From d1efc21baad487f9c062d7155c48ab1ef3617847 Mon Sep 17 00:00:00 2001 From: Daniel van Strien Date: Fri, 30 Oct 2026 13:45:28 +0000 Subject: [PATCH 01/10] Add Run on Jobs Space generated from the recipes A static catalogue of recipes with first-run examples, a searchable list of models and copy-paste commands. tools/build-catalog.py generates the Space files, recipes.json and AGENTS.md by parsing the scripts without running them. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_016isSPpM5PRqNUzwsDntoG4 --- .github/workflows/check-run-on-jobs.yml | 18 + .github/workflows/sync-run-on-jobs.yml | 15 + run-on-jobs/AGENTS.md | 22 + run-on-jobs/README.md | 42 ++ run-on-jobs/assets/nasa-food-list.jpg | Bin 0 -> 106447 bytes run-on-jobs/index.html | 376 +++++++++++ run-on-jobs/recipes.json | 796 ++++++++++++++++++++++++ tools/build-catalog.py | 395 ++++++++++++ tools/run-on-jobs-template.html | 290 +++++++++ 9 files changed, 1954 insertions(+) create mode 100644 .github/workflows/check-run-on-jobs.yml create mode 100644 .github/workflows/sync-run-on-jobs.yml create mode 100644 run-on-jobs/AGENTS.md create mode 100644 run-on-jobs/README.md create mode 100644 run-on-jobs/assets/nasa-food-list.jpg create mode 100644 run-on-jobs/index.html create mode 100644 run-on-jobs/recipes.json create mode 100644 tools/build-catalog.py create mode 100644 tools/run-on-jobs-template.html diff --git a/.github/workflows/check-run-on-jobs.yml b/.github/workflows/check-run-on-jobs.yml new file mode 100644 index 0000000..4b646b2 --- /dev/null +++ b/.github/workflows/check-run-on-jobs.yml @@ -0,0 +1,18 @@ +name: Check run-on-jobs catalogue +# Fails when a recipe changes without rebuilding the Space, or when a declared model leaves the Hub. +on: + pull_request: + schedule: + - cron: '0 7 * * 1' + workflow_dispatch: + +jobs: + check: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.12' + - name: Space files match the recipes, and declared models exist + run: python3 tools/build-catalog.py --check --verify-models diff --git a/.github/workflows/sync-run-on-jobs.yml b/.github/workflows/sync-run-on-jobs.yml new file mode 100644 index 0000000..5f3db9c --- /dev/null +++ b/.github/workflows/sync-run-on-jobs.yml @@ -0,0 +1,15 @@ +name: Sync run-on-jobs → HF +on: + push: + branches: [main] + paths: ['run-on-jobs/**', '.github/workflows/sync-run-on-jobs.yml'] + +jobs: + sync: + uses: ./.github/workflows/_sync-folder.yml + with: + folder: run-on-jobs + repo_id: uv-scripts/run-on-jobs + repo_type: space + secrets: + HF_TOKEN: ${{ secrets.HF_TOKEN }} diff --git a/run-on-jobs/AGENTS.md b/run-on-jobs/AGENTS.md new file mode 100644 index 0000000..b19686d --- /dev/null +++ b/run-on-jobs/AGENTS.md @@ -0,0 +1,22 @@ +# Run a model on Hugging Face Jobs + +`recipes.json` lists one-command batch recipes. Find the model in `recipes[].models`. + + hf download uv-scripts/run-on-jobs recipes.json --repo-type space + +Each recipe has: + +- `kind`: `run` applies a model to data; `train` fine-tunes one. +- `command`: the command to start from. `INPUT` and `OUTPUT` are usually dataset IDs; read the script to confirm. +- `source`: how far to trust the command. + - `header`: the script carries its launch settings. + - `documented`: an example from the script's docs. It may set its own hardware flags. + - `flags-needed`: `command` is null. Read the script and choose the options yourself. + +Before you start a job: + +1. Read the options: open the recipe's `url`. +2. Preview without spending: `hf jobs uv run --dry-run `. Check the image and flavor; without a header a GPU recipe can resolve to `cpu-basic`. +3. Start small: pass `--max-samples` if the script has it. +4. Monitor: `hf jobs ps`, `hf jobs logs `. +5. Check the output. A completed job is not proof that its rows are correct. diff --git a/run-on-jobs/README.md b/run-on-jobs/README.md new file mode 100644 index 0000000..6d72c9c --- /dev/null +++ b/run-on-jobs/README.md @@ -0,0 +1,42 @@ +--- +title: Run on Jobs +emoji: ⚡ +colorFrom: blue +colorTo: gray +sdk: static +pinned: false +short_description: Run this model over your own data on HF Jobs +models: + - allenai/olmOCR-2-7B-1025-FP8 + - ATH-MaaS/OvisOCR2 + - baidu/Qianfan-OCR + - baidu/Unlimited-OCR + - baidu/Unlimited-OCR-2 + - datalab-to/lift + - datalab-to/surya-ocr-2 + - deepseek-ai/DeepSeek-OCR + - deepseek-ai/DeepSeek-OCR-2 + - facebook/sam3 + - fastino/gliner2.5-multi-v1 + - HuggingFaceTB/SmolLM3-3B + - lightonai/LightOnOCR-2-1B + - lightonai/LightOnOCR-3-4B + - NemoStation/Marlin-2B + - numind/NuExtract3 + - nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B + - PaddlePaddle/PaddleOCR-VL-1.6 + - Qwen/Qwen2-VL-7B-Instruct + - Qwen/Qwen3.6-35B-A3B + - Qwen/Qwen3.8-27B + - rednote-hilab/dots.mocr + - rednote-hilab/dots.ocr + - sentence-transformers/all-MiniLM-L6-v2 + - tencent/HunyuanOCR + - zai-org/GLM-OCR +--- + +# Run on Jobs + +A catalogue of [uv-scripts](https://huggingface.co/uv-scripts) recipes: one command runs a model over your own data on [Hugging Face Jobs](https://huggingface.co/docs/hub/jobs). + +This Space is generated from [davanstrien/uv-scripts](https://github.com/davanstrien/uv-scripts). To change it, edit the recipes or `tools/build-catalog.py` there. 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+

Run Hub models on your own data

+

One command starts a GPU on Hugging Face Jobs, runs the model over your whole dataset and saves the results to the Hub. There is nothing to clone.

+
+
+

Read seven scanned NASA pages with GLM-OCR and save the text to a new dataset on your account.

hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
+    uv-scripts/ocr-demo your-username/ocr-demo-results
The scan
Scanned page listing Space Shuttle foods
What the Job wrote
Space Shuttle Food and Beverage List
+
+Foods*
+
+Applesauce (T)
+Apricots, dried (IM)
+Asparagus (R)
+Bananas (FD)
+Beef almondine (R)
+Beef, corned (I) (T)
+Beef and gravy (T)
+Beef, ground w/pickle sauce (T)
+Beef jerky (IM)
+Beef patty (R)
+Beef, slices w/barbeque sauce (T)
+Beef steak (I) (T)
+Beef stroganoff w/noodles (R)
+Bread, seedless rye (I) (NF)
+Broccoli au gratin (R)
+Breakfast roll (I) (NF)
+Candy, Life Savers*, assorted flavors (NF)
+Cauliflower w/cheese (R)
+Cereal, bran flakes (R)
+Cereal, cornflakes (R)
+Cereal, granola (R)
+Cereal, granola w/blueberries (R)
+Cereal, granola w/raisins (R)
+Cheddar cheese spread (T)
+Chicken a la king (T)
+
+Chicken and noodles (R)
+Chicken and rice (R)
+Chili mac w/beef (R)
+Cookies, pecan (NF)
+Cookies, shortbread (NF)
+Crackers, graham (NF)
+Eggs, scrambled (R)
+Food bar, almond crunch (NF)
+Food bar, chocolate chip (NF)
+Food bar, granola (NF)
+Food bar, granola/raisin (NF)
+Food bar, peanut butter/granola (NF)
+Frankfurters (Vienna sausage) (T)
+Fruitcake (NF)
+Fruit cocktail (T)
+Green beans, french w/mushrooms (R)
+Green beans and broccoli (R)
+Ham (I) (T)
+Jam/Jelly (T)
+Macaroni and cheese (R)
+Meatballs w/barbeque sauce (T)
+Nuts, almonds (NF)
+Nuts, cashews (NF)
+Nuts, peanuts (NF)
+
+Peach ambrosia (R)
+Peaches, dried (IM)
+Peaches, (T)
+Peanut butter
+Pears (FD)
+Pears (T)
+Peas w/butter sauce (R)
+Pineapple, crushed (T)
+Pudding, butterscotch (T)
+Pudding, chocolate (R) (T)
+Pudding, lemon (T)
+Pudding, vanilla (R) (T)
+Rice pilaf (R)
+Salmon (T)
+Sausage patty (R)
+Shrimp creole (R)
+Shrimp cocktail (R)
+Soup, cream of mushroom (R)
+Spaghetti w/meatless sauce (R)
+Strawberries (R)
+Tomatoes, stewed (T)
+Tuna (T)
+Turkey and gravy (T)
+Turkey, smoked/sliced (I) (T)
+Turkey tetrazzini (R)
+Vegetables, mixed Italian (R)
+
+Beverages
+
+Apple drink
+Cocoa
+Coffee, black
+Coffee w/cream
+Coffee w/cream and sugar
+Coffee w/sugar
+Grape drink
+Grapefruit drink
+Instant breakfast, chocolate
+Instant breakfast, strawberry
+
+Instant breakfast, vanilla
+Lemonade
+Orange drink
+Orange-grapefruit drink
+Orange-pineapple drink
+Strawberry drink
+Tea
+Tea w/lemon and sugar
+Tea w/sugar
+Tropical punch
+
+Condiments
+
+Barbeque sauce
+Catsup
+Mustard
+Pepper
+Salt
+Hot pepper sauce
+Mayonnaise
+
+*Abbreviations in parentheses indicate type of food T = thermostabilized, I = irradiated, IM = intermediate moisture, FD = freeze dried, R = rehydratable, and NF = natural form.
+
+Shuttle Menu
+
+The Space Shuttle menu currently features more than 70 food items and 20 beverages. Shuttle travelers will have a varied menu every day for six days rather than the personal preference meals used in previous space missions. Each day, three meals are allowed, with a repeat of menus after six days. The pantry also provides plenty of foods for snacks and between meal beverages and for individual menu changes. The pantry also stores additional contingency food to last 96 hours.
+
+Food System Constraints
+
+The primary objective in designing a food system for the Space Shuttle is to provide food that is safe and nutritious, light in weight and compact, and is packaged in a convenient form that allows easy manipulation in the weightless environment of an orbiting spacecraft. To achieve this objective requires a careful consideration of three important factors: biological, operational, and engineering. Under each factor are many constraints that affect the final choice of food and how it is

Our run: davanstrien/ocr-demo-results. 7 pages, 2.4 minutes of processing on a10g-small, after a few minutes of setup.

+

Transcribe four minutes of a NASA podcast, with speaker tags and word timestamps, into your own Bucket.

hf buckets create your-username/transcripts --private
+hf jobs uv run --timeout 45m \
+    -v hf://buckets/uv-scripts/transcription-demo/podcast:/input:ro \
+    -v hf://buckets/your-username/transcripts/podcast:/output \
+    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/nemotron-transcribe.py \
+    /input /output --timestamps
<spk:0> Jim, thank you so much for coming on the podcast today. ... <spk:3> It is, uh, the mission control room, of course, is now open for business ...

Our run: about 7.5 minutes on rtx-pro-6000, about $0.34. Most of that was model setup.

+

Embed the first 100 IMDb reviews and save them as a private dataset.

hf jobs uv run --flavor l4x1 -s HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings.py \
+    stanfordnlp/imdb your-username/imdb-embeddings \
+    --column text --max-samples 100 --private

One L4 GPU embeds about 900 rows per second with all-MiniLM-L6-v2.

+
+

You need the hf CLI (1.32 or newer), a login and pay-as-you-go credit. Add --dry-run to see the GPU and settings without starting a job.

+

Find your model

+

In the commands, INPUT and OUTPUT are dataset IDs unless the recipe says otherwise.

+ + + +

OCR

  • allenai/olmOCR-2-7B-1025-FP8ready · a10g-small

    olmocr2-vllm.py ready to run · a10g-small

    Convert document images to markdown using olmOCR-2 with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \
+    INPUT_DATASET OUTPUT_DATASET
  • ATH-MaaS/OvisOCR2ready · a10g-small

    ovis-ocr2.py ready to run · a10g-small

    Convert document images to markdown using OvisOCR2 with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2.py \
+    INPUT_DATASET OUTPUT_DATASET
    1 more recipe

    ovis-ocr2-saturate.py ready to run · a10g-small

    Convert document images to markdown using OvisOCR2 via saturate.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2-saturate.py \
+    INPUT_DATASET OUTPUT_DATASET
  • baidu/Qianfan-OCRready · a10g-small

    qianfan-ocr.py ready to run · a10g-small

    Convert document images to markdown using Qianfan-OCR with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/qianfan-ocr.py \
+    INPUT_DATASET OUTPUT_DATASET
  • baidu/Unlimited-OCRready · a10g-small

    unlimited-ocr-vllm.py ready to run · a10g-small

    Convert document images to markdown using Baidu Unlimited-OCR with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr-vllm.py \
+    INPUT_DATASET OUTPUT_DATASET
  • baidu/Unlimited-OCR-2ready · rtx-pro-6000

    unlimited-ocr2.py ready to run · rtx-pro-6000

    OCR whole documents (book volumes, folders of scans) with Baidu Unlimited-OCR-2.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2.py \
+    INPUT OUTPUT
    2 more recipes

    unlimited-ocr2-live.py ready to run, experimental · rtx-pro-6000

    unlimited-ocr2.py with a live view you can watch (and screen-record) while the Job runs.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2-live.py \
+    INPUT OUTPUT

    unlimited-ocr2-saturate.py ready to run, experimental · rtx-pro-6000

    OCR whole documents (book volumes, PDFs) with Baidu Unlimited-OCR-2 via saturate.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2-saturate.py \
+    INPUT OUTPUT
  • datalab-to/liftready · a100-large

    lift-extract.py ready to run · a100-large

    Extract structured JSON from document images OR multi-page PDFs using Datalab's lift model (datalab-to/lift, 9B, Qwen3.5-based).

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lift-extract.py \
+    INPUT_DATASET OUTPUT_DATASET --schema SCHEMA
  • datalab-to/surya-ocr-2ready · a10g-small

    surya-ocr.py ready to run · a10g-small

    Document intelligence on images OR multi-page PDFs with Datalab's Surya OCR 2 (datalab-to/surya-ocr-2, 650M, Qwen3.5-style).

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/surya-ocr.py \
+    INPUT_DATASET OUTPUT_DATASET
  • deepseek-ai/DeepSeek-OCRready · a10g-small

    deepseek-ocr-vllm.py ready to run · a10g-small

    Convert document images to markdown using DeepSeek-OCR with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \
+    INPUT_DATASET OUTPUT_DATASET
  • deepseek-ai/DeepSeek-OCR-2ready · a10g-small

    deepseek-ocr2-vllm.py ready to run · a10g-small

    Convert document images to markdown using DeepSeek-OCR-2 with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr2-vllm.py \
+    INPUT_DATASET OUTPUT_DATASET
  • lightonai/LightOnOCR-2-1Bready · a10g-small

    lighton-ocr2.py ready to run · a10g-small

    Convert document images to markdown using LightOnOCR-2 with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \
+    INPUT_DATASET OUTPUT_DATASET
    1 more recipe

    lighton-ocr2-saturate.py ready to run · a10g-small

    Convert document images to markdown using LightOnOCR-2 via saturate.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2-saturate.py \
+    INPUT_DATASET OUTPUT_DATASET
  • lightonai/LightOnOCR-3-4Bready · a10g-small

    lighton-ocr3-bucket.py ready to run · a10g-small

    OCR images and PDFs from an HF Bucket (or a directory) using LightOnOCR-3, writing markdown files.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr3-bucket.py \
+    INPUT OUTPUT
    1 more recipe

    lighton-ocr3.py ready to run · a10g-small

    Convert document images to markdown (or layout blocks) using LightOnOCR-3 with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr3.py \
+    INPUT_DATASET OUTPUT_DATASET
  • numind/NuExtract3ready · a10g-small

    nuextract3.py ready to run · a10g-small

    Convert document images to markdown OR extract structured JSON using NuExtract3 with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nuextract3.py \
+    INPUT_DATASET OUTPUT_DATASET
  • openbmb/MiniCPM-OCR-1.0ready · a10g-large

    minicpm-ocr.py ready to run, experimental · a10g-large

    Convert document images to markdown with OpenBMB's MiniCPM-OCR 1.0 pipeline.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/minicpm-ocr.py \
+    INPUT OUTPUT
  • PaddlePaddle/PaddleOCR-VL-1.6ready · a10g-small

    paddleocr-vl-1.6.py ready to run · a10g-small

    Convert document images to text/tables/formulas using PaddleOCR-VL-1.6 with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.6.py \
+    INPUT_DATASET OUTPUT_DATASET
  • rednote-hilab/dots.mocrready · a10g-small

    dots-mocr.py ready to run · a10g-small

    Convert document images to markdown using dots.mocr with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-mocr.py \
+    INPUT_DATASET OUTPUT_DATASET
  • rednote-hilab/dots.ocrready · a10g-small

    dots-ocr.py ready to run · a10g-small

    Convert document images to markdown using DoTS.ocr with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py \
+    INPUT_DATASET OUTPUT_DATASET
  • tencent/HunyuanOCRready · a10g-small

    hunyuan-ocr-1.5.py ready to run · a10g-small

    Convert document images to markdown using HunyuanOCR-1.5 with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/hunyuan-ocr-1.5.py \
+    INPUT_DATASET OUTPUT_DATASET
  • zai-org/GLM-OCRready · a10g-small

    glm-ocr.py ready to run · a10g-small

    Convert document images to markdown using GLM-OCR with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
+    INPUT_DATASET OUTPUT_DATASET

Transcription

  • CohereLabs/cohere-transcribe-03-2026options needed

    cohere-transcribe-vllm.py set options first

    Transcribe audio files from a directory using Cohere Transcribe via vLLM.

    Read the script for its options. It has no launch settings yet.

    2 more recipes

    cohere-transcribe.py set options first

    Transcribe audio files from a directory using Cohere Transcribe via transformers.

    Read the script for its options. It has no launch settings yet.

    easytranscriber-transcribe.py set options first

    Transcribe audio with word-level timestamps using kb-labb/easytranscriber.

    Read the script for its options. It has no launch settings yet.

  • distil-whisper/distil-large-v3.5options needed

    easytranscriber-transcribe.py set options first

    Transcribe audio with word-level timestamps using kb-labb/easytranscriber.

    Read the script for its options. It has no launch settings yet.

  • facebook/mms-1b-alloptions needed

    easytranscriber-transcribe.py set options first

    Transcribe audio with word-level timestamps using kb-labb/easytranscriber.

    Read the script for its options. It has no launch settings yet.

  • ibm-granite/granite-speech-5.0-470m-turboctcoptions needed

    granite-turboctc-transcribe.py set options first

    Transcribe English audio files with IBM Granite Speech 5.0 TurboCTC (470M).

    Read the script for its options. It has no launch settings yet.

  • nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4Bready · rtx-pro-6000

    nemotron-transcribe.py ready to run · rtx-pro-6000

    Transcribe audio with NVIDIA Nemotron 3.5 Transcribe via an in-job vLLM server.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/nemotron-transcribe.py \
+    INPUT_DIR OUTPUT_DIR
  • OpenMOSS-Team/MOSS-Transcribe-Diarizeoptions needed

    moss-transcribe-diarize.py set options first

    Transcribe + diarize audio files using MOSS-Transcribe-Diarize (0.9B).

