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-4xh200	alignment-handbook new-dpo generated_from_trainer	Anthropic/hh-rlhf	name results llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802

llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802

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

• Loss: 0.5131
• Fcm Dpo/beta: 0.0057
• Fcm Dpo/q T: 0.3538
• Fcm Dpo/delta: 0.0491
• Fcm Dpo/margin: 139.8486
• Margin Dpo/margin Mean: 139.8486
• Margin Dpo/margin Std: 209.5895
• Logps/chosen: -511.8030
• Logps/rejected: -659.3984
• Logps/ref Chosen: -79.0510
• Logps/ref Rejected: -86.7979
• Logits/chosen: 0.2560
• Logits/rejected: 0.2786

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
0.8697	0.2937	200	0.5858	0.0198	0.3795	0.0634	38.2719	38.2719	74.7381	-148.6721	-194.6909	-79.0510	-86.7979	-0.4905	-0.4710
0.8159	0.5874	400	0.5362	0.0070	0.3603	0.0726	111.7486	111.7486	182.2796	-360.3431	-479.8386	-79.0510	-86.7979	0.0750	0.0972
0.8894	0.8811	600	0.5131	0.0057	0.3538	0.0491	139.8486	139.8486	209.5895	-511.8030	-659.3984	-79.0510	-86.7979	0.2560	0.2786

Framework versions

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