micro-terse

<div align="center"> <picture> <img src="https://raw.githubusercontent.com/michelangeloromerochisco/micro-terse/main/resources/logo.png" width="30%" alt="Micro-Terse"> </picture> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://github.com/michelangeloromerochisco/micro-terse" target="_blank"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-Micro--Terse-181717?logo=github&logoColor=white"/></a> <a href="https://github.com/michelangeloromerochisco/micro-terse/blob/main/docs/papers/terse-micro-technical-report.md" target="_blank"><img alt="Technical Report" src="https://img.shields.io/badge/%F0%9F%93%84%20Technical%20Report-Micro--Terse-blue"/></a> <img alt="License" src="https://img.shields.io/badge/License-Apache_2.0-green.svg"/> </div>

1. Model Introduction

Micro-Terse is a 423M-parameter (≈320M active) ternary-weight language model trained from scratch for ≈$150, deployable as a 182 MB CPU-only GGUF. Its weights are constrained to {−1, 0, +1} (≈1.58 bits), so TQ2_0 packs them exactly; the released 182 MB file pairs that with a Q6_K tied embedding.

It is a research proof-of-concept, not a production assistant. At an 8B-token budget it is data-limited: fluent for a clause or two, near chance on knowledge benchmarks. The point is capability per megabyte and per joule — a from-scratch ternary model an individual can train and run on owned hardware.

Key Features

• Ternary weights {−1, 0, +1} on all internal projections.
• Clean-room architecture and ternary training operator.
• 182 MB GGUF (ternary weights packed exactly; Q6_K tied embedding), CPU-only inference.
• Trained from scratch for ≈$150 on a single RTX A6000.

Model Variants

2. Model Overview

3. Training

4. Evaluation (measured)

Standard academic benchmarks (MMLU/HellaSwag/ARC) were not run; at this data budget knowledge accuracy is expected near chance. What we measured:

• Perplexity (held-out English, lower better): base 56.7, SFT 97.5, ORPO 125.0.
• Identity preference (mean log-prob margin, charter vs "ChatGPT", 4 probes): base −1.81 (0/4) → SFT −1.09 (0/4) → ORPO +0.90 (3/4).
• Single-token factual recall (base, top-1): "…painted by Leonardo da" → Vinci (90%), "…Neil" → Armstrong (84%), "hydrogen and" → oxygen (73%), "…revolves around the" → sun (66%). ≈14/18 curated prompts correct.

5. Quickstart

The model uses a custom terse architecture, so it needs the small llama.cpp fork (branch terse-arch). After building it:

huggingface-cli download MicheRomChis/micro-terse terse-micro-sft.TQ2_0.gguf --local-dir .
./llama-cli -m terse-micro-sft.TQ2_0.gguf -p "Hello" -n 128

Use terse-micro-base.TQ2_0.gguf for completion and terse-micro-orpo.TQ2_0.gguf for identity-aligned output.

6. Limitations

• Not a production assistant. Free-generation is incoherent beyond a clause or two (GPT-2-medium-class); it is data-limited.
• Near-chance on knowledge/reasoning benchmarks is expected. Do not use for factual QA without retrieval.
• May hallucinate and reflect web-text biases; no safety tuning beyond the ORPO pass.
• Ternary gives no training-memory savings (STE keeps fp masters); the win is inference footprint/energy.

7. License

Apache-2.0.

8. Citation

@techreport{romerochisco2026tersemicro,
  title  = {Terse-Micro: A 423M-Parameter Ternary-Weight Language Model Trained From Scratch for \$150},
  author = {Romero Chisco, Michelangelo},
  year   = {2026},
  note   = {Apache-2.0. github.com/michelangeloromerochisco/micro-terse}
}