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Running on Zero
Running on Zero
| 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. | |
| --- | |
| ## Demo | |
| [](https://youtu.be/hDbIDtjjiB0) | |
| 💻 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 | |
| --- | |
| ## 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. | |
| --- | |
| ## 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) | | |
| --- | |
| ## 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. | |
| --- | |
| ## Get started | |
| ```bash | |
| 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 | | |
| --- | |
| ## Hardware | |
| | | Minimum | Recommended | | |
| |---|---|---| | |
| | RAM | 8 GB | 16 GB | | |
| | VRAM | CPU-only works | 4 GB GPU speeds up inference | | |
| | Disk | ~4 GB | ~4 GB | | |
| --- | |
| ## 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](docs/architecture.md) · [Setup](docs/setup.md) · [Get Started](docs/get-started.md) · [Folder Structure](docs/folder-structure.md) | |
| --- | |
| ## About | |
| Built by **[Sriharsha C R](https://www.linkedin.com/in/sriharsha-cr)** — AI Engineer and Cloud Native developer. | |
| [](https://www.linkedin.com/in/sriharsha-cr) | |
| [](https://x.com/sriharsha_cr) | |
| [](https://huggingface.co/sriharsha-cr) | |
| [](https://github.com/SriharshaCR) | |