AGENTS.md

For coding agents

This repo is a curated collection of ready-to-run OCR scripts — each one self-contained via UV inline metadata, runnable over the network via hf jobs uv run. No clone, no install, no setup.

Don't rely on this doc — discover the current state

This file will go stale. Prefer these sources of truth:

• hf jobs uv run --help — job submission flags (volumes, secrets, flavors, timeouts)
• hf jobs hardware — current GPU flavors and pricing
• hf auth whoami — check HF token is set
• hf jobs ps / hf jobs logs <id> — monitor running jobs
• ls the repo to see which scripts actually exist (bucket variants especially)
• README.md — the table of scripts with model sizes and notes

Picking a script

The README.md table lists every script with model size, backend, and a short note. Axes that matter:

• Model size vs accuracy vs GPU cost. Smaller = cheaper per doc.
• Backend: vLLM scripts are usually fastest at scale. transformers and falcon-perception are alternatives for specific models.
• Task support: most scripts do plain text; some expose --task-mode (table, formula, layout, etc.) — check the script's own docstring.

For the authoritative benchmark numbers on any model in the table, query the model card programmatically — every OCR model publishes eval results on its card:

from huggingface_hub import HfApi
info = HfApi().model_info("tiiuae/Falcon-OCR", expand=["evalResults"])
for r in info.eval_results:
    print(r.dataset_id, r.value)

See the leaderboard data guide for the full API. This is more reliable than any markdown table that might drift.

Getting help from a specific script

Each script has a docstring at the top with a description and usage examples. To read it without downloading:

curl -s https://huggingface.co/datasets/uv-scripts/ocr/raw/main/<script>.py | head -100

Or open the URL in a browser. Running uv run <url> --help locally may fail if the script has GPU-only dependencies — reading the docstring is more reliable.

The main pattern: dataset → dataset

Most scripts take an input HF dataset ID and push results to an output HF dataset ID:

hf jobs uv run --flavor l4x1 -s HF_TOKEN \
  https://huggingface.co/datasets/uv-scripts/ocr/raw/main/<script>.py \
  <input-dataset-id> <output-dataset-id> [--max-samples N] [--shuffle]

The script adds a markdown column to the input dataset and pushes the merged result to the output dataset ID on the Hub.

Alternative: directory → directory (bucket variants)

A couple of scripts have -bucket.py variants (currently falcon-ocr-bucket.py and glm-ocr-bucket.py) that read from a mounted directory and write one .md per image (or per PDF page). Useful with HF Buckets via -v:

hf jobs uv run --flavor l4x1 -s HF_TOKEN \
  -v hf://buckets/<user>/<input>:/input:ro \
  -v hf://buckets/<user>/<output>:/output \
  https://huggingface.co/datasets/uv-scripts/ocr/raw/main/<script>-bucket.py \
  /input /output

ls the repo to check whether a -bucket.py variant exists for the model you want before assuming it's available.

Common flags across dataset-mode scripts

Most scripts support: --max-samples, --shuffle, --seed, --split, --image-column, --output-column, --private, --config, --create-pr, --verbose. Read the script's docstring for the authoritative list — individual scripts may add model-specific options like --task-mode.

Gotchas

• Secrets: pass -s HF_TOKEN to forward the user's token into the job.
• GPU required: all scripts exit if CUDA isn't available. l4x1 is the cheapest GPU flavor and works for models up to ~3B. Check hf jobs hardware for current options.
• First run is slow: model download + torch.compile / vLLM warmup dominates small runs. Cost per doc drops sharply past a few hundred images — test with --max-samples 10 first, then scale.
• Don't poll jobs: jobs run async. Submit once, check status later with hf jobs ps or hf jobs logs <id>.