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README.md

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 ---
-license: mit
+license: gemma
+license_link: https://ai.google.dev/gemma/terms
+base_model: google/gemma-4-E2B-it
+base_model_relation: quantized
+language:
+  - en
+pipeline_tag: text-generation
+library_name: gguf
+tags:
+  - gguf
+  - ternary
+  - bitnet
+  - 1.58-bit
+  - gemma
+  - gemma-4
+  - quantization-aware-training
+  - distillation
+  - sovereign-ai
+  - edge
+  - efficient-inference
+  - llama.cpp
 ---
+
+<div align="center">
+
+# Terna‑E2B (GGUF)
+### A ternary (~1.6‑bit) distillation of Gemma‑4‑E2B · **Pre‑release** 🍁
+
+**GoAutomate AI Institute — Canadian Sovereign AI**
+
+[Organization](https://huggingface.co/GoAutomateAI) · [Website](https://www.goautomate.institute) · [Technical Report TR‑2026‑001](https://www.goautomate.institute)
+
+</div>
+
+---
+
+**Terna** is the GoAutomate AI Institute's family of **ternary‑weight** language models. The name comes from the Latin *terni* — "three each" — a nod to the three values every weight is constrained to: **{ −1, 0, +1 }**, the ternary representation at the heart of the family. That constraint is our defining bet: models that are **≈8–10× smaller** and **multiply‑free**, engineered to *subtract cost, not intelligence* — capable from the edge to legacy datacenter GPUs to modern accelerators. **Terna‑E2B** is the first member of the family.
+
+---
+
+> ## ⚠️ Pre‑release checkpoint — read first
+> These weights are an **early checkpoint trained on ~1B tokens**, published to demonstrate the method and invite community evaluation. They are **not a finished model.** Expect fluent‑but‑confidently‑wrong answers, and verify every output. **Production weights (~15B tokens) with full capability benchmarks will follow and replace this checkpoint.** We deliberately defer quantitative capability claims to that release.
+
+---
+
+## Model at a glance
+
+| | |
+|---|---|
+| **Base / teacher** | [`google/gemma-4-E2B-it`](https://ai.google.dev/gemma) (capability‑dense, Western open‑weight lineage) |
+| **Method** | Quantization‑aware **distillation** to ternary weights — *learned*, not post‑hoc rounded |
+| **Weight representation** | Ternary — each weight ∈ **{ −1, 0, +1 }** (≈1.58 bits; ≈8–10× smaller than FP16, ≈2× smaller than 4‑bit) |
+| **Format** | GGUF (`Q2_K`), runs on **llama.cpp** |
+| **Language** | English |
+| **Training tokens (this release)** | ~1B (pre‑release checkpoint) |
+| **License** | [Gemma Terms of Use](https://ai.google.dev/gemma/terms) |
+
+---
+
+## What is ternary?
+
+**Ternary** constrains every weight to one of three values — **{ −1, 0, +1 }** — which does two things at once:
+
+- **Footprint collapses** to ≈1.58 bits per weight (log₂3), roughly an order of magnitude below half precision.
+- **The multiply disappears:** `w · x` with `w ∈ {−1, 0, +1}` is just *add x*, *subtract x*, or *skip* — a general matrix‑multiply becomes a sparse signed sum, with ~⅓ of the work vanishing as structured sparsity.
+
+Crucially, we reach ternary through **distillation** — training a ternary "student" to reproduce a high‑precision Gemma‑4‑E2B "teacher" — so the constraint is *learned*, not crudely imposed on a finished model. The guiding principle: **subtract cost, not intelligence.**
+
+Full mathematics, methodology, and engineering are in **[TR‑2026‑001 — *Ternary Foundations for Efficient, Sovereign AI*](https://www.goautomate.institute)**.
+
+---
+
+## Files
+
+| File | Size | Format |
+|---|---|---|
+| `terna-e2b-Q2_K.gguf` | ~3.6 GB | GGUF (`Q2_K`) — runs on **llama.cpp** |
+
+---
+
+## Usage
+
+This is a standard **GGUF** and runs on [`llama.cpp`](https://github.com/ggml-org/llama.cpp).
