CanisAI: Precise. Efficient. Sustainable.

Open, practical AI for learning and teaching — from synthetic data to fine-tuned tutors you can run locally.

• Mission: Build transparent, modular AI that educators can understand, improve, and trust.
• Focus: Socratic tutoring (questions before answers), register-realistic student language, and classroom-first deployment paths.
• Values: Privacy awareness, reproducibility, honest evaluation, and open collaboration.

Projects

Canis.teach (Gemma 4 — R3)

Gemma 4 tutors trained on messy, multilingual K–12 dialogue — not polished ChatGPT-style prompts.

• Published adapter (51k multi-turn): CanisAI/teach-multilingual-gemma-4-e2b-r3
• Dataset: CanisAI/teach-r3-multilingual (51,870 dialogues, 6 languages)
• Stack: Gemma 4 E2B + Unsloth QLoRA; local serve via llama.cpp / Canis CLI; Canis Studio demo at https://canis.appwrite.network
• Submission repo: https://github.com/crasyK/canis-gemma4good
• Video: https://www.youtube.com/watch?v=QbxPs0jLiZY

Canis.teach (Qwen3 & Ministral — earlier ELMs)

Subject-tuned Qwen3-4B tutors from the original Canis paper line (math, science, humanities, language, generalist).

• Base: Qwen/Qwen3-4B-Instruct-2507
• Artifacts: LoRA adapters and optional merged checkpoints on the Hub under CanisAI/*
• Evidence: R1-era research A/B tooling (base vs math vs generalist) — see paper and lesson/ in the public repo

Canis.lab

Lightweight toolchain to generate, transform, and validate tutoring datasets.

• Role-structured dialogues, quality gates, HF-ready exports
• R3 corpus generated with Gemma 4 26B + Canis.lab seeds/workflows
• GitHub: https://github.com/crasyK/Canis.lab

Get started

Try Gemma 4 R3 (recommended for Gemma 4 Good):

1. Base: unsloth/gemma-4-E2B-unsloth-bnb-4bit
2. Adapter: CanisAI/teach-multilingual-gemma-4-e2b-r3
3. Or follow model/load_adapter.py in the submission repo

Try legacy Qwen3 tutors:

1. Base: Qwen/Qwen3-4B-Instruct-2507
2. Apply a subject LoRA from CanisAI (e.g. generalist / math)

Build data with Canis.lab: https://github.com/crasyK/Canis.lab

Safety and limitations

• Educational support with human oversight — not a replacement for teachers.
• Models may hallucinate or oversimplify; verify critical facts (use curriculum / RAG where needed).
• R3 training data is synthetic; Gemma weights subject to Gemma Terms of Use.
• Comply with local privacy and data-handling policies.

Contribute

• Educators: tasks, rubrics, and feedback on tutoring style
• Researchers: datasets, evals, adapters (especially multilingual)
• Partners: pilots and deployments — contact via GitHub issues on public repos

Teach boldly. Build openly.