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lambda-qwen2.5-14b-dpo-test - GGUF

Original model description:

library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-14B-Instruct tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: lambda-qwen2.5-14b-dpo-test results: []

lambda-qwen2.5-14b-dpo-test

This model is a fine-tuned version of Qwen/Qwen2.5-14B-Instruct on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4919
  • Rewards/chosen: -2.4745
  • Rewards/rejected: -3.3729
  • Rewards/accuracies: 0.7400
  • Rewards/margins: 0.8984
  • Logps/rejected: -832.0724
  • Logps/chosen: -737.5234
  • Logits/rejected: -1.2739
  • Logits/chosen: -1.2560

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: 8
  • total_train_batch_size: 128
  • 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
0.5269 0.2094 100 0.5333 -1.6756 -2.3320 0.7000 0.6564 -727.9815 -657.6356 -1.3952 -1.3850
0.5086 0.4187 200 0.5044 -2.0906 -2.9287 0.7040 0.8381 -787.6511 -699.1298 -1.2939 -1.2773
0.4787 0.6281 300 0.4948 -2.2927 -3.1689 0.7320 0.8762 -811.6696 -719.3386 -1.2846 -1.2646
0.4825 0.8375 400 0.4924 -2.4470 -3.3410 0.7400 0.8939 -828.8748 -734.7765 -1.2644 -1.2477

Framework versions

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