library_name, license, base_model, tags, datasets, model-index

library_name	license	base_model	tags	datasets	model-index
transformers	apache-2.0	jackf857/qwen3-8b-base-sft-hh-harmless-4xh200-batch-64-20260417-214452	alignment-handbook new-dpo generated_from_trainer	Anthropic/hh-rlhf	name results qwen3-8b-base-new-dpo-hh-harmless-4xh200-batch-64-q_t-0.45-s_star-0.6

qwen3-8b-base-new-dpo-hh-harmless-4xh200-batch-64-q_t-0.45-s_star-0.6

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: 0.5368
• Fcm Dpo/beta: 0.0530
• Margin Dpo/margin Mean: 11.7010
• Margin Dpo/margin Std: 18.7863
• Logps/chosen: -92.3002
• Logps/rejected: -113.7958
• Logps/ref Chosen: -86.9018
• Logps/ref Rejected: -96.6964
• Logits/chosen: 1.6311
• Logits/rejected: 1.4933

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	Margin Dpo/margin Mean	Margin Dpo/margin Std	Logps/chosen	Logps/rejected	Logps/ref Chosen	Logps/ref Rejected	Logits/chosen	Logits/rejected
1.3231	0.1512	100	0.6549	0.1000	0.8834	1.9512	-86.2078	-96.8858	-86.9018	-96.6964	1.6085	1.4983
1.1388	0.3023	200	0.5426	0.2115	2.9544	4.9177	-81.7111	-94.4601	-86.9018	-96.6964	1.6653	1.5454
1.1386	0.4535	300	0.5411	0.1198	5.2175	8.5703	-85.9098	-100.9220	-86.9018	-96.6964	1.5470	1.4237
1.209	0.6047	400	0.5380	0.0890	6.8529	11.1007	-85.3711	-102.0186	-86.9018	-96.6964	1.6431	1.5118
1.0608	0.7559	500	0.5388	0.0570	10.7654	17.4069	-90.8454	-111.4054	-86.9018	-96.6964	1.2932	1.1717
1.1399	0.9070	600	0.5368	0.0530	11.7010	18.7863	-92.3002	-113.7958	-86.9018	-96.6964	1.6311	1.4933

Framework versions

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