Model release

SuperApriel-15B-Instruct

SuperApriel-15B-Instruct is an open-weight model release in the Apriel family from ServiceNow (SLAM Labs)1 3, version SuperApriel-15B-Instruct, released Apr 2026. Its weights can be downloaded by the public1 2 under the MIT License1.

A 15B-parameter instruction-tuned "supernet" in which each of 48 decoder layers carries four token-mixer variants: full attention, sliding-window attention, Gated DeltaNet, and Kimi Delta Attention. Choosing one mixer per layer gives deployment presets that trade output quality for decoding speed from a single checkpoint. It was derived from Apriel-1.6-15b-Thinker by distillation followed by supervised fine-tuning.1 3

On AI on Hugging Face: ServiceNow-AI/SuperApriel-15B-Instruct

Last reviewedSep 29, 2026Entry updated Sep 29, 2026Documented release Apr 2026

Availability and license

Overall availability

Public

Downloadable from Hugging Face without gating. Loading uses custom model code shipped in the repository (trust_remote_code), and presets are selected by copying a preset configuration file.1 2

Availability fact review: not separately recorded

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

MIT License (external site: huggingface.co)1
Applies to: Weights · SPDX MIT · on the rubric’s OSI-approved software-license list · fact-level rights review not recorded
Plain-language guide to the MIT License
Apache License 2.0 (Fast-LLM) (external site: raw.githubusercontent.com)5
Applies to: Code · SPDX Apache-2.0 · on the rubric’s OSI-approved software-license list · fact-level rights review not recorded
Plain-language guide to the Apache License 2.0

The weights license is stated as MIT in the model card's metadata and License section; the Hugging Face repository has no separate LICENSE file. Fast-LLM's LICENSE file applies Apache 2.0 to all files "unless otherwise noted".1 2 5

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.

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 SuperApriel-15B-Instruct
Item	Status	Notes and evidence
WeightsCan the general public download the model parameters for this release?	Public	One supernet checkpoint with preset configurations is downloadable from Hugging Face.1 2
Inference codeIs code for running the model published?	Public	The card documents inference with Hugging Face Transformers (custom modeling code in the repository) and with vLLM through a Fast-LLM plugin published on a feature branch.1
Training codeIs the code used to train the model published?	Public	The paper states that the Fast-LLM training code is released; Fast-LLM's main branch contains an apriel2 model implementation.3 6
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 paper says distillation used the Apriel pretraining corpus and SFT data, and that the SFT stage used instruction-tuning data; the datasets are not documented as released.4
Training recipeAre the training configuration and procedure documented in enough detail to follow?	Partial	The card and paper describe two-stage stochastic distillation from a frozen Apriel 1.6 teacher followed by targeted SFT, with token counts, batch shape, and GPU counts, and link training logs. This catalog did not confirm that complete configurations are published.1 4
Evaluation materialsPublic = evaluation code or prompts that let others re-run the evaluations are published. Partial = results only.	Partial	The card and paper report benchmark results for each preset; the paper says it uses LM Evaluation Harness task formulations, but no evaluation configuration for this release was found.1 4

What it is useful for

The card lists code assistance, multi-step reasoning, question answering, function calling, instruction following, and agent use cases, and describes serving one preset for a fixed deployment or switching presets at runtime. It says the model is not intended for safety-critical use without human oversight.1

Run and use notes

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

The model card recommends vLLM with the Fast-LLM plugin. It states that single-preset mode loads approximately 27 GiB of weights in bf16 and supernet mode (all four mixers per layer, allowing runtime preset switching) approximately 46 GiB in bf16, excluding KV cache.1
Presets that use Gated DeltaNet or Kimi Delta Attention layers need the causal-conv1d and mamba-ssm packages when run with Transformers; attention-only presets do not.1

Organization context

ServiceNow

Provenance and derivatives

Derived from ServiceNow's Apriel-1.6-15b-Thinker: shared parameters (feed-forward layers, embeddings, and norms) are inherited from Apriel 1.6, and the new mixer weights were trained by distillation from a frozen Apriel 1.6 teacher, then supervised fine-tuning. The Apriel 1.x line began from Mistral AI's Pixtral-12B-Base-2409, and the model keeps a Pixtral vision encoder.1 4 7

Derived from: Apriel-1.6-15b-Thinker — Teacher model and source of the shared weights.
Derived from: SuperApriel-15b-Base (external site: huggingface.co) — Stage 1 distilled supernet; no separate catalog record.
Derived from: Pixtral-12B-Base-2409 (Mistral AI) (external site: huggingface.co) — Starting checkpoint of the Apriel 1.5 line.

Other releases in the Apriel family

Apriel-1.6-15B-ThinkerModel-disclosure tier (USASI rubric v0.2): Open-weight

Apriel 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-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.-governed project

The model is published by ServiceNow's ServiceNow-AI organization on Hugging Face, and the accompanying report is credited to ServiceNow's SLAM Labs. ServiceNow, Inc. is a Delaware corporation headquartered in Santa Clara, California, per its 2025 Form 10-K. Its lineage traces through Apriel 1.6 and 1.5 to Pixtral-12B-Base-2409, published by Mistral AI; that base is recorded under provenance and is not treated as U.S.-developed.1 3 8 7

Assessed Sep 29, 2026

Sources

1.
ServiceNow-AI/SuperApriel-15B-Instruct model card (external site: huggingface.co)
ServiceNow · Model card · accessed Sep 29, 2026
2.
Hugging Face model API metadata for ServiceNow-AI/SuperApriel-15B-Instruct (external site: huggingface.co)
Hugging Face · Other · accessed Sep 29, 2026
3.
Super Apriel: One Checkpoint, Many Speeds (arXiv 2604.19877) (external site: arxiv.org)
ServiceNow (SLAM Labs) · Paper · published Apr 21, 2026 · accessed Sep 29, 2026
4.
Super Apriel: One Checkpoint, Many Speeds (arXiv 2604.19877, HTML full text) (external site: arxiv.org)
ServiceNow (SLAM Labs) · Paper · published Apr 21, 2026 · accessed Sep 29, 2026
5.
Fast-LLM LICENSE (external site: raw.githubusercontent.com)
ServiceNow · License · accessed Sep 29, 2026
6.
Fast-LLM fast_llm_external_models directory (includes apriel2) (external site: github.com)
ServiceNow · Repository · accessed Sep 29, 2026
7.
Apriel-1.5-15b-Thinker (arXiv 2510.01141) (external site: arxiv.org)
ServiceNow (SLAM Lab) · Paper · published Oct 1, 2025 · accessed Sep 29, 2026
8.
ServiceNow, Inc. Form 10-K for the fiscal year ended December 31, 2025 (external site: sec.gov)
U.S. Securities and Exchange Commission (ServiceNow filing) · Filing · published Jan 28, 2026 · accessed Sep 29, 2026

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APA style

USASI. (2026, September 29). SuperApriel-15B-Instruct. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/superapriel-15b-instruct/

BibTeX

@misc{usasi_superapriel_15b_instruct,
  author = {{USASI}},
  title = {SuperApriel-15B-Instruct},
  year = {2026},
  month = sep,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/superapriel-15b-instruct/}},
  note = {United States of America Superintelligence. Last updated 2026-09-29}
}

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