Configuration Reference

All configuration is managed through Python dataclasses defined in src/weather_doc_extractor/config.py. There is no external config file; values are changed by passing CLI flags or by editing the dataclasses directly for programmatic use.

AppConfig

The top-level configuration object, composed of the sections below.

IngestConfig

Controls where the ingest stage reads data from.

Field	Default	Description
images_dir	Daily_rainfall_sample/images	Directory of document images
transcriptions_dir	Daily_rainfall_sample/transcriptions	Directory of JSON transcriptions
output_dir	data/dataset	Where to write processed dataset records

ModelConfig

Controls which model is used for inference.

Field	Default	Description
model_name	HuggingFaceTB/SmolVLM-500M-Instruct	HuggingFace model ID or local adapter path
max_new_tokens	2048	Maximum tokens the model may generate
temperature	0.0	Sampling temperature (0 = greedy)
device	auto	Device placement (auto, cpu, cuda)

Model presets

The --model CLI flag accepts short preset names as well as full HuggingFace model IDs or local adapter paths.

Preset	Model ID	Notes
smolvlm	HuggingFaceTB/SmolVLM-500M-Instruct	Lightweight baseline (~500 M params)
smolvlm2	HuggingFaceTB/SmolVLM2-2.2B-Instruct	SmolVLM2, 2.2 B
granite	ibm-granite/granite-vision-3.2-2b	IBM Granite Vision 3.2, 2 B
granite4	ibm-granite/granite-vision-4.1-4b	IBM Granite Vision 4.1, 4 B
gemma3	google/gemma-3-4b-it	Google Gemma 3, 4 B; uses pan-and-scan tiling for high-res scans
gemma4	google/gemma-4-E4B-it	Google Gemma 4, 4 B edge; variable-resolution token budget
ministral	mistralai/Mistral-Small-3.1-24B-Instruct-2503	Mistral Small 3.1, 24 B; Pixtral vision encoder, 128 k context

TrainingConfig

Controls the fine-tuning process.

Field	Default	Description
output_dir	outputs/checkpoints	Root directory for saved adapters
learning_rate	2e-4	AdamW learning rate
epochs	3	Number of training epochs
batch_size	1	Per-device training batch size
gradient_accumulation_steps	8	Effective batch = batch_size × this
eval_split	0.1	Fraction of data used for validation
lora_r	8	LoRA rank
lora_alpha	16	LoRA alpha scaling factor
lora_dropout	0.05	Dropout applied to LoRA layers
lora_target_modules	None	Linear layers to adapt; None = all

ProjectPaths

General-purpose path configuration (used internally by some pipeline functions).

Field	Default
data_dir	data
raw_images_dir	data/raw_images
annotations_dir	data/annotations
outputs_dir	outputs
models_dir	models