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goldsounds/
jetpack-model-pt

Name: jetpack-model-pt
Availability: InStock

generated_from_trainer

bigcode/starcoder2-7b

adapter

other

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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

goldsounds/jetpack-model-pt

sft

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

goldsounds/
jetpack-model-pt