README.md

metadata

title: ZeroGPU Batch Embedder
emoji: 🚀
colorFrom: blue
colorTo: green
sdk: gradio
app_file: app.py
pinned: false
short_description: On-demand H200 batch embeddings for the RAG benchmark

ZeroGPU batch embedder

Free H200 batch embedding for the GoodKnowledge RAG benchmark. Offloads the corpus-embedding step (the local-Metal bottleneck) so the full 512k EnterpriseRAG-Bench is runnable.

Deploy

1. Create a ZeroGPU Space (requires HF PRO), Gradio SDK.
2. Add HF_TOKEN as a Space secret (embeddinggemma is gated).
3. Push app.py + requirements.txt, set Hardware → ZeroGPU in Space settings.

Use from the bench harness

Set the embedder to hf-zerogpu:<your-username>/<space-name> (see _zerogpu_embed in experiments/bench/bench_sweep.py). The harness batches texts to fit the 60–120 s per-call limit and caches the returned vectors. PRO = 25 min H200/day; 512k docs ≈ 2–4 min of GPU.