library_name, base_model, tags, datasets, model-index

library_name	base_model	tags	datasets	model-index
transformers	W-61/llama-3-8b-base-sft-ultrachat-8xh200	alignment-handbook beta-dpo generated_from_trainer	HuggingFaceH4/ultrafeedback_binarized	name results llama-3-8b-base-beta-dpo-ultrafeedback-4xh200-batch-128-20260424-044124

llama-3-8b-base-beta-dpo-ultrafeedback-4xh200-batch-128-20260424-044124

This model is a fine-tuned version of W-61/llama-3-8b-base-sft-ultrachat-8xh200 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

• Loss: 0.6357
• Beta Dpo/gap Mean: 28.0227
• Beta Dpo/gap Std: 50.3673
• Beta Dpo/beta Used Raw: 0.0207
• Beta Dpo/beta Used: 0.0430
• Beta Dpo/mask Keep Frac: 1.0
• Logits/chosen: -0.8454
• Logits/rejected: -0.8282

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: 4
• gradient_accumulation_steps: 8
• total_train_batch_size: 128
• total_eval_batch_size: 16
• 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

Training Loss	Epoch	Step	Validation Loss	Beta Dpo/gap Mean	Beta Dpo/gap Std	Beta Dpo/beta Used Raw	Beta Dpo/beta Used	Beta Dpo/mask Keep Frac	Logits/chosen	Logits/rejected
5.1899	0.4188	200	0.6122	23.1744	48.2593	0.0140	0.0346	1.0	-0.8157	-0.8025
3.5281	0.8377	400	0.6357	28.0227	50.3673	0.0207	0.0430	1.0	-0.8454	-0.8282

Framework versions

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