Modelpublic

fausap/peft-smollm2-lora-gtx1660

sourceHugging Faceapache-2.0updated 11mo agoView on Hugging Face

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Model Card

peft-smollm2-lora-gtx1660

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-360M on an unknown dataset. It achieves the following results on the evaluation set:

—Loss: 3.6778

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: 0.0005
—trainbatchsize: 1
—evalbatchsize: 1
—seed: 42
—gradientaccumulationsteps: 16
—totaltrainbatch_size: 16
—optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
—lrschedulertype: cosine
—lrschedulerwarmup_steps: 5
—training_steps: 500

Training results

Training Loss	Epoch	Step	Validation Loss
4.0994	0.02	10	4.0259
3.9631	0.04	20	3.8910
3.915	0.06	30	3.8351
3.8301	0.08	40	3.7982
3.813	0.1	50	3.7773
3.7831	0.12	60	3.7633
3.7447	0.14	70	3.7478
3.7448	0.16	80	3.7437
3.7424	0.18	90	3.7297
3.7015	0.2	100	3.7205
3.7006	0.22	110	3.7144
3.6684	0.24	120	3.7020
3.6689	0.26	130	3.6980
3.6341	0.28	140	3.6918
3.6516	0.3	150	3.6897
3.6409	0.32	160	3.6922
3.6305	0.34	170	3.6829
3.617	0.36	180	3.6834
3.6111	0.38	190	3.6810
3.6092	0.4	200	3.6814
3.5892	0.42	210	3.6795
3.5968	0.44	220	3.6739
3.5732	0.46	230	3.6803
3.586	0.48	240	3.6729
3.5805	0.5	250	3.6765
3.5651	0.52	260	3.6788
3.5532	0.54	270	3.6749
3.556	0.56	280	3.6752
3.5717	0.58	290	3.6752
3.5333	0.6	300	3.6755
3.5652	0.62	310	3.6790
3.5473	0.64	320	3.6774
3.5352	0.66	330	3.6765
3.5369	0.68	340	3.6757
3.5356	0.7	350	3.6779
3.5418	0.72	360	3.6773
3.5458	0.74	370	3.6758
3.5502	0.76	380	3.6777
3.5114	0.78	390	3.6776
3.5532	0.8	400	3.6779
3.5411	0.82	410	3.6787
3.5357	0.84	420	3.6774
3.5353	0.86	430	3.6778
3.5408	0.88	440	3.6779
3.5562	0.9	450	3.6786
3.5272	0.92	460	3.6779
3.545	0.94	470	3.6776
3.5353	0.96	480	3.6776
3.5436	0.98	490	3.6778
3.5326	1.0	500	3.6778

Framework versions

—PEFT 0.17.1
—Transformers 4.57.1
—Pytorch 2.8.0+cu128
—Datasets 4.2.0
—Tokenizers 0.22.1