README.md

metadata

title: Transformers Playground
emoji: 🛝
colorFrom: yellow
colorTo: gray
sdk: gradio
sdk_version: 6.30.0
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_expiration_minutes: 480
hf_oauth_scopes:
  - jobs
  - read-repos
short_description: Copy official transformers snippets or run them on HF Jobs

Transformers Playground

Pick any transformers model on the Hub, get the usage snippets from its model_doc page (adapted to your checkpoint), copy them, or run them on Hugging Face Jobs with your own account.

How it works

• indexer.py sparse-clones docs/source/en/model_doc from transformers, extracts the <hfoptions id="usage"> tabs and usage code blocks, drops training and quantization examples (v1 is inference only), and writes snippets.json.
• app.py matches a model's config.json model_type to its doc page, swaps the doc's checkpoint for the selected model, adds a generic pipeline() snippet from the model's task, and recommends the cheapest Jobs flavor that fits the weights (live prices from list_jobs_hardware).
• Runs go through run_job with the user's OAuth token, so each user pays for their own compute. Jobs need a PRO, Team or Enterprise account.

Setup

1. Create the Space from these files. On first boot it builds the index itself (about 30 seconds).
2. Optional but recommended: publish the index as a dataset and refresh it nightly, then set a Space variable INDEX_REPO=<you>/transformers-snippets.

hf jobs scheduled run @daily python:3.12 --secrets HF_TOKEN -- bash -c \
  "pip install -q huggingface_hub && \
   curl -sL https://huggingface.co/spaces/<you>/transformers-playground/raw/main/indexer.py -o indexer.py && \
   python indexer.py --push <you>/transformers-snippets"