llama-3-8b-base-epsilon-dpo-hh-harmless-4xh200-batch-64-20260418-003215

• Loss: 0.5778
• Epsilon Dpo/beta: 0.0075
• Epsilon Dpo/loss Margin Mean: 44.6202
• Epsilon Dpo/beta Margin Mean: 0.3297
• Epsilon Dpo/beta Margin Std: 0.5411
• Epsilon Dpo/beta Margin Grad Mean: -0.4237
• Epsilon Dpo/beta Margin Grad Std: 0.1239
• Rewards/chosen: -0.6980
• Rewards/rejected: -1.0277
• Rewards/accuracies: 0.7192
• Rewards/margins: 0.3297
• Logps/chosen: -168.1115
• Logps/rejected: -217.4212
• Logps/ref Chosen: -74.8595
• Logps/ref Rejected: -79.5490
• Logits/chosen: 0.0396
• Logits/rejected: -0.0641
• Kl/p Epsilon Steps: 0.7196
• Kl/n Epsilon Steps: 0.2799

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 1

Training results

Framework versions

• Transformers 4.51.0
• Pytorch 2.3.1+cu121
• Datasets 2.21.0
• Tokenizers 0.21.4