Model release

OpenELM 3B Instruct

OpenELM 3B Instruct is an open-weight model release in the OpenELM family from Apple3 1, version OpenELM-3B-Instruct, released Apr 2024. Its weights can be downloaded by the public2 3 under the Apple Machine Learning Research Model License Agreement3 2.

OpenELM 3B Instruct is the 3B-parameter instruction-tuned model in Apple's April 2024 OpenELM release. It was pretrained with Apple's CoreNet library on about 1.8 trillion tokens of public data and then instruction-tuned on the UltraFeedback dataset using the Hugging Face Alignment Handbook.1 7

Last reviewedSep 29, 2026Entry updated Sep 29, 2026Documented release Apr 2024

Availability and license

Overall availability

Public

Downloadable from Hugging Face without an access gate, under a license that permits only non-commercial research use.2 3

Availability fact review: not separately recorded

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

Apple Machine Learning Research Model License Agreement (external site: huggingface.co)3 2
Applies to: Weights · custom license (no SPDX identifier) · fact-level rights review not recorded
Plain-language guide to the Apple Machine Learning Research Model License Agreement
Apple software license (CoreNet) (external site: raw.githubusercontent.com)4
Applies to: Code · custom license (no SPDX identifier) · fact-level rights review not recorded

The weights license grants a personal, non-exclusive, revocable license to use, copy, modify, distribute, and create derivatives only for non-commercial scientific research, excluding product development and commercial use; derivatives are held to the same limit. Redistribution requires passing on the agreement with a specified attribution notice and identifying modifications. No patent rights are granted, and the agreement is governed by California law. The card's metadata uses the license tag "apple-amlr" but still gives "apple-sample-code-license" as the license name, while the LICENSE file in the repository (copyright 2025) is the research-only agreement. CoreNet's own license is a permissive Apple software license that grants no patent rights.3 2 4

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 OpenELM 3B Instruct
Item	Status	Notes and evidence
WeightsCan the general public download the model parameters for this release?	Public	Weights are published on Hugging Face without an access gate, under a research-only license.2 3
Inference codeIs code for running the model published?	Public	The Hugging Face repository includes model code and a generate_openelm.py script for inference with Transformers; CoreNet documents conversion to Apple's MLX.1 2 5
Training codeIs the code used to train the model published?	Public	CoreNet documents OpenELM pre-training, and the instruction-tuning recipe uses the public Alignment Handbook with a configuration published in CoreNet.5 6 7
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	Pre-training used public datasets (RefinedWeb, deduplicated PILE, a subset of RedPajama, and a subset of Dolma v1.6, about 1.8T tokens), and CoreNet documents where to download them and how the subsets were specified; instruction tuning used UltraFeedback. Current availability of each source dataset was not verified.1 6 7
Training recipeAre the training configuration and procedure documented in enough detail to follow?	Public	Apple published pre-training configurations and training logs, and the instruction-tuning README gives the recipe file and per-size hyperparameters.8 7
Evaluation materialsPublic = evaluation code or prompts that let others re-run the evaluations are published. Partial = results only.	Public	The model card gives the lm-evaluation-harness commit, tasks, and shot settings used to reproduce its reported zero-shot and few-shot results.1

What it is useful for

The card presents the model for open research on language models, notes it was trained on public data without safety guarantees, and asks users to do their own safety testing and filtering. Its license limits use to non-commercial research.1 3

Run and use notes

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

The card's evaluation commands load the model with trust_remote_code and pair it with the Llama 2 tokenizer (meta-llama/Llama-2-7b-hf); the CoreNet project notes that the models use the LLaMA v1/v2 tokenizer, which is downloaded separately.1 5

Organization context

Apple

Provenance and derivatives

Pretrained from scratch by Apple on public datasets using CoreNet, then instruction-tuned by Apple. The models use Meta's Llama tokenizer, which is not distributed with them.1 5 7

Derived from: OpenELM-3B (pretrained) (external site: huggingface.co) — Pretrained base checkpoint; no separate catalog record.

Other releases in the OpenELM family

No other releases in this family have been assessed.

OpenELM 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. headquarters

The model license states the model is developed and released by Apple Inc. Apple Inc. has its principal executive offices in Cupertino, California, per its Form 10-K for fiscal year 2025.3 9

Assessed Sep 29, 2026

Sources

1.
apple/OpenELM-3B-Instruct model card (external site: huggingface.co)
Apple (Hugging Face) · Model card · accessed Sep 29, 2026
2.
Hugging Face model metadata for apple/OpenELM-3B-Instruct (external site: huggingface.co)
Hugging Face · Model card · accessed Sep 29, 2026
3.
OpenELM LICENSE (Apple Machine Learning Research Model License Agreement) (external site: huggingface.co)
Apple Inc. (Hugging Face) · License · accessed Sep 29, 2026
4.
apple/corenet LICENSE (external site: raw.githubusercontent.com)
Apple Inc. (GitHub) · License · accessed Sep 29, 2026
5.
CoreNet projects/openelm README (external site: raw.githubusercontent.com)
Apple (GitHub) · Repository · accessed Sep 29, 2026
6.
Pretraining OpenELM (CoreNet projects/openelm/README-pretraining.md) (external site: raw.githubusercontent.com)
Apple (GitHub) · Documentation · accessed Sep 29, 2026
7.
OpenELM Instruction Tuning (CoreNet projects/openelm/README-instruct.md) (external site: raw.githubusercontent.com)
Apple (GitHub) · Documentation · accessed Sep 29, 2026
8.
OpenELM: An Efficient Language Model Family with Open Training and Inference Framework (arXiv 2404.14619) (external site: arxiv.org)
arXiv · Paper · published Apr 22, 2024 · accessed Sep 29, 2026
9.
Apple Inc. Form 10-K for the fiscal year ended September 27, 2025 (external site: sec.gov)
Apple Inc. (via U.S. Securities and Exchange Commission EDGAR) · Filing · 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). OpenELM 3B Instruct. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/openelm-3b-instruct/

BibTeX

@misc{usasi_openelm_3b_instruct,
  author = {{USASI}},
  title = {OpenELM 3B Instruct},
  year = {2026},
  month = sep,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/openelm-3b-instruct/}},
  note = {United States of America Superintelligence. Last updated 2026-09-29}
}

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