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Kudod/LLama3-2-1B-distortion-fold-4-1a-v2

sourceHugging Facellama3.2updated 8mo agoView on Hugging Face

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LLama3-2-1B-distortion-fold-4-1a-v2

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on an unknown dataset. It achieves the following results on the evaluation set:

—Loss: 5.6704
—Accuracy: 0.4263
—Precision Macro: 0.4247
—Recall Macro: 0.4059
—F1 Macro: 0.4109
—Precision Weighted: 0.4319
—Recall Weighted: 0.4263
—F1 Weighted: 0.4249

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: 2e-05
—trainbatchsize: 16
—evalbatchsize: 16
—seed: 42
—optimizer: Use adamwtorchfused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
—lrschedulertype: linear
—lrschedulerwarmup_ratio: 0.06
—num_epochs: 30
—mixedprecisiontraining: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision Macro	Recall Macro	F1 Macro	Precision Weighted	Recall Weighted	F1 Weighted
2.5618	1.0	60	2.0527	0.2759	0.4290	0.2497	0.2274	0.4430	0.2759	0.2474
1.4356	2.0	120	2.2665	0.2978	0.3373	0.2943	0.2856	0.3611	0.2978	0.2887
0.8489	3.0	180	2.8696	0.3323	0.4108	0.3130	0.2867	0.4402	0.3323	0.3130
0.4164	4.0	240	3.6011	0.3542	0.3799	0.3353	0.3209	0.3917	0.3542	0.3362
0.156	5.0	300	4.6426	0.3166	0.3580	0.2810	0.2853	0.3665	0.3166	0.3140
0.0726	6.0	360	6.2002	0.3323	0.3676	0.3088	0.3028	0.3679	0.3323	0.3139
0.0909	7.0	420	6.5484	0.3636	0.3858	0.3475	0.3411	0.4023	0.3636	0.3574
0.0308	8.0	480	6.1449	0.4013	0.3950	0.3789	0.3704	0.4116	0.4013	0.3911
0.054	9.0	540	6.1597	0.4232	0.4269	0.4020	0.3929	0.4305	0.4232	0.4084
0.0299	10.0	600	6.2312	0.3981	0.4147	0.3731	0.3688	0.4143	0.3981	0.3802
0.0151	11.0	660	6.3685	0.4107	0.4299	0.4024	0.4063	0.4444	0.4107	0.4178
0.0013	12.0	720	5.9043	0.4044	0.4190	0.3759	0.3827	0.4243	0.4044	0.3990
0.0004	13.0	780	5.6635	0.4263	0.4246	0.4039	0.4094	0.4330	0.4263	0.4250
0.0	14.0	840	5.6663	0.4295	0.4285	0.4080	0.4134	0.4362	0.4295	0.4282
0.0	15.0	900	5.6662	0.4263	0.4247	0.4059	0.4109	0.4319	0.4263	0.4249
0.0	16.0	960	5.6688	0.4263	0.4247	0.4059	0.4109	0.4319	0.4263	0.4249
0.0	17.0	1020	5.6704	0.4263	0.4247	0.4059	0.4109	0.4319	0.4263	0.4249

Framework versions

—Transformers 4.57.1
—Pytorch 2.9.1+cu128
—Datasets 4.4.1
—Tokenizers 0.22.1