docs/setup.md

Setup

What you need

Hardware

	Minimum (CPU)	Recommended (GPU)
RAM	8 GB	8 GB+
VRAM	—	4 GB (e.g. NVIDIA T4)
Disk	~4 GB free	~4 GB free
Inference speed	Slow (float32)	Fast (float16, auto device map)

The default model (Qwen2.5-1.5B-Instruct) fits in 4 GB VRAM. Larger models from the dropdown (e.g. Phi-3.5-mini) need more headroom.

Software

• Python 3.10 or above

Network

• Internet required on first run only — model weights (~3.5 GB total) download from HuggingFace and are cached locally
• Fully offline after that

Steps

1. Navigate to the project folder

cd app

2. Create and activate the virtual environment

python -m venv .venv
.venv\Scripts\activate        # Windows
# source .venv/bin/activate   # macOS/Linux

3. Install dependencies

pip install -r requirements.txt

4. Optionally tweak the defaults

You can override any of these via environment variables if needed:

Variable	Default	What it does
LLM_MODEL	Qwen/Qwen2.5-1.5B-Instruct	The model used for compression
EMBEDDER_MODEL	sentence-transformers/all-MiniLM-L6-v2	Used to score compression quality
DB_PATH	tinypress.db	Where the SQLite database lives
PORT	7860	Port the Gradio app listens on

5. Run it

python app.py

The first time you run it, model weights will download from HuggingFace automatically. After that, everything runs from local cache.

Managing dependencies

Installing a new package

pip install <package-name>
pip freeze > requirements.txt

Removing a package

pip uninstall <package-name>
pip freeze > requirements.txt

Always run pip freeze > requirements.txt after any install or uninstall — that keeps the file in sync with what's actually in your environment.

Deactivating the virtual environment

When you're done, just run:

deactivate

That drops you back to your system Python. Next time, activate again with .venv\Scripts\activate before working on the project.

🏠 README.md