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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`.
```bash
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"
```
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