lmind_hotpot_train8000_eval7405_v1_qa_Qwen_Qwen1.5-4B_3e-4_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: 3.6823
• Accuracy: 0.4917

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: 0.0003
• 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
2.248	1.0	250	2.3110	0.5173
1.9103	2.0	500	2.3740	0.5157
1.4896	3.0	750	2.5266	0.5112
1.109	4.0	1000	2.7830	0.5037
0.7757	5.0	1250	3.0311	0.4987
0.5994	6.0	1500	3.2256	0.4979
0.4921	7.0	1750	3.3517	0.4958
0.4575	8.0	2000	3.4321	0.4946
0.4233	9.0	2250	3.5151	0.4961
0.4178	10.0	2500	3.5280	0.4950
0.3987	11.0	2750	3.5547	0.4951
0.4033	12.0	3000	3.5601	0.4954
0.3932	13.0	3250	3.5859	0.4932
0.4012	14.0	3500	3.5944	0.4927
0.3895	15.0	3750	3.6038	0.4939
0.396	16.0	4000	3.6504	0.4932
0.3847	17.0	4250	3.6602	0.4912
0.3942	18.0	4500	3.6515	0.4914
0.3809	19.0	4750	3.7304	0.4923
0.3805	20.0	5000	3.6823	0.4917

Framework versions

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