| # 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. 🐾 | |