• File size measurements show the distribution of size of files.
• Files are classified in four categories based on their size (lines of code): 1-100 (very small files), 101-200 (small files), 201-500 (medium size files), 501-1000 (long files), 1001+(very long files).
• It is a good practice to keep files small. Long files may become "bloaters", code that have increased to such gargantuan proportions that they are hard to work with.

• There are 73 files with 16,047 lines of code.
• 0 very long files (0 lines of code)
• 13 long files (9,819 lines of code)
• 14 medium size files (4,413 lines of codeclsfd_ftr_w_mp_ins)
• 3 small files (502 lines of code)
• 43 very small files (1,313 lines of code)

There are 39 files with lines longer than 120 characters. In total, there are 389 long lines.

File	# lines	# units	# long lines
trial.ipynb in 1_synthetic-qa-generation/auto-evolve-instruct	393	-	43
glan_tutorial.ipynb in 1_synthetic-qa-generation/glan-instruct	831	-	31
1_training_mlflow_phi.ipynb in 2_slm-fine-tuning-mlstudio/phi	866	-	25
1_training_custom_phi.ipynb in 2_slm-fine-tuning-mlstudio/phi	863	-	25
1_training_mlflow_florence2.ipynb in 2_slm-fine-tuning-mlstudio/florence2-VQA	640	-	21
jsonl simple_qna_data_en.jsonl in 3_llmops-aifoundry/3_3_optimizing/data	19	-	19
make_qa_multimodal_pdf_docai.ipynb in 1_synthetic-qa-generation/seed	901	-	19
contentsafety_with_code_ja.ipynb in 3_llmops-aifoundry/3_4_operationalizing	579	-	16
promptflow_with_code.ipynb in 3_llmops-aifoundry/3_2_prototyping	486	-	16
make_qa_multimodal_pdf_oss.ipynb in 1_synthetic-qa-generation/seed	679	-	15
contentsafety_with_code_en.ipynb in 3_llmops-aifoundry/3_4_operationalizing	559	-	14
3_optimization_olive.ipynb in 2_slm-fine-tuning-mlstudio/phi	599	-	12
2_serving_florence2.ipynb in 2_slm-fine-tuning-mlstudio/florence2-VQA	811	-	12
evolve.py in 1_synthetic-qa-generation/reasoningplaning	697	48	12
glan.py in 1_synthetic-qa-generation/glan-instruct	378	13	11
train_mlflow.py in 2_slm-fine-tuning-mlstudio/florence2-VQA/src_train	206	10	10
jinja2 groundedness_score.jinja2 in 3_llmops-aifoundry/3_3_optimizing/evaluation	30	-	10
evolve.py in 1_synthetic-qa-generation/evolve-instruct	361	14	10
2_serving_basic_phi.ipynb in 2_slm-fine-tuning-mlstudio/phi	923	-	9
2_serving_llm_phi.ipynb in 2_slm-fine-tuning-mlstudio/phi	871	-	8
jsonl simple_math_data_en.jsonl in 3_llmops-aifoundry/3_3_optimizing/data	20	-	8
promptflow_with_evaluation_code.ipynb in 3_llmops-aifoundry/3_3_optimizing	345	-	8
jsonl qna_outdoor.jsonl in 3_llmops-aifoundry/3_3_optimizing/data	5	-	5
2_prompty.ipynb in 0_lab_preparation	192	-	4
1_get_started.ipynb in 0_lab_preparation	276	-	3
1_data-preparation-basic.ipynb in 2_slm-fine-tuning-mlstudio/phi/dataset-preparation	189	-	3
make_qa_only_image_pdf.ipynb in 1_synthetic-qa-generation/seed	416	-	3
make_qa_only_image_multiple_pdf.ipynb in 1_synthetic-qa-generation/seed	288	-	3
combine_tokenizer.py in 2_slm-fine-tuning-mlstudio/phi/dataset-preparation	39	2	2
jsonl questions_outdoor.jsonl in 3_llmops-aifoundry/3_2_prototyping/data	4	-	2
make_qa_csv.ipynb in 1_synthetic-qa-generation/seed	236	-	2
score.py in 2_slm-fine-tuning-mlstudio/phi/src_serve	28	2	1
score.py in 2_slm-fine-tuning-mlstudio/florence2-VQA/src_serve	57	3	1
phi35_finetuned.py in 3_llmops-aifoundry/3_2_prototyping/chat-context	42	2	1
phi35_chatcompletion.py in 3_llmops-aifoundry/3_2_prototyping/chat	44	2	1
phi35_finetuned.py in 3_llmops-aifoundry/3_2_prototyping/chat	43	2	1
convert.py in 1_synthetic-qa-generation/evolve-instruct	34	1	1
common_utils.py in 1_synthetic-qa-generation/seed/util	68	4	1
generate_answer_only.py in 1_synthetic-qa-generation/glan-instruct	28	-	1