qwen_rag_sft

This model is a fine-tuned version of Qwen/Qwen3-0.6B-Base on an unknown dataset. It achieves the following results on the evaluation set:

Model description

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The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_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: linear
lr_scheduler_warmup_steps: 100
num_epochs: 1

Training Loss	Epoch	Step	Validation Loss
0.7189	0.0287	200	0.7363
0.6892	0.0574	400	0.7126
0.6684	0.0861	600	0.6995
0.6602	0.1148	800	0.6907
0.6835	0.1435	1000	0.6842
0.6806	0.1723	1200	0.6790
0.6515	0.2010	1400	0.6751
0.6698	0.2297	1600	0.6716
0.6389	0.2584	1800	0.6690
0.6508	0.2871	2000	0.6670
0.6226	0.3158	2200	0.6651
0.6541	0.3445	2400	0.6631
0.6413	0.3732	2600	0.6616
0.6344	0.4019	2800	0.6605
0.6427	0.4306	3000	0.6593
0.6401	0.4593	3200	0.6584
0.6378	0.4880	3400	0.6574
0.6747	0.5168	3600	0.6567
0.6145	0.5455	3800	0.6562
0.6439	0.5742	4000	0.6556
0.6516	0.6029	4200	0.6552
0.6607	0.6316	4400	0.6548
0.6227	0.6603	4600	0.6543
0.6368	0.6890	4800	0.6541
0.6656	0.7177	5000	0.6539
0.631	0.7464	5200	0.6537
0.639	0.7751	5400	0.6536
0.6428	0.8038	5600	0.6535
0.6292	0.8326	5800	0.6534
0.6412	0.8613	6000	0.6533
0.6408	0.8900	6200	0.6533
0.6631	0.9187	6400	0.6532
0.6471	0.9474	6600	0.6531
0.6514	0.9761	6800	0.6532