GoAutomateAI/terna-e2b-GGUF

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<img src="https://www.goautomate.institute/logo1.png" alt="GoAutomate AI Institute" width="90" />

<h1>Terna‑E2B (GGUF)</h1>

<h3>A ternary (~1.6‑bit) distillation of Gemma‑4‑E2B · <b>Pre‑release</b> 🍁</h3>

GoAutomate AI Institute — Canadian Sovereign AI


  

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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

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://doi.org/10.5281/zenodo.21110909)* (Zenodo, DOI 10.5281/zenodo.21110909).

Files

Usage

This is a standard GGUF and runs on `llama.cpp`.

Quick test (CLI):

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:

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

@techreport{goautomate2026ternary,
  title       = {Ternary Foundations for Efficient, Sovereign AI},
  author      = {{GoAutomate AI Institute}},
  institution = {GoAutomate AI Institute},
  number      = {TR-2026-001},
  year        = {2026},
  doi         = {10.5281/zenodo.21110909},
  url         = {https://doi.org/10.5281/zenodo.21110909},
  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"> _{© 2026 GoAutomate AI Institute · Canadian Sovereign AI · Responsible Adoption · Public Benefit} </div>