---
title: Beexly Studio Chat
emoji: 🧠
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
short_description: Chat with an open-weights model on free ZeroGPU
tags:
  - zero-gpu
---

# Beexly Studio Chat (ZeroGPU)

Personal chat tool. **Not connected to any Galaxy Sports Edge product, data,
or prediction engine — this is Garrett's own tooling, unrelated to GSE.**

- **Model**: `Qwen/Qwen2.5-72B-Instruct`, loaded 4-bit (nf4, bitsandbytes)
- **License**: `qwen` (Alibaba's custom Qwen license, confirmed from the
  model card metadata — not Apache-2.0/MIT; read the full terms on the
  [model card](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) before any
  commercial or redistribution use, this Space is personal-use only)
- **Measured limits** (live-tested, see the deploy report): packed footprint
  44.5GB, ~13.4 tokens/sec including overhead, max 1024 output tokens per
  reply (calibrated so a full-length reply stays under ZeroGPU's 120s grant
  with margin — a 1024-token reply measured 84.0s), cold start ~4-5 min
  (full weight re-download; no persistent storage on the free tier)

## Features

- System-prompt picker: General / Code / Writing / Structured JSON output
- Streaming responses
- Copy button on every reply (hover a message)
- Chat history capped at the last 12 turns to bound context growth

## Tried and reverted: document-grounded chat (RAG)

A Documents tab (upload PDF/DOCX/PPTX, parsed via `docling`, retrieved with
BM25) was built and pushed, then reverted. Real cause, not a code bug: this
Space's own `MODEL_ID` (72B) downloads **145GB** of raw weights before
4-bit quantization, against a **150GB per-Space storage cap** — docling's
own supplementary models pushed it over, and the container was evicted
("Workload evicted, storage limit exceeded (150G)"). There isn't enough
storage headroom left on this Space for anything else meaningful. If
document-grounded chat is wanted later, it needs either a smaller base
model here, or to live on a Space that isn't already this close to the
storage ceiling (e.g. `studio-media`, whose models total well under half
that footprint).

## Fixed: a real gradio_client bug

ZeroGPU force-pins `gradio[oauth]==5.9.1`. Its bundled `gradio_client`
(1.5.2) has a real bug: its JSON-schema parser assumes every schema node is
a `dict`, but valid JSON Schema allows a bare boolean (e.g.
`additionalProperties: true`) — this crashed *every* API call to this
Space with `TypeError: argument of type 'bool' is not iterable`. The
earlier fix (pinning `pydantic==2.10.3`, which happens to avoid generating
that schema shape) was a workaround, not a fix, and it broke `docling`'s
own serialization elsewhere. The real fix, now in `app.py`: a small
monkeypatch on `gradio_client.utils._json_schema_to_python_type` that
handles non-dict schema nodes directly. No pydantic pin needed.

## Swapping the model

Change `MODEL_ID` in `app.py` to any other instruct-tuned model on the Hub.
Verified working sizes so far (4-bit, packed footprint): 32B → ~20.7GB,
72B → 44.5GB, both loading and generating cleanly. If a bigger model OOMs
or times out, the previous known-good `MODEL_ID` is
`Qwen/Qwen2.5-32B-Instruct`.