    Read the script for its options. It has no launch settings yet.

    1 more recipe

    moss-transcribe-diarize-server.py set options first

    High-throughput transcription + diarization via an in-job sgl-omni server.

    Read the script for its options. It has no launch settings yet.

Segmentation

  • facebook/sam3example

    detect-objects.py example from the script

    Detect objects in images using Meta's SAM3 (Segment Anything Model 3).

    hf jobs uv run --flavor a100-large \
+    -s HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \
+    input-dataset output-dataset \
+    --class-name photograph \
+    --confidence-threshold 0.5
    1 more recipe

    segment-objects.py example from the script

    Segment objects in images using Meta's SAM3 (Segment Anything Model 3).

    hf jobs uv run --flavor a100-large \
+    -s HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/sam3/raw/main/segment-objects.py \
+    input-dataset output-dataset \
+    --class-name photograph

Object detection

  • Qwen/Qwen3.6-35B-A3Bready · a100-large

    qwen3vl-detect.py ready to run · a100-large

    Instruction-oriented object detection with Qwen3-VL via vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/vlm-object-detection/raw/main/qwen3vl-detect.py \
+    INPUT_DATASET OUTPUT_DATASET
    1 more recipe

    qwen3vl-detect-fewshot.py ready to run · a100-large

    [FEW-SHOT VARIANT] Instruction-oriented object detection with Qwen3-VL via vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/vlm-object-detection/raw/main/qwen3vl-detect-fewshot.py \
+    INPUT_DATASET OUTPUT_DATASET

Video

  • NemoStation/Marlin-2Bready · a10g-small

    marlin-caption.py ready to run · a10g-small

    Caption videos with timestamped events using Marlin-2B, writing a resumable dataset.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/video/raw/main/marlin-caption.py \
+    INPUT_DIR OUTPUT
  • Qwen/Qwen3.8-27Bready · a100-large

    qwen38-caption.py ready to run · a100-large

    Caption whole videos — no chunking — with Qwen3.8-27B, writing a resumable dataset.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/video/raw/main/qwen38-caption.py \
+    INPUT_DIR OUTPUT

Classification

  • fastino/gliner2.5-multi-v1ready · t4-small

    classify-gliner2.py ready to run · t4-small

    Classify a text column of a Hub dataset with GLiNER2 — zero-shot, or with your fine-tuned model.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-gliner2.py \
+    INPUT_DATASET OUTPUT_DATASET
  • HuggingFaceTB/SmolLM3-3Bready · l4x1

    classify-dataset-sglang.py ready to run · l4x1

    Classify text columns in Hugging Face datasets using SGLang with reasoning-aware models.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset-sglang.py \
+    --input-dataset INPUT_DATASET --column COLUMN --labels LABELS --output-dataset OUTPUT_DATASET
    1 more recipe

    classify-dataset.py ready to run · l4x1

    Classify text columns in Hugging Face datasets using vLLM with structured outputs.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset.py \
+    --input-dataset INPUT_DATASET --column COLUMN --labels LABELS --output-dataset OUTPUT_DATASET

Embeddings

  • BAAI/bge-base-en-v1.5example

    embed-to-lance.py example from the script

    Embed a Hugging Face dataset and push it back as a Lance vector index — a Hub dataset that IS a searchable vector database.

    hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/embed-to-lance.py \
+    stanfordnlp/imdb your-name/imdb-vecdb --column text --model BAAI/bge-base-en-v1.5 --private
  • Qwen/Qwen3-Embedding-0.6Bexample

    generate-embeddings-vllm.py example from the script

    High-throughput embedding generation with vLLM pooling mode — the "scale" variant of generate-embeddings.py, for large *decoder* embedding models (e.g

    hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings-vllm.py \
+    stanfordnlp/imdb your-name/imdb-embeddings --column text --model Qwen/Qwen3-Embedding-0.6B --private
  • sentence-transformers/all-MiniLM-L6-v2example

    generate-embeddings.py example from the script

    Generate embeddings for a Hugging Face dataset (text OR images) with sentence-transformers, and push the result back to the Hub as a new dataset with 

    hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings.py \
+    stanfordnlp/imdb  your-name/imdb-embeddings \
+    --column text --model sentence-transformers/all-MiniLM-L6-v2
    1 more recipe

    launch-embedding-fleet.py set options first

    Fan one embedding run out across N Hugging Face Jobs, then consolidate.

    Read the script for its options. It has no launch settings yet.

Text generation

  • Qwen/Qwen2-VL-7B-Instructready · l40sx1

    vlm-classify.py ready to run · l40sx1

    Classify images using Vision Language Models with vLLM.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/vllm/raw/main/vlm-classify.py \
+    INPUT_DATASET OUTPUT_DATASET --classes CLASSES
  • Qwen/Qwen3-30B-A3B-Instruct-2507ready · a100-large

    generate-responses.py ready to run · a100-large

    Generate responses for prompts in a dataset using vLLM for efficient GPU inference.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-responses.py \
+    SRC_DATASET_HUB_ID OUTPUT_DATASET_HUB_ID

Fine-tuning

  • fastino/gliner2.5-multi-v1ready · t4-small

    train-gliner2.py ready to run · t4-small

    Fine-tune GLiNER2 into a text classifier — a small model (74M to 287M parameters, depending on the base checkpoint) that already works zero-shot.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-gliner2.py \
+    INPUT_DATASET OUTPUT_REPO
  • LiquidAI/LFM2.5-Encoder-350Mready · l4x1

    train-classifier.py ready to run · l4x1

    Fine-tune a text-classification encoder on a Hub dataset and push the trained model to the Hub.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-classifier.py \
+    INPUT_DATASET OUTPUT_REPO
  • sentence-transformers/all-MiniLM-L6-v2example

    train-setfit.py example from the script

    Few-shot text classification with SetFit — train on 8-64 labelled examples per class, on CPU or GPU.

    hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-setfit.py \
+    fancyzhx/ag_news username/ag-news-setfit \
+    --num-samples 8
+
+ +

For agents

+

Read AGENTS.md and recipes.json.

+
+
+ + + diff --git a/run-on-jobs/recipes.json b/run-on-jobs/recipes.json new file mode 100644 index 0000000..144b42d --- /dev/null +++ b/run-on-jobs/recipes.json @@ -0,0 +1,796 @@ +{ + "recipes": [ + { + "kind": "run", + "task": "Classification", + "script": "classification/classify-dataset-sglang.py", + "url": "https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset-sglang.py", + "models": [ + "HuggingFaceTB/SmolLM3-3B" + ], + "support": null, + "source": "header", + "flavor": "l4x1", + "timeout": "1h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset-sglang.py \\\n --input-dataset INPUT_DATASET --column COLUMN --labels LABELS --output-dataset OUTPUT_DATASET", + "summary": "Classify text columns in Hugging Face datasets using SGLang with reasoning-aware models.", + "declared": true + }, + { + "kind": "run", + "task": "Classification", + "script": "classification/classify-dataset.py", + "url": "https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset.py", + "models": [ + "HuggingFaceTB/SmolLM3-3B" + ], + "support": null, + "source": "header", + "flavor": "l4x1", + "timeout": "1h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset.py \\\n --input-dataset INPUT_DATASET --column COLUMN --labels LABELS --output-dataset OUTPUT_DATASET", + "summary": "Classify text columns in Hugging Face datasets using vLLM with structured outputs.", + "declared": true + }, + { + "kind": "run", + "task": "Classification", + "script": "classification/classify-gliner2.py", + "url": "https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-gliner2.py", + "models": [ + "fastino/gliner2.5-multi-v1" + ], + "support": null, + "source": "header", + "flavor": "t4-small", + "timeout": "1h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-gliner2.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Classify a text column of a Hub dataset with GLiNER2 — zero-shot, or with your fine-tuned model.", + "declared": true + }, + { + "kind": "train", + "task": "Fine-tuning", + "script": "classification/train-classifier.py", + "url": "https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-classifier.py", + "models": [ + "LiquidAI/LFM2.5-Encoder-350M" + ], + "support": null, + "source": "header", + "flavor": "l4x1", + "timeout": "1h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-classifier.py \\\n INPUT_DATASET OUTPUT_REPO", + "summary": "Fine-tune a text-classification encoder on a Hub dataset and push the trained model to the Hub.", + "declared": false + }, + { + "kind": "train", + "task": "Fine-tuning", + "script": "classification/train-gliner2.py", + "url": "https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-gliner2.py", + "models": [ + "fastino/gliner2.5-multi-v1" + ], + "support": null, + "source": "header", + "flavor": "t4-small", + "timeout": "1h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-gliner2.py \\\n INPUT_DATASET OUTPUT_REPO", + "summary": "Fine-tune GLiNER2 into a text classifier — a small model (74M to 287M parameters, depending on the base checkpoint) that already works zero-shot.", + "declared": false + }, + { + "kind": "train", + "task": "Fine-tuning", + "script": "classification/train-setfit.py", + "url": "https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-setfit.py", + "models": [ + "sentence-transformers/all-MiniLM-L6-v2" + ], + "support": null, + "source": "documented", + "flavor": null, + "timeout": null, + "command": "hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN \\\n https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-setfit.py \\\n fancyzhx/ag_news username/ag-news-setfit \\\n --num-samples 8", + "summary": "Few-shot text classification with SetFit — train on 8-64 labelled examples per class, on CPU or GPU.", + "declared": false + }, + { + "kind": "run", + "task": "Embeddings", + "script": "embeddings/embed-to-lance.py", + "url": "https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/embed-to-lance.py", + "models": [ + "BAAI/bge-base-en-v1.5" + ], + "support": null, + "source": "documented", + "flavor": null, + "timeout": null, + "command": "hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/embed-to-lance.py \\\n stanfordnlp/imdb your-name/imdb-vecdb --column text --model BAAI/bge-base-en-v1.5 --private", + "summary": "Embed a Hugging Face dataset and push it back as a Lance vector index — a Hub dataset that IS a searchable vector database.", + "declared": false + }, + { + "kind": "run", + "task": "Embeddings", + "script": "embeddings/generate-embeddings-vllm.py", + "url": "https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings-vllm.py", + "models": [ + "Qwen/Qwen3-Embedding-0.6B" + ], + "support": null, + "source": "documented", + "flavor": null, + "timeout": null, + "command": "hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings-vllm.py \\\n stanfordnlp/imdb your-name/imdb-embeddings --column text --model Qwen/Qwen3-Embedding-0.6B --private", + "summary": "High-throughput embedding generation with vLLM pooling mode — the \"scale\" variant of generate-embeddings.py, for large *decoder* embedding models (e.g", + "declared": false + }, + { + "kind": "run", + "task": "Embeddings", + "script": "embeddings/generate-embeddings.py", + "url": "https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings.py", + "models": [ + "sentence-transformers/all-MiniLM-L6-v2" + ], + "support": null, + "source": "documented", + "flavor": null, + "timeout": null, + "command": "hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings.py \\\n stanfordnlp/imdb your-name/imdb-embeddings \\\n --column text --model sentence-transformers/all-MiniLM-L6-v2", + "summary": "Generate embeddings for a Hugging Face dataset (text OR images) with sentence-transformers, and push the result back to the Hub as a new dataset with ", + "declared": true + }, + { + "kind": "run", + "task": "Embeddings", + "script": "embeddings/launch-embedding-fleet.py", + "url": "https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/launch-embedding-fleet.py", + "models": [ + "sentence-transformers/all-MiniLM-L6-v2" + ], + "support": null, + "source": "flags-needed", + "flavor": null, + "timeout": null, + "command": null, + "summary": "Fan one embedding run out across N Hugging Face Jobs, then consolidate.", + "declared": false + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/deepseek-ocr-vllm.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py", + "models": [ + "deepseek-ai/DeepSeek-OCR" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown using DeepSeek-OCR with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/deepseek-ocr2-vllm.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr2-vllm.py", + "models": [ + "deepseek-ai/DeepSeek-OCR-2" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr2-vllm.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown using DeepSeek-OCR-2 with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/dots-mocr.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-mocr.py", + "models": [ + "rednote-hilab/dots.mocr" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-mocr.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown using dots.mocr with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/dots-ocr.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py", + "models": [ + "rednote-hilab/dots.ocr" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown using DoTS.ocr with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/glm-ocr.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py", + "models": [ + "zai-org/GLM-OCR" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown using GLM-OCR with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/hunyuan-ocr-1.5.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/hunyuan-ocr-1.5.py", + "models": [ + "tencent/HunyuanOCR" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/hunyuan-ocr-1.5.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown using HunyuanOCR-1.5 with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/lift-extract.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lift-extract.py", + "models": [ + "datalab-to/lift" + ], + "support": "core", + "source": "header", + "flavor": "a100-large", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lift-extract.py \\\n INPUT_DATASET OUTPUT_DATASET --schema SCHEMA", + "summary": "Extract structured JSON from document images OR multi-page PDFs using Datalab's lift model (datalab-to/lift, 9B, Qwen3.5-based).", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/lighton-ocr2-saturate.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2-saturate.py", + "models": [ + "lightonai/LightOnOCR-2-1B" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2-saturate.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown using LightOnOCR-2 via saturate.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/lighton-ocr2.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py", + "models": [ + "lightonai/LightOnOCR-2-1B" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown using LightOnOCR-2 with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/lighton-ocr3-bucket.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr3-bucket.py", + "models": [ + "lightonai/LightOnOCR-3-4B" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr3-bucket.py \\\n INPUT OUTPUT", + "summary": "OCR images and PDFs from an HF Bucket (or a directory) using LightOnOCR-3, writing markdown files.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/lighton-ocr3.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr3.py", + "models": [ + "lightonai/LightOnOCR-3-4B" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr3.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown (or layout blocks) using LightOnOCR-3 with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/minicpm-ocr.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/minicpm-ocr.py", + "models": [ + "openbmb/MiniCPM-OCR-1.0" + ], + "support": "experimental", + "source": "header", + "flavor": "a10g-large", + "timeout": "2h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/minicpm-ocr.py \\\n INPUT OUTPUT", + "summary": "Convert document images to markdown with OpenBMB's MiniCPM-OCR 1.0 pipeline.", + "declared": false + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/nuextract3.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nuextract3.py", + "models": [ + "numind/NuExtract3" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nuextract3.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown OR extract structured JSON using NuExtract3 with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/olmocr2-vllm.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py", + "models": [ + "allenai/olmOCR-2-7B-1025-FP8" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown using olmOCR-2 with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/ovis-ocr2-saturate.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2-saturate.py", + "models": [ + "ATH-MaaS/OvisOCR2" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2-saturate.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown using OvisOCR2 via saturate.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/ovis-ocr2.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2.py", + "models": [ + "ATH-MaaS/OvisOCR2" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown using OvisOCR2 with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/paddleocr-vl-1.6.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.6.py", + "models": [ + "PaddlePaddle/PaddleOCR-VL-1.6" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.6.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to text/tables/formulas using PaddleOCR-VL-1.6 with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/qianfan-ocr.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/qianfan-ocr.py", + "models": [ + "baidu/Qianfan-OCR" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/qianfan-ocr.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown using Qianfan-OCR with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/surya-ocr.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/surya-ocr.py", + "models": [ + "datalab-to/surya-ocr-2" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/surya-ocr.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Document intelligence on images OR multi-page PDFs with Datalab's Surya OCR 2 (datalab-to/surya-ocr-2, 650M, Qwen3.5-style).", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/unlimited-ocr-vllm.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr-vllm.py", + "models": [ + "baidu/Unlimited-OCR" + ], + "support": "core", + "source": "header", + "flavor": "a10g-small", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr-vllm.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Convert document images to markdown using Baidu Unlimited-OCR with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/unlimited-ocr2-live.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2-live.py", + "models": [ + "baidu/Unlimited-OCR-2" + ], + "support": "experimental", + "source": "header", + "flavor": "rtx-pro-6000", + "timeout": "4h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2-live.py \\\n INPUT OUTPUT", + "summary": "unlimited-ocr2.py with a live view you can watch (and screen-record) while the Job runs.", + "declared": false + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/unlimited-ocr2-saturate.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2-saturate.py", + "models": [ + "baidu/Unlimited-OCR-2" + ], + "support": "experimental", + "source": "header", + "flavor": "rtx-pro-6000", + "timeout": "4h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2-saturate.py \\\n INPUT OUTPUT", + "summary": "OCR whole documents (book volumes, PDFs) with Baidu Unlimited-OCR-2 via saturate.", + "declared": false + }, + { + "kind": "run", + "task": "OCR", + "script": "ocr/unlimited-ocr2.py", + "url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2.py", + "models": [ + "baidu/Unlimited-OCR-2" + ], + "support": "core", + "source": "header", + "flavor": "rtx-pro-6000", + "timeout": "4h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2.py \\\n INPUT OUTPUT", + "summary": "OCR whole documents (book volumes, folders of scans) with Baidu Unlimited-OCR-2.", + "declared": true + }, + { + "kind": "run", + "task": "Segmentation", + "script": "sam3/detect-objects.py", + "url": "https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py", + "models": [ + "facebook/sam3" + ], + "support": null, + "source": "documented", + "flavor": null, + "timeout": null, + "command": "hf jobs uv run --flavor a100-large \\\n -s HF_TOKEN \\\n https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \\\n input-dataset output-dataset \\\n --class-name photograph \\\n --confidence-threshold 0.5", + "summary": "Detect objects in images using Meta's SAM3 (Segment Anything Model 3).", + "declared": true + }, + { + "kind": "run", + "task": "Segmentation", + "script": "sam3/segment-objects.py", + "url": "https://huggingface.co/datasets/uv-scripts/sam3/raw/main/segment-objects.py", + "models": [ + "facebook/sam3" + ], + "support": null, + "source": "documented", + "flavor": null, + "timeout": null, + "command": "hf jobs uv run --flavor a100-large \\\n -s HF_TOKEN \\\n https://huggingface.co/datasets/uv-scripts/sam3/raw/main/segment-objects.py \\\n input-dataset output-dataset \\\n --class-name photograph", + "summary": "Segment objects in images using Meta's SAM3 (Segment Anything Model 3).", + "declared": true + }, + { + "kind": "run", + "task": "Transcription", + "script": "transcription/cohere-transcribe-vllm.py", + "url": "https://huggingface.co/datasets/uv-scripts/transcription/raw/main/cohere-transcribe-vllm.py", + "models": [ + "CohereLabs/cohere-transcribe-03-2026" + ], + "support": null, + "source": "flags-needed", + "flavor": null, + "timeout": null, + "command": null, + "summary": "Transcribe audio files from a directory using Cohere Transcribe via vLLM.", + "declared": false + }, + { + "kind": "run", + "task": "Transcription", + "script": "transcription/cohere-transcribe.py", + "url": "https://huggingface.co/datasets/uv-scripts/transcription/raw/main/cohere-transcribe.py", + "models": [ + "CohereLabs/cohere-transcribe-03-2026" + ], + "support": null, + "source": "flags-needed", + "flavor": null, + "timeout": null, + "command": null, + "summary": "Transcribe audio files from a directory using Cohere Transcribe via transformers.", + "declared": false + }, + { + "kind": "run", + "task": "Transcription", + "script": "transcription/easytranscriber-transcribe.py", + "url": "https://huggingface.co/datasets/uv-scripts/transcription/raw/main/easytranscriber-transcribe.py", + "models": [ + "CohereLabs/cohere-transcribe-03-2026", + "distil-whisper/distil-large-v3.5", + "facebook/mms-1b-all" + ], + "support": null, + "source": "flags-needed", + "flavor": null, + "timeout": null, + "command": null, + "summary": "Transcribe audio with word-level timestamps using kb-labb/easytranscriber.", + "declared": false + }, + { + "kind": "run", + "task": "Transcription", + "script": "transcription/granite-turboctc-transcribe.py", + "url": "https://huggingface.co/datasets/uv-scripts/transcription/raw/main/granite-turboctc-transcribe.py", + "models": [ + "ibm-granite/granite-speech-5.0-470m-turboctc" + ], + "support": null, + "source": "flags-needed", + "flavor": null, + "timeout": null, + "command": null, + "summary": "Transcribe English audio files with IBM Granite Speech 5.0 TurboCTC (470M).", + "declared": false + }, + { + "kind": "run", + "task": "Transcription", + "script": "transcription/moss-transcribe-diarize-server.py", + "url": "https://huggingface.co/datasets/uv-scripts/transcription/raw/main/moss-transcribe-diarize-server.py", + "models": [ + "OpenMOSS-Team/MOSS-Transcribe-Diarize" + ], + "support": null, + "source": "flags-needed", + "flavor": null, + "timeout": null, + "command": null, + "summary": "High-throughput transcription + diarization via an in-job sgl-omni server.", + "declared": false + }, + { + "kind": "run", + "task": "Transcription", + "script": "transcription/moss-transcribe-diarize.py", + "url": "https://huggingface.co/datasets/uv-scripts/transcription/raw/main/moss-transcribe-diarize.py", + "models": [ + "OpenMOSS-Team/MOSS-Transcribe-Diarize" + ], + "support": null, + "source": "flags-needed", + "flavor": null, + "timeout": null, + "command": null, + "summary": "Transcribe + diarize audio files using MOSS-Transcribe-Diarize (0.9B).", + "declared": false + }, + { + "kind": "run", + "task": "Transcription", + "script": "transcription/nemotron-transcribe.py", + "url": "https://huggingface.co/datasets/uv-scripts/transcription/raw/main/nemotron-transcribe.py", + "models": [ + "nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B" + ], + "support": null, + "source": "header", + "flavor": "rtx-pro-6000", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/transcription/raw/main/nemotron-transcribe.py \\\n INPUT_DIR OUTPUT_DIR", + "summary": "Transcribe audio with NVIDIA Nemotron 3.5 Transcribe via an in-job vLLM server.", + "declared": true + }, + { + "kind": "run", + "task": "Video", + "script": "video/marlin-caption.py", + "url": "https://huggingface.co/datasets/uv-scripts/video/raw/main/marlin-caption.py", + "models": [ + "NemoStation/Marlin-2B" + ], + "support": null, + "source": "header", + "flavor": "a10g-small", + "timeout": "2h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/video/raw/main/marlin-caption.py \\\n INPUT_DIR OUTPUT", + "summary": "Caption videos with timestamped events using Marlin-2B, writing a resumable dataset.", + "declared": true + }, + { + "kind": "run", + "task": "Video", + "script": "video/qwen38-caption.py", + "url": "https://huggingface.co/datasets/uv-scripts/video/raw/main/qwen38-caption.py", + "models": [ + "Qwen/Qwen3.8-27B" + ], + "support": null, + "source": "header", + "flavor": "a100-large", + "timeout": null, + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/video/raw/main/qwen38-caption.py \\\n INPUT_DIR OUTPUT", + "summary": "Caption whole videos — no chunking — with Qwen3.8-27B, writing a resumable dataset.", + "declared": true + }, + { + "kind": "run", + "task": "Text generation", + "script": "vllm/generate-responses.py", + "url": "https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-responses.py", + "models": [ + "Qwen/Qwen3-30B-A3B-Instruct-2507" + ], + "support": null, + "source": "header", + "flavor": "a100-large", + "timeout": "2h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-responses.py \\\n SRC_DATASET_HUB_ID OUTPUT_DATASET_HUB_ID", + "summary": "Generate responses for prompts in a dataset using vLLM for efficient GPU inference.", + "declared": false + }, + { + "kind": "run", + "task": "Text generation", + "script": "vllm/vlm-classify.py", + "url": "https://huggingface.co/datasets/uv-scripts/vllm/raw/main/vlm-classify.py", + "models": [ + "Qwen/Qwen2-VL-7B-Instruct" + ], + "support": null, + "source": "header", + "flavor": "l40sx1", + "timeout": "1h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/vllm/raw/main/vlm-classify.py \\\n INPUT_DATASET OUTPUT_DATASET --classes CLASSES", + "summary": "Classify images using Vision Language Models with vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "Object detection", + "script": "vlm-object-detection/qwen3vl-detect-fewshot.py", + "url": "https://huggingface.co/datasets/uv-scripts/vlm-object-detection/raw/main/qwen3vl-detect-fewshot.py", + "models": [ + "Qwen/Qwen3.6-35B-A3B" + ], + "support": null, + "source": "header", + "flavor": "a100-large", + "timeout": "1h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/vlm-object-detection/raw/main/qwen3vl-detect-fewshot.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "[FEW-SHOT VARIANT] Instruction-oriented object detection with Qwen3-VL via vLLM.", + "declared": true + }, + { + "kind": "run", + "task": "Object detection", + "script": "vlm-object-detection/qwen3vl-detect.py", + "url": "https://huggingface.co/datasets/uv-scripts/vlm-object-detection/raw/main/qwen3vl-detect.py", + "models": [ + "Qwen/Qwen3.6-35B-A3B" + ], + "support": null, + "source": "header", + "flavor": "a100-large", + "timeout": "1h", + "command": "hf jobs uv run \\\n https://huggingface.co/datasets/uv-scripts/vlm-object-detection/raw/main/qwen3vl-detect.py \\\n INPUT_DATASET OUTPUT_DATASET", + "summary": "Instruction-oriented object detection with Qwen3-VL via vLLM.", + "declared": true + } + ], + "data_tools": [ + { + "repo": "uv-scripts/object-detection", + "description": "Check and convert detection datasets" + }, + { + "repo": "uv-scripts/deduplication", + "description": "Remove near-duplicate text" + }, + { + "repo": "uv-scripts/build-atlas", + "description": "Build an embedding atlas" + }, + { + "repo": "uv-scripts/iiif-tiles", + "description": "Make IIIF deep-zoom tiles" + }, + { + "repo": "uv-scripts/data-processing", + "description": "Convert uploads to optimized Parquet" + } + ] +} diff --git a/tools/build-catalog.py b/tools/build-catalog.py new file mode 100644 index 0000000..e6318c8 --- /dev/null +++ b/tools/build-catalog.py @@ -0,0 +1,395 @@ +#!/usr/bin/env python3 +"""Generate the Run on Jobs Space from the recipes in this repo. + + python3 tools/build-catalog.py # write run-on-jobs/ + python3 tools/build-catalog.py --check # fail if run-on-jobs/ is stale + python3 tools/build-catalog.py --verify-models # also check each declared model on the Hub + +Scripts are only parsed, never imported or run. Standard library only. +""" + +import argparse +import ast +import html +import json +import re +import sys +import tomllib +import urllib.error +import urllib.request +from pathlib import Path + +ROOT = Path(__file__).resolve().parent.parent +OUTPUT_DIR = ROOT / "run-on-jobs" +HUB_BASE = "https://huggingface.co/datasets/uv-scripts" + +TASKS = { + "ocr": "OCR", + "transcription": "Transcription", + "sam3": "Segmentation", + "vlm-object-detection": "Object detection", + "video": "Video", + "classification": "Classification", + "gliner": "Entities", + "embeddings": "Embeddings", + "vllm": "Text generation", +} +TRAINING_TASK = "Fine-tuning" +TASK_ORDER = [*TASKS.values(), TRAINING_TASK] + +DATA_TOOLS = { + "object-detection": "Check and convert detection datasets", + "deduplication": "Remove near-duplicate text", + "build-atlas": "Build an embedding atlas", + "iiif-tiles": "Make IIIF deep-zoom tiles", + "data-processing": "Convert uploads to optimized Parquet", +} + +# Recipes without a [tool.hf-jobs] header whose documented command passed a smoke Job with checked output. +# Only these and header recipes are declared on model pages. +SMOKE_TESTED_EXAMPLES = { + "embeddings/generate-embeddings.py", + "sam3/detect-objects.py", + "sam3/segment-objects.py", +} + +# Left out of the Space until they run again. +BROKEN = { + "gliner/extract-entities.py", # the default model's tokenizer needs tiktoken, which the script doesn't declare +} + +# Listed on the page, but not declared on model pages until the plain command works for the model. +NOT_DECLARED = { + "vllm/generate-responses.py", # the default model needs --max-model-len on the header's a100-large +} + +# Recipe metadata that names a family rather than a Hub repo. +NOT_HUB_MODELS = {"PaddlePaddle/PP-OCRv6"} + +OCR_SUPPORT_LEVELS = {"core", "experimental"} +SKIP_SCRIPT = re.compile(r"(^_|^test_|conftest|setup|benchmark|judge|compare)", re.IGNORECASE) +ALTERNATIVE_SCRIPT = re.compile(r"saturate|live|server|fleet|fewshot", re.IGNORECASE) +MODEL_ID = re.compile(r"^[A-Za-z0-9][\w.-]*/[\w.+-]+$") +PLACEHOLDER_OWNERS = {"org", "datasets", "models", "hf", "bucket", "buckets", "input", "output", "user", "your", "username", "path", "dir", "my-org", "uv-scripts"} + +STATUS = {"header": "ready to run", "documented": "example from the script", "flags-needed": "set options first"} +ROW_STATUS = {"header": "ready", "documented": "example", "flags-needed": "options needed"} + + +def read_header(text): + match = re.search(r"(?m)^# /// script\n((?:#.*\n)*?)# ///", text) + if not match: + return {} + lines = [line[2:] if line.startswith("# ") else line[1:] for line in match.group(1).splitlines()] + try: + return tomllib.loads("\n".join(lines)).get("tool", {}).get("hf-jobs", {}) + except tomllib.TOMLDecodeError: + return {} + + +def read_arguments(tree): + """Return module string constants and the add_argument calls, using only the syntax tree.""" + constants = {} + for node in tree.body: + if isinstance(node, ast.Assign) and len(node.targets) == 1 and isinstance(node.targets[0], ast.Name): + if isinstance(node.value, ast.Constant) and isinstance(node.value.value, str): + constants[node.targets[0].id] = node.value.value + + arguments = [] + for node in ast.walk(tree): + if not (isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute) and node.func.attr == "add_argument"): + continue + names = [arg.value for arg in node.args if isinstance(arg, ast.Constant) and isinstance(arg.value, str)] + if not names: + continue + keywords = {keyword.arg: keyword.value for keyword in node.keywords if keyword.arg} + default = keywords.get("default") + if isinstance(default, ast.Constant): + default = default.value + elif isinstance(default, ast.Name): + default = constants.get(default.id) + else: + default = None + required = isinstance(keywords.get("required"), ast.Constant) and keywords["required"].value is True + arguments.append({"name": names[0], "default": default, "positional": not names[0].startswith("-"), "required": required}) + return constants, arguments + + +def find_models(constants, arguments): + candidates = [] + for argument in arguments: + if isinstance(argument["default"], str) and "model" in argument["name"].lower() and MODEL_ID.match(argument["default"]): + candidates.append(argument["default"]) + for name, value in constants.items(): + if "MODEL" in name.upper() and MODEL_ID.match(value): + candidates.append(value) + unique = dict.fromkeys(candidates) + return [model for model in unique if model.split("/")[0].lower() not in PLACEHOLDER_OWNERS] + + +def first_documented_command(text, script_name, url): + match = re.search(r"hf jobs uv run(?:[^\n]*\\\n)*[^\n]*", text) + if not match or script_name not in match.group(0): + return None + lines = [re.sub(r"^\s*(#\s*)?", "", line).rstrip(" \\") for line in match.group(0).split("\n")] + command = " \\\n ".join(line for line in lines if line) + if url not in command: + command = re.sub(r"(?
{html.escape(command)}
' + + +def recipe_html(recipe): + status = STATUS[recipe["source"]] + if recipe["support"] == "experimental": + status += ", experimental" + status_class = "warn" if recipe["source"] == "flags-needed" else "muted" + flavor = f" · {html.escape(recipe['flavor'])}" if recipe["flavor"] else "" + summary = f'

