jetpack-model-pt

goldsounds/jetpack-model-pt

jetpack-model-pt is a machine learning model from goldsounds. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as other.

should probably proofread and complete it, then remove this comment. --

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Repo size

34 GB

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Hugging Face

Repo makeup

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.pt22.7 GB · 66%

At a glance

Library: peft
License: other
Access: Public
Created: Mar 14, 2024
Updated: Mar 14, 2024
SHA: 7111b874

Base models

bigcode/starcoder2-7b

Model card

From the Hugging Face model README

sft

This model is a fine-tuned version of bigcode/starcoder2-7b on the starcoder_jetpack dataset. It achieves the following results on the evaluation set:

Loss: 0.6761

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-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 3.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.2469	0.02	100	0.8479
1.3584	0.03	200	0.8368
1.0651	0.05	300	0.8281
0.9209	0.06	400	0.8185
0.8306	0.08	500	0.8132
0.9175	0.1	600	0.8064
0.8157	0.11	700	0.8023
0.9469	0.13	800	0.7996
0.8872	0.14	900	0.7955
0.8842	0.16	1000	0.7913
0.7909	0.18	1100	0.7863
0.8196	0.19	1200	0.7844
0.9341	0.21	1300	0.7825
0.8801	0.22	1400	0.7787
0.9109	0.24	1500	0.7777
0.8964	0.26	1600	0.7759
0.9265	0.27	1700	0.7742
0.8632	0.29	1800	0.7699
1.0081	0.3	1900	0.7693
0.7651	0.32	2000	0.7664
1.0037	0.34	2100	0.7654
0.8768	0.35	2200	0.7642
0.8052	0.37	2300	0.7618
0.7271	0.38	2400	0.7595
0.9615	0.4	2500	0.7582
0.8284	0.42	2600	0.7555
0.8631	0.43	2700	0.7540
1.0507	0.45	2800	0.7518
0.8247	0.46	2900	0.7512
0.9835	0.48	3000	0.7496
0.8407	0.49	3100	0.7496
0.7417	0.51	3200	0.7467
0.7449	0.53	3300	0.7473
0.8562	0.54	3400	0.7437
0.9222	0.56	3500	0.7429
0.9242	0.57	3600	0.7413
0.8092	0.59	3700	0.7403
0.7279	0.61	3800	0.7394
0.7774	0.62	3900	0.7385
0.8942	0.64	4000	0.7364
0.9286	0.65	4100	0.7348
0.7703	0.67	4200	0.7354
0.8322	0.69	4300	0.7330
0.9851	0.7	4400	0.7324
0.8712	0.72	4500	0.7317
0.7871	0.73	4600	0.7310
0.7156	0.75	4700	0.7284
0.7856	0.77	4800	0.7277
0.7906	0.78	4900	0.7255
0.7917	0.8	5000	0.7250
0.6395	0.81	5100	0.7237
0.7567	0.83	5200	0.7232
0.8551	0.85	5300	0.7220
0.7392	0.86	5400	0.7226
0.9312	0.88	5500	0.7205
0.8323	0.89	5600	0.7196
0.7312	0.91	5700	0.7197
1.0	0.93	5800	0.7182
0.6164	0.94	5900	0.7177
0.7484	0.96	6000	0.7147
0.7924	0.97	6100	0.7144
0.9389	0.99	6200	0.7145
0.7108	1.01	6300	0.7136
0.8076	1.02	6400	0.7154
0.7232	1.04	6500	0.7147
0.6456	1.05	6600	0.7122
0.5862	1.07	6700	0.7133
0.6935	1.09	6800	0.7112
0.7522	1.1	6900	0.7103
1.0525	1.12	7000	0.7106
0.8285	1.13	7100	0.7099
0.6116	1.15	7200	0.7079
0.5617	1.17	7300	0.7087
0.6514	1.18	7400	0.7072
0.6729	1.2	7500	0.7052
0.6401	1.21	7600	0.7055
0.8089	1.23	7700	0.7052
0.8166	1.25	7800	0.7041
