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imagination-ai

Multimodal Imagination model stack: Gradio apps, Colab/FastAPI backends, training tooling, and an optional Next.js chat UI. This repo holds versioned runtimes under versions/ plus shared modules/ (weights and assets are not committed—see below).

Repository layout

Path Role
versions/v1.1.2/ Gradio app entry app.py, runtime imagination_runtime/
versions/v1.2.1/ Gradio + optional QLoRA training (train_imagination.py)
versions/v1.3/ Colab/FastAPI backend (colab_backend.py, imagination_runtime/chat_http.py)
v0-imagination-ui/ Next.js frontend (separate git history; submodule-style nested repo)
modules/ Expected on-disk model trees (CAD, reasoning, research, image, …)
.github/workflows/ CI (e.g. validate-v112.yml)

Agent-oriented notes for Cursor and automation live in AGENTS.md.

Requirements

  • Python 3.10+ for app code under versions/
  • Node.js 20+ (or pnpm/npm) only if you work on v0-imagination-ui/

Environment: IMAGINATION_ROOT

Runtimes expect IMAGINATION_ROOT to point at a directory that contains the expected modules/ layout (and temp/ as needed). Without real weights you can use a minimal fake tree for import/UI smoke tests—see AGENTS.md and version-specific READMEs.

Quick starts (by version)

Gradio (v1.1.2)

cd versions/v1.1.2
pip install -r requirements.txt
export IMAGINATION_ROOT=/path/to/your/root
python app.py

Gradio + training hooks (v1.2.1)

cd versions/v1.2.1
pip install -r requirements.txt
export IMAGINATION_ROOT=/path/to/your/root
python app.py

FastAPI / Colab (v1.3)

From versions/v1.3/ (after dependencies and IMAGINATION_ROOT are set), see colab_backend.py and imagination_runtime/chat_http.py. Typical flow: run Uvicorn on the FastAPI app and point the Next UI at the public URL via NEXT_PUBLIC_API_URL.

Next.js UI (v0-imagination-ui)

cd v0-imagination-ui
pnpm install   # or npm install
cp .env.example .env.local   # if present; set NEXT_PUBLIC_API_URL to your backend origin
pnpm dev

Contributing & license

Disclaimer

Imagination is a research/engineering stack. Model outputs can be wrong or unsafe; deploy with appropriate safeguards for your use case.

About

A lightweight AI model designed for multimodal inputs, as well as outputs such as images, code, and more. Lightweight model for mobile, search engines, etc.

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