{html.escape(recipe["summary"])}

' if recipe["summary"] else "" + if recipe["source"] == "flags-needed": + body = f'

Read the script for its options. It has no launch settings yet.

' + else: + body = command_block(recipe["command"]) + name = recipe["script"].split("/")[-1] + return ( + f'

{html.escape(name)} ' + f'{status}{flavor}

{summary}{body}
' + ) + + +def model_row_html(task, model, recipes): + ranked = sorted(recipes, key=rank) + best = ranked[0] + row_status = ROW_STATUS[best["source"]] + (f" · {best['flavor']}" if best["flavor"] else "") + row_class = "warn" if best["source"] == "flags-needed" else "muted" + search_text = " ".join([model, task] + [f"{recipe['script']} {recipe['summary']}" for recipe in ranked]).lower() + others = ranked[1:] + more = "" + if others: + label = f"{len(others)} more recipe{'s' if len(others) > 1 else ''}" + more = f'
{label}' + "".join(recipe_html(recipe) for recipe in others) + "
" + return ( + f'
  • ' + f'{html.escape(model)}{html.escape(row_status)}' + + recipe_html(best) + more + "
    • " + ) + + +def render_index(recipes): + template = (ROOT / "tools" / "run-on-jobs-template.html").read_text() + entries = {} + for recipe in recipes: + for model in recipe["models"]: + entries.setdefault((recipe["task"], model), []).append(recipe) + + groups = [] + used_tasks = [] + for task in TASK_ORDER: + models = sorted((model for entry_task, model in entries if entry_task == task), key=str.lower) + if not models: + continue + used_tasks.append(task) + rows = "".join(model_row_html(task, model, entries[(task, model)]) for model in models) + groups.append(f'

    {html.escape(task)}

      {rows}
    ') + + filters = '' + "".join( + f'' for task in used_tasks + ) + tools = "".join( + f'
  • {html.escape(label)}
    • ' for folder, label in DATA_TOOLS.items() + ) + return template.replace("{{FILTERS}}", filters).replace("{{GROUPS}}", "".join(groups)).replace("{{TOOLS}}", tools) + + +def render_readme(recipes): + model_lines = "\n".join(f" - {model}" for model in declared_models(recipes)) + return f"""--- +title: Run on Jobs +emoji: ⚡ +colorFrom: blue +colorTo: gray +sdk: static +pinned: false +short_description: Run this model over your own data on HF Jobs +models: +{model_lines} +--- + +# Run on Jobs + +A catalogue of [uv-scripts](https://huggingface.co/uv-scripts) recipes: one command runs a model over your own data on [Hugging Face Jobs](https://huggingface.co/docs/hub/jobs). + +This Space is generated from [davanstrien/uv-scripts](https://github.com/davanstrien/uv-scripts). To change it, edit the recipes or `tools/build-catalog.py` there. Agents should read [AGENTS.md](./AGENTS.md). +""" + + +def render_agents(): + return """# Run a model on Hugging Face Jobs + +`recipes.json` lists one-command batch recipes. Find the model in `recipes[].models`. + + hf download uv-scripts/run-on-jobs recipes.json --repo-type space + +Each recipe has: + +- `kind`: `run` applies a model to data; `train` fine-tunes one. +- `command`: the command to start from. `INPUT` and `OUTPUT` are usually dataset IDs; read the script to confirm. +- `source`: how far to trust the command. + - `header`: the script carries its launch settings. + - `documented`: an example from the script's docs. It may set its own hardware flags. + - `flags-needed`: `command` is null. Read the script and choose the options yourself. + +Before you start a job: + +1. Read the options: open the recipe's `url`. +2. Preview without spending: `hf jobs uv run --dry-run `. Check the image and flavor; without a header a GPU recipe can resolve to `cpu-basic`. +3. Start small: pass `--max-samples` if the script has it. +4. Monitor: `hf jobs ps`, `hf jobs logs `. +5. Check the output. A completed job is not proof that its rows are correct. +""" + + +def generate(): + recipes = load_recipes() + catalog = { + "recipes": sorted(recipes, key=lambda recipe: recipe["script"]), + "data_tools": [{"repo": f"uv-scripts/{folder}", "description": label} for folder, label in DATA_TOOLS.items()], + } + return { + "index.html": render_index(recipes), + "README.md": render_readme(recipes), + "AGENTS.md": render_agents(), + "recipes.json": json.dumps(catalog, indent=1, ensure_ascii=False) + "\n", + } + + +def verify_models(recipes): + missing = [] + for model in declared_models(recipes): + try: + urllib.request.urlopen(f"https://huggingface.co/api/models/{model}", timeout=30) + except urllib.error.HTTPError as error: + missing.append(f"{model} (HTTP {error.code})") + return missing + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--check", action="store_true", help="Fail if run-on-jobs/ does not match the recipes") + parser.add_argument("--verify-models", action="store_true", help="Check that declared models exist on the Hub") + args = parser.parse_args() + + files = generate() + if args.check: + stale = [name for name, content in files.items() if not (OUTPUT_DIR / name).exists() or (OUTPUT_DIR / name).read_text() != content] + if stale: + print(f"Stale: {', '.join(stale)}. Run python3 tools/build-catalog.py and commit the result.") + return 1 + else: + for name, content in files.items(): + (OUTPUT_DIR / name).write_text(content) + + recipes = load_recipes() + print(f"{len(recipes)} recipes, {len(declared_models(recipes))} declared models") + if args.verify_models: + missing = verify_models(recipes) + if missing: + print("Declared models not found:", ", ".join(missing)) + return 1 + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tools/run-on-jobs-template.html b/tools/run-on-jobs-template.html new file mode 100644 index 0000000..cb74182 --- /dev/null +++ b/tools/run-on-jobs-template.html @@ -0,0 +1,290 @@ + + + + + +Run on Jobs + + + + + +
    +

    Run Hub models on your own data

    +

    One command starts a GPU on Hugging Face Jobs, runs the model over your whole dataset and saves the results to the Hub. There is nothing to clone.