0.8685	1.26	7900	0.7026
0.6945	1.28	8000	0.7043
0.6955	1.29	8100	0.7010
0.734	1.31	8200	0.7022
0.5586	1.32	8300	0.7017
0.7299	1.34	8400	0.6999
1.089	1.36	8500	0.6994
0.5733	1.37	8600	0.6994
0.5409	1.39	8700	0.6987
0.8848	1.4	8800	0.6976
0.5739	1.42	8900	0.6971
0.728	1.44	9000	0.6963
0.7503	1.45	9100	0.6953
0.706	1.47	9200	0.6951
0.7926	1.48	9300	0.6945
0.6019	1.5	9400	0.6945
0.6707	1.52	9500	0.6943
0.7269	1.53	9600	0.6940
0.7216	1.55	9700	0.6923
0.6394	1.56	9800	0.6920
0.7608	1.58	9900	0.6909
1.034	1.6	10000	0.6908
0.7934	1.61	10100	0.6892
0.627	1.63	10200	0.6902
0.5849	1.64	10300	0.6897
0.7257	1.66	10400	0.6889
0.8931	1.68	10500	0.6890
0.6831	1.69	10600	0.6875
0.4995	1.71	10700	0.6879
0.757	1.72	10800	0.6873
0.4664	1.74	10900	0.6876
0.78	1.76	11000	0.6865
0.5564	1.77	11100	0.6865
0.7858	1.79	11200	0.6858
0.6989	1.8	11300	0.6851
0.705	1.82	11400	0.6841
0.5795	1.84	11500	0.6842
0.6989	1.85	11600	0.6837
0.6877	1.87	11700	0.6838
0.6484	1.88	11800	0.6835
0.8525	1.9	11900	0.6832
0.7547	1.92	12000	0.6823
0.8118	1.93	12100	0.6819
0.8859	1.95	12200	0.6823
0.738	1.96	12300	0.6811
0.7051	1.98	12400	0.6816
0.5598	2.0	12500	0.6802
0.6194	2.01	12600	0.6812
0.7101	2.03	12700	0.6817
0.7027	2.04	12800	0.6815
0.9432	2.06	12900	0.6810
0.5931	2.08	13000	0.6817
0.5412	2.09	13100	0.6810
0.6237	2.11	13200	0.6815
0.5871	2.12	13300	0.6812
0.8331	2.14	13400	0.6817
0.4528	2.15	13500	0.6812
0.6292	2.17	13600	0.6814
0.6219	2.19	13700	0.6800
0.6281	2.2	13800	0.6798
0.6949	2.22	13900	0.6803
0.6701	2.23	14000	0.6791
0.6467	2.25	14100	0.6795
0.6579	2.27	14200	0.6800
0.5978	2.28	14300	0.6802
0.7032	2.3	14400	0.6793
0.6347	2.31	14500	0.6787
0.9034	2.33	14600	0.6788
0.6166	2.35	14700	0.6781
0.7327	2.36	14800	0.6786
0.7159	2.38	14900	0.6777
0.6283	2.39	15000	0.6779
0.6113	2.41	15100	0.6776
0.5951	2.43	15200	0.6781
0.6747	2.44	15300	0.6777
0.5935	2.46	15400	0.6779
0.6435	2.47	15500	0.6776
0.637	2.49	15600	0.6772
0.4617	2.51	15700	0.6774
0.7937	2.52	15800	0.6771
0.7187	2.54	15900	0.6768
0.657	2.55	16000	0.6767
0.8606	2.57	16100	0.6767
0.4392	2.59	16200	0.6768
0.5675	2.6	16300	0.6769
0.6454	2.62	16400	0.6768
0.5787	2.63	16500	0.6767
0.6111	2.65	16600	0.6766
0.6106	2.67	16700	0.6767
0.5947	2.68	16800	0.6763
0.5576	2.7	16900	0.6763
0.659	2.71	17000	0.6762
0.787	2.73	17100	0.6761
0.5503	2.75	17200	0.6760
0.5558	2.76	17300	0.6760
0.6324	2.78	17400	0.6761
0.5846	2.79	17500	0.6761
0.9542	2.81	17600	0.6760
0.5755	2.83	17700	0.6761
0.7841	2.84	17800	0.6761
0.5662	2.86	17900	0.6761
0.8085	2.87	18000	0.6761
0.7389	2.89	18100	0.6761
0.736	2.91	18200	0.6761
0.5604	2.92	18300	0.6761
0.6156	2.94	18400	0.6761
0.5473	2.95	18500	0.6761
0.7286	2.97	18600	0.6761
0.5932	2.98	18700	0.6761

Framework versions

PEFT 0.9.0
Transformers 4.39.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Library: peft
License: other
Created: Mar 14, 2024
Updated: Mar 14, 2024