# CanisAI: Precise. Efficient. Sustainable.

Open, practical AI for learning and teaching — from data tools to fine‑tuned tutors.

- Mission: Build transparent, modular AI that educators can understand, improve, and trust.
- Projects:
  - Canis.teach — subject‑tuned tutors
  - Canis.lab — dataset and tooling suite for building Expert Language Models
- Values: Classroom‑first design, privacy awareness, reproducibility, and open collaboration

## Projects

### Canis.teach
Fine‑tuned Qwen3‑based models for subject‑aware tutoring dialogs, optimized for clarity, hints, and step‑by‑step support.

- Base: Qwen/Qwen3‑4B‑Instruct‑2507
- Variants: math, science, humanities, language, and generalist
- Artifacts: LoRA adapters (lightweight) and optionally merged checkpoints
- Cards: Model cards include dataset provenance, training setup, and usage guidance
- Tag: `canis-teach`

Why: Students need didactic dialogue, not just short answers. Our models emphasize teaching structure, metacognitive hints, and rubrics‑aligned responses.

### Canis.lab
A lightweight toolchain to generate, transform, and validate tutoring datasets and pipelines.

- Capabilities:
  - Generate and refine dialogue data with role‑structured turns
  - Apply chat templates and unify formatting for HF datasets
- Output: Ready‑to‑train datasets for Expert Language Models (ELM)

Why: Good tutors start with good data. Canis.lab standardizes data flow so educators and researchers can iterate quickly and reproducibly.

## Get started

- Try a Canis.teach model:
  1) Load base model: `Qwen/Qwen3-4B-Instruct-2507`
  2) Apply the chosen subject’s LoRA adapter
  3) Or use the ggufs provided inside of Ollama

- Build with Canis.lab:
  - Check out the Github page: https://github.com/crasyK/Canis.lab 

## Safety and limitations

- Intended for educational support with human oversight.
- May hallucinate or oversimplify; verify critical facts.
- Use RAG or curriculum documents for fact‑heavy topics.
- Comply with local privacy and data‑handling policies.

## Contribute

- Educators: share tasks, rubrics, and feedback to improve tutoring quality.
- Researchers: extend datasets, add evals, or submit fine‑tuned adapters.
- Partners: contact us for pilots, evaluations, or deployments.

Teach boldly. Build openly. 🐾