Update README.md

README.md

@@ -1,10 +1,96 @@
+<div align="center">
+
+# GoAutomate AI Institute
+### Canadian Sovereign AI, built for public benefit 🍁
+
+**Accessible · Responsible · Canadian‑governed**
+
+[Website](https://goautomate.ai) · [Collaborate](mailto:info@goautomate.ai)
+
+</div>
+
+---
+
+The **GoAutomate AI Institute** is a not‑for‑profit advancing **Canadian sovereign artificial intelligence** — AI models, tools, and research designed for Canadian organizations and aligned with Canadian values, ethics, and governance.
+
+We don't believe Canada has to win only the race toward ever‑larger frontier models. **Purpose‑built, efficient, Canadian‑governed models** can deliver real outcomes in real environments — hospitals, health authorities, public institutions, businesses, researchers, and not‑for‑profits — while keeping people in control and accountable.
+
+This organization is where we publish our open‑weight models, inference research, and technical reports.
+
+---
+
+## 🔬 Our research bet: **ternary**
+
+Modern AI isn't limited by intelligence — it's limited by **the cost of storing and moving it**. Most of the cost of running a language model is the memory bandwidth spent streaming billions of weights for every token, and the arithmetic of multiplying them.
+
+**Ternary** representation constrains every weight to one of three values — **{ −1, 0, +1 }** — and attacks both costs at once:
+
+- **≈8–10× smaller.** Each weight carries ≈1.58 bits of information (log₂3) instead of 16 — roughly an order of magnitude below half precision, and about half the size of 4‑bit quantization.
+- **Multiply‑free inference.** A weight in { −1, 0, +1 } turns the expensive multiply into *add*, *subtract*, or *skip*. Roughly a third of the work disappears as structured sparsity.
+- **Capability preserved.** We reach ternary through **distillation** — training a ternary student to reproduce a high‑precision teacher — so the constraint is *learned*, not crudely imposed after the fact. The guiding principle: **subtract cost, not intelligence.**
+
+The payoff spans hardware generations: ternary's footprint lets capable models **fit on memory‑constrained edge devices** and **revive legacy datacenter GPUs** that modern models had outgrown — extending the useful life of existing silicon — while on modern accelerators the same savings mean **far more concurrent users per device**. It's an environmental story as much as a performance one: less energy per token, fewer accelerators per unit of served intelligence, and a slower path to e‑waste.
+
+---
+
+## 📦 What you'll find in this organization
+
+**Ternary‑distilled Gemma‑4‑E2B** — our first release, and the first in a planned series.
+
+A ternary (≈1.6‑bit) distillation of **Gemma‑4‑E2B**, a capability‑dense model with a **transparent, Western open‑weight lineage**. Distributed as **GGUF** for efficient local and datacenter serving.
+
+> ⚠️ **Pre‑release checkpoint.** The published weights are an early checkpoint (~1B training tokens), shared to demonstrate the method and invite community evaluation. **Production weights (~15B tokens) with full capability benchmarks will follow and replace them.** We defer quantitative capability claims to that release rather than over‑state a pre‑release checkpoint.
+
+Browse the model repositories in this organization for files, quantizations, and usage.
+
 ---
-title: README
-emoji: 🐠
-colorFrom: red
-colorTo: yellow
-sdk: static
-pinned: false
+
+## 🎯 Why Gemma‑4 as the base
+
+- **Exceptional intelligence‑per‑FLOP.** An already capability‑dense family; ternary distillation compounds that efficiency.
+- **Elastic architecture.** The E2B ("effective‑2B") design fits edge and multi‑tier deployment — and ternary lowers the floor of that range further.
+- **Provenance you can defend.** For regulated and sovereignty‑sensitive sectors — healthcare, public sector, critical infrastructure — the *provenance* of a model's knowledge is a governance requirement, not a preference. A transparent, auditable, Western open‑weight base gives the resulting ternary model a clear, documentable lineage. **Efficiency should not come at the cost of provenance.**
+
+---
+
+## ⚙️ An engine built to spend ternary's footprint on throughput
+
+Ternary's memory savings are only as valuable as a serving stack's ability to convert them into useful work. We maintain an inference engine (a fork of the open‑source llama.cpp lineage) tuned so footprint becomes **concurrency and throughput**:
+
+- **Footprint → concurrent contexts** — small weights leave far more device memory for KV cache, so one accelerator serves many more simultaneous sessions.
+- **Hardware‑best matmul dispatch** — the engine picks the path that fits the device by batch size, so capability‑dense models serve efficiently even on GPUs that lack dedicated matrix‑multiply units.
+- **Device‑side sampling** and **shared‑prefix / cross‑session cache reuse** — agentic workloads pay for a large shared system prompt once, not once per user.
+
+We validate this across **deliberately different accelerator families** — legacy datacenter GPUs and earlier‑generation tensor‑core GPUs — to prove the ternary‑plus‑engine approach spans hardware generations. Every serving optimization is held to a **token‑identical correctness gate** before it ships: throughput work never silently degrades output.
+
+---
+
+## 📄 Research
+
+- **TR‑2026‑001 — *Ternary Foundations for Efficient, Sovereign AI*** — the mathematics of ternary, why Gemma‑4, the engine that turns footprint into throughput, and the implications for legacy and modern hardware.
+- **TR‑2026‑002 — *Quantization‑Aware Training and Ternary Weights: Method vs. Representation*** — clears the "QAT vs ternary" category error and places both on one efficiency curve.
+
+Further reports — on serving engines across accelerator families, multi‑GPU parallelism for heterogeneous fleets, shared‑context concurrency, ternary at larger scales, and governable agentic orchestration — are on the Institute's roadmap.
+
+---
+
+## 🤝 What we stand for
+
+- **Sovereignty** — reduce dependency on foreign‑controlled AI models and infrastructure; support Canadian laws, governance, and interests.
+- **Responsibility** — steerable, explainable, accountable, secure systems that keep humans at the helm.
+- **Accessibility** — capable models, tools, and documentation available to organizations across Canada.
+- **Efficiency** — capability that scales **down** in cost as readily as it scales up in ability.
+
+---
+
+## 📬 Get involved
+
+The GoAutomate AI Institute welcomes collaboration with Canadian organizations, healthcare systems, public institutions, researchers, policymakers, and technology leaders who share our commitment to responsible and accessible AI.
+
+**→ [info@goautomate.ai](mailto:info@goautomate.ai)  ·  [goautomate.ai](https://www.goautomate.ai)**
+
 ---
 
-Edit this `README.md` markdown file to author your organization card.
+<div align="center">
+<sub>© 2026 GoAutomate AI Institute · Canadian Sovereign AI · Responsible Adoption · Public Benefit</sub>
+</div>