README.md

metadata

title: BitNet Text Summarizer
emoji: 📝
colorFrom: purple
colorTo: indigo
sdk: docker
app_port: 8501
pinned: false
short_description: Streamlit summarizer using microsoft/bitnet-b1.58-2B-4T

📝 BitNet Text Summarizer (Streamlit)

An open‑source text summarizer running on microsoft/bitnet-b1.58-2B-4T with a map‑reduce strategy for long inputs. Deployed on Hugging Face Spaces using Docker + Streamlit.

Features

• BitNet (local) inference inside the Space (GPU recommended)
• HF Inference API fallback (optional) if you provide HF_TOKEN
• Map‑Reduce summarization for long documents
• Adjustable generation parameters (temperature, top_p, token budgets)

Quickstart

1. Create/Duplicate a Space with SDK: Docker.
2. Add files from this portfolio: Dockerfile, requirements.txt, README.md, and src/ folder.
3. In Settings → Hardware, pick a GPU for faster startup (CPU works but is slower).
4. (Optional) Add a repo secret HF_TOKEN to enable the fallback engine.
5. Open the app, paste text, and click Summarize.

Local Dev (optional)

python -m venv .venv && source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -r requirements.txt
streamlit run src/streamlit_app.py