+
+**Quick test (CLI):**
+
+```bash
+llama-cli -m terna-e2b-Q2_K.gguf -p "Explain what a mitochondrion does, in two sentences." --temp 0.7
+```
+
+**Serve an OpenAI‑compatible endpoint:**
+
+```bash
+llama-server \
+  -m terna-e2b-Q2_K.gguf \
+  -c 4096 --host 127.0.0.1 --port 8000
+```
+
+Then POST to `http://127.0.0.1:8000/v1/chat/completions`. The **Gemma‑4 chat template** ships in the GGUF and is applied automatically when you use the chat endpoint.
+
+### Recommended generation settings
+
+This checkpoint has a **low‑entropy** output distribution (very confident). For anything beyond short answers, soften it:
+
+- `temperature: 0.7`
+- a mild repetition penalty (e.g. `--repeat-penalty 1.2`) for long‑form generations, to avoid rigidity/repetition
+
+---
+
+## Intended use
+
+- **Research and community evaluation** of ternary distillation and efficient serving.
+- **Efficiency / systems experimentation** — edge, memory‑constrained, and legacy‑GPU serving where footprint dominates.
+- Best behaved in **well‑covered domains**: general science, biology, medicine (educational), mathematics, and computer science.
+
+### Out of scope / not recommended (for this pre‑release)
+
+- **Production or high‑stakes use** of any kind. This is an early checkpoint.
+- **Unverified factual, medical, legal, or safety‑critical output.** The model is confident even when wrong — a human must verify.
+- Long‑context or long‑form tasks without the softened sampling above.
+
+---
+
+## Limitations & known behaviors
+
+Honest notes from our own evaluation of this checkpoint:
+
+- **Confidently wrong.** High top‑1 confidence means errors are stated as fluently and assertively as correct answers. Do not treat outputs as facts without checking.
+- **Low entropy → rigidity.** Peaked output distribution can make long generations repetitive or rigid; mitigate with the recommended sampling.
+- **Echo‑loops on out‑of‑distribution inputs.** Limited chat‑format training means unusual phrasings or niche topics can trigger repetition/echoing. Stays healthiest in the well‑covered domains listed above.
+- **English‑centric**, small effective size, and inherits any biases/limitations of the Gemma‑4‑E2B base.
+- **Pre‑release quality.** Capability is not yet benchmarked; the production (~15B‑token) release is the intended quality bar.
+
+---
+
+## Training & method (summary)
+
+The ternary student is trained under **quantization‑aware objectives** that align its outputs and intermediate representations to the Gemma‑4‑E2B teacher, so the { −1, 0, +1 } constraint is learned during training rather than applied afterward. Language‑model linear layers are ternarized; embeddings, the LM head, and normalization layers are kept at higher precision. Serving optimizations are held to a **token‑identical correctness gate** against a reference path, so throughput work never silently changes outputs.
+
+Exact data mixes, hyperparameters, and kernel/encoding internals are held proprietary; the *methods and mental models* are described in the Institute's technical reports.
+
+---
+
+## About the GoAutomate AI Institute
+
+The **[GoAutomate AI Institute](https://www.goautomate.institute)** is a not‑for‑profit advancing **Canadian sovereign AI** — accessible, responsible, Canadian‑governed models for organizations across Canada, with a focus on efficiency and on sectors where **provenance and governability matter** (healthcare, public sector, critical infrastructure). Ternary is our bet on the next wave of efficient AI: capability that scales **down** in cost as readily as it scales up in ability.
+
+---
+
+## License
+
+This model is a derivative of **Gemma‑4‑E2B** and is governed by the **[Gemma Terms of Use](https://ai.google.dev/gemma/terms)**. By downloading or using these weights you agree to those terms. Gemma is provided under and subject to the Gemma Terms of Use.
+
+---
+
+## Citation
+
+```bibtex
+@techreport{goautomate2026ternary,
+  title       = {Ternary Foundations for Efficient, Sovereign AI},
+  author      = {{GoAutomate AI Institute}},
+  institution = {GoAutomate AI Institute},
+  number      = {TR-2026-001},
+  year        = {2026},
+  note        = {Pre-release; ternary-distilled Gemma-4-E2B}
+}
+```
+
+---
+
+## Contact
+
+Questions, evaluation feedback, or collaboration: **[info@goautomate.ai](mailto:info@goautomate.ai)** · **[goautomate.institute](https://www.goautomate.institute)**
+
+<div align="center">
+<sub>© 2026 GoAutomate AI Institute · Canadian Sovereign AI · Responsible Adoption · Public Benefit</sub>
+</div>