Model release

Trinity Mini

Trinity Mini is an open-weight model release in the Trinity family from Arcee AI1, version Mini, released Dec 1, 2025. Its weights can be downloaded by the public1 under the OpenMDW License Agreement, version 1.12 1.

Trinity Mini is a 26B-parameter mixture-of-experts model with 3B active parameters, 128 experts (8 active plus 1 shared), and a 128k-token context window. It was trained on 10 trillion tokens and tuned for reasoning.1

Model hub: Model card (external site: huggingface.co)
License: License (OpenMDW-1.1) (external site: huggingface.co)
Release notes: Release announcement (external site: arcee.ai)
Paper: Technical report (external site: arxiv.org)

On AI on Hugging Face: arcee-ai/Trinity-Mini

Last reviewedSep 29, 2026Entry updated Sep 29, 2026Documented release Dec 1, 2025

Availability and license

Overall availability

Public

Weights download from Hugging Face without an access gate. Also served through Arcee's API and OpenRouter.1 6

Availability fact review: not separately recorded

Availability is separate from permission: read the license before using or redistributing.

OpenMDW License Agreement, version 1.1 (external site: huggingface.co)2 1
Applies to: Weights and code · custom license (no SPDX identifier) · fact-level rights review not recorded
Plain-language guide to the OpenMDW License Agreement, version 1.1

Arcee released Trinity Mini on 2025-12-01 under Apache 2.0. On 2026-05-28 the Hugging Face repository changed its license to OpenMDW-1.1 as part of Arcee's move of all Trinity releases to that license. OpenMDW-1.1 requires keeping the license and notices when redistributing, and ends the rights of anyone who sues claiming the materials infringe a patent or copyright.6 3 7 2

Component reuse rights

A readable or downloadable component is not automatically reusable. These indicators concern recorded license evidence, not system certification.

weights: Unknown — no complete fact-level rights review
code: Unknown — no complete fact-level rights review
data: Unknown — no complete fact-level rights review
documentation: Unknown — no complete fact-level rights review

No complete system-rights review is recorded for this release.

Model-disclosure tier

Computed from the checklist below using USASI rubric v0.2. An editorial category, not a certification.

Open-weight

The model parameters for this release can be downloaded by the public. License terms may still restrict use, redistribution, or commercial use.

The weights are under a license that is not on the rubric's OSI-approved list. Read its terms before use.

How tiers are computed

Public materials checklist

Items for a model under USASI rubric v0.2. Unknown means unassessed or insufficient evidence.

Public materials checklist for Trinity Mini
Item	Status	Notes and evidence
WeightsCan the general public download the model parameters for this release?	Public	Weights are in the ungated Hugging Face repository; a GGUF conversion is published as a separate repository.1
Inference codeIs code for running the model published?	Public	The repository includes modeling code (modeling_afmoe.py), and the model card documents Transformers, vLLM, llama.cpp, and LM Studio.1 4
Training codeIs the code used to train the model published?	Unknown	The technical report says training used a modified version of TorchTitan but does not link a release.8
Training-data informationDoes the information cover provenance, scope, acquisition, selection, labeling, processing, and where data or alternatives can be obtained? Access alone does not establish completeness.	Unknown	Not assessed.
Training-data accessCan the training data be obtained? This is independent of information completeness and reuse rights; original unshareable data need not be downloadable.	Unknown	Not assessed.
Complete training pipelineIs the complete base-training and preprocessing pipeline published, including configuration? Fine-tuning code or an inference SDK alone is insufficient.	Unknown	Not assessed.
Legacy data assessment (v0.1)Historical assessment combining download access and disclosure. Preserved for traceability; excluded from the v0.2 tier calculation. See the new separate assessments above.	Partial	The technical report describes the 10-trillion-token, three-phase pretraining mix for Trinity Nano and Mini. No public release of the data itself was found.8
Training recipeAre the training configuration and procedure documented in enough detail to follow?	Partial	The technical report documents architecture and optimizer choices for the family; post-training details for Mini are limited.8
Evaluation materialsPublic = evaluation code or prompts that let others re-run the evaluations are published. Partial = results only.	Partial	The model card shows a benchmark chart and the technical report includes results; evaluation code is not linked.1 8

What it is useful for

Arcee presents Trinity Mini as the mid-sized model in the family, for cloud or on-premises deployment.5

Run and use notes

Documented facts only. No hardware or performance claims are made without a cited source and stated assumptions.

The model card documents support in vLLM 0.11.1, llama.cpp release b7061, LM Studio, and Transformers (main branch, or a released version with trust_remote_code enabled).1

Organization context

Arcee AI

Provenance and derivatives

Post-trained from Arcee's Trinity-Mini-Base checkpoint. The model card says the training data was gathered and curated with Datology and training ran on a cluster provided by Prime Intellect.1

Derived from: Trinity-Mini-Base (external site: huggingface.co) — Same organization; pretrained base checkpoint.

Other releases in the Trinity family

Trinity-Large-ThinkingModel-disclosure tier (USASI rubric v0.2): Open-weight

Trinity family overview

What this catalog does not know

Unknown means the sources reviewed for this record do not document it. It is not evidence that something does not exist.

Training code: unknown.
Training-data information: unknown.
Training-data access: unknown.
Complete training pipeline: unknown.

Have a primary source? How to report a correction.

U.S. eligibility

Project eligibility rests on documented governing or maintaining entities, not on contributors.

Eligible · basis: U.S. headquarters

Developed and published by Arcee AI, Inc., a U.S. company with a documented San Francisco base (see the arcee-ai record). Arcee describes the Trinity models as trained end to end in the United States.1 6

Assessed Sep 29, 2026

Sources

1.
arcee-ai/Trinity-Mini model card (external site: huggingface.co)
Arcee AI · Model card · accessed Sep 29, 2026
2.
Trinity-Mini LICENSE (OpenMDW-1.1) (external site: huggingface.co)
Arcee AI · License · accessed Sep 29, 2026
3.
Trinity-Mini repository commit history (external site: huggingface.co)
Hugging Face · Repository · accessed Sep 29, 2026
4.
Trinity-Mini repository file listing (external site: huggingface.co)
Hugging Face · Repository · accessed Sep 29, 2026
5.
Trinity Models (external site: arcee.ai)
Arcee AI · Official page · accessed Sep 29, 2026
6.
The Trinity Manifesto (external site: arcee.ai)
Arcee AI · Announcement · published Dec 1, 2025 · accessed Sep 29, 2026
7.
Trinity is moving to OpenMDW-1.1 (external site: arcee.ai)
Arcee AI · Announcement · published May 29, 2026 · accessed Sep 29, 2026
8.
Arcee Trinity Large Technical Report (external site: arxiv.org)
arXiv · Paper · published Feb 19, 2026 · accessed Sep 29, 2026

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This listing is not an endorsement, a safety assessment, or a federal approval.

Cite this page

APA style

USASI. (2026, September 29). Trinity Mini. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/trinity-mini/

BibTeX

@misc{usasi_trinity_mini,
  author = {{USASI}},
  title = {Trinity Mini},
  year = {2026},
  month = sep,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/trinity-mini/}},
  note = {United States of America Superintelligence. Last updated 2026-09-29}
}

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