    +
    +
    +

    Read seven scanned NASA pages with GLM-OCR and save the text to a new dataset on your account.

    hf jobs uv run \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
+    uv-scripts/ocr-demo your-username/ocr-demo-results
    The scan
    Scanned page listing Space Shuttle foods
    What the Job wrote
    Space Shuttle Food and Beverage List
+
+Foods*
+
+Applesauce (T)
+Apricots, dried (IM)
+Asparagus (R)
+Bananas (FD)
+Beef almondine (R)
+Beef, corned (I) (T)
+Beef and gravy (T)
+Beef, ground w/pickle sauce (T)
+Beef jerky (IM)
+Beef patty (R)
+Beef, slices w/barbeque sauce (T)
+Beef steak (I) (T)
+Beef stroganoff w/noodles (R)
+Bread, seedless rye (I) (NF)
+Broccoli au gratin (R)
+Breakfast roll (I) (NF)
+Candy, Life Savers*, assorted flavors (NF)
+Cauliflower w/cheese (R)
+Cereal, bran flakes (R)
+Cereal, cornflakes (R)
+Cereal, granola (R)
+Cereal, granola w/blueberries (R)
+Cereal, granola w/raisins (R)
+Cheddar cheese spread (T)
+Chicken a la king (T)
+
+Chicken and noodles (R)
+Chicken and rice (R)
+Chili mac w/beef (R)
+Cookies, pecan (NF)
+Cookies, shortbread (NF)
+Crackers, graham (NF)
+Eggs, scrambled (R)
+Food bar, almond crunch (NF)
+Food bar, chocolate chip (NF)
+Food bar, granola (NF)
+Food bar, granola/raisin (NF)
+Food bar, peanut butter/granola (NF)
+Frankfurters (Vienna sausage) (T)
+Fruitcake (NF)
+Fruit cocktail (T)
+Green beans, french w/mushrooms (R)
+Green beans and broccoli (R)
+Ham (I) (T)
+Jam/Jelly (T)
+Macaroni and cheese (R)
+Meatballs w/barbeque sauce (T)
+Nuts, almonds (NF)
+Nuts, cashews (NF)
+Nuts, peanuts (NF)
+
+Peach ambrosia (R)
+Peaches, dried (IM)
+Peaches, (T)
+Peanut butter
+Pears (FD)
+Pears (T)
+Peas w/butter sauce (R)
+Pineapple, crushed (T)
+Pudding, butterscotch (T)
+Pudding, chocolate (R) (T)
+Pudding, lemon (T)
+Pudding, vanilla (R) (T)
+Rice pilaf (R)
+Salmon (T)
+Sausage patty (R)
+Shrimp creole (R)
+Shrimp cocktail (R)
+Soup, cream of mushroom (R)
+Spaghetti w/meatless sauce (R)
+Strawberries (R)
+Tomatoes, stewed (T)
+Tuna (T)
+Turkey and gravy (T)
+Turkey, smoked/sliced (I) (T)
+Turkey tetrazzini (R)
+Vegetables, mixed Italian (R)
+
+Beverages
+
+Apple drink
+Cocoa
+Coffee, black
+Coffee w/cream
+Coffee w/cream and sugar
+Coffee w/sugar
+Grape drink
+Grapefruit drink
+Instant breakfast, chocolate
+Instant breakfast, strawberry
+
+Instant breakfast, vanilla
+Lemonade
+Orange drink
+Orange-grapefruit drink
+Orange-pineapple drink
+Strawberry drink
+Tea
+Tea w/lemon and sugar
+Tea w/sugar
+Tropical punch
+
+Condiments
+
+Barbeque sauce
+Catsup
+Mustard
+Pepper
+Salt
+Hot pepper sauce
+Mayonnaise
+
+*Abbreviations in parentheses indicate type of food T = thermostabilized, I = irradiated, IM = intermediate moisture, FD = freeze dried, R = rehydratable, and NF = natural form.
+
+Shuttle Menu
+
+The Space Shuttle menu currently features more than 70 food items and 20 beverages. Shuttle travelers will have a varied menu every day for six days rather than the personal preference meals used in previous space missions. Each day, three meals are allowed, with a repeat of menus after six days. The pantry also provides plenty of foods for snacks and between meal beverages and for individual menu changes. The pantry also stores additional contingency food to last 96 hours.
+
+Food System Constraints
+
+The primary objective in designing a food system for the Space Shuttle is to provide food that is safe and nutritious, light in weight and compact, and is packaged in a convenient form that allows easy manipulation in the weightless environment of an orbiting spacecraft. To achieve this objective requires a careful consideration of three important factors: biological, operational, and engineering. Under each factor are many constraints that affect the final choice of food and how it is

    Our run: davanstrien/ocr-demo-results. 7 pages, 2.4 minutes of processing on a10g-small, after a few minutes of setup.

    +

    Transcribe four minutes of a NASA podcast, with speaker tags and word timestamps, into your own Bucket.

    hf buckets create your-username/transcripts --private
+hf jobs uv run --timeout 45m \
+    -v hf://buckets/uv-scripts/transcription-demo/podcast:/input:ro \
+    -v hf://buckets/your-username/transcripts/podcast:/output \
+    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/nemotron-transcribe.py \
+    /input /output --timestamps
    <spk:0> Jim, thank you so much for coming on the podcast today. ... <spk:3> It is, uh, the mission control room, of course, is now open for business ...

    Our run: about 7.5 minutes on rtx-pro-6000, about $0.34. Most of that was model setup.

    +

    Embed the first 100 IMDb reviews and save them as a private dataset.

    hf jobs uv run --flavor l4x1 -s HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings.py \
+    stanfordnlp/imdb your-username/imdb-embeddings \
+    --column text --max-samples 100 --private

    One L4 GPU embeds about 900 rows per second with all-MiniLM-L6-v2.

    +
    +

    You need the hf CLI (1.32 or newer), a login and pay-as-you-go credit. Add --dry-run to see the GPU and settings without starting a job.

    +

    Find your model

    +

    In the commands, INPUT and OUTPUT are dataset IDs unless the recipe says otherwise.

    + + + +{{GROUPS}} +
    +

    Data tools with no model

      {{TOOLS}}
    +

    For agents

    +

    Read AGENTS.md and recipes.json.

    +
    +
    + + + From 37ab46377204cff7ba8fbbdca02598d7dcf1ea7f Mon Sep 17 00:00:00 2001 From: Daniel van Strien Date: Fri, 30 Oct 2026 13:46:07 +0000 Subject: [PATCH 02/10] tools/build-catalog: satisfy ruff Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_016isSPpM5PRqNUzwsDntoG4 --- tools/build-catalog.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) mode change 100644 => 100755 tools/build-catalog.py diff --git a/tools/build-catalog.py b/tools/build-catalog.py old mode 100644 new mode 100755 index e6318c8..196a129 --- a/tools/build-catalog.py +++ b/tools/build-catalog.py @@ -91,9 +91,9 @@ def read_arguments(tree): """Return module string constants and the add_argument calls, using only the syntax tree.""" constants = {} for node in tree.body: - if isinstance(node, ast.Assign) and len(node.targets) == 1 and isinstance(node.targets[0], ast.Name): - if isinstance(node.value, ast.Constant) and isinstance(node.value.value, str): - constants[node.targets[0].id] = node.value.value + is_single_assign = isinstance(node, ast.Assign) and len(node.targets) == 1 and isinstance(node.targets[0], ast.Name) + if is_single_assign and isinstance(node.value, ast.Constant) and isinstance(node.value.value, str): + constants[node.targets[0].id] = node.value.value arguments = [] for node in ast.walk(tree): From 38411c53d9f2c2e5c8700c01643a06677e25bdb6 Mon Sep 17 00:00:00 2001 From: Daniel van Strien Date: Fri, 30 Oct 2026 13:49:34 +0000 Subject: [PATCH 03/10] run-on-jobs: open the intro to fine-tuning, add setup steps for agents Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_016isSPpM5PRqNUzwsDntoG4 --- run-on-jobs/AGENTS.md | 25 +++++++++++++++++++++++-- run-on-jobs/index.html | 4 ++-- tools/build-catalog.py | 25 +++++++++++++++++++++++-- tools/run-on-jobs-template.html | 4 ++-- 4 files changed, 50 insertions(+), 8 deletions(-) diff --git a/run-on-jobs/AGENTS.md b/run-on-jobs/AGENTS.md index b19686d..f89fb52 100644 --- a/run-on-jobs/AGENTS.md +++ b/run-on-jobs/AGENTS.md @@ -1,9 +1,30 @@ # Run a model on Hugging Face Jobs -`recipes.json` lists one-command batch recipes. Find the model in `recipes[].models`. +`recipes.json` lists one-command batch and fine-tuning recipes. + +## Set up + +Check for the `hf` CLI (1.32 or newer) with `hf version`. If it's missing, install it, then ask the user to sign in: + + curl -LsSf https://hf.co/cli/install.sh | bash + hf auth login + +Jobs need pay-as-you-go credit on the user's account, and each job costs money, so confirm with the user before starting one. + +Learn the commands from the CLI, because it stays current: + + hf --help + hf jobs uv run --help + hf jobs hardware + +For a persistent reference, install the `hf-cli` skill with `hf skills add`. The installer above adds it by default. + +## Find a recipe hf download uv-scripts/run-on-jobs recipes.json --repo-type space +Find the model in `recipes[].models`. + Each recipe has: - `kind`: `run` applies a model to data; `train` fine-tunes one. @@ -13,7 +34,7 @@ Each recipe has: - `documented`: an example from the script's docs. It may set its own hardware flags. - `flags-needed`: `command` is null. Read the script and choose the options yourself. -Before you start a job: +## Run it 1. Read the options: open the recipe's `url`. 2. Preview without spending: `hf jobs uv run --dry-run `. Check the image and flavor; without a header a GPU recipe can resolve to `cpu-basic`. diff --git a/run-on-jobs/index.html b/run-on-jobs/index.html index 64fe801..cab1ef0 100644 --- a/run-on-jobs/index.html +++ b/run-on-jobs/index.html @@ -81,8 +81,8 @@
    -

    Run Hub models on your own data

    -

    One command starts a GPU on Hugging Face Jobs, runs the model over your whole dataset and saves the results to the Hub. There is nothing to clone.

    +

    Use Hub models on your own data

    +

    Run a model over your whole dataset, or fine-tune it on your own labels. One command starts a GPU on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.

    Read seven scanned NASA pages with GLM-OCR and save the text to a new dataset on your account.

    hf jobs uv run \
diff --git a/tools/build-catalog.py b/tools/build-catalog.py
index 196a129..7752cb5 100755
--- a/tools/build-catalog.py
+++ b/tools/build-catalog.py
@@ -318,10 +318,31 @@ def render_readme(recipes):
 def render_agents():
     return """# Run a model on Hugging Face Jobs
 
-`recipes.json` lists one-command batch recipes. Find the model in `recipes[].models`.
+`recipes.json` lists one-command batch and fine-tuning recipes.
+
+## Set up
+
+Check for the `hf` CLI (1.32 or newer) with `hf version`. If it's missing, install it, then ask the user to sign in:
+
+    curl -LsSf https://hf.co/cli/install.sh | bash
+    hf auth login
+
+Jobs need pay-as-you-go credit on the user's account, and each job costs money, so confirm with the user before starting one.
+
+Learn the commands from the CLI, because it stays current:
+
+    hf --help
+    hf jobs uv run --help
+    hf jobs hardware
+
+For a persistent reference, install the `hf-cli` skill with `hf skills add`. The installer above adds it by default.
+
+## Find a recipe
 
     hf download uv-scripts/run-on-jobs recipes.json --repo-type space
 
+Find the model in `recipes[].models`.
+
 Each recipe has:
 
 - `kind`: `run` applies a model to data; `train` fine-tunes one.
@@ -331,7 +352,7 @@ def render_agents():
   - `documented`: an example from the script's docs. It may set its own hardware flags.
   - `flags-needed`: `command` is null. Read the script and choose the options yourself.
 
-Before you start a job:
+## Run it
 
 1. Read the options: open the recipe's `url`.
 2. Preview without spending: `hf jobs uv run --dry-run  `. Check the image and flavor; without a header a GPU recipe can resolve to `cpu-basic`.
diff --git a/tools/run-on-jobs-template.html b/tools/run-on-jobs-template.html
index cb74182..0cf2ac4 100644
--- a/tools/run-on-jobs-template.html
+++ b/tools/run-on-jobs-template.html
@@ -81,8 +81,8 @@
 
 
 
    
-
    Run Hub models on your own data
    
-
    One command starts a GPU on Hugging Face Jobs, runs the model over your whole dataset and saves the results to the Hub. There is nothing to clone.
    
+
    Use Hub models on your own data
    
+
    Run a model over your whole dataset, or fine-tune it on your own labels. One command starts a GPU on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
 
    
 
    
 
    Read seven scanned NASA pages with GLM-OCR and save the text to a new dataset on your account.
    hf jobs uv run \

From f7d9e1cd1c73684245f6c45f0ba3361bcd7153fb Mon Sep 17 00:00:00 2001
From: Daniel van Strien 
Date: Fri, 30 Oct 2026 13:50:05 +0000
Subject: [PATCH 04/10] run-on-jobs: keep AGENTS.md to short pointers

Co-Authored-By: Claude Opus 5.5 
Claude-Session: https://claude.ai/code/session_016isSPpM5PRqNUzwsDntoG4
---
 run-on-jobs/AGENTS.md  | 48 ++++++------------------------------------
 tools/build-catalog.py | 48 ++++++------------------------------------
 2 files changed, 14 insertions(+), 82 deletions(-)

diff --git a/run-on-jobs/AGENTS.md b/run-on-jobs/AGENTS.md
index f89fb52..498be9f 100644
--- a/run-on-jobs/AGENTS.md
+++ b/run-on-jobs/AGENTS.md
@@ -1,43 +1,9 @@
 # Run a model on Hugging Face Jobs
 
-`recipes.json` lists one-command batch and fine-tuning recipes.
-
-## Set up
-
-Check for the `hf` CLI (1.32 or newer) with `hf version`. If it's missing, install it, then ask the user to sign in:
-
-    curl -LsSf https://hf.co/cli/install.sh | bash
-    hf auth login
-
-Jobs need pay-as-you-go credit on the user's account, and each job costs money, so confirm with the user before starting one.
-
-Learn the commands from the CLI, because it stays current:
-
-    hf --help
-    hf jobs uv run --help
-    hf jobs hardware
-
-For a persistent reference, install the `hf-cli` skill with `hf skills add`. The installer above adds it by default.
-
-## Find a recipe
-
-    hf download uv-scripts/run-on-jobs recipes.json --repo-type space
-
-Find the model in `recipes[].models`.
-
-Each recipe has:
-
-- `kind`: `run` applies a model to data; `train` fine-tunes one.
-- `command`: the command to start from. `INPUT` and `OUTPUT` are usually dataset IDs; read the script to confirm.
-- `source`: how far to trust the command.
-  - `header`: the script carries its launch settings.
-  - `documented`: an example from the script's docs. It may set its own hardware flags.
-  - `flags-needed`: `command` is null. Read the script and choose the options yourself.
-
-## Run it
-
-1. Read the options: open the recipe's `url`.
-2. Preview without spending: `hf jobs uv run --dry-run  `. Check the image and flavor; without a header a GPU recipe can resolve to `cpu-basic`.
-3. Start small: pass `--max-samples` if the script has it.
-4. Monitor: `hf jobs ps`, `hf jobs logs `.
-5. Check the output. A completed job is not proof that its rows are correct.
+- **Set up:** `hf version` (needs 1.32+). If missing: `curl -LsSf https://hf.co/cli/install.sh | bash`, then `hf auth login`. Jobs need pay-as-you-go credit.
+- **Learn the CLI:** `hf --help`, `hf jobs uv run --help`, `hf jobs hardware`. Install the skill with `hf skills add`.
+- **Find a recipe:** `hf download uv-scripts/run-on-jobs recipes.json --repo-type space`, then look for the model in `recipes[].models`.
+- **Trust the command by `source`:** `header` carries its launch settings; `documented` is an example from the script's docs; `flags-needed` has no command, so read the script.
+- **Preview for free:** `hf jobs uv run --dry-run  `. Check the flavor: without a header, a GPU recipe can resolve to `cpu-basic`.
+- **Spend carefully:** confirm with the user first, and start small with `--max-samples` if the script has it.
+- **Check the result:** `hf jobs ps`, `hf jobs logs `, then look at the output rows. A completed job doesn't prove they're correct.
diff --git a/tools/build-catalog.py b/tools/build-catalog.py
index 7752cb5..ea823b1 100755
--- a/tools/build-catalog.py
+++ b/tools/build-catalog.py
@@ -318,47 +318,13 @@ def render_readme(recipes):
 def render_agents():
     return """# Run a model on Hugging Face Jobs
 
-`recipes.json` lists one-command batch and fine-tuning recipes.
-
-## Set up
-
-Check for the `hf` CLI (1.32 or newer) with `hf version`. If it's missing, install it, then ask the user to sign in:
-
-    curl -LsSf https://hf.co/cli/install.sh | bash
-    hf auth login
-
-Jobs need pay-as-you-go credit on the user's account, and each job costs money, so confirm with the user before starting one.
-
-Learn the commands from the CLI, because it stays current:
-
-    hf --help
-    hf jobs uv run --help
-    hf jobs hardware
-
-For a persistent reference, install the `hf-cli` skill with `hf skills add`. The installer above adds it by default.
-
-## Find a recipe
-
-    hf download uv-scripts/run-on-jobs recipes.json --repo-type space
-
-Find the model in `recipes[].models`.
-
-Each recipe has:
-
-- `kind`: `run` applies a model to data; `train` fine-tunes one.
-- `command`: the command to start from. `INPUT` and `OUTPUT` are usually dataset IDs; read the script to confirm.
-- `source`: how far to trust the command.
-  - `header`: the script carries its launch settings.
-  - `documented`: an example from the script's docs. It may set its own hardware flags.
-  - `flags-needed`: `command` is null. Read the script and choose the options yourself.
-
-## Run it
-
-1. Read the options: open the recipe's `url`.
-2. Preview without spending: `hf jobs uv run --dry-run  `. Check the image and flavor; without a header a GPU recipe can resolve to `cpu-basic`.
-3. Start small: pass `--max-samples` if the script has it.
-4. Monitor: `hf jobs ps`, `hf jobs logs `.
-5. Check the output. A completed job is not proof that its rows are correct.
+- **Set up:** `hf version` (needs 1.32+). If missing: `curl -LsSf https://hf.co/cli/install.sh | bash`, then `hf auth login`. Jobs need pay-as-you-go credit.
+- **Learn the CLI:** `hf --help`, `hf jobs uv run --help`, `hf jobs hardware`. Install the skill with `hf skills add`.
+- **Find a recipe:** `hf download uv-scripts/run-on-jobs recipes.json --repo-type space`, then look for the model in `recipes[].models`.
+- **Trust the command by `source`:** `header` carries its launch settings; `documented` is an example from the script's docs; `flags-needed` has no command, so read the script.
+- **Preview for free:** `hf jobs uv run --dry-run  `. Check the flavor: without a header, a GPU recipe can resolve to `cpu-basic`.
+- **Spend carefully:** confirm with the user first, and start small with `--max-samples` if the script has it.
+- **Check the result:** `hf jobs ps`, `hf jobs logs `, then look at the output rows. A completed job doesn't prove they're correct.
 """
 
 

From f4367992057f82fcc2ed36e0de12f17f6ea0f580 Mon Sep 17 00:00:00 2001
From: Daniel van Strien 
Date: Fri, 30 Oct 2026 13:51:15 +0000
Subject: [PATCH 05/10] run-on-jobs: add usage pointers to AGENTS.md

Co-Authored-By: Claude Opus 5.5 
Claude-Session: https://claude.ai/code/session_016isSPpM5PRqNUzwsDntoG4
---
 run-on-jobs/AGENTS.md  | 41 ++++++++++++++++++++++++++++++++++-------
 tools/build-catalog.py | 41 ++++++++++++++++++++++++++++++++++-------
 2 files changed, 68 insertions(+), 14 deletions(-)

diff --git a/run-on-jobs/AGENTS.md b/run-on-jobs/AGENTS.md
index 498be9f..97b769c 100644
--- a/run-on-jobs/AGENTS.md
+++ b/run-on-jobs/AGENTS.md
@@ -1,9 +1,36 @@
 # Run a model on Hugging Face Jobs
 
