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

W-61/llama-3-8b-base-beta-dpo-ultrafeedback-4xh200-batch-128-20260424-044124

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

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

W-61/
llama-3-8b-base-beta-dpo-ultrafeedback-4xh200-batch-128-20260424-044124