import gradio as gr

ABOUT = """
# HyperPEER

**Compressing a large model into a small student that runs on a single 16 GB consumer GPU** — by replacing each transformer layer's feed-forward / mixture-of-experts block with a *hypernetwork that generates a per-token low-rank expert*, instead of storing a full expert bank. Attention and embeddings are inherited and frozen; only the generator is trained, by feature distillation against the teacher's per-layer outputs.

The footprint becomes the size of the generator, not the size of everything it can generate.

Proof-of-concept target: **google/gemma-4-26B-A4B**, compressed to run on one consumer card.

## Validated on a 3B testbed (single 16 GB card)

- Generating experts costs **no quality** versus storing them: held-out perplexity **25.9** (generated) vs **26.2** (stored) at convergence.
- A **larger hypernetwork making smaller experts wins** — capacity has to live in the generator.
- **Feature distillation** (matching each block's output to the teacher's) beats next-token prediction and logit-KL.
- Runs in about **2.85 GB of VRAM**, under half the teacher's, at a third of the parameters.

## What's in this repo

- `gemma/` — the Gemma-4-26B capture + layer-local distillation pipeline.
- `testbed/` — the 3B validation code and result JSONs.
- `PHASE1_REPORT.md`, `PHASE2_PLAN.md` — the report and the full plan.

The recipe is validated end to end; the remaining step is the Gemma-4-26B run. The blocker is purely compute. Everything will be released openly.

— Mikey Bee
"""

with gr.Blocks(title="HyperPEER") as demo:
    gr.Markdown(ABOUT)

if __name__ == "__main__":
    demo.launch()