-- **Set up:** `hf version` (needs 1.32+). If missing: `curl -LsSf https://hf.co/cli/install.sh | bash`, then `hf auth login`. Jobs need pay-as-you-go credit.
-- **Learn the CLI:** `hf --help`, `hf jobs uv run --help`, `hf jobs hardware`. Install the skill with `hf skills add`.
-- **Find a recipe:** `hf download uv-scripts/run-on-jobs recipes.json --repo-type space`, then look for the model in `recipes[].models`.
-- **Trust the command by `source`:** `header` carries its launch settings; `documented` is an example from the script's docs; `flags-needed` has no command, so read the script.
-- **Preview for free:** `hf jobs uv run --dry-run  `. Check the flavor: without a header, a GPU recipe can resolve to `cpu-basic`.
-- **Spend carefully:** confirm with the user first, and start small with `--max-samples` if the script has it.
-- **Check the result:** `hf jobs ps`, `hf jobs logs `, then look at the output rows. A completed job doesn't prove they're correct.
+Each recipe is one script, run from its URL with `hf jobs uv run`. There is nothing to clone or install except the `hf` CLI.
+
+## Set up
+
+- Check `hf version` (needs 1.32+). If it's missing: `curl -LsSf https://hf.co/cli/install.sh | bash`, then `hf auth login`.
+- Jobs need pay-as-you-go credit, and each job costs money. Confirm with the user before starting one.
+- The CLI is the current reference: `hf --help`, `hf jobs uv run --help`, `hf jobs hardware`. For a skill, use `hf skills add`.
+
+## Find a recipe
+
+- `hf download uv-scripts/run-on-jobs recipes.json --repo-type space`, then look for the model in `recipes[].models`.
+- `kind`: `run` applies a model to data; `train` fine-tunes one.
+- `source`: `header` carries its launch settings; `documented` is an example from the script's docs; `flags-needed` has no command, so read the script first.
+- Read a script's docstring and options: `curl -s  | head -100`. Running it locally may fail on GPU-only dependencies.
+- Each task's README explains its recipes, for example https://huggingface.co/datasets/uv-scripts/ocr. OCR also has `models.json`, with language coverage and support levels.
+
+## Run it
+
+Most recipes go from dataset to dataset:
+
+    hf jobs uv run    --max-samples 10
+
+Recipes that read files use mounts. A local folder also works as a source:
+
+    hf jobs uv run -v hf://buckets//:/input:ro -v hf://buckets//:/output  /input /output
+
+- Preview for free with `--dry-run`, and check the flavor: without a header, a GPU recipe can resolve to `cpu-basic`.
+- Flags before the URL configure the job and override the header, such as `--flavor` and `--timeout`. Flags after it go to the script. If a script flag has the same name as a job flag, put `--` before the URL.
+- Start with a small `--max-samples` if the script has it, and add `--private` if the output shouldn't be public.
+
+## Check it
+
+- Use `--detach` for long runs, then `hf jobs logs -f `, `hf jobs wait ` or `hf jobs inspect `. `hf jobs cancel ` stops one.
+- Look at the output rows. A completed job doesn't prove they're correct.
diff --git a/tools/build-catalog.py b/tools/build-catalog.py
index ea823b1..5665f46 100755
--- a/tools/build-catalog.py
+++ b/tools/build-catalog.py
@@ -318,13 +318,40 @@ def render_readme(recipes):
 def render_agents():
     return """# Run a model on Hugging Face Jobs
 
-- **Set up:** `hf version` (needs 1.32+). If missing: `curl -LsSf https://hf.co/cli/install.sh | bash`, then `hf auth login`. Jobs need pay-as-you-go credit.
-- **Learn the CLI:** `hf --help`, `hf jobs uv run --help`, `hf jobs hardware`. Install the skill with `hf skills add`.
-- **Find a recipe:** `hf download uv-scripts/run-on-jobs recipes.json --repo-type space`, then look for the model in `recipes[].models`.
-- **Trust the command by `source`:** `header` carries its launch settings; `documented` is an example from the script's docs; `flags-needed` has no command, so read the script.
-- **Preview for free:** `hf jobs uv run --dry-run  `. Check the flavor: without a header, a GPU recipe can resolve to `cpu-basic`.
-- **Spend carefully:** confirm with the user first, and start small with `--max-samples` if the script has it.
-- **Check the result:** `hf jobs ps`, `hf jobs logs `, then look at the output rows. A completed job doesn't prove they're correct.
+Each recipe is one script, run from its URL with `hf jobs uv run`. There is nothing to clone or install except the `hf` CLI.
+
+## Set up
+
+- Check `hf version` (needs 1.32+). If it's missing: `curl -LsSf https://hf.co/cli/install.sh | bash`, then `hf auth login`.
+- Jobs need pay-as-you-go credit, and each job costs money. Confirm with the user before starting one.
+- The CLI is the current reference: `hf --help`, `hf jobs uv run --help`, `hf jobs hardware`. For a skill, use `hf skills add`.
+
+## Find a recipe
+
+- `hf download uv-scripts/run-on-jobs recipes.json --repo-type space`, then look for the model in `recipes[].models`.
+- `kind`: `run` applies a model to data; `train` fine-tunes one.
+- `source`: `header` carries its launch settings; `documented` is an example from the script's docs; `flags-needed` has no command, so read the script first.
+- Read a script's docstring and options: `curl -s  | head -100`. Running it locally may fail on GPU-only dependencies.
+- Each task's README explains its recipes, for example https://huggingface.co/datasets/uv-scripts/ocr. OCR also has `models.json`, with language coverage and support levels.
+
+## Run it
+
+Most recipes go from dataset to dataset:
+
+    hf jobs uv run    --max-samples 10
+
+Recipes that read files use mounts. A local folder also works as a source:
+
+    hf jobs uv run -v hf://buckets//:/input:ro -v hf://buckets//:/output  /input /output
+
+- Preview for free with `--dry-run`, and check the flavor: without a header, a GPU recipe can resolve to `cpu-basic`.
+- Flags before the URL configure the job and override the header, such as `--flavor` and `--timeout`. Flags after it go to the script. If a script flag has the same name as a job flag, put `--` before the URL.
+- Start with a small `--max-samples` if the script has it, and add `--private` if the output shouldn't be public.
+
+## Check it
+
+- Use `--detach` for long runs, then `hf jobs logs -f `, `hf jobs wait ` or `hf jobs inspect `. `hf jobs cancel ` stops one.
+- Look at the output rows. A completed job doesn't prove they're correct.
 """
 
 

From f3683e9a8dbbfb995859c1f5fa1f05f79bff6e37 Mon Sep 17 00:00:00 2001
From: Daniel van Strien 
Date: Fri, 30 Oct 2026 13:52:39 +0000
Subject: [PATCH 06/10] run-on-jobs: say examples rather than labels in the
 intro

Co-Authored-By: Claude Opus 5.5 
Claude-Session: https://claude.ai/code/session_016isSPpM5PRqNUzwsDntoG4
---
 run-on-jobs/index.html          | 2 +-
 tools/run-on-jobs-template.html | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/run-on-jobs/index.html b/run-on-jobs/index.html
index cab1ef0..ed7c87e 100644
--- a/run-on-jobs/index.html
+++ b/run-on-jobs/index.html
@@ -82,7 +82,7 @@
 
 
    
 
    Use Hub models on your own data
    
-
    Run a model over your whole dataset, or fine-tune it on your own labels. One command starts a GPU on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
+
    Run a model over your whole dataset, or fine-tune it on your own examples. One command starts a GPU on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
 
    
 
    
 
    Read seven scanned NASA pages with GLM-OCR and save the text to a new dataset on your account.
    hf jobs uv run \
diff --git a/tools/run-on-jobs-template.html b/tools/run-on-jobs-template.html
index 0cf2ac4..ee41626 100644
--- a/tools/run-on-jobs-template.html
+++ b/tools/run-on-jobs-template.html
@@ -82,7 +82,7 @@
 
 
    
 
    Use Hub models on your own data
    
-
    Run a model over your whole dataset, or fine-tune it on your own labels. One command starts a GPU on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
+
    Run a model over your whole dataset, or fine-tune it on your own examples. One command starts a GPU on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
 
    
 
    
 
    Read seven scanned NASA pages with GLM-OCR and save the text to a new dataset on your account.
    hf jobs uv run \

From 33d989fd118d1f67ebf4dfb1db85e4fdec999fe4 Mon Sep 17 00:00:00 2001
From: Daniel van Strien 
Date: Fri, 30 Oct 2026 14:00:33 +0000
Subject: [PATCH 07/10] run-on-jobs: take commands from script docs and
 arguments, apply review fixes

Header recipes now keep their documented arguments and mounts, and path
arguments get generated mounts, so no command is hard-coded. Clearer labels,
intro and placeholders; clipboard fallback; reduced motion.

Co-Authored-By: Claude Opus 5.5 
Claude-Session: https://claude.ai/code/session_016isSPpM5PRqNUzwsDntoG4
---
 run-on-jobs/AGENTS.md           |   4 +-
 run-on-jobs/README.md           |   2 +-
 run-on-jobs/index.html          | 167 ++++++++++++++++++++++----------
 run-on-jobs/recipes.json        |  90 ++++++++---------
 tools/build-catalog.py          |  95 ++++++++++++------
 tools/run-on-jobs-template.html |  13 ++-
 6 files changed, 239 insertions(+), 132 deletions(-)

diff --git a/run-on-jobs/AGENTS.md b/run-on-jobs/AGENTS.md
index 97b769c..da29bcd 100644
--- a/run-on-jobs/AGENTS.md
+++ b/run-on-jobs/AGENTS.md
@@ -6,7 +6,7 @@ Each recipe is one script, run from its URL with `hf jobs uv run`. There is noth
 
 - Check `hf version` (needs 1.32+). If it's missing: `curl -LsSf https://hf.co/cli/install.sh | bash`, then `hf auth login`.
 - Jobs need pay-as-you-go credit, and each job costs money. Confirm with the user before starting one.
-- The CLI is the current reference: `hf --help`, `hf jobs uv run --help`, `hf jobs hardware`. For a skill, use `hf skills add`.
+- The CLI is the current reference: `hf --help`, `hf jobs uv run --help`, `hf jobs hardware`. To install the Hugging Face CLI skill, run `hf skills add`.
 
 ## Find a recipe
 
@@ -18,7 +18,7 @@ Each recipe is one script, run from its URL with `hf jobs uv run`. There is noth
 
 ## Run it
 
-Most recipes go from dataset to dataset:
+Start from the recipe's `command` field. Most recipes go from dataset to dataset:
 
     hf jobs uv run    --max-samples 10
 
diff --git a/run-on-jobs/README.md b/run-on-jobs/README.md
index 6d72c9c..60b42d9 100644
--- a/run-on-jobs/README.md
+++ b/run-on-jobs/README.md
@@ -5,7 +5,7 @@ colorFrom: blue
 colorTo: gray
 sdk: static
 pinned: false
-short_description: Run this model over your own data on HF Jobs
+short_description: Batch-run this model on your own data with HF Jobs
 models:
   - allenai/olmOCR-2-7B-1025-FP8
   - ATH-MaaS/OvisOCR2
diff --git a/run-on-jobs/index.html b/run-on-jobs/index.html
index ed7c87e..e8c535d 100644
--- a/run-on-jobs/index.html
+++ b/run-on-jobs/index.html
@@ -77,15 +77,16 @@
 body .more{margin-left:16px}
 
 
+@media (prefers-reduced-motion:reduce){summary::before{transition:none}}
 
 
 
 
    
 
    Use Hub models on your own data
    
-
    Run a model over your whole dataset, or fine-tune it on your own examples. One command starts a GPU on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
+
    Run a model over your whole dataset, or fine-tune it on your own examples. Each command starts a paid batch job on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
 
    
 
    
-
    Read seven scanned NASA pages with GLM-OCR and save the text to a new dataset on your account.
    hf jobs uv run \
+
    Read seven scanned NASA pages with GLM-OCR and save the text to a new public dataset on your account. Add --private to keep it private.
    hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
     uv-scripts/ocr-demo your-username/ocr-demo-results
    The scan
    Scanned page listing Space Shuttle foods
    What the Job wrote
    Space Shuttle Food and Beverage List
 
@@ -225,96 +226,160 @@ 
    Use Hub models on your own data
    
 
    
 
    You need the hf CLI (1.32 or newer), a login and pay-as-you-go credit. Add --dry-run to see the GPU and settings without starting a job.
    
 
    Find your model
    
-
    In the commands, INPUT and OUTPUT are dataset IDs unless the recipe says otherwise.
    
+
    Replace the capitalised placeholders and your-username. Check the script if you're unsure what a value should be.
    
 
 
