library_name, tags, datasets, model-index

library_name	tags	datasets	model-index
transformers	alignment-handbook beta-dpo generated_from_trainer	Anthropic/hh-rlhf	name results qwen3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260418-012645

qwen3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260418-012645

This model is a fine-tuned version of /scratch/qu.yang1/dynamic-dpo-v4/outputs/qwen3-8b-base-sft-hh-helpful-4xh200-batch-64-20260417-214452 on the Anthropic/hh-rlhf dataset. It achieves the following results on the evaluation set:

• Loss: 0.6518
• Beta Dpo/beta: 0.1628
• Beta Dpo/loss Margin Mean: 8.6336
• Beta Dpo/beta Margin Mean: 1.6614
• Beta Dpo/beta Margin Std: 2.5181
• Beta Dpo/beta Margin Grad Mean: -0.3428
• Beta Dpo/beta Margin Grad Std: 0.2482
• Beta Dpo/gap Mean: 7.7476
• Beta Dpo/gap Std: 14.8995
• Beta Dpo/beta Used Raw: 0.1532
• Beta Dpo/beta Used: 0.1628
• Beta Dpo/mask Keep Frac: 1.0
• Logits/chosen: 1.2071
• Logits/rejected: 1.1403

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	Beta Dpo/beta	Beta Dpo/loss Margin Mean	Beta Dpo/beta Margin Mean	Beta Dpo/beta Margin Std	Beta Dpo/beta Margin Grad Mean	Beta Dpo/beta Margin Grad Std	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
1.3654	0.1512	100	0.6641	0.1110	0.6060	0.0717	0.2130	-0.4824	0.0521	0.4233	1.6301	0.1110	0.1110	1.0	1.3014	1.2543
0.9954	0.3023	200	0.6120	0.0999	4.1607	0.5099	0.9505	-0.4145	0.1620	4.2136	9.3730	0.0968	0.0999	1.0	0.9179	0.8638
1.2007	0.4535	300	0.6115	0.1346	6.6135	1.0588	1.7069	-0.3695	0.2169	6.1583	12.2942	0.1273	0.1346	1.0	1.2327	1.1646
1.3823	0.6047	400	0.5951	0.1019	6.9792	0.8735	1.3552	-0.3881	0.1738	7.2064	12.6529	0.0864	0.1019	1.0	1.2965	1.2260
1.2301	0.7559	500	0.6311	0.1290	8.6766	1.3653	2.0186	-0.3625	0.2097	8.4351	15.6317	0.1145	0.1290	1.0	1.2194	1.1512
1.0426	0.9070	600	0.6518	0.1628	8.6336	1.6614	2.5181	-0.3428	0.2482	7.7476	14.8995	0.1532	0.1628	1.0	1.2071	1.1403

Framework versions

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