lmind_hotpot_train8000_eval7405_v1_reciteonly_qa_Qwen_Qwen1.5-4B_3e-5_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: 1.8696
• Accuracy: 0.6658

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: 3e-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
1.4742	1.0	250	1.5313	0.6748
1.45	2.0	500	1.5196	0.6757
1.4269	3.0	750	1.5134	0.6761
1.3999	4.0	1000	1.5120	0.6762
1.3614	5.0	1250	1.5192	0.6760
1.3303	6.0	1500	1.5266	0.6755
1.2946	7.0	1750	1.5446	0.6747
1.2518	8.0	2000	1.5590	0.6745
1.2082	9.0	2250	1.5717	0.6740
1.19	10.0	2500	1.6022	0.6727
1.1523	11.0	2750	1.6098	0.6726
1.1193	12.0	3000	1.6345	0.6716
1.0736	13.0	3250	1.6748	0.6707
1.0414	14.0	3500	1.6880	0.6701
1.0069	15.0	3750	1.7182	0.6694
0.9654	16.0	4000	1.7522	0.6685
0.9337	17.0	4250	1.7826	0.6677
0.9	18.0	4500	1.8080	0.6672
0.8704	19.0	4750	1.8350	0.6663
0.8407	20.0	5000	1.8696	0.6658

Framework versions

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