README.md

metadata

title: Router Control Room (ZeroGPU)
emoji: 🛰️
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit
short_description: ZeroGPU UI for CourseGPT-Pro router checkpoints

🛰️ Router Control Room — ZeroGPU

This Space exposes the CourseGPT-Pro router checkpoints (Gemma3 27B + Qwen3 32B) with an opinionated Gradio UI. It runs entirely on ZeroGPU hardware using 8-bit loading so you can validate router JSON plans without paying for dedicated GPUs.

✨ What’s Included

• Router-specific prompt builder – inject difficulty, tags, context, acceptance criteria, and additional guidance into the canonical router system prompt.
• Two curated checkpoints – Router-Qwen3-32B-8bit and Router-Gemma3-27B-8bit, both merged and quantized for ZeroGPU.
• JSON extraction + validation – output is parsed automatically and checked for the required router fields (route_plan, todo_list, metrics, etc.).
• Raw output + prompt debug – inspect the verbatim generation and the exact prompt string sent to the checkpoint.
• One-click clear – reset the UI between experiments without reloading models.

🔄 Workflow

1. Describe the user task / homework prompt in the main textbox.
2. Optionally provide context, acceptance criteria, and extra guidance.
3. Choose the difficulty tier, tags, model, and decoding parameters.
4. Click Generate Router Plan.
5. Review:
   • Raw Model Output – plain text returned by the LLM.
   • Parsed Router Plan – JSON tree extracted from the output.
   • Validation Panel – confirms whether all required fields are present.
   • Full Prompt – copy/paste for repro or benchmarking.

If JSON parsing fails, the validation panel will surface the error so you can tweak decoding parameters or the prompt.

🧠 Supported Models

Name	Base	Notes
Router-Qwen3-32B-8bit	Qwen3 32B	Best overall acceptance on CourseGPT-Pro benchmarks.
Router-Gemma3-27B-8bit	Gemma3 27B	Slightly smaller, tends to favour math-first plans.

Both checkpoints are merged + quantized in the Alovestocode namespace and require HF_TOKEN with read access.

⚙️ Local Development

cd Milestone-6/router-agent/zero-gpu-space
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
export HF_TOKEN=hf_xxx
python app.py

📝 Notes

• The app always attempts 8-bit loading first (bitsandbytes). If that fails, it falls back to bf16/fp16/fp32.
• The UI enforces single-turn router generations; conversation history and web search are intentionally omitted to match the Milestone 6 deliverable.
• If you need to re-enable web search or more checkpoints, extend MODELS and adjust the prompt builder accordingly.
• Benchmarking: run python Milestone-6/router-agent/tests/run_router_space_benchmark.py --space Alovestocode/ZeroGPU-LLM-Inference --limit 32 (requires pip install gradio_client) to call the Space, dump predictions, and evaluate against the Milestone 5 hard suite + thresholds.