azure / azure-llm-fine-tuning

Duplication

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Longest Duplicates

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Size	#	Folders	Files	Lines	Code
275	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_mlflow_phi3.ipynb	38:312 (31%) 30:304 (31%)	view
105	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_mlflow_phi3.ipynb	418:522 (12%) 406:510 (12%)	view
102	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_mlflow_phi3.ipynb	665:766 (11%) 657:758 (11%)	view
72	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_custom_phi3.ipynb	47:118 (11%) 57:128 (8%)	view
72	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_mlflow_phi3.ipynb	47:118 (11%) 49:120 (8%)	view
53	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_custom_phi3.ipynb	468:520 (8%) 673:725 (6%)	view
53	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_mlflow_phi3.ipynb	468:520 (8%) 665:717 (6%)	view
50	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_mlflow_phi3.ipynb	584:633 (5%) 572:621 (5%)	view
41	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_mlflow_phi3.ipynb	764:804 (4%) 763:803 (4%)	view
39	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_mlflow_phi3.ipynb	171:209 (6%) 234:272 (4%)	view
39	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_custom_phi3.ipynb	171:209 (6%) 242:280 (4%)	view
38	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_mlflow_phi3.ipynb	8:45 (5%) 8:45 (4%)	view
36	x 2	phi3/src_train phi3/src_train	train.py train_mlflow.py	105:152 (18%) 128:175 (18%)	view
36	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_mlflow_phi3.ipynb	412:447 (5%) 582:617 (4%)	view
36	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_mlflow_phi3.ipynb	828:863 (4%) 831:866 (4%)	view
36	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_custom_phi3.ipynb	412:447 (5%) 594:629 (4%)	view
35	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_mlflow_phi3.ipynb	548:582 (4%) 536:570 (4%)	view
34	x 2	phi3/src_train phi3/src_train	train.py train_mlflow.py	18:62 (17%) 43:87 (17%)	view
29	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_custom_phi3.ipynb	210:238 (4%) 283:311 (3%)	view
29	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_mlflow_phi3.ipynb	210:238 (4%) 275:303 (3%)	view
27	x 2	phi3/src_train phi3/src_train	train.py train_mlflow.py	75:101 (14%) 89:115 (14%)	view
25	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_custom_phi3.ipynb	367:391 (3%) 548:572 (2%)	view
25	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_mlflow_phi3.ipynb	367:391 (3%) 536:560 (2%)	view
23	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_mlflow_phi3.ipynb	343:365 (3%) 512:534 (2%)	view
23	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_custom_phi3.ipynb	343:365 (3%) 524:546 (2%)	view
23	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_mlflow_phi3.ipynb	524:546 (2%) 512:534 (2%)	view
22	x 2	phi3/src_train phi3/src_train	train.py train_mlflow.py	243:274 (11%) 267:298 (11%)	view
21	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_mlflow_phi3.ipynb	522:542 (3%) 721:741 (2%)	view
21	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_custom_phi3.ipynb	522:542 (3%) 729:749 (2%)	view
20	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_mlflow_phi3.ipynb	276:295 (3%) 444:463 (2%)	view
20	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_custom_phi3.ipynb	276:295 (3%) 456:475 (2%)	view
20	x 2	aoai florence2-VQA	aoai_finetune.ipynb 2_serving_florence2.ipynb	360:379 (5%) 779:798 (2%)	view
19	x 2	phi3 phi3/dataset-preparation	1_training_custom_phi3.ipynb 1_data-preparation-basic.ipynb	831:849 (2%) 165:183 (10%)	view
19	x 2	phi3 phi3/dataset-preparation	1_training_mlflow_phi3.ipynb 1_data-preparation-basic.ipynb	834:852 (2%) 165:183 (10%)	view
18	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_mlflow_phi3.ipynb	349:366 (2%) 339:356 (2%)	view
18	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_mlflow_phi3.ipynb	809:826 (2%) 810:827 (2%)	view
18	x 2	florence2-VQA florence2-VQA	1_training_mlflow_florence2.ipynb 1_training_mlflow_florence2.ipynb	251:268 (2%) 282:299 (2%)	view
17	x 2	florence2-VQA florence2-VQA	1_training_mlflow_florence2.ipynb 2_serving_florence2.ipynb	46:62 (2%) 43:59 (2%)	view
17	x 2	florence2-VQA phi3	2_serving_florence2.ipynb 3_optimization_olive.ipynb	43:59 (2%) 29:45 (2%)	view
17	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_custom_phi3.ipynb	575:591 (2%) 791:807 (1%)	view
17	x 2	florence2-VQA phi3	2_serving_florence2.ipynb 3_optimization_olive.ipynb	66:82 (2%) 94:110 (2%)	view
17	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 3_optimization_olive.ipynb	46:62 (2%) 29:45 (2%)	view
16	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_mlflow_phi3.ipynb	462:477 (1%) 419:434 (1%)	view
16	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_mlflow_phi3.ipynb	648:663 (1%) 641:656 (1%)	view
16	x 2	florence2-VQA phi3	2_serving_florence2.ipynb 1_training_custom_phi3.ipynb	44:59 (1%) 57:72 (1%)	view
16	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_custom_phi3.ipynb	249:264 (2%) 429:444 (1%)	view
16	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_custom_phi3.ipynb	431:446 (1%) 462:477 (1%)	view
16	x 2	phi3 phi3	1_training_custom_phi3.ipynb 3_optimization_olive.ipynb	57:72 (1%) 30:45 (2%)	view
16	x 2	phi3 phi3	1_training_custom_phi3.ipynb 1_training_mlflow_phi3.ipynb	431:446 (1%) 450:465 (1%)	view
16	x 2	florence2-VQA phi3	1_training_mlflow_florence2.ipynb 1_training_mlflow_phi3.ipynb	249:264 (2%) 417:432 (1%)	view