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.

Each recipe is a uv script: one Python file that lists its own dependencies, so it runs straight from its URL.

Find your model · New to Jobs? Try an example below, or read the quickstart · Follow uv-scripts for new recipes

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

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.

More recipes and options in uv-scripts/ocr.

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.

More recipes and options in uv-scripts/transcription.

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.

More recipes and options in uv-scripts/embeddings.

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.

More recipes and options in uv-scripts/classification.

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, and run hf jobs uv run --help for all options.

Find your model

Replace the capitalised placeholders and your-username. Check the script if you're unsure what a value should be.

OCR

Transcription

Segmentation

Object detection

Video

Classification

Entities

Embeddings

Bulk inference

Fine-tuning

Change a recipe

Download a script, edit it and run your copy: hf jobs uv run ./my-script.py. Flags before the script, such as --flavor, change the hardware.

Learn more

For agents

Read AGENTS.md and recipes.json.