 
-
    OCR
    • allenai/olmOCR-2-7B-1025-FP8ready · a10g-small
      olmocr2-vllm.py ready to run · a10g-small
      Convert document images to markdown using olmOCR-2 with vLLM.
      hf jobs uv run \
+
      OCR
      • allenai/olmOCR-2-7B-1025-FP8command ready · a10g-small
        olmocr2-vllm.py command ready · a10g-small
        Convert document images to markdown using olmOCR-2 with vLLM.
        hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \
-    INPUT_DATASET OUTPUT_DATASET
      • ATH-MaaS/OvisOCR2ready · a10g-small
        ovis-ocr2.py ready to run · a10g-small
        Convert document images to markdown using OvisOCR2 with vLLM.
        hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET
      • ATH-MaaS/OvisOCR2command ready · a10g-small
        ovis-ocr2.py command ready · a10g-small
        Convert document images to markdown using OvisOCR2 with vLLM.
        hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2.py \
-    INPUT_DATASET OUTPUT_DATASET
        1 more recipe
        ovis-ocr2-saturate.py ready to run · a10g-small
        Convert document images to markdown using OvisOCR2 via saturate.
        hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET --batch-size 16
        1 more recipe
        ovis-ocr2-saturate.py command ready · a10g-small
        Convert document images to markdown using OvisOCR2 via saturate.
        hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2-saturate.py \
-    INPUT_DATASET OUTPUT_DATASET
      • baidu/Qianfan-OCRready · a10g-small
        qianfan-ocr.py ready to run · a10g-small
        Convert document images to markdown using Qianfan-OCR with vLLM.
        hf jobs uv run \
+    <INPUT_DATASET> <OUTPUT_DATASET>
    • baidu/Qianfan-OCRcommand ready · a10g-small
      qianfan-ocr.py command ready · a10g-small
      Convert document images to markdown using Qianfan-OCR with vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/qianfan-ocr.py \
-    INPUT_DATASET OUTPUT_DATASET
    • baidu/Unlimited-OCRready · a10g-small
      unlimited-ocr-vllm.py ready to run · a10g-small
      Convert document images to markdown using Baidu Unlimited-OCR with vLLM.
      hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET --max-samples 10
    • baidu/Unlimited-OCRcommand ready · a10g-small
      unlimited-ocr-vllm.py command ready · a10g-small
      Convert document images to markdown using Baidu Unlimited-OCR with vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr-vllm.py \
-    INPUT_DATASET OUTPUT_DATASET
    • baidu/Unlimited-OCR-2ready · rtx-pro-6000
      unlimited-ocr2.py ready to run · rtx-pro-6000
      OCR whole documents (book volumes, folders of scans) with Baidu Unlimited-OCR-2.
      hf jobs uv run \
+    your-input-dataset your-output-dataset --max-samples 10
    • baidu/Unlimited-OCR-2command ready · rtx-pro-6000
      unlimited-ocr2.py command ready · rtx-pro-6000
      OCR whole documents (book volumes, folders of scans) with Baidu Unlimited-OCR-2.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2.py \
-    INPUT OUTPUT
      2 more recipes
      unlimited-ocr2-live.py ready to run, experimental · rtx-pro-6000
      unlimited-ocr2.py with a live view you can watch (and screen-record) while the Job runs.
      hf jobs uv run \
+    "hf://buckets/biglam/britannica/source/pages/1771EncyclopediaBritannicaNLS/*.jp2" \
+    <user>/britannica-1771
      2 more recipes
      unlimited-ocr2-live.py command ready, experimental · rtx-pro-6000
      unlimited-ocr2.py with a live view you can watch (and screen-record) while the Job runs.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2-live.py \
-    INPUT OUTPUT
      unlimited-ocr2-saturate.py ready to run, experimental · rtx-pro-6000
      OCR whole documents (book volumes, PDFs) with Baidu Unlimited-OCR-2 via saturate.
      hf jobs uv run \
+    "hf://buckets/biglam/britannica/source/pages/1771EncyclopediaBritannicaNLS/*.jp2" \
+    <user>/britannica-1771 --live-port 7860
      unlimited-ocr2-saturate.py command ready, experimental · rtx-pro-6000
      OCR whole documents (book volumes, PDFs) with Baidu Unlimited-OCR-2 via saturate.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2-saturate.py \
-    INPUT OUTPUT
    • datalab-to/liftready · a100-large
      lift-extract.py ready to run · a100-large
      Extract structured JSON from document images OR multi-page PDFs using Datalab's lift model (datalab-to/lift, 9B, Qwen3.5-based).
      hf jobs uv run \
+    "hf://buckets/biglam/britannica/source/pages/1771EncyclopediaBritannicaNLS/*.jp2" \
+    <user>/britannica-1771-uocr2
    • datalab-to/liftcommand ready · a100-large
      lift-extract.py command ready · a100-large
      Extract structured JSON from document images OR multi-page PDFs using Datalab's lift model (datalab-to/lift, 9B, Qwen3.5-based).
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lift-extract.py \
-    INPUT_DATASET OUTPUT_DATASET --schema SCHEMA
    • datalab-to/surya-ocr-2ready · a10g-small
      surya-ocr.py ready to run · a10g-small
      Document intelligence on images OR multi-page PDFs with Datalab's Surya OCR 2 (datalab-to/surya-ocr-2, 650M, Qwen3.5-style).
      hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET \
+    --schema '{"type":"object","properties":{"title":{"type":"string"}}}' \
+    --max-samples 5 --shuffle --seed 42
    • datalab-to/surya-ocr-2command ready · a10g-small
      surya-ocr.py command ready · a10g-small
      Document intelligence on images OR multi-page PDFs with Datalab's Surya OCR 2 (datalab-to/surya-ocr-2, 650M, Qwen3.5-style).
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/surya-ocr.py \
-    INPUT_DATASET OUTPUT_DATASET
    • deepseek-ai/DeepSeek-OCRready · a10g-small
      deepseek-ocr-vllm.py ready to run · a10g-small
      Convert document images to markdown using DeepSeek-OCR with vLLM.
      hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET \
+    --max-samples 5 --shuffle --seed 42
    • deepseek-ai/DeepSeek-OCRcommand ready · a10g-small
      deepseek-ocr-vllm.py command ready · a10g-small
      Convert document images to markdown using DeepSeek-OCR with vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \
-    INPUT_DATASET OUTPUT_DATASET
    • deepseek-ai/DeepSeek-OCR-2ready · a10g-small
      deepseek-ocr2-vllm.py ready to run · a10g-small
      Convert document images to markdown using DeepSeek-OCR-2 with vLLM.
      hf jobs uv run \
+    my-dataset my-output --max-samples 10
    • deepseek-ai/DeepSeek-OCR-2command ready · a10g-small
      deepseek-ocr2-vllm.py command ready · a10g-small
      Convert document images to markdown using DeepSeek-OCR-2 with vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr2-vllm.py \
-    INPUT_DATASET OUTPUT_DATASET
    • lightonai/LightOnOCR-2-1Bready · a10g-small
      lighton-ocr2.py ready to run · a10g-small
      Convert document images to markdown using LightOnOCR-2 with vLLM.
      hf jobs uv run \
+    my-dataset my-output --max-samples 10
    • lightonai/LightOnOCR-2-1Bcommand ready · a10g-small
      lighton-ocr2.py command ready · a10g-small
      Convert document images to markdown using LightOnOCR-2 with vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \
-    INPUT_DATASET OUTPUT_DATASET
      1 more recipe
      lighton-ocr2-saturate.py ready to run · a10g-small
      Convert document images to markdown using LightOnOCR-2 via saturate.
      hf jobs uv run \
+    <INPUT_DATASET> <OUTPUT_DATASET>
      1 more recipe
      lighton-ocr2-saturate.py command ready · a10g-small
      Convert document images to markdown using LightOnOCR-2 via saturate.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2-saturate.py \
-    INPUT_DATASET OUTPUT_DATASET
    • lightonai/LightOnOCR-3-4Bready · a10g-small
      lighton-ocr3-bucket.py ready to run · a10g-small
      OCR images and PDFs from an HF Bucket (or a directory) using LightOnOCR-3, writing markdown files.
      hf jobs uv run \
+    <INPUT_DATASET> <OUTPUT_DATASET>
    • lightonai/LightOnOCR-3-4Bcommand ready · a10g-small
      lighton-ocr3-bucket.py command ready · a10g-small
      OCR images and PDFs from an HF Bucket (or a directory) using LightOnOCR-3, writing markdown files.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr3-bucket.py \
-    INPUT OUTPUT
      1 more recipe
      lighton-ocr3.py ready to run · a10g-small
      Convert document images to markdown (or layout blocks) using LightOnOCR-3 with vLLM.
      hf jobs uv run \
+    hf://buckets/biglam/britannica/source/pages/00070476bsb \
+    hf://buckets/your-username/ocr-output/britannica --mode grounding --max-samples 20
      1 more recipe
      lighton-ocr3.py command ready · a10g-small
      Convert document images to markdown (or layout blocks) using LightOnOCR-3 with vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr3.py \
-    INPUT_DATASET OUTPUT_DATASET
    • numind/NuExtract3ready · a10g-small
      nuextract3.py ready to run · a10g-small
      Convert document images to markdown OR extract structured JSON using NuExtract3 with vLLM.
      hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET --max-samples 10
    • numind/NuExtract3command ready · a10g-small
      nuextract3.py command ready · a10g-small
      Convert document images to markdown OR extract structured JSON using NuExtract3 with vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nuextract3.py \
-    INPUT_DATASET OUTPUT_DATASET
    • openbmb/MiniCPM-OCR-1.0ready · a10g-large
      minicpm-ocr.py ready to run, experimental · a10g-large
      Convert document images to markdown with OpenBMB's MiniCPM-OCR 1.0 pipeline.
      hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET --max-samples 5 --shuffle --seed 42
    • openbmb/MiniCPM-OCR-1.0command ready · a10g-large
      minicpm-ocr.py command ready, experimental · a10g-large
      Convert document images to markdown with OpenBMB's MiniCPM-OCR 1.0 pipeline.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/minicpm-ocr.py \
-    INPUT OUTPUT
    • PaddlePaddle/PaddleOCR-VL-1.6ready · a10g-small
      paddleocr-vl-1.6.py ready to run · a10g-small
      Convert document images to text/tables/formulas using PaddleOCR-VL-1.6 with vLLM.
      hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET --max-samples 20
    • PaddlePaddle/PaddleOCR-VL-1.6command ready · a10g-small
      paddleocr-vl-1.6.py command ready · a10g-small
      Convert document images to text/tables/formulas using PaddleOCR-VL-1.6 with vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.6.py \
-    INPUT_DATASET OUTPUT_DATASET
    • rednote-hilab/dots.mocrready · a10g-small
      dots-mocr.py ready to run · a10g-small
      Convert document images to markdown using dots.mocr with vLLM.
      hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET
    • rednote-hilab/dots.mocrcommand ready · a10g-small
      dots-mocr.py command ready · a10g-small
      Convert document images to markdown using dots.mocr with vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-mocr.py \
-    INPUT_DATASET OUTPUT_DATASET
    • rednote-hilab/dots.ocrready · a10g-small
      dots-ocr.py ready to run · a10g-small
      Convert document images to markdown using DoTS.ocr with vLLM.
      hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET
    • rednote-hilab/dots.ocrcommand ready · a10g-small
      dots-ocr.py command ready · a10g-small
      Convert document images to markdown using DoTS.ocr with vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py \
-    INPUT_DATASET OUTPUT_DATASET
    • tencent/HunyuanOCRready · a10g-small
      hunyuan-ocr-1.5.py ready to run · a10g-small
      Convert document images to markdown using HunyuanOCR-1.5 with vLLM.
      hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET
    • tencent/HunyuanOCRcommand ready · a10g-small
      hunyuan-ocr-1.5.py command ready · a10g-small
      Convert document images to markdown using HunyuanOCR-1.5 with vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/hunyuan-ocr-1.5.py \
-    INPUT_DATASET OUTPUT_DATASET
    • zai-org/GLM-OCRready · a10g-small
      glm-ocr.py ready to run · a10g-small
      Convert document images to markdown using GLM-OCR with vLLM.
      hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET
    • zai-org/GLM-OCRcommand ready · a10g-small
      glm-ocr.py command ready · a10g-small
      Convert document images to markdown using GLM-OCR with vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
-    INPUT_DATASET OUTPUT_DATASET
    Transcription
    • CohereLabs/cohere-transcribe-03-2026options needed
      cohere-transcribe-vllm.py set options first
      Transcribe audio files from a directory using Cohere Transcribe via vLLM.
      Read the script for its options. It has no launch settings yet.
      2 more recipes
      cohere-transcribe.py set options first
      Transcribe audio files from a directory using Cohere Transcribe via transformers.
      Read the script for its options. It has no launch settings yet.
      easytranscriber-transcribe.py set options first
      Transcribe audio with word-level timestamps using kb-labb/easytranscriber.
      Read the script for its options. It has no launch settings yet.
    • distil-whisper/distil-large-v3.5options needed
      easytranscriber-transcribe.py set options first
      Transcribe audio with word-level timestamps using kb-labb/easytranscriber.
      Read the script for its options. It has no launch settings yet.
    • facebook/mms-1b-alloptions needed
      easytranscriber-transcribe.py set options first
      Transcribe audio with word-level timestamps using kb-labb/easytranscriber.
      Read the script for its options. It has no launch settings yet.
    • ibm-granite/granite-speech-5.0-470m-turboctcoptions needed
      granite-turboctc-transcribe.py set options first
      Transcribe English audio files with IBM Granite Speech 5.0 TurboCTC (470M).
      Read the script for its options. It has no launch settings yet.
    • nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4Bready · rtx-pro-6000
      nemotron-transcribe.py ready to run · rtx-pro-6000
      Transcribe audio with NVIDIA Nemotron 3.5 Transcribe via an in-job vLLM server.
      hf jobs uv run \
+    {source_dataset} \
+    <OUTPUT_DATASET> \
+    --image-column {image_column} \
+    --batch-size {batch_size} \
+    --task {task}
    Transcription
    • CohereLabs/cohere-transcribe-03-2026example command
      cohere-transcribe.py example command
      Transcribe audio files from a directory using Cohere Transcribe via transformers.
      hf jobs uv run --flavor l4x1 \
+    -s HF_TOKEN \
+    -v hf://buckets/your-username/audio-input:/input:ro \
+    -v hf://buckets/your-username/transcripts:/output \
+    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/cohere-transcribe.py \
+    /input /output --language en --compile
      2 more recipes
      easytranscriber-transcribe.py example command
      Transcribe audio with word-level timestamps using kb-labb/easytranscriber.
      hf jobs uv run --flavor l4x1 -s HF_TOKEN \
+    -e UV_TORCH_BACKEND=cu128 \
+    -v hf://buckets/your-username/audio-files:/input:ro \
+    -v hf://buckets/your-username/transcripts-aligned:/output \
+    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/easytranscriber-transcribe.py \
+    /input /output \
+    --language en --emit-txt --emit-srt
      cohere-transcribe-vllm.py read the script first
      Transcribe audio files from a directory using Cohere Transcribe via vLLM.
      Read the script for its options. There is no ready-made command yet.
    • distil-whisper/distil-large-v3.5example command
      easytranscriber-transcribe.py example command
      Transcribe audio with word-level timestamps using kb-labb/easytranscriber.
      hf jobs uv run --flavor l4x1 -s HF_TOKEN \
+    -e UV_TORCH_BACKEND=cu128 \
+    -v hf://buckets/your-username/audio-files:/input:ro \
+    -v hf://buckets/your-username/transcripts-aligned:/output \
+    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/easytranscriber-transcribe.py \
+    /input /output \
+    --language en --emit-txt --emit-srt
    • facebook/mms-1b-allexample command
      easytranscriber-transcribe.py example command
      Transcribe audio with word-level timestamps using kb-labb/easytranscriber.
      hf jobs uv run --flavor l4x1 -s HF_TOKEN \
+    -e UV_TORCH_BACKEND=cu128 \
+    -v hf://buckets/your-username/audio-files:/input:ro \
+    -v hf://buckets/your-username/transcripts-aligned:/output \
+    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/easytranscriber-transcribe.py \
+    /input /output \
+    --language en --emit-txt --emit-srt
    • ibm-granite/granite-speech-5.0-470m-turboctcexample command
      granite-turboctc-transcribe.py example command
      Transcribe English audio files with IBM Granite Speech 5.0 TurboCTC (470M).
      hf jobs uv run --flavor l4x1 -s HF_TOKEN \
+    -e UV_TORCH_BACKEND=cu128 \
+    -v hf://buckets/your-username/audio-input:/input:ro \
+    -v hf://buckets/your-username/transcripts:/output \
+    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/granite-turboctc-transcribe.py \
+    /input /output
    • nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4Bcommand ready · rtx-pro-6000
      nemotron-transcribe.py command ready · rtx-pro-6000
      Transcribe audio with NVIDIA Nemotron 3.5 Transcribe via an in-job vLLM server.
      hf jobs uv run \
+    -v hf://buckets/your-username/audio-files:/input:ro \
+    -v hf://buckets/your-username/transcripts:/output \
     https://huggingface.co/datasets/uv-scripts/transcription/raw/main/nemotron-transcribe.py \
-    INPUT_DIR OUTPUT_DIR
    • OpenMOSS-Team/MOSS-Transcribe-Diarizeoptions needed
      moss-transcribe-diarize.py set options first
      Transcribe + diarize audio files using MOSS-Transcribe-Diarize (0.9B).
      Read the script for its options. It has no launch settings yet.
      1 more recipe
      moss-transcribe-diarize-server.py set options first
      High-throughput transcription + diarization via an in-job sgl-omni server.
      Read the script for its options. It has no launch settings yet.
    Segmentation
    • facebook/sam3example
      detect-objects.py example from the script
      Detect objects in images using Meta's SAM3 (Segment Anything Model 3).
      hf jobs uv run --flavor a100-large \
+    /input /output
    • OpenMOSS-Team/MOSS-Transcribe-Diarizeexample command
      moss-transcribe-diarize.py example command
      Transcribe + diarize audio files using MOSS-Transcribe-Diarize (0.9B).
      hf jobs uv run --flavor l4x1 -s HF_TOKEN \
+    -e UV_TORCH_BACKEND=cu128 \
+    -v hf://buckets/your-username/audio-files:/input:ro \
+    -v hf://buckets/your-username/transcripts:/output \
+    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/moss-transcribe-diarize.py \
+    /input /output --emit-txt --emit-srt
      1 more recipe
      moss-transcribe-diarize-server.py read the script first
      High-throughput transcription + diarization via an in-job sgl-omni server.
      Read the script for its options. There is no ready-made command yet.
    Segmentation
    • facebook/sam3example command
      detect-objects.py example command
      Detect objects in images using Meta's SAM3 (Segment Anything Model 3).
      hf jobs uv run --flavor a100-large \
     -s HF_TOKEN \
     https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \
-    input-dataset output-dataset \
+    INPUT_DATASET OUTPUT_DATASET \
     --class-name photograph \
-    --confidence-threshold 0.5
      1 more recipe
      segment-objects.py example from the script
      Segment objects in images using Meta's SAM3 (Segment Anything Model 3).
      hf jobs uv run --flavor a100-large \
+    --confidence-threshold 0.5
      1 more recipe
      segment-objects.py example command
      Segment objects in images using Meta's SAM3 (Segment Anything Model 3).
      hf jobs uv run --flavor a100-large \
     -s HF_TOKEN \
     https://huggingface.co/datasets/uv-scripts/sam3/raw/main/segment-objects.py \
-    input-dataset output-dataset \
-    --class-name photograph
    Object detection
    • Qwen/Qwen3.6-35B-A3Bready · a100-large
      qwen3vl-detect.py ready to run · a100-large
      Instruction-oriented object detection with Qwen3-VL via vLLM.
      hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET \
+    --class-name photograph
    Object detection
    • Qwen/Qwen3.6-35B-A3Bcommand ready · a100-large
      qwen3vl-detect.py command ready · a100-large
      Instruction-oriented object detection with Qwen3-VL via vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/vlm-object-detection/raw/main/qwen3vl-detect.py \
-    INPUT_DATASET OUTPUT_DATASET
      1 more recipe
      qwen3vl-detect-fewshot.py ready to run · a100-large
      [FEW-SHOT VARIANT] Instruction-oriented object detection with Qwen3-VL via vLLM.
      hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET
      1 more recipe
      qwen3vl-detect-fewshot.py command ready · a100-large
      Instruction-oriented object detection with Qwen3-VL via vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/vlm-object-detection/raw/main/qwen3vl-detect-fewshot.py \
-    INPUT_DATASET OUTPUT_DATASET
    Video
    • NemoStation/Marlin-2Bready · a10g-small
      marlin-caption.py ready to run · a10g-small
      Caption videos with timestamped events using Marlin-2B, writing a resumable dataset.
      hf jobs uv run \
+    INPUT_DATASET OUTPUT_DATASET
    Video
    • NemoStation/Marlin-2Bcommand ready · a10g-small
      marlin-caption.py command ready · a10g-small
      Caption videos with timestamped events using Marlin-2B, writing a resumable dataset.
      hf jobs uv run \
+    -v hf://buckets/your-username/my-videos:/input:ro \
     https://huggingface.co/datasets/uv-scripts/video/raw/main/marlin-caption.py \
-    INPUT_DIR OUTPUT
    • Qwen/Qwen3.8-27Bready · a100-large
      qwen38-caption.py ready to run · a100-large
      Caption whole videos — no chunking — with Qwen3.8-27B, writing a resumable dataset.
      hf jobs uv run \
+    /input hf://buckets/your-username/my-videos/captions
    • Qwen/Qwen3.8-27Bcommand ready · a100-large
      qwen38-caption.py command ready · a100-large
      Caption whole videos — no chunking — with Qwen3.8-27B, writing a resumable dataset.
      hf jobs uv run \
+    -v hf://buckets/your-username/my-videos:/input:ro \
     https://huggingface.co/datasets/uv-scripts/video/raw/main/qwen38-caption.py \
-    INPUT_DIR OUTPUT
    Classification
    • fastino/gliner2.5-multi-v1ready · t4-small
      classify-gliner2.py ready to run · t4-small
      Classify a text column of a Hub dataset with GLiNER2 — zero-shot, or with your fine-tuned model.
      hf jobs uv run \
+    /input hf://buckets/your-username/my-videos/captions
    Classification
    • fastino/gliner2.5-multi-v1command ready · t4-small
      classify-gliner2.py command ready · t4-small
      Classify a text column of a Hub dataset with GLiNER2 — zero-shot, or with your fine-tuned model.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-gliner2.py \
-    INPUT_DATASET OUTPUT_DATASET
    • HuggingFaceTB/SmolLM3-3Bready · l4x1
      classify-dataset-sglang.py ready to run · l4x1
      Classify text columns in Hugging Face datasets using SGLang with reasoning-aware models.
      hf jobs uv run \
+    fancyzhx/ag_news your-username/ag-news-gliner2 \
+    --labels World Sports Business "Science and technology" --max-samples 1000
    • HuggingFaceTB/SmolLM3-3Bcommand ready · l4x1
      classify-dataset-sglang.py command ready · l4x1
      Classify text columns in Hugging Face datasets using SGLang with reasoning-aware models.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset-sglang.py \
-    --input-dataset INPUT_DATASET --column COLUMN --labels LABELS --output-dataset OUTPUT_DATASET
      1 more recipe
      classify-dataset.py ready to run · l4x1
      Classify text columns in Hugging Face datasets using vLLM with structured outputs.
      hf jobs uv run \
+    --input-dataset your-username/emails \
+    --column content \
+    --labels "spam,ham" \
+    --output-dataset your-username/emails-classified \
+    --reasoning
      1 more recipe
      classify-dataset.py command ready · l4x1
      Classify text columns in Hugging Face datasets using vLLM with structured outputs.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset.py \
-    --input-dataset INPUT_DATASET --column COLUMN --labels LABELS --output-dataset OUTPUT_DATASET
    Embeddings
    • BAAI/bge-base-en-v1.5example
      embed-to-lance.py example from the script
      Embed a Hugging Face dataset and push it back as a Lance vector index — a Hub dataset that IS a searchable vector database.
      hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/embed-to-lance.py \
-    stanfordnlp/imdb your-name/imdb-vecdb --column text --model BAAI/bge-base-en-v1.5 --private
    • Qwen/Qwen3-Embedding-0.6Bexample
      generate-embeddings-vllm.py example from the script
      High-throughput embedding generation with vLLM pooling mode — the "scale" variant of generate-embeddings.py, for large *decoder* embedding models (e.g
      hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings-vllm.py \
-    stanfordnlp/imdb your-name/imdb-embeddings --column text --model Qwen/Qwen3-Embedding-0.6B --private
    • sentence-transformers/all-MiniLM-L6-v2example
      generate-embeddings.py example from the script
      Generate embeddings for a Hugging Face dataset (text OR images) with sentence-transformers, and push the result back to the Hub as a new dataset with 
      hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings.py \
-    stanfordnlp/imdb  your-name/imdb-embeddings \
-    --column text --model sentence-transformers/all-MiniLM-L6-v2
      1 more recipe
      launch-embedding-fleet.py set options first
      Fan one embedding run out across N Hugging Face Jobs, then consolidate.
      Read the script for its options. It has no launch settings yet.
    Text generation
    • Qwen/Qwen2-VL-7B-Instructready · l40sx1
      vlm-classify.py ready to run · l40sx1
      Classify images using Vision Language Models with vLLM.
      hf jobs uv run \
+    --input-dataset your-username/emails \
+    --column content \
+    --labels "spam,ham" \
+    --output-dataset your-username/emails-classified \
+    --enable-reasoning
    Embeddings
    • BAAI/bge-base-en-v1.5example command
      embed-to-lance.py example command
      Embed a Hugging Face dataset and push it back as a Lance vector index — a Hub dataset that IS a searchable vector database.
      hf jobs uv run --flavor l4x1 -s HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/embed-to-lance.py \
+    stanfordnlp/imdb your-username/imdb-vecdb --column text --model BAAI/bge-base-en-v1.5 --private
    • Qwen/Qwen3-Embedding-0.6Bexample command
      generate-embeddings-vllm.py example command
      High-throughput embedding generation with vLLM pooling mode — the "scale" variant of generate-embeddings.py, for large decoder embedding models (e.g.
      hf jobs uv run --flavor l4x1 -s HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings-vllm.py \
+    stanfordnlp/imdb your-username/imdb-embeddings --column text --model Qwen/Qwen3-Embedding-0.6B --private
    • sentence-transformers/all-MiniLM-L6-v2example command
      generate-embeddings.py example command
      Generate embeddings for a Hugging Face dataset (text OR images) with sentence-transformers, and push the result back to the Hub as a new dataset with…
      hf jobs uv run --flavor l4x1 -s HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings.py \
+    stanfordnlp/imdb  your-username/imdb-embeddings \
+    --column text --model sentence-transformers/all-MiniLM-L6-v2
      1 more recipe
      launch-embedding-fleet.py read the script first
      Fan one embedding run out across N Hugging Face Jobs, then consolidate.
      Read the script for its options. There is no ready-made command yet.
    Text generation
    • Qwen/Qwen2-VL-7B-Instructcommand ready · l40sx1
      vlm-classify.py command ready · l40sx1
      Classify images using Vision Language Models with vLLM.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/vllm/raw/main/vlm-classify.py \
-    INPUT_DATASET OUTPUT_DATASET --classes CLASSES
    • Qwen/Qwen3-30B-A3B-Instruct-2507ready · a100-large
      generate-responses.py ready to run · a100-large
      Generate responses for prompts in a dataset using vLLM for efficient GPU inference.
      hf jobs uv run \
+    your-username/INPUT_DATASET \
+    your-username/OUTPUT_DATASET \
+    --classes "title-page,content,index,other"
    • Qwen/Qwen3-30B-A3B-Instruct-2507command ready · a100-large
      generate-responses.py command ready · a100-large
      Generate responses for prompts in a dataset using vLLM for efficient GPU inference.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-responses.py \
-    SRC_DATASET_HUB_ID OUTPUT_DATASET_HUB_ID
    Fine-tuning
    • fastino/gliner2.5-multi-v1ready · t4-small
      train-gliner2.py ready to run · t4-small
      Fine-tune GLiNER2 into a text classifier — a small model (74M to 287M parameters, depending on the base checkpoint) that already works zero-shot.
      hf jobs uv run \
+    input output --max-model-len 32768
    Fine-tuning
    • fastino/gliner2.5-multi-v1command ready · t4-small
      train-gliner2.py command ready · t4-small
      Fine-tune GLiNER2 into a text classifier — a small model (74M to 287M parameters, depending on the base checkpoint) that already works zero-shot.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-gliner2.py \
-    INPUT_DATASET OUTPUT_REPO
    • LiquidAI/LFM2.5-Encoder-350Mready · l4x1
      train-classifier.py ready to run · l4x1
      Fine-tune a text-classification encoder on a Hub dataset and push the trained model to the Hub.
      hf jobs uv run \
+    biglam/blbooksgenre your-username/gliner2-blbooks-genre \
+    --dataset-config title_genre_classifiction --text-column title
    • LiquidAI/LFM2.5-Encoder-350Mcommand ready · l4x1
      train-classifier.py command ready · l4x1
      Fine-tune a text-classification encoder on a Hub dataset and push the trained model to the Hub.
      hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-classifier.py \
-    INPUT_DATASET OUTPUT_REPO
    • sentence-transformers/all-MiniLM-L6-v2example
      train-setfit.py example from the script
      Few-shot text classification with SetFit — train on 8-64 labelled examples per class, on CPU or GPU.
      hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN \
+    fancyzhx/ag_news your-username/my-news-classifier \
+    --max-samples 2000 --epochs 1
    • sentence-transformers/all-MiniLM-L6-v2example command
      train-setfit.py example command
      Few-shot text classification with SetFit — train on 8-64 labelled examples per class, on CPU or GPU.
      hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN \
     https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-setfit.py \
-    fancyzhx/ag_news username/ag-news-setfit \
+    fancyzhx/ag_news your-username/ag-news-setfit \
     --num-samples 8
    
