lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3_3e-4_lora2

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the tyzhu/lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3 dataset. It achieves the following results on the evaluation set:

• Loss: 2.4500
• Accuracy: 0.6438

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: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 4
• 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: 50.0

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss
1.2608	1.0	187	0.6692	1.1864
1.0703	2.0	375	0.6702	1.1833
0.8377	3.0	562	0.6683	1.2356
0.5925	4.0	750	0.6631	1.3472
0.3927	5.0	937	0.6587	1.4937
0.2434	6.0	1125	0.6541	1.6856
0.1579	7.0	1312	0.6532	1.8412
0.1164	8.0	1500	0.6522	1.9470
0.0915	9.0	1687	0.6519	2.0593
0.084	10.0	1875	0.6507	2.1807
0.0787	11.0	2062	0.6511	2.1614
0.078	12.0	2250	0.6499	2.2384
0.0778	13.0	2437	0.6497	2.2370
0.0783	14.0	2625	0.6507	2.2405
0.0819	15.0	2812	0.6492	2.1894
0.085	16.0	3000	0.6494	2.1976
0.0801	17.0	3187	0.6487	2.1847
0.0812	18.0	3375	0.6487	2.2310
0.0785	19.0	3562	0.6492	2.2172
0.0771	20.0	3750	0.6490	2.2268
0.0747	21.0	3937	0.6479	2.2741
0.0849	22.0	4125	0.6489	2.2643
0.0799	23.0	4312	0.6481	2.2515
0.0815	24.0	4500	0.6488	2.2567
0.0728	25.0	4687	0.6485	2.2895
0.0717	26.0	4875	0.6483	2.3063
0.0706	27.0	5062	0.6479	2.3848
0.0722	28.0	5250	0.6469	2.3759
0.0728	29.0	5437	0.6479	2.3156
0.0731	30.0	5625	0.6476	2.2922
0.0752	31.0	5812	0.6464	2.3409
0.0768	32.0	6000	0.6458	2.2928
0.0709	33.0	6187	0.6462	2.3183
0.072	34.0	6375	0.6456	2.3278
0.0711	35.0	6562	0.6462	2.3750
0.0718	36.0	6750	0.6463	2.4077
0.0724	37.0	6937	0.6450	2.4183
0.0718	38.0	7125	0.6449	2.3822
0.0729	39.0	7312	0.6461	2.4076
0.0729	40.0	7500	0.6444	2.3906
0.069	41.0	7687	0.6453	2.4055
0.0706	42.0	7875	0.6442	2.3809
0.0702	43.0	8062	0.6451	2.3951
0.0708	44.0	8250	0.6440	2.3934
0.0708	45.0	8437	0.6448	2.4016
0.07	46.0	8625	0.6447	2.4118
0.0723	47.0	8812	0.6453	2.4068
0.0735	48.0	9000	0.6444	2.4201
0.0683	49.0	9187	0.6440	2.4153
0.0696	49.87	9350	0.6438	2.4500

Framework versions

• Transformers 4.34.0
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.14.1