| name | uv-recipes |
| description | Run self-contained UV-script recipes over Hugging Face datasets on Hugging Face Jobs (`hf jobs uv run <url>`): OCR & document extraction, audio transcription, object detection & segmentation, NER/entity extraction, text & image classification, embeddings & dataset maps, synthetic data, and batch LLM/VLM inference. Each recipe reads a Hub dataset and writes a new one, so recipes chain into pipelines. Use when the user wants to batch-process a dataset at scale, pre-label data for human review, run a model over many images/audio/documents/rows, or build a data pipeline on Hugging Face — locally with `uv run` or on a managed GPU with `hf jobs uv run`. Recipes live at huggingface.co/uv-scripts (one dataset repo per task family); discover them and read a recipe's header before running. |
uv-recipes
A recipe is one self-contained Python file (a PEP 723 UV script) that reads a Hugging Face dataset and writes a new one. Recipes take arguments in the same INPUT_DATASET OUTPUT_DATASET order and run straight from a URL — no clone, no venv, no install. They're self-describing: the dependency block, docstring, and --help say what each needs.
Requires
uv — curl -LsSf https://astral.sh/uv/install.sh | sh.- The
hf CLI — curl -LsSf https://hf.co/cli/install.sh | bash -s (or uv tool install "huggingface_hub[cli]"). Authenticate with hf auth login, or set HF_TOKEN. Jobs is pay-as-you-go (no subscription needed) — it just needs a Hugging Face account with credit.
- Strongly recommended — install the Hugging Face skills:
hf skills add. This installs the hf-cli skill (the full hf surface: jobs, auth, repos, datasets, buckets, webhooks) and stays current via hf skills update. The two compose: uv-recipes picks and runs a recipe, hf-cli drives everything else on the Hub.
Run a recipe
Jobs is recommended, not required. It gives a managed GPU — pay-per-second, no local CUDA — which matters because most recipes need a GPU you may not have locally. Run a recipe on Jobs:
hf jobs uv run --flavor l4x1 --secrets HF_TOKEN \
https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
INPUT_DATASET OUTPUT_DATASET --max-samples 10
--secrets HF_TOKEN forwards the user's token so the job can write the output dataset back.--flavor picks hardware: cpu-basic, t4-small, l4x1 (good default for ≤3B vision models), a10g-large, a100-large. Run hf jobs hardware for live prices.- Default timeout is 30 min. For training or large batches add
--timeout 2h.
- Default image is
astral-sh/uv:python3.12-bookworm. Some vLLM recipes need --image vllm/vllm-openai — the recipe's docstring has the exact command; read it first.
- Track a run:
hf jobs logs <job-id>, hf jobs ps, hf jobs inspect <job-id>.
- Jobs concepts, hardware flavors, and pricing: https://huggingface.co/docs/hub/jobs
Or run locally — the same file works with uv run <url> INPUT OUTPUT when your machine has the hardware it needs (usually a CUDA GPU). Inspect without running: uv run <url> --help.
Discover recipes (no fixed list — read it live)
curl -s "https://huggingface.co/api/datasets?author=uv-scripts" | jq -r '.[].id'
curl -s "https://huggingface.co/api/datasets/uv-scripts/ocr/tree/main" \
| jq -r '.[].path | select(endswith(".py"))'
uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py --help
Repo name is the task family (e.g. ocr, transcription, sam3, gliner, classification, build-atlas); a few repos are utilities, not recipes. Prefer a smaller model first (0.3–1B on l4x1); scale up only if quality demands it.
Adapt a recipe
A recipe is one readable file, so you can copy and modify it instead of only running it as-is — change the prompt, add or rename an output column, swap the model id, add a CLI flag or a row filter, or add a dependency to the PEP 723 block at the top. Fetch it, edit, and run the local copy (hf jobs uv run takes a local path or a URL; a local file is packaged into a temporary job repo):
curl -sLO https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py
hf jobs uv run --flavor l4x1 --secrets HF_TOKEN ./glm-ocr.py INPUT OUTPUT --max-samples 10
Keep the inline dependency block valid (new deps install at runtime); update the docstring if you change behaviour; test on --max-samples 10 first.
Compose a pipeline
Each recipe's output dataset is the next recipe's input (handoff through the Hub): e.g. dataset-creation (PDFs→dataset) → ocr/glm-ocr.py (→markdown) → deduplication → build-atlas (embed + map). A pipeline can also end in a trained model.
Pre-label, then review
A recipe's prediction column is a set of suggestions, not final labels. To put a human in the loop, build a small, task-specific review UI on the spot — a single-file Gradio app or static HTML that shows each input beside its predicted label with an accept / edit control, writing corrections back to a Hub dataset. Tailor it to the task: image + markdown side-by-side for OCR, image with box overlay for detection, text with highlighted spans for NER. Keep it minimal.
To spend review effort well (active learning), order the queue by informativeness first — by model uncertainty (low confidence from a classification recipe) or by diversity (embed with build-atlas, sample to cover the space). Review the most-informative first; optionally fine-tune on the verified labels and re-predict, repeating until predictions stop changing. A scheduled job or webhook can re-run pre-labelling as new data lands.
Rules that keep jobs working
- Jobs disk is ephemeral — write outputs to the Hub, never local paths. The default pattern is a dataset in, a dataset out (
push_to_hub). Local input files don't need an upload step: -v ./scans:/input syncs the folder to a private bucket and mounts it in the Job (re-runs only sync changed files; huggingface_hub ≥ 1.22). For data you rewrite often (mutable, incremental writes, checkpoints, one file per item), use a storage bucket — mount one with -v hf://buckets/<user>/<bucket>:/mnt, or read/write hf://buckets/… paths via fsspec. See buckets.md.
- GPU recipes check
torch.cuda.is_available() and exit clearly if missing — match --flavor to the recipe.
- Test on
--max-samples 10 first.
If a recipe fails
Most failures are environment or usage, not recipe bugs — triage before reporting:
- 401 / auth → add
--secrets HF_TOKEN, or run hf auth login.
- OOM / CUDA error → flavor too small; bump
--flavor (l4x1 → a10g-large → a100-large).
- Hit the 30-min timeout → add
--timeout 2h.
nvcc / engine-init crash → try the recipe's documented --image vllm/vllm-openai fallback (see its header).- Model gated / license error → accept the terms on the model's Hub page.
- Transient build failure on a nightly wheel → wait and retry.
- Check the docs — the recipe's
--help / header and the Jobs docs for the exact invocation and known constraints.
If it still fails, reproduce minimally: re-run with --max-samples 10 on a small public dataset appropriate to the task (e.g. davanstrien/ufo-ColPali for image/OCR recipes; a public audio set for transcription, a public text set for NER) to confirm it's the recipe, not your data.
Only if that is a genuine, reproducible defect in the recipe itself, open an issue at https://github.com/davanstrien/uv-scripts-for-ai/issues — but first search existing issues (one per distinct failure) and confirm with the user. The report must give a full reproducer the maintainer can run as-is: a public input dataset (one appropriate to the task) and the exact command — never the user's private data. Redact every token/secret from the command and logs. Include the recipe URL, flavor + image, the error tail, the job ID/URL, and what you already tried. Do not open issues for usage errors, flavor/OOM, gated models, or transient infra.