 
    
 
@@ -363,13 +428,15 @@ 
    For agents
    
   const button = event.target;
   const pre = button.previousElementSibling;
   const done = () => { button.textContent = 'Copied'; setTimeout(() => { button.textContent = 'Copy'; }, 1400); };
-  navigator.clipboard.writeText(pre.textContent).then(done).catch(() => {
+  const selectText = () => {
     const range = document.createRange();
     range.selectNodeContents(pre);
     getSelection().removeAllRanges();
     getSelection().addRange(range);
     button.textContent = 'Selected';
-  });
+  };
+  if (!navigator.clipboard) { selectText(); return; }
+  navigator.clipboard.writeText(pre.textContent).then(done).catch(selectText);
 });
 
 
diff --git a/run-on-jobs/recipes.json b/run-on-jobs/recipes.json
index 144b42d..deaa39d 100644
--- a/run-on-jobs/recipes.json
+++ b/run-on-jobs/recipes.json
@@ -12,7 +12,7 @@
    "source": "header",
    "flavor": "l4x1",
    "timeout": "1h",
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset-sglang.py \\\n    --input-dataset INPUT_DATASET --column COLUMN --labels LABELS --output-dataset OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset-sglang.py \\\n    --input-dataset your-username/emails \\\n    --column content \\\n    --labels \"spam,ham\" \\\n    --output-dataset your-username/emails-classified \\\n    --reasoning",
    "summary": "Classify text columns in Hugging Face datasets using SGLang with reasoning-aware models.",
    "declared": true
   },
@@ -28,7 +28,7 @@
    "source": "header",
    "flavor": "l4x1",
    "timeout": "1h",
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset.py \\\n    --input-dataset INPUT_DATASET --column COLUMN --labels LABELS --output-dataset OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset.py \\\n    --input-dataset your-username/emails \\\n    --column content \\\n    --labels \"spam,ham\" \\\n    --output-dataset your-username/emails-classified \\\n    --enable-reasoning",
    "summary": "Classify text columns in Hugging Face datasets using vLLM with structured outputs.",
    "declared": true
   },
@@ -44,7 +44,7 @@
    "source": "header",
    "flavor": "t4-small",
    "timeout": "1h",
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-gliner2.py \\\n    INPUT_DATASET OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-gliner2.py \\\n    fancyzhx/ag_news your-username/ag-news-gliner2 \\\n    --labels World Sports Business \"Science and technology\" --max-samples 1000",
    "summary": "Classify a text column of a Hub dataset with GLiNER2 — zero-shot, or with your fine-tuned model.",
    "declared": true
   },
@@ -60,7 +60,7 @@
    "source": "header",
    "flavor": "l4x1",
    "timeout": "1h",
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-classifier.py \\\n    INPUT_DATASET OUTPUT_REPO",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-classifier.py \\\n    fancyzhx/ag_news your-username/my-news-classifier \\\n    --max-samples 2000 --epochs 1",
    "summary": "Fine-tune a text-classification encoder on a Hub dataset and push the trained model to the Hub.",
    "declared": false
   },
@@ -76,7 +76,7 @@
    "source": "header",
    "flavor": "t4-small",
    "timeout": "1h",
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-gliner2.py \\\n    INPUT_DATASET OUTPUT_REPO",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-gliner2.py \\\n    biglam/blbooksgenre your-username/gliner2-blbooks-genre \\\n    --dataset-config title_genre_classifiction --text-column title",
    "summary": "Fine-tune GLiNER2 into a text classifier — a small model (74M to 287M parameters, depending on the base checkpoint) that already works zero-shot.",
    "declared": false
   },
@@ -92,7 +92,7 @@
    "source": "documented",
    "flavor": null,
    "timeout": null,
-   "command": "hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN \\\n    https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-setfit.py \\\n    fancyzhx/ag_news username/ag-news-setfit \\\n    --num-samples 8",
+   "command": "hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN \\\n    https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-setfit.py \\\n    fancyzhx/ag_news your-username/ag-news-setfit \\\n    --num-samples 8",
    "summary": "Few-shot text classification with SetFit — train on 8-64 labelled examples per class, on CPU or GPU.",
    "declared": false
   },
@@ -108,7 +108,7 @@
    "source": "documented",
    "flavor": null,
    "timeout": null,
-   "command": "hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/embed-to-lance.py \\\n    stanfordnlp/imdb your-name/imdb-vecdb --column text --model BAAI/bge-base-en-v1.5 --private",
+   "command": "hf jobs uv run --flavor l4x1 -s HF_TOKEN \\\n    https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/embed-to-lance.py \\\n    stanfordnlp/imdb your-username/imdb-vecdb --column text --model BAAI/bge-base-en-v1.5 --private",
    "summary": "Embed a Hugging Face dataset and push it back as a Lance vector index — a Hub dataset that IS a searchable vector database.",
    "declared": false
   },
@@ -124,8 +124,8 @@
    "source": "documented",
    "flavor": null,
    "timeout": null,
-   "command": "hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings-vllm.py \\\n    stanfordnlp/imdb your-name/imdb-embeddings --column text --model Qwen/Qwen3-Embedding-0.6B --private",
-   "summary": "High-throughput embedding generation with vLLM pooling mode — the \"scale\" variant of generate-embeddings.py, for large *decoder* embedding models (e.g",
+   "command": "hf jobs uv run --flavor l4x1 -s HF_TOKEN \\\n    https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings-vllm.py \\\n    stanfordnlp/imdb your-username/imdb-embeddings --column text --model Qwen/Qwen3-Embedding-0.6B --private",
+   "summary": "High-throughput embedding generation with vLLM pooling mode — the \"scale\" variant of generate-embeddings.py, for large decoder embedding models (e.g.",
    "declared": false
   },
   {
@@ -140,8 +140,8 @@
    "source": "documented",
    "flavor": null,
    "timeout": null,
-   "command": "hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings.py \\\n    stanfordnlp/imdb  your-name/imdb-embeddings \\\n    --column text --model sentence-transformers/all-MiniLM-L6-v2",
-   "summary": "Generate embeddings for a Hugging Face dataset (text OR images) with sentence-transformers, and push the result back to the Hub as a new dataset with ",
+   "command": "hf jobs uv run --flavor l4x1 -s HF_TOKEN \\\n    https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings.py \\\n    stanfordnlp/imdb  your-username/imdb-embeddings \\\n    --column text --model sentence-transformers/all-MiniLM-L6-v2",
+   "summary": "Generate embeddings for a Hugging Face dataset (text OR images) with sentence-transformers, and push the result back to the Hub as a new dataset with…",
    "declared": true
   },
   {
@@ -172,7 +172,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \\\n    INPUT_DATASET OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \\\n    my-dataset my-output --max-samples 10",
    "summary": "Convert document images to markdown using DeepSeek-OCR with vLLM.",
    "declared": true
   },
@@ -188,7 +188,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr2-vllm.py \\\n    INPUT_DATASET OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr2-vllm.py \\\n    my-dataset my-output --max-samples 10",
    "summary": "Convert document images to markdown using DeepSeek-OCR-2 with vLLM.",
    "declared": true
   },
@@ -236,7 +236,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \\\n    INPUT_DATASET OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \\\n    {source_dataset} \\\n     \\\n    --image-column {image_column} \\\n    --batch-size {batch_size} \\\n    --task {task}",
    "summary": "Convert document images to markdown using GLM-OCR with vLLM.",
    "declared": true
   },
@@ -268,7 +268,7 @@
    "source": "header",
    "flavor": "a100-large",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lift-extract.py \\\n    INPUT_DATASET OUTPUT_DATASET --schema SCHEMA",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lift-extract.py \\\n    INPUT_DATASET OUTPUT_DATASET \\\n    --schema '{\"type\":\"object\",\"properties\":{\"title\":{\"type\":\"string\"}}}' \\\n    --max-samples 5 --shuffle --seed 42",
    "summary": "Extract structured JSON from document images OR multi-page PDFs using Datalab's lift model (datalab-to/lift, 9B, Qwen3.5-based).",
    "declared": true
   },
@@ -284,7 +284,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2-saturate.py \\\n    INPUT_DATASET OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2-saturate.py \\\n     ",
    "summary": "Convert document images to markdown using LightOnOCR-2 via saturate.",
    "declared": true
   },
@@ -300,7 +300,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \\\n    INPUT_DATASET OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \\\n     ",
    "summary": "Convert document images to markdown using LightOnOCR-2 with vLLM.",
    "declared": true
   },
@@ -316,7 +316,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr3-bucket.py \\\n    INPUT OUTPUT",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr3-bucket.py \\\n    hf://buckets/biglam/britannica/source/pages/00070476bsb \\\n    hf://buckets/your-username/ocr-output/britannica --mode grounding --max-samples 20",
    "summary": "OCR images and PDFs from an HF Bucket (or a directory) using LightOnOCR-3, writing markdown files.",
    "declared": true
   },
@@ -332,7 +332,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr3.py \\\n    INPUT_DATASET OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr3.py \\\n    INPUT_DATASET OUTPUT_DATASET --max-samples 10",
    "summary": "Convert document images to markdown (or layout blocks) using LightOnOCR-3 with vLLM.",
    "declared": true
   },
@@ -348,7 +348,7 @@
    "source": "header",
    "flavor": "a10g-large",
    "timeout": "2h",
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/minicpm-ocr.py \\\n    INPUT OUTPUT",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/minicpm-ocr.py \\\n    INPUT_DATASET OUTPUT_DATASET --max-samples 20",
    "summary": "Convert document images to markdown with OpenBMB's MiniCPM-OCR 1.0 pipeline.",
    "declared": false
   },
@@ -364,7 +364,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nuextract3.py \\\n    INPUT_DATASET OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nuextract3.py \\\n    INPUT_DATASET OUTPUT_DATASET --max-samples 5 --shuffle --seed 42",
    "summary": "Convert document images to markdown OR extract structured JSON using NuExtract3 with vLLM.",
    "declared": true
   },
@@ -396,7 +396,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2-saturate.py \\\n    INPUT_DATASET OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2-saturate.py \\\n     ",
    "summary": "Convert document images to markdown using OvisOCR2 via saturate.",
    "declared": true
   },
@@ -412,7 +412,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2.py \\\n    INPUT_DATASET OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2.py \\\n    INPUT_DATASET OUTPUT_DATASET --batch-size 16",
    "summary": "Convert document images to markdown using OvisOCR2 with vLLM.",
    "declared": true
   },
@@ -444,7 +444,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/qianfan-ocr.py \\\n    INPUT_DATASET OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/qianfan-ocr.py \\\n    INPUT_DATASET OUTPUT_DATASET --max-samples 10",
    "summary": "Convert document images to markdown using Qianfan-OCR with vLLM.",
    "declared": true
   },
@@ -460,7 +460,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/surya-ocr.py \\\n    INPUT_DATASET OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/surya-ocr.py \\\n    INPUT_DATASET OUTPUT_DATASET \\\n    --max-samples 5 --shuffle --seed 42",
    "summary": "Document intelligence on images OR multi-page PDFs with Datalab's Surya OCR 2 (datalab-to/surya-ocr-2, 650M, Qwen3.5-style).",
    "declared": true
   },
@@ -476,7 +476,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr-vllm.py \\\n    INPUT_DATASET OUTPUT_DATASET",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr-vllm.py \\\n    your-input-dataset your-output-dataset --max-samples 10",
    "summary": "Convert document images to markdown using Baidu Unlimited-OCR with vLLM.",
    "declared": true
   },
@@ -492,7 +492,7 @@
    "source": "header",
    "flavor": "rtx-pro-6000",
    "timeout": "4h",
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2-live.py \\\n    INPUT OUTPUT",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2-live.py \\\n    \"hf://buckets/biglam/britannica/source/pages/1771EncyclopediaBritannicaNLS/*.jp2\" \\\n    /britannica-1771 --live-port 7860",
    "summary": "unlimited-ocr2.py with a live view you can watch (and screen-record) while the Job runs.",
    "declared": false
   },
@@ -508,7 +508,7 @@
    "source": "header",
    "flavor": "rtx-pro-6000",
    "timeout": "4h",
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2-saturate.py \\\n    INPUT OUTPUT",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2-saturate.py \\\n    \"hf://buckets/biglam/britannica/source/pages/1771EncyclopediaBritannicaNLS/*.jp2\" \\\n    /britannica-1771-uocr2",
    "summary": "OCR whole documents (book volumes, PDFs) with Baidu Unlimited-OCR-2 via saturate.",
    "declared": false
   },
@@ -524,7 +524,7 @@
    "source": "header",
    "flavor": "rtx-pro-6000",
    "timeout": "4h",
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2.py \\\n    INPUT OUTPUT",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr2.py \\\n    \"hf://buckets/biglam/britannica/source/pages/1771EncyclopediaBritannicaNLS/*.jp2\" \\\n    /britannica-1771",
    "summary": "OCR whole documents (book volumes, folders of scans) with Baidu Unlimited-OCR-2.",
    "declared": true
   },
@@ -540,7 +540,7 @@
    "source": "documented",
    "flavor": null,
    "timeout": null,
-   "command": "hf jobs uv run --flavor a100-large \\\n    -s HF_TOKEN \\\n    https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \\\n    input-dataset output-dataset \\\n    --class-name photograph \\\n    --confidence-threshold 0.5",
+   "command": "hf jobs uv run --flavor a100-large \\\n    -s HF_TOKEN \\\n    https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \\\n    INPUT_DATASET OUTPUT_DATASET \\\n    --class-name photograph \\\n    --confidence-threshold 0.5",
    "summary": "Detect objects in images using Meta's SAM3 (Segment Anything Model 3).",
    "declared": true
   },
@@ -556,7 +556,7 @@
    "source": "documented",
    "flavor": null,
    "timeout": null,
-   "command": "hf jobs uv run --flavor a100-large \\\n    -s HF_TOKEN \\\n    https://huggingface.co/datasets/uv-scripts/sam3/raw/main/segment-objects.py \\\n    input-dataset output-dataset \\\n    --class-name photograph",
+   "command": "hf jobs uv run --flavor a100-large \\\n    -s HF_TOKEN \\\n    https://huggingface.co/datasets/uv-scripts/sam3/raw/main/segment-objects.py \\\n    INPUT_DATASET OUTPUT_DATASET \\\n    --class-name photograph",
    "summary": "Segment objects in images using Meta's SAM3 (Segment Anything Model 3).",
    "declared": true
   },
@@ -585,10 +585,10 @@
     "CohereLabs/cohere-transcribe-03-2026"
    ],
    "support": null,
-   "source": "flags-needed",
+   "source": "documented",
    "flavor": null,
    "timeout": null,
-   "command": null,
+   "command": "hf jobs uv run --flavor l4x1 \\\n    -s HF_TOKEN \\\n    -v hf://buckets/your-username/audio-input:/input:ro \\\n    -v hf://buckets/your-username/transcripts:/output \\\n    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/cohere-transcribe.py \\\n    /input /output --language en --compile",
    "summary": "Transcribe audio files from a directory using Cohere Transcribe via transformers.",
    "declared": false
   },
@@ -603,10 +603,10 @@
     "facebook/mms-1b-all"
    ],
    "support": null,
-   "source": "flags-needed",
+   "source": "documented",
    "flavor": null,
    "timeout": null,
-   "command": null,
+   "command": "hf jobs uv run --flavor l4x1 -s HF_TOKEN \\\n    -e UV_TORCH_BACKEND=cu128 \\\n    -v hf://buckets/your-username/audio-files:/input:ro \\\n    -v hf://buckets/your-username/transcripts-aligned:/output \\\n    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/easytranscriber-transcribe.py \\\n    /input /output \\\n    --language en --emit-txt --emit-srt",
    "summary": "Transcribe audio with word-level timestamps using kb-labb/easytranscriber.",
    "declared": false
   },
@@ -619,10 +619,10 @@
     "ibm-granite/granite-speech-5.0-470m-turboctc"
    ],
    "support": null,
-   "source": "flags-needed",
+   "source": "documented",
    "flavor": null,
    "timeout": null,
-   "command": null,
+   "command": "hf jobs uv run --flavor l4x1 -s HF_TOKEN \\\n    -e UV_TORCH_BACKEND=cu128 \\\n    -v hf://buckets/your-username/audio-input:/input:ro \\\n    -v hf://buckets/your-username/transcripts:/output \\\n    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/granite-turboctc-transcribe.py \\\n    /input /output",
    "summary": "Transcribe English audio files with IBM Granite Speech 5.0 TurboCTC (470M).",
    "declared": false
   },
@@ -651,10 +651,10 @@
     "OpenMOSS-Team/MOSS-Transcribe-Diarize"
    ],
    "support": null,
-   "source": "flags-needed",
+   "source": "documented",
    "flavor": null,
    "timeout": null,
-   "command": null,
+   "command": "hf jobs uv run --flavor l4x1 -s HF_TOKEN \\\n    -e UV_TORCH_BACKEND=cu128 \\\n    -v hf://buckets/your-username/audio-files:/input:ro \\\n    -v hf://buckets/your-username/transcripts:/output \\\n    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/moss-transcribe-diarize.py \\\n    /input /output --emit-txt --emit-srt",
    "summary": "Transcribe + diarize audio files using MOSS-Transcribe-Diarize (0.9B).",
    "declared": false
   },
@@ -670,7 +670,7 @@
    "source": "header",
    "flavor": "rtx-pro-6000",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/nemotron-transcribe.py \\\n    INPUT_DIR OUTPUT_DIR",
+   "command": "hf jobs uv run \\\n    -v hf://buckets/your-username/audio-files:/input:ro \\\n    -v hf://buckets/your-username/transcripts:/output \\\n    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/nemotron-transcribe.py \\\n    /input /output",
    "summary": "Transcribe audio with NVIDIA Nemotron 3.5 Transcribe via an in-job vLLM server.",
    "declared": true
   },
@@ -686,7 +686,7 @@
    "source": "header",
    "flavor": "a10g-small",
    "timeout": "2h",
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/video/raw/main/marlin-caption.py \\\n    INPUT_DIR OUTPUT",
+   "command": "hf jobs uv run \\\n    -v hf://buckets/your-username/my-videos:/input:ro \\\n    https://huggingface.co/datasets/uv-scripts/video/raw/main/marlin-caption.py \\\n    /input hf://buckets/your-username/my-videos/captions",
    "summary": "Caption videos with timestamped events using Marlin-2B, writing a resumable dataset.",
    "declared": true
   },
@@ -702,7 +702,7 @@
    "source": "header",
    "flavor": "a100-large",
    "timeout": null,
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/video/raw/main/qwen38-caption.py \\\n    INPUT_DIR OUTPUT",
+   "command": "hf jobs uv run \\\n    -v hf://buckets/your-username/my-videos:/input:ro \\\n    https://huggingface.co/datasets/uv-scripts/video/raw/main/qwen38-caption.py \\\n    /input hf://buckets/your-username/my-videos/captions",
    "summary": "Caption whole videos — no chunking — with Qwen3.8-27B, writing a resumable dataset.",
    "declared": true
   },
@@ -718,7 +718,7 @@
    "source": "header",
    "flavor": "a100-large",
    "timeout": "2h",
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-responses.py \\\n    SRC_DATASET_HUB_ID OUTPUT_DATASET_HUB_ID",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-responses.py \\\n    input output --max-model-len 32768",
    "summary": "Generate responses for prompts in a dataset using vLLM for efficient GPU inference.",
    "declared": false
   },
@@ -734,7 +734,7 @@
    "source": "header",
    "flavor": "l40sx1",
    "timeout": "1h",
-   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/vllm/raw/main/vlm-classify.py \\\n    INPUT_DATASET OUTPUT_DATASET --classes CLASSES",
+   "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/vllm/raw/main/vlm-classify.py \\\n    your-username/INPUT_DATASET \\\n    your-username/OUTPUT_DATASET \\\n    --classes \"title-page,content,index,other\"",
    "summary": "Classify images using Vision Language Models with vLLM.",
    "declared": true
   },
@@ -751,7 +751,7 @@
    "flavor": "a100-large",
    "timeout": "1h",
    "command": "hf jobs uv run \\\n    https://huggingface.co/datasets/uv-scripts/vlm-object-detection/raw/main/qwen3vl-detect-fewshot.py \\\n    INPUT_DATASET OUTPUT_DATASET",
-   "summary": "[FEW-SHOT VARIANT] Instruction-oriented object detection with Qwen3-VL via vLLM.",
+   "summary": "Instruction-oriented object detection with Qwen3-VL via vLLM.",
    "declared": true
   },
   {
diff --git a/tools/build-catalog.py b/tools/build-catalog.py
index 5665f46..f588c56 100755
--- a/tools/build-catalog.py
+++ b/tools/build-catalog.py
@@ -69,11 +69,12 @@
 OCR_SUPPORT_LEVELS = {"core", "experimental"}
 SKIP_SCRIPT = re.compile(r"(^_|^test_|conftest|setup|benchmark|judge|compare)", re.IGNORECASE)
 ALTERNATIVE_SCRIPT = re.compile(r"saturate|live|server|fleet|fewshot", re.IGNORECASE)
+PATH_ARGUMENT = re.compile(r"^(input|output)$|_dir$|_path$|_folder$")
 MODEL_ID = re.compile(r"^[A-Za-z0-9][\w.-]*/[\w.+-]+$")
 PLACEHOLDER_OWNERS = {"org", "datasets", "models", "hf", "bucket", "buckets", "input", "output", "user", "your", "username", "path", "dir", "my-org", "uv-scripts"}
 
