library_name, base_model, tags, datasets, model-index

library_name	base_model	tags	datasets	model-index
transformers	jackf857/qwen3-8b-base-sft-hh-harmless-4xh200-batch-64-20260417-214452	alignment-handbook epsilon-dpo generated_from_trainer	Anthropic/hh-rlhf	name results qwen3-8b-base-epsilon-dpo-hh-harmless-4xh200-batch-64-20260424-040415

qwen3-8b-base-epsilon-dpo-hh-harmless-4xh200-batch-64-20260424-040415

This model is a fine-tuned version of jackf857/qwen3-8b-base-sft-hh-harmless-4xh200-batch-64-20260417-214452 on the Anthropic/hh-rlhf dataset. It achieves the following results on the evaluation set:

• Loss: 0.5633
• Rewards/chosen: -0.6512
• Rewards/rejected: -1.0488
• Rewards/accuracies: 0.7328
• Rewards/margins: 0.3976
• Logps/chosen: -149.9445
• Logps/rejected: -198.7271
• Logps/ref Chosen: -86.9018
• Logps/ref Rejected: -96.6964
• Logits/chosen: -1.3052
• Logits/rejected: -1.4319
• Kl/p Epsilon Steps: 0.7289
• Kl/n Epsilon Steps: 0.2689

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	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
1.3249	0.1512	100	0.6600	0.0570	-0.0181	0.6822	0.0751	-86.2224	-96.9165	-86.9018	-96.6964	-0.7700	-0.7965	0.6761	0.3226
1.1555	0.3023	200	0.5709	-0.2620	-0.7050	0.7091	0.4431	-91.2424	-108.5375	-86.9018	-96.6964	-1.2620	-1.3218	0.6923	0.3050
1.1837	0.4535	300	0.5444	-0.9429	-1.5861	0.7377	0.6432	-110.7582	-137.0542	-86.9018	-96.6964	-1.3035	-1.4158	0.7306	0.2689
1.2239	0.6047	400	0.5372	-0.9501	-1.5951	0.7430	0.6451	-124.1300	-159.5326	-86.9018	-96.6964	-1.2494	-1.3749	0.7372	0.2614
1.0454	0.7559	500	0.5415	-0.9468	-1.5190	0.7350	0.5722	-145.2611	-190.7861	-86.9018	-96.6964	-1.2671	-1.3971	0.7293	0.2680
1.1764	0.9070	600	0.5633	-0.6512	-1.0488	0.7328	0.3976	-149.9445	-198.7271	-86.9018	-96.6964	-1.3052	-1.4319	0.7289	0.2689

Framework versions

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