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_star0.6-4xh200-batch-64-20260421-214335-rerun

llama-3-8b-base-new-dpo-hh-helpful-s_star0.6-4xh200-batch-64-20260421-214335-rerun

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: 0.5572
• Fcm Dpo/beta: 0.0034
• Fcm Dpo/q T: 0.3914
• Fcm Dpo/delta: 0.0523
• Fcm Dpo/margin: 156.2329
• Margin Dpo/margin Mean: 156.2329
• Margin Dpo/margin Std: 271.4757
• Logps/chosen: -459.3008
• Logps/rejected: -623.2806
• Logps/ref Chosen: -79.0510
• Logps/ref Rejected: -86.7979
• Logits/chosen: -0.5276
• Logits/rejected: -0.5133

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
0.9767	0.2937	200	0.5985	0.0106	0.4061	0.0551	47.4977	47.4977	101.7798	-179.9633	-235.2079	-79.0510	-86.7979	-0.4084	-0.3900
0.9245	0.5874	400	0.5729	0.0038	0.3973	0.0663	134.6704	134.6704	251.6155	-454.1283	-596.5456	-79.0510	-86.7979	-0.4387	-0.4223
0.9964	0.8811	600	0.5572	0.0034	0.3914	0.0523	156.2329	156.2329	271.4757	-459.3008	-623.2806	-79.0510	-86.7979	-0.5276	-0.5133

Framework versions

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