qwen3-8b-base-new-dpo-ultrafeedback-4xh200-batch-128-q_t-0.45-s_star-0.4/README.md

library_name, base_model, tags, datasets, model-index

library_name	base_model	tags	datasets	model-index
transformers	jackf857/qwen3-8b-base-sft-ultrachat-4xh200-batch-128	alignment-handbook new-dpo generated_from_trainer	HuggingFaceH4/ultrafeedback_binarized	name results qwen3-8b-base-new-dpo-ultrafeedback-4xh200-batch-128-q_t-0.45-s_star-0.4

qwen3-8b-base-new-dpo-ultrafeedback-4xh200-batch-128-q_t-0.45-s_star-0.4

This model is a fine-tuned version of jackf857/qwen3-8b-base-sft-ultrachat-4xh200-batch-128 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

• Loss: 0.5766
• Fcm Dpo/beta: 0.0072
• Margin Dpo/margin Mean: 47.1411
• Margin Dpo/margin Std: 72.3220
• Logps/chosen: -314.3019
• Logps/rejected: -345.6718
• Logps/ref Chosen: -280.4167
• Logps/ref Rejected: -264.6455
• Kl/chosen Kl Mean: -33.8852
• Kl/rejected Kl Mean: -81.0263
• Kl/mean: -57.4557
• Kl/std: 62.9633
• Logits/chosen: 1.4237
• Logits/rejected: 1.4794

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: 4
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 8
• total_train_batch_size: 128
• total_eval_batch_size: 8
• 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	Kl/chosen Kl Mean	Kl/rejected Kl Mean	Kl/mean	Kl/std	Logits/chosen	Logits/rejected
4.8731	0.4188	200	0.5751	0.0154	23.0896	36.1352	-284.8843	-292.2027	-280.4167	-264.6455	-4.4676	-27.5572	-16.0124	31.6575	1.6198	1.6611
4.5255	0.8377	400	0.5766	0.0072	47.1411	72.3220	-314.3019	-345.6718	-280.4167	-264.6455	-33.8852	-81.0263	-57.4557	62.9633	1.4237	1.4794

Framework versions

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