---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- text-generation
- information-extraction
- structured-output
- json
- ner
- small-language-model
- edge-ai
- on-device
- ollama
- gguf
- calendar
pipeline_tag: text-generation
library_name: gguf
model-index:
- name: AILO-152M-Events-EN
  results: []
---

# AILO-152M-Events-EN  Natural language → calendar-event JSON ⚡

> **A 152M-parameter specialist** that turns an English sentence into a clean **event JSON** — title, date, time, location, participants — and runs on almost anything.

This is a **task-specialist** built on AILO-152M. It does one thing and does it well: read an event description in plain English and output structured JSON. Tiny, fast, deterministic — ideal as the parsing brain of a calendar app, assistant, or automation.

```bash
ollama run Alieno/ailo-152m-events-en
>>> Lunch with Sarah tomorrow at 1pm at the new Italian place
{"title": "lunch", "date": "tomorrow", "time": "13:00", "location": "the new Italian place", "participants": ["Sarah"]}
```

## Schema

```json
{"title": str, "date": str|null, "time": "HH:MM"|null, "location": str|null, "participants": [str]}
```

- **time is normalized** to 24h `HH:MM` — *"at 3pm"* → `15:00`, *"half past 7"* → `07:30`, *"at noon"* → `12:00`.
- **date is extracted as written** (*"tomorrow"*, *"next Friday"*, *"March 15"*) — it is **not** resolved to a calendar date (the model has no clock).
- Missing fields → `null`; no participants → `[]`.

## Benchmarks (held-out test set, 1500 unseen examples)

| Metric | Score |
|---|---|
| **Valid JSON** | **100%** |
| Full object exact-match | 83.7% |
| `title` | 97.3% |
| `date` | 88.3% |
| `time` (normalized) | **100%** |
| `location` | 97.0% |
| `participants` | 97.3% |

It also **generalizes to real, free-form sentences** (it learned to *copy spans*, not classify to a fixed list): *"Call mom tonight"* → `{"title": "call mom", ...}`, *"Birthday party Saturday at Jake's place with everyone"* → `{"title": "birthday party", "location": "Jake's place", "participants": ["everyone"]}`.

## Use it in an app

```bash
curl http://localhost:11434/api/chat -d '{
  "model": "Alieno/ailo-152m-events-en",
  "messages": [{"role": "user", "content": "Quick sync with the dev team Monday 10am on Zoom"}],
  "stream": false,
  "options": {"temperature": 0.0}
}'
# -> {"title":"quick sync","date":"Monday","time":"10:00","location":"on Zoom","participants":["the dev team"]}
```

Tags: `:latest` / `:q8_0` (best, 156 MB) · `:q4_k_m` (smallest, 97 MB) · `:f16` (291 MB).
Run with **temperature 0** for deterministic JSON. `repeat_penalty` is kept low (1.05) so JSON punctuation isn't penalized.

## Details

| Property | Value |
|---|---|
| Parameters | 151.9M |
| Architecture | Decoder-only Transformer (LayerNorm · RoPE · SwiGLU), 12L/768/12H, ctx 512 |
| Base | AILO-152M-v2 → specialized on event-extraction |
| Training | 26k synthetic (sentence → JSON) pairs, open/compositional vocabulary (~2000 unique titles) so the model learns to **copy spans** |
| Formats | GGUF (q4_k_m, q8_0, f16) + PyTorch |

## Limitations

- **Dates are not resolved** to absolute dates — the phrase is extracted as-is.
- Unusual date phrasings (*"the 23rd of March"*) may drop the day number.
- Single event per input; English only; 512-token context (short sentences).
- For exact calendar entries, resolve the relative date downstream with the user's timezone/clock.

## License & contact

Dual-license: **CC BY-NC-SA 4.0** (free for research/education/personal) + **commercial** by separate agreement.
**Riccardo Sparacino** — [LinkedIn](https://www.linkedin.com/in/riccardo-sparacino-developer-php-javascript-mysql-app-ios-android/)

```bibtex
@misc{ailo152m_events_en_2026,
  title  = {AILO-152M-Events-EN: A tiny natural-language-to-event-JSON specialist},
  author = {Sparacino, Riccardo}, year = {2026},
  note   = {Dual-licensed CC BY-NC-SA 4.0 / commercial}
}
```