library_name, base_model, tags, datasets, model-index

library_name	base_model	tags	datasets	model-index
transformers	W-61/llama-3-8b-base-sft-hh-helpful-8xh200	alignment-handbook epsilon-dpo generated_from_trainer	Anthropic/hh-rlhf	name results llama-3-8b-base-epsilon-dpo-hh-helpful-8xh200-20260410-233108

llama-3-8b-base-epsilon-dpo-hh-helpful-8xh200-20260410-233108

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

• Loss: 0.6443
• Rewards/chosen: -0.4430
• Rewards/rejected: -0.5884
• Rewards/accuracies: 0.6402
• Rewards/margins: 0.1454
• Logps/chosen: -208.1799
• Logps/rejected: -243.5746
• Logps/ref Chosen: -87.8236
• Logps/ref Rejected: -82.8189
• Logits/chosen: -0.8325
• Logits/rejected: -0.7259
• Kl/p Epsilon Steps: 0.6150
• Kl/n Epsilon Steps: 0.3850

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: 16
• eval_batch_size: 16
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• total_train_batch_size: 128
• total_eval_batch_size: 128
• 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	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/chosen	Logps/rejected	Logps/ref Chosen	Logps/ref Rejected	Logits/chosen	Logits/rejected	Kl/p Epsilon Steps	Kl/n Epsilon Steps
0.6236	0.2941	100	0.6636	-0.3033	-0.3895	0.6124	0.0862	-127.6472	-134.3018	-87.8236	-82.8189	-1.2261	-1.1807	0.5760	0.4236
0.5934	0.5882	200	0.6429	-0.5156	-0.6820	0.6324	0.1663	-182.9084	-209.3034	-87.8236	-82.8189	-0.9817	-0.8951	0.6037	0.3958
0.6156	0.8824	300	0.6443	-0.4430	-0.5884	0.6402	0.1454	-208.1799	-243.5746	-87.8236	-82.8189	-0.8325	-0.7259	0.6150	0.3850

Framework versions

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