# Training guide

Abjad fine-tuning uses MLX-VLM LoRA on Apple Silicon. The repository keeps previous adapters and datasets so old
experiments remain reproducible; new runs should use a new output name instead of overwriting a previous adapter.

## 1. Prepare data

Start with manually verified JSONL. Copy `configs/data_list.example.txt` and edit the paths for the run. For a
small correction run, a single JSONL file is enough:

```bash
cp configs/data_list.example.txt configs/data_list.local.txt
```

Review the rows before training, especially Arabic names, Arabic-Indic digits, tables, stamps, and signatures.

## 2. Run LoRA

The low-level entry point is:

```bash
.venv/bin/python run_lora_training.py \
  --model mlx-community/Qwen3-VL-4B-Instruct-4bit \
  --dataset dataset/abjad_0_2/train_compact.jsonl \
  --output adapters/abjad_0_3_domain.safetensors \
  --iters 300 \
  --image-size 224 \
  --max-seq-length 1024
```

The command writes `training_status.json` and `logs/lora_training.log`. Checkpoints are saved during the run.
Use `--resume-adapter` only when the new run intentionally continues the same adapter lineage.

## 3. Verify before fusion

Evaluate on pages that were not used for training. Look for:

- omitted or duplicated phrases;
- Arabic and Western digit confusion;
- wrong names or diacritics;
- reading-order errors in columns and tables;
- watermark, stamp, and signature contamination;
- output that stops early because of the token limit.

Do not fuse an adapter only because the training loss decreased. Keep the best checkpoint based on held-out OCR
quality and the document types that matter to the deployment.

## 4. Fuse for standalone inference

The current fused model was created from the final selected adapter and can be run with `--no-adapter`. Keep the
adapter backup separately so the fusion can be audited or repeated.

## 5. Publishing checklist

Before uploading a model or dataset:

1. Remove private filesystem paths and tokens.
2. Confirm every source document may be redistributed.
3. State the base model, training method, cumulative row count, and overlap caveat.
4. Include a test example and known limitations.
5. Never claim zero OCR errors; report evaluation results on a fixed held-out set.