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+# CanisAI
+
+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 tutors
+- 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 “all” (blended)
+- 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. 🐾