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amazingvince/llama2_xs_233m_GQA-llama-1028-interleaved-deduped-v1-tb-interleaved-deduped-1028-0919

sourceHugging Faceupdated 3y agoView on Hugging Face

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Model Card

llama2xs233m_GQA-llama-1028-interleaved-deduped-v1-tb-interleaved-deduped-1028-0919

This model is a fine-tuned version of amazingvince/llama2_xs_233m_GQA-llama-1028-interleaved-deduped-v1-tb-interleaved-deduped-1028-0919 on the None dataset. It achieves the following results on the evaluation set:

—Loss: 3.1626
—Accuracy: 0.4030

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: 0.0002
—trainbatchsize: 16
—evalbatchsize: 16
—seed: 17404
—distributed_type: multi-GPU
—num_devices: 2
—gradientaccumulationsteps: 4
—totaltrainbatch_size: 128
—totalevalbatch_size: 32
—optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-06
—lrschedulertype: cosine
—lrschedulerwarmup_ratio: 0.01
—num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.2561	0.09	250	3.4413	0.3719
3.2492	0.18	500	3.4013	0.3763
3.1923	0.27	750	3.3739	0.3789
3.1947	0.36	1000	3.3508	0.3817
3.2014	0.45	1250	3.3310	0.3837
3.187	0.54	1500	3.3098	0.3859
3.1083	0.63	1750	3.2901	0.3879
3.0937	0.72	2000	3.2718	0.3900
3.0772	0.81	2250	3.2543	0.3920
3.0102	0.9	2500	3.2394	0.3935
3.0455	0.98	2750	3.2249	0.3955
3.0091	1.07	3000	3.2157	0.3965
2.9399	1.16	3250	3.2067	0.3975
2.9885	1.25	3500	3.1967	0.3987
2.9979	1.34	3750	3.1885	0.4002
2.9405	1.43	4000	3.1823	0.4002
2.9643	1.52	4250	3.1754	0.4012
2.9571	1.61	4500	3.1702	0.4020
2.9677	1.7	4750	3.1668	0.4024
2.9564	1.79	5000	3.1640	0.4028
2.932	1.88	5250	3.1629	0.4030
2.9532	1.97	5500	3.1626	0.4030

Framework versions

—Transformers 4.34.0.dev0
—Pytorch 2.2.0.dev20230906+cu121
—Datasets 2.14.5
—Tokenizers 0.13.3