lmind_hotpot_train8000_eval7405_v1_reciteonly_qa_Qwen_Qwen1.5-4B_5e-4_lora2

This model is a fine-tuned version of Qwen/Qwen1.5-4B on the tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa dataset. It achieves the following results on the evaluation set:

• Loss: 2.6245
• Accuracy: 0.6596

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: 0.0005
• train_batch_size: 1
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.4465	1.0	250	1.4944	0.6773
1.2673	2.0	500	1.5207	0.6758
1.0289	3.0	750	1.6096	0.6728
0.8374	4.0	1000	1.7156	0.6700
0.6351	5.0	1250	1.8476	0.6668
0.5167	6.0	1500	1.9508	0.6650
0.3838	7.0	1750	2.0580	0.6636
0.3269	8.0	2000	2.1726	0.6622
0.2495	9.0	2250	2.2331	0.6618
0.2321	10.0	2500	2.3444	0.6616
0.1914	11.0	2750	2.3785	0.6613
0.1895	12.0	3000	2.4416	0.6613
0.1628	13.0	3250	2.4870	0.6601
0.1679	14.0	3500	2.4965	0.6600
0.1478	15.0	3750	2.5362	0.6606
0.1571	16.0	4000	2.5291	0.6608
0.1385	17.0	4250	2.5737	0.6600
0.146	18.0	4500	2.5954	0.6594
0.1335	19.0	4750	2.6082	0.6596
0.1438	20.0	5000	2.6245	0.6596

Framework versions

• PEFT 0.5.0
• Transformers 4.40.2
• Pytorch 2.3.0
• Datasets 2.19.1
• Tokenizers 0.19.1