327 -632.5449 -690.0779 -1.9447 -2.0015
0.6473 0.8 1400 0.6528 0.3378 0.2021 0.6080 0.1357 -632.6981 -689.9300 -1.9072 -1.9621
0.6447 0.86 1500 0.6526 0.3221 0.1869 0.6080 0.1352 -634.2156 -691.5005 -1.9226 -1.9781
0.6546 0.91 1600 0.6525 0.3303 0.1941 0.6074 0.1362 -633.5018 -690.6824 -1.9134 -1.9684
0.6725 0.97 1700 0.6525 0.3312 0.1950 0.6074 0.1363 -633.4115 -690.5892 -1.9098 -1.9645

Framework versions

  • Transformers 4.39.0.dev0
  • Pytorch 2.3.0+cu121
  • Datasets 2.14.6
  • Tokenizers 0.15.2
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