Modelpublic

Swephoenix/smollm2-lora-hrz1ceio-1742335328

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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Model Card

smollm2-lora-hrz1ceio-1742335328

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-1.7B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

—Loss: 2.4598
—Perplexity: 11.7017

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: 5e-06
—trainbatchsize: 1
—evalbatchsize: 1
—seed: 42
—gradientaccumulationsteps: 16
—totaltrainbatch_size: 16
—optimizer: Use adamwtorch with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
—lrschedulertype: linear
—lrschedulerwarmup_ratio: 0.01
—num_epochs: 50
—mixedprecisiontraining: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Perplexity
3.8247	1.48	10	3.8893	48.8773
3.7821	2.96	20	3.8282	45.9783
3.7187	4.32	30	3.7582	42.8707
3.6627	5.8	40	3.6850	39.8460
3.5609	7.16	50	3.6080	36.8919
3.4883	8.64	60	3.5351	34.2986
3.3997	10.0	70	3.4620	31.8798
3.3753	11.48	80	3.3897	29.6568
3.249	12.96	90	3.3187	27.6251
3.2619	14.32	100	3.2492	25.7684
3.1184	15.8	110	3.1800	24.0468
3.0852	17.16	120	3.1133	22.4950
2.9999	18.64	130	3.0489	21.0926
2.9315	20.0	140	2.9865	19.8159
2.848	21.48	150	2.9260	18.6519
2.8046	22.96	160	2.8679	17.6005
2.7379	24.32	170	2.8153	16.6979
2.704	25.8	180	2.7637	15.8585
2.6349	27.16	190	2.7168	15.1316
2.5972	28.64	200	2.6749	14.5109
2.5585	30.0	210	2.6327	13.9107
2.5502	31.48	220	2.5979	13.4359
2.5166	32.96	230	2.5670	13.0264
2.4733	34.32	240	2.5395	12.6737
2.4502	35.8	250	2.5152	12.3691
2.4268	37.16	260	2.4965	12.1393
2.365	38.64	270	2.4808	11.9507
2.4208	40.0	280	2.4707	11.8304
2.3818	41.48	290	2.4623	11.7311
2.391	42.96	300	2.4598	11.7017

Framework versions

—PEFT 0.14.0
—Transformers 4.48.2
—Pytorch 2.1.0+cu118
—Datasets 3.4.1
—Tokenizers 0.21.1