lmind_hotpot_train8000_eval7405_v1_qa_Qwen_Qwen1.5-4B_5e-5_lora2

This model is a fine-tuned version of Qwen/Qwen1.5-4B on the tyzhu/lmind_hotpot_train8000_eval7405_v1_qa dataset. It achieves the following results on the evaluation set:

• Loss: 3.9023
• Accuracy: 0.4878

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-05
• 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
2.2742	1.0	250	2.3355	0.5148
2.1673	2.0	500	2.3223	0.5169
2.0189	3.0	750	2.3526	0.5157
1.8684	4.0	1000	2.4020	0.5140
1.7028	5.0	1250	2.4910	0.5100
1.5685	6.0	1500	2.5835	0.5072
1.4161	7.0	1750	2.6927	0.5036
1.2904	8.0	2000	2.8047	0.5009
1.1397	9.0	2250	2.9097	0.4979
1.0366	10.0	2500	3.0555	0.4951
0.9114	11.0	2750	3.1526	0.4928
0.8301	12.0	3000	3.2417	0.4928
0.7283	13.0	3250	3.3687	0.4894
0.6717	14.0	3500	3.5007	0.4899
0.6055	15.0	3750	3.5470	0.4894
0.5667	16.0	4000	3.6736	0.4876
0.5271	17.0	4250	3.7100	0.4870
0.5081	18.0	4500	3.7920	0.4877
0.4758	19.0	4750	3.8548	0.4868
0.4682	20.0	5000	3.9023	0.4878

Framework versions

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