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--- |
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license: agpl-3.0 |
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tags: |
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- text-generation |
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- education |
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- curriculum |
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- course-design |
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pipeline_tag: text-generation |
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language: |
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- en |
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datasets: |
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- BAAI/Infinity-Instruct |
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base_model: |
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- mistralai/Mistral-7B-Instruct-v0.3 |
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--- |
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# Antlia-Learn-7B |
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## Summary |
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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). |
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## Intended use |
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* Draft micro-courses with one goal, a 3–4 point outline (minutes in parentheses), and 1–2 short practice tasks. |
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* Draft long-form course outlines with section objectives, activities, checkpoints, and assessment ideas. |
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* Rewrite learning objectives in plain language and propose short Socratic prompts. |
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**Out of scope** |
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Safety-critical or regulated domains (medical, legal, financial), processing of personal/regulated data, and unsupervised deployment without human review. |
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## Inputs and outputs |
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**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”). |
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**Output:** English text: for micro-courses, a goal + outline + practice tasks; for longer courses, multi-section plans with objectives, activities, checkpoints, and assessment ideas. |
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## Recommended decoding |
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The repository includes `generation_config.json` tuned for concise, low-repetition drafting: |
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* `do_sample: true` |
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* `temperature: 0.35` |
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* `top_p: 0.85` |
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* `top_k: 40` |
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* `repetition_penalty: 1.3` |
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* `no_repeat_ngram_size: 5` |
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* `max_new_tokens: 120` |
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* `min_new_tokens: 16` |
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## Model details |
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* **Architecture:** Decoder-only transformer, \~7B parameters. |
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* **Precision:** Standard HF weights; quantized inference supported downstream. |
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* **Alignment:** Supervised instruction-tuning focused on clear, compact educational scaffolds; no RLHF in this release. |
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## Training |
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Supervised instruction-tuning across staged passes to encourage concise goals, minute-budgeted outlines, checkpoints, and practice prompts. Exact mixtures, volumes, and hyperparameters are proprietary. |
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## Limitations and risks |
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* Not a knowledge base; facts may be incomplete or outdated. |
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* Tends toward brevity; for richer syllabi, include explicit section counts, activities, and assessment style. |
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* Can produce generic phrasing for very broad prompts—be specific. |
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## Safety |
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All outputs require human review for accuracy and appropriateness. Do not use to generate harmful, discriminatory, or unsafe content. |
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## License |
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* **Weights:** GNU Affero General Public License v3.0 |
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* **Use:** Your use must comply with the license and any applicable upstream licenses. |
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## Third-party notice (required) |
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This model’s training process made use of content from **BAAI/Infinity-Instruct**, licensed under **Creative Commons Attribution-ShareAlike 4.0 International**. |
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License: [https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/) |
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Dataset page: [https://huggingface.co/datasets/BAAI/Infinity-Instruct](https://huggingface.co/datasets/BAAI/Infinity-Instruct) |
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## Contact |
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Maintainer: **OOMU** — **[antlia@oomu.ai](mailto:antlia@oomu.ai)** |
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