Update README.md

README.md

@@ -5,7 +5,7 @@ Open, practical AI for learning and teaching — from data tools to fine‑tuned
 - 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
+  - Canis.lab — dataset and tooling suite for building Expert Language Models
 - Values: Classroom‑first design, privacy awareness, reproducibility, and open collaboration
 
 ## Projects
@@ -14,7 +14,7 @@ Open, practical AI for learning and teaching — from data tools to fine‑tuned
 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)
+- 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`
@@ -37,6 +37,7 @@ Why: Good tutors start with good data. Canis.lab standardizes data flow so educa
   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