| --- |
| title: "PASITA: plain text to Markdown" |
| emoji: 📝 |
| colorFrom: gray |
| colorTo: red |
| sdk: gradio |
| sdk_version: 6.26.0 |
| app_file: app.py |
| short_description: Faithful text to Markdown with the 88M PASITA model |
| python_version: "3.12" |
| --- |
| |
| # PASITA: plain text to faithful Markdown |
|
|
| Demo Space for [**OpceanAI/PASITA**](https://huggingface.co/OpceanAI/PASITA), an 88M-parameter |
| decoder-only language model trained from scratch for one task: turning plain text into valid |
| Markdown without inventing content. It adds structure, it does not rewrite the source. |
|
|
| Paste OCR output, pasted HTML, meeting notes, a report or an article, and copy the result from |
| the rendered view or download it as `pasita-*.md` from the source view. The Details tab of each |
| run reports the route taken, numbers kept, content coverage and time. |
|
|
| ## How this Space converts |
|
|
| The app runs a small harness around the model instead of calling it once: |
|
|
| 1. Free-form prose goes to PASITA. Three samples are generated with a fixed seed |
| (`temperature 0.3`, adaptive length, adjustable 1-3 in Settings), each one is post-processed, |
| and the reranker keeps the best candidate by number fidelity, content coverage, list and table |
| preservation and GFM validity. |
| 2. Inputs detected as HTML or as a delimited data dump are converted with a deterministic |
| converter. The model does not handle raw markup or table synthesis at this size, and the demo |
| does not pretend otherwise. |
| 3. The status line under the header reports what actually happened for each run: which path ran, |
| how many of the source numbers survived, content coverage and elapsed time. |
|
|
| The same pipeline is available three ways: |
|
|
| 1. **OpenAI-style REST API**: point any OpenAI SDK at `https://opceanai-pasita.hf.space/v1` |
| (`/v1/chat/completions` with streaming, `/v1/convert`, `/v1/models`). Interactive docs at |
| [`/docs`](https://opceanai-pasita.hf.space/docs). No API key required. |
|
|
| ```python |
| from openai import OpenAI |
| |
| client = OpenAI(base_url="https://opceanai-pasita.hf.space/v1", api_key="not-needed") |
| resp = client.chat.completions.create( |
| model="pasita-v1", |
| messages=[{"role": "user", "content": "<plain text>"}], |
| ) |
| print(resp.choices[0].message.content) |
| ``` |
|
|
| 2. **MCP server**: add `https://opceanai-pasita.hf.space/gradio_api/mcp/` to any MCP client. |
| 3. **Gradio API**: the `convert` endpoint, documented in the `?view=api` drawer. |
|
|
| Sampling temperature is fixed at 0.3 because that is the measured recipe; both REST |
| endpoints accept an optional `max_tokens` cap. The Gradio v2 call endpoint also accepts |
| the legacy `{"data": [...]}` body shape for compatibility. |
|
|
| Errors use the OpenAI envelope (`{"error": {"message", "type", "param", "code"}}`); ZeroGPU |
| quota exhaustion answers with HTTP 429. |
|
|
| ## What works, what does not |
|
|
| Valid regime: medium and long documents, in Spanish or English. The model is fragile on 1 to 3 |
| line inputs, and it can truncate digits, drop secondary data or continue past completion. The |
| harness cuts degenerate continuations and repair headings, but it cannot add information the |
| model never generated. |
|
|
| ## Model |
|
|
| | | | |
| |---|---| |
| | Parameters | 88,099,584 (~88M) in bfloat16 | |
| | Architecture | `LlamaForCausalLM`, 12 layers, hidden 768, GQA 12Q/4KV, ctx 2048 | |
| | Tokenizer | custom 16k byte-level BPE | |
| | Training | SFT + DPO + GRPO on markdown-derived ES/EN data | |
| | License | Apache-2.0 | |
|
|
| Benchmarks on the held-out PASITA-bench-1000, as reported on the model card: GFM validity 0.956, |
| faithfulness 0.927, semantic faithfulness 0.888, table fidelity 1.000. |
|
|
| ## Files |
|
|
| - `app.py`: Gradio interface, ZeroGPU handlers and the OpenAI-style `/v1` API. |
| - `pasita.py`: prompt, generation, post-processing, scoring and reranking harness. |
| - `DESIGN.md`: visual direction, motion settings and the reason for each decision. |
| - `DEPLOY.md`: step-by-step deployment guide for a local PC and for Hugging Face Spaces. |
|
|
| ## Links |
|
|
| - Model card: https://huggingface.co/OpceanAI/PASITA |
| - API (OpenAI-style): https://opceanai-pasita.hf.space/v1 and docs at /docs |
| - MCP: https://opceanai-pasita.hf.space/gradio_api/mcp/ |
| - Gradio API: append `?view=api` to this Space URL. |
| |
