README.md

metadata

title: Tiny Press
emoji: 📊
colorFrom: indigo
colorTo: gray
sdk: gradio
sdk_version: 6.15.2
python_version: '3.12'
app_file: app.py
pinned: false
license: mit
short_description: Compress any text to a token budget locally.
models:
  - Qwen/Qwen2.5-1.5B-Instruct
tags:
  - gradio
  - build-small-hackathon
  - thousand-token-wood
  - text-compression
  - prompt-optimization
  - local-inference

TinyPress — Prompt Compression Engine

HuggingFace Build Small Hackathon · Track: Thousand Token Wood

The constraint is the feature. Give TinyPress a long piece of text, set a token budget, and get back a compressed version that still carries the meaning — scored, saved, and diffed so you can see exactly what was kept and what was shed.

No cloud. No API bill. Two small models running quietly on your machine.

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Demo

💻 Try @ https://huggingface.co/spaces/build-small-hackathon/tiny-press

👩‍💻Notebook @ https://colab.research.google.com/github/SriharshaCR/tiny-press/blob/task/bootstrap/tinypress_colab.ipynb

Social Media Posts

• https://x.com/sriharsha_cr/status/2065662576684650879
• https://www.linkedin.com/posts/sriharsha-cr_tinypress-prompt-compression-engine-activity-7471426128331624448-aKfe

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Why this fits Thousand Token Wood

Working inside a tight token budget is not a limitation to work around — it is the problem worth solving. LLM context windows are finite, prompt costs are real, and bloated inputs degrade output quality. TinyPress treats the token count as a hard constraint and makes compression the primary interaction: you set the budget, the model meets it, and a quality score tells you how much meaning survived.

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Features

🗜️ Token-budget compression	Set a target (100–1000 tokens) and compress to exactly that budget
📊 Quality score	Cosine similarity between original and compressed text — 0 to 1, higher is better
🟢🔴 Live readiness banner	Green when input is over budget and compression will run; red when already within budget
🔍 Token highlight panel	Every token rendered as a colour-coded chip so you can see where your budget is going
🔀 Model hot-swap	Switch the compression LLM mid-session without a restart (5 curated models, or any HF model ID)
🎯 Embedder hot-swap	Switch the scoring embedder with per-model trade-off info (speed vs quality vs RAM)
👍👎 Feedback capture	Rate every result, add an optional text note — saved instantly to SQLite
📜 Run history	Every compression persisted locally with full metrics and configurable column visibility
🔎 Side-by-side diff	Word-level colour diff — dropped (red), rewritten (amber), inserted (green), unchanged (plain)

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Models

Role	Default	Alternatives
Compression LLM	Qwen/Qwen2.5-1.5B-Instruct	Qwen2.5-0.5B, SmolLM2-1.7B, Phi-3.5-mini, Llama-3.2-1B
Quality scorer	sentence-transformers/all-MiniLM-L6-v2	mpnet-base, bge-small, bge-base, mxbai-large, gte-Qwen2-1.5B

All models are open-weight and under 32B. Everything runs locally — no API calls, no data leaves your machine.

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Get started

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

pip install -r requirements.txt
python app.py

Open http://localhost:7860. That's it.

Run it in Colab: open tinypress_colab.ipynb — it installs dependencies, loads the models, and launches a public Gradio share URL. GPU runtime recommended for faster inference.

Optional environment overrides:

Variable	Default	Description
LLM_MODEL	Qwen/Qwen2.5-1.5B-Instruct	Compression model
EMBEDDER_MODEL	sentence-transformers/all-MiniLM-L6-v2	Scoring embedder
DB_PATH	tinypress.db	SQLite database path
PORT	7860	Gradio server port

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Hardware

	Minimum	Recommended
RAM	8 GB	16 GB
VRAM	CPU-only works	4 GB GPU speeds up inference
Disk	~4 GB	~4 GB

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Architecture

Input text + token budget
        │
  core/compressor.py     — builds prompt, calls LLM, hard-trims if it overshoots
        │
  models/model_loader.py — Qwen2.5-1.5B (or swapped model), loaded once, reused
        │
  core/scorer.py         — cosine similarity via sentence-transformer embedder
        │
  db/store.py            — saves run to SQLite
        │
  ui/compress_tab.py     — shows result, metrics, feedback UI

Thin UI layer — Gradio handlers pass inputs to core/, return outputs. All logic lives in core/ and db/.

Full docs: Architecture · Setup · Get Started · Folder Structure

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About

Built by Sriharsha C R — AI Engineer and Cloud Native developer.