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-harmless-4xh200	alignment-handbook new-dpo generated_from_trainer	Anthropic/hh-rlhf	name results llama-3-8b-base-new-dpo-hh-harmless-s_star1.0-4xh200-batch-64-20260421-213851

llama-3-8b-base-new-dpo-hh-harmless-s_star1.0-4xh200-batch-64-20260421-213851

This model is a fine-tuned version of W-61/llama-3-8b-base-sft-hh-harmless-4xh200 on the Anthropic/hh-rlhf dataset. It achieves the following results on the evaluation set:

• Loss: 0.5467
• Fcm Dpo/beta: 0.2268
• Fcm Dpo/q T: 0.3412
• Fcm Dpo/delta: -0.0017
• Fcm Dpo/margin: 4.4089
• Margin Dpo/margin Mean: 4.4089
• Margin Dpo/margin Std: 7.2504
• Logps/chosen: -82.5570
• Logps/rejected: -91.6554
• Logps/ref Chosen: -74.8595
• Logps/ref Rejected: -79.5490
• Logits/chosen: 0.2724
• Logits/rejected: 0.2290

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: 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

Training Loss	Epoch	Step	Validation Loss	Fcm Dpo/beta	Fcm Dpo/q T	Fcm Dpo/delta	Fcm Dpo/margin	Margin Dpo/margin Mean	Margin Dpo/margin Std	Logps/chosen	Logps/rejected	Logps/ref Chosen	Logps/ref Rejected	Logits/chosen	Logits/rejected
0.9878	0.3023	200	0.5709	0.4418	0.3475	0.0198	2.2173	2.2173	3.8804	-79.5152	-86.4220	-74.8595	-79.5490	0.2313	0.1912
0.966	0.6047	400	0.5573	0.2747	0.3451	0.0194	3.5687	3.5687	6.0461	-81.6850	-89.9432	-74.8595	-79.5490	0.2557	0.2135
1.122	0.9070	600	0.5467	0.2268	0.3412	-0.0017	4.4089	4.4089	7.2504	-82.5570	-91.6554	-74.8595	-79.5490	0.2724	0.2290

Framework versions

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