---
license: agpl-3.0
tags:
- text-generation
- education
- curriculum
- course-design
pipeline_tag: text-generation
language:
- en
datasets:
- BAAI/Infinity-Instruct
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
---

# Antlia-Learn-7B

## Summary

Antlia-Learn-7B is a 7B-parameter instruction-tuned model for instructional design. It drafts concise short micro-courses and complex long-form course outlines, including learning outcomes, section structure, short practice activities, and lightweight checkpoints. This model is released under the GNU Affero General Public License v3.0 (AGPL-3.0).

## Intended use

* Draft micro-courses with one goal, a 3–4 point outline (minutes in parentheses), and 1–2 short practice tasks.
* Draft long-form course outlines with section objectives, activities, checkpoints, and assessment ideas.
* Rewrite learning objectives in plain language and propose short Socratic prompts.

**Out of scope**
Safety-critical or regulated domains (medical, legal, financial), processing of personal/regulated data, and unsupervised deployment without human review.

## Inputs and outputs

**Input:** English prompts describing topic and scope (e.g., “Create a micro-course on threading a needle” or “Design a 3–4 hour course on building a car engine for advanced beginners”).
**Output:** English text: for micro-courses, a goal + outline + practice tasks; for longer courses, multi-section plans with objectives, activities, checkpoints, and assessment ideas.

## Recommended decoding

The repository includes `generation_config.json` tuned for concise, low-repetition drafting:

* `do_sample: true`
* `temperature: 0.35`
* `top_p: 0.85`
* `top_k: 40`
* `repetition_penalty: 1.3`
* `no_repeat_ngram_size: 5`
* `max_new_tokens: 120`
* `min_new_tokens: 16`

## Model details

* **Architecture:** Decoder-only transformer, \~7B parameters.
* **Precision:** Standard HF weights; quantized inference supported downstream.
* **Alignment:** Supervised instruction-tuning focused on clear, compact educational scaffolds; no RLHF in this release.

## Training

Supervised instruction-tuning across staged passes to encourage concise goals, minute-budgeted outlines, checkpoints, and practice prompts. Exact mixtures, volumes, and hyperparameters are proprietary.

## Limitations and risks

* Not a knowledge base; facts may be incomplete or outdated.
* Tends toward brevity; for richer syllabi, include explicit section counts, activities, and assessment style.
* Can produce generic phrasing for very broad prompts—be specific.

## Safety

All outputs require human review for accuracy and appropriateness. Do not use to generate harmful, discriminatory, or unsafe content.

## License

* **Weights:** GNU Affero General Public License v3.0
* **Use:** Your use must comply with the license and any applicable upstream licenses.

## Third-party notice (required)

This model’s training process made use of content from **BAAI/Infinity-Instruct**, licensed under **Creative Commons Attribution-ShareAlike 4.0 International**.
License: [https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/)
Dataset page: [https://huggingface.co/datasets/BAAI/Infinity-Instruct](https://huggingface.co/datasets/BAAI/Infinity-Instruct)

## Contact

Maintainer: **OOMU** — **[antlia@oomu.ai](mailto:antlia@oomu.ai)**