-STATUS = {"header": "ready to run", "documented": "example from the script", "flags-needed": "set options first"}
-ROW_STATUS = {"header": "ready", "documented": "example", "flags-needed": "options needed"}
+STATUS = {"header": "command ready", "documented": "example command", "flags-needed": "read the script first"}
+ROW_STATUS = {"header": "command ready", "documented": "example command", "flags-needed": "no command yet"}
 
 
 def read_header(text):
@@ -128,31 +129,66 @@ def find_models(constants, arguments):
 
 
 def first_documented_command(text, script_name, url):
-    match = re.search(r"hf jobs uv run(?:[^\n]*\\\n)*[^\n]*", text)
-    if not match or script_name not in match.group(0):
-        return None
-    lines = [re.sub(r"^\s*(#\s*)?", "", line).rstrip(" \\") for line in match.group(0).split("\n")]
-    command = " \\\n    ".join(line for line in lines if line)
-    if url not in command:
-        command = re.sub(r"(?{html.escape(recipe["summary"])}
    ' if recipe["summary"] else ""
     if recipe["source"] == "flags-needed":
-        body = f'
    Read the script for its options. It has no launch settings yet.
    '
+        body = f'
    Read the script for its options. There is no ready-made command yet.
    '
     else:
         body = command_block(recipe["command"])
     name = recipe["script"].split("/")[-1]
@@ -302,7 +339,7 @@ def render_readme(recipes):
 colorTo: gray
 sdk: static
 pinned: false
-short_description: Run this model over your own data on HF Jobs
+short_description: Batch-run this model on your own data with HF Jobs
 models:
 {model_lines}
 ---
@@ -324,7 +361,7 @@ def render_agents():
 
 - Check `hf version` (needs 1.32+). If it's missing: `curl -LsSf https://hf.co/cli/install.sh | bash`, then `hf auth login`.
 - Jobs need pay-as-you-go credit, and each job costs money. Confirm with the user before starting one.
-- The CLI is the current reference: `hf --help`, `hf jobs uv run --help`, `hf jobs hardware`. For a skill, use `hf skills add`.
+- The CLI is the current reference: `hf --help`, `hf jobs uv run --help`, `hf jobs hardware`. To install the Hugging Face CLI skill, run `hf skills add`.
 
 ## Find a recipe
 
@@ -336,7 +373,7 @@ def render_agents():
 
 ## Run it
 
-Most recipes go from dataset to dataset:
+Start from the recipe's `command` field. Most recipes go from dataset to dataset:
 
     hf jobs uv run    --max-samples 10
 
diff --git a/tools/run-on-jobs-template.html b/tools/run-on-jobs-template.html
index ee41626..fcf097e 100644
--- a/tools/run-on-jobs-template.html
+++ b/tools/run-on-jobs-template.html
@@ -77,15 +77,16 @@
 body .more{margin-left:16px}
 
 
+@media (prefers-reduced-motion:reduce){summary::before{transition:none}}
 
 
 
 
    
 
    Use Hub models on your own data
    
-
    Run a model over your whole dataset, or fine-tune it on your own examples. One command starts a GPU on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
+
    Run a model over your whole dataset, or fine-tune it on your own examples. Each command starts a paid batch job on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
 
    
 
    
-
    Read seven scanned NASA pages with GLM-OCR and save the text to a new dataset on your account.
    hf jobs uv run \
+
    Read seven scanned NASA pages with GLM-OCR and save the text to a new public dataset on your account. Add --private to keep it private.
    hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
     uv-scripts/ocr-demo your-username/ocr-demo-results
    The scan
    Scanned page listing Space Shuttle foods
    What the Job wrote
    Space Shuttle Food and Beverage List
 
@@ -225,7 +226,7 @@ 
    Use Hub models on your own data
    
 
    
 
    You need the hf CLI (1.32 or newer), a login and pay-as-you-go credit. Add --dry-run to see the GPU and settings without starting a job.
    
 
    Find your model
    
-
    In the commands, INPUT and OUTPUT are dataset IDs unless the recipe says otherwise.
    
+
    Replace the capitalised placeholders and your-username. Check the script if you're unsure what a value should be.
    
 
 
 
@@ -277,13 +278,15 @@ 
    For agents
    
   const button = event.target;
   const pre = button.previousElementSibling;
   const done = () => { button.textContent = 'Copied'; setTimeout(() => { button.textContent = 'Copy'; }, 1400); };
-  navigator.clipboard.writeText(pre.textContent).then(done).catch(() => {
+  const selectText = () => {
     const range = document.createRange();
     range.selectNodeContents(pre);
     getSelection().removeAllRanges();
     getSelection().addRange(range);
     button.textContent = 'Selected';
-  });
+  };
+  if (!navigator.clipboard) { selectText(); return; }
+  navigator.clipboard.writeText(pre.textContent).then(done).catch(selectText);
 });
 
 

From afe984daadaf478f6bea5288b2758d9d2ba88872 Mon Sep 17 00:00:00 2001
From: Daniel van Strien 
Date: Fri, 30 Oct 2026 14:04:01 +0000
Subject: [PATCH 08/10] run-on-jobs: simplify the intro

Co-Authored-By: Claude Opus 5.5 
Claude-Session: https://claude.ai/code/session_016isSPpM5PRqNUzwsDntoG4
---
 run-on-jobs/index.html          | 2 +-
 tools/run-on-jobs-template.html | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/run-on-jobs/index.html b/run-on-jobs/index.html
index e8c535d..bbfbe9b 100644
--- a/run-on-jobs/index.html
+++ b/run-on-jobs/index.html
@@ -83,7 +83,7 @@
 
 
    
 
    Use Hub models on your own data
    
-
    Run a model over your whole dataset, or fine-tune it on your own examples. Each command starts a paid batch job on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
+
    Run a model over your whole dataset, or fine-tune it on your own examples. Each command starts a job on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
 
    
 
    
 
    Read seven scanned NASA pages with GLM-OCR and save the text to a new public dataset on your account. Add --private to keep it private.
    hf jobs uv run \
diff --git a/tools/run-on-jobs-template.html b/tools/run-on-jobs-template.html
index fcf097e..da60e96 100644
--- a/tools/run-on-jobs-template.html
+++ b/tools/run-on-jobs-template.html
@@ -83,7 +83,7 @@
 
 
    
 
    Use Hub models on your own data
    
-
    Run a model over your whole dataset, or fine-tune it on your own examples. Each command starts a paid batch job on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
+
    Run a model over your whole dataset, or fine-tune it on your own examples. Each command starts a job on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
 
    
 
    
 
    Read seven scanned NASA pages with GLM-OCR and save the text to a new public dataset on your account. Add --private to keep it private.
    hf jobs uv run \

From a42a92080e403bf8fdc7d06f061f2b891b29b5cc Mon Sep 17 00:00:00 2001
From: Daniel van Strien 
Date: Fri, 30 Oct 2026 14:04:21 +0000
Subject: [PATCH 09/10] run-on-jobs: keep inline code on one line

Co-Authored-By: Claude Opus 5.5 
Claude-Session: https://claude.ai/code/session_016isSPpM5PRqNUzwsDntoG4
---
 run-on-jobs/index.html          | 2 +-
 tools/run-on-jobs-template.html | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/run-on-jobs/index.html b/run-on-jobs/index.html
index bbfbe9b..b194797 100644
--- a/run-on-jobs/index.html
+++ b/run-on-jobs/index.html
@@ -21,7 +21,7 @@
 .muted{color:var(--muted)}
 .small{font-size:14.5px}
 code,pre,input,summary{font-family:var(--mono)}
-code{font-size:.86em}
+code{font-size:.86em;white-space:nowrap}
 pre{font-size:12.5px;line-height:1.55;margin:0;padding:10px 12px;background:var(--code);border-radius:4px;white-space:pre;overflow-x:auto;min-width:0}
 .cmd{display:grid;grid-template-columns:minmax(0,1fr) auto;gap:6px;align-items:start;margin:6px 0 8px;min-width:0}
 .copy{font:500 12px var(--text);padding:3px 8px;background:var(--bg);color:var(--fg);border:1px solid var(--rule);border-radius:4px;cursor:pointer}
diff --git a/tools/run-on-jobs-template.html b/tools/run-on-jobs-template.html
index da60e96..d03f679 100644
--- a/tools/run-on-jobs-template.html
+++ b/tools/run-on-jobs-template.html
@@ -21,7 +21,7 @@
 .muted{color:var(--muted)}
 .small{font-size:14.5px}
 code,pre,input,summary{font-family:var(--mono)}
-code{font-size:.86em}
+code{font-size:.86em;white-space:nowrap}
 pre{font-size:12.5px;line-height:1.55;margin:0;padding:10px 12px;background:var(--code);border-radius:4px;white-space:pre;overflow-x:auto;min-width:0}
 .cmd{display:grid;grid-template-columns:minmax(0,1fr) auto;gap:6px;align-items:start;margin:6px 0 8px;min-width:0}
 .copy{font:500 12px var(--text);padding:3px 8px;background:var(--bg);color:var(--fg);border:1px solid var(--rule);border-radius:4px;cursor:pointer}

From cffc81473624fbad2ba8b74e3c5fd61d85fce05d Mon Sep 17 00:00:00 2001
From: Daniel van Strien 
Date: Fri, 30 Oct 2026 14:08:50 +0000
Subject: [PATCH 10/10] run-on-jobs: add a SetFit fine-tuning example and skill
 pointers

Co-Authored-By: Claude Opus 5.5 
Claude-Session: https://claude.ai/code/session_016isSPpM5PRqNUzwsDntoG4
---
 run-on-jobs/AGENTS.md           | 1 +
 run-on-jobs/index.html          | 7 ++++++-
 tools/build-catalog.py          | 1 +
 tools/run-on-jobs-template.html | 7 ++++++-
 4 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/run-on-jobs/AGENTS.md b/run-on-jobs/AGENTS.md
index da29bcd..5d73744 100644
--- a/run-on-jobs/AGENTS.md
+++ b/run-on-jobs/AGENTS.md
@@ -15,6 +15,7 @@ Each recipe is one script, run from its URL with `hf jobs uv run`. There is noth
 - `source`: `header` carries its launch settings; `documented` is an example from the script's docs; `flags-needed` has no command, so read the script first.
 - Read a script's docstring and options: `curl -s  | head -100`. Running it locally may fail on GPU-only dependencies.
 - Each task's README explains its recipes, for example https://huggingface.co/datasets/uv-scripts/ocr. OCR also has `models.json`, with language coverage and support levels.
+- Fuller guides: [uv-recipes](https://github.com/davanstrien/uv-scripts/tree/main/skills/uv-recipes) and, for few-shot fine-tuning, [setfit-on-jobs](https://github.com/davanstrien/uv-scripts/tree/main/skills/setfit-on-jobs).
 
 ## Run it
 
diff --git a/run-on-jobs/index.html b/run-on-jobs/index.html
index b194797..2c955ea 100644
--- a/run-on-jobs/index.html
+++ b/run-on-jobs/index.html
@@ -85,7 +85,7 @@
 
    Use Hub models on your own data
    
 
    Run a model over your whole dataset, or fine-tune it on your own examples. Each command starts a job on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
 
    
-
    
+
    
 
    Read seven scanned NASA pages with GLM-OCR and save the text to a new public dataset on your account. Add --private to keep it private.
    hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
     uv-scripts/ocr-demo your-username/ocr-demo-results
    The scan
    Scanned page listing Space Shuttle foods
    What the Job wrote
    Space Shuttle Food and Beverage List
@@ -223,6 +223,11 @@ 
    Use Hub models on your own data
    
     https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings.py \
     stanfordnlp/imdb your-username/imdb-embeddings \
     --column text --max-samples 100 --private
    One L4 GPU embeds about 900 rows per second with all-MiniLM-L6-v2.
    
+
    Train a news topic classifier from 16 examples, with no GPU, and save the model to your account.
    hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-setfit.py \
+    fancyzhx/ag_news your-username/ag-news-setfit \
+    --num-samples 4 --max-train-pool 2000 --max-eval-samples 100 \
+    --max-seq-length 128 --sampling-strategy undersampling --max-minutes 5
    Our run: 0.77 accuracy on 100 test articles, against 0.32 for always guessing the most common topic. Training took 20 seconds on cpu-basic.
    
 
    
 
    You need the hf CLI (1.32 or newer), a login and pay-as-you-go credit. Add --dry-run to see the GPU and settings without starting a job.
    
 
    Find your model
    
diff --git a/tools/build-catalog.py b/tools/build-catalog.py
index f588c56..a387360 100755
--- a/tools/build-catalog.py
+++ b/tools/build-catalog.py
@@ -370,6 +370,7 @@ def render_agents():
 - `source`: `header` carries its launch settings; `documented` is an example from the script's docs; `flags-needed` has no command, so read the script first.
 - Read a script's docstring and options: `curl -s  | head -100`. Running it locally may fail on GPU-only dependencies.
 - Each task's README explains its recipes, for example https://huggingface.co/datasets/uv-scripts/ocr. OCR also has `models.json`, with language coverage and support levels.
+- Fuller guides: [uv-recipes](https://github.com/davanstrien/uv-scripts/tree/main/skills/uv-recipes) and, for few-shot fine-tuning, [setfit-on-jobs](https://github.com/davanstrien/uv-scripts/tree/main/skills/setfit-on-jobs).
 
 ## Run it
 
diff --git a/tools/run-on-jobs-template.html b/tools/run-on-jobs-template.html
index d03f679..5fee821 100644
--- a/tools/run-on-jobs-template.html
+++ b/tools/run-on-jobs-template.html
@@ -85,7 +85,7 @@
 
    Use Hub models on your own data
    
 
    Run a model over your whole dataset, or fine-tune it on your own examples. Each command starts a job on Hugging Face Jobs and saves the results to the Hub. There is nothing to clone.
    
 
    
-
    
+
    
 
    Read seven scanned NASA pages with GLM-OCR and save the text to a new public dataset on your account. Add --private to keep it private.
    hf jobs uv run \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
     uv-scripts/ocr-demo your-username/ocr-demo-results
    The scan
    Scanned page listing Space Shuttle foods
    What the Job wrote
    Space Shuttle Food and Beverage List
@@ -223,6 +223,11 @@ 
    Use Hub models on your own data
    
     https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings.py \
     stanfordnlp/imdb your-username/imdb-embeddings \
     --column text --max-samples 100 --private
    One L4 GPU embeds about 900 rows per second with all-MiniLM-L6-v2.
    
+
    Train a news topic classifier from 16 examples, with no GPU, and save the model to your account.
    hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/classification/raw/main/train-setfit.py \
+    fancyzhx/ag_news your-username/ag-news-setfit \
+    --num-samples 4 --max-train-pool 2000 --max-eval-samples 100 \
+    --max-seq-length 128 --sampling-strategy undersampling --max-minutes 5
    Our run: 0.77 accuracy on 100 test articles, against 0.32 for always guessing the most common topic. Training took 20 seconds on cpu-basic.
    
 
    
 
    You need the hf CLI (1.32 or newer), a login and pay-as-you-go credit. Add --dry-run to see the GPU and settings without starting a job.
    
 
    Find your model