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.
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.
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 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. 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
1@techreport{goautomate2026ternary,
2 title = {Ternary Foundations for Efficient, Sovereign AI},
3 author = {{GoAutomate AI Institute}},
4 institution = {GoAutomate AI Institute},
5 number = {TR-2026-001},
6 year = {2026},
7 doi = {10.5281/zenodo.21110909},
8 url = {https://doi.org/10.5281/zenodo.21110909},
9 note = {Pre-release; ternary-distilled Gemma-4-E2B}
10}
