Model release

Rnj-1.5 Instruct

Rnj-1.5 Instruct is an open-weight model release in the Rnj family from Essential AI1, version rnj-1.5-instruct, released 2026. Its weights can be downloaded by the public1 under the Apache License 2.01.

A long-context follow-up to Rnj-1 Instruct that extends the context window from 32K to 160K tokens. Essential AI built it from the Rnj-1 base model, switching most attention layers to block-local attention with a group of global layers in the middle, and added long-context mid-training data and more software-engineering training data.1

Model hub: Model card (Hugging Face) (external site: huggingface.co)
Model hub: Rnj-1 base model card (external site: huggingface.co)

On AI on Hugging Face: EssentialAI/rnj-1.5-instruct

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

Availability and license

Overall availability

Public

Downloadable from Hugging Face without gating.1

Availability fact review: not separately recorded

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

Apache License 2.0 (external site: huggingface.co)1
Applies to: Weights · 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 card states that the repository and model weights are licensed under Apache 2.0 and links to the license file in the rnj-1-instruct repository.1

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 Rnj-1.5 Instruct
Item	Status	Notes and evidence
WeightsCan the general public download the model parameters for this release?	Public	1
Inference codeIs code for running the model published?	Public	The card points to the Rnj-1 usage instructions and says support was added to vLLM in v0.20.0, with the block-local attention implemented in Triton.1 2
Training codeIs the code used to train the model published?	Unknown	Not assessed.
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 card describes the added mid-training data (science PDFs converted to text with olmOCR 2 and repository-level code with fill-in-the-middle examples), synthetic long-context tasks, and about 600k synthetic software-engineering trajectories from three teacher models it does not name. No dataset links are given.1
Training recipeAre the training configuration and procedure documented in enough detail to follow?	Partial	The card describes the attention-layer pattern and the kinds of mid-training and synthetic task data at a high level, without hyperparameters.1
Evaluation materialsPublic = evaluation code or prompts that let others re-run the evaluations are published. Partial = results only.	Partial	The card reports benchmark results, including a "lookback" long-context evaluation Essential AI built from GitHub repositories; no evaluation data or code links are given for this release.1

What it is useful for

Coding, including agentic software-engineering tasks run in harnesses such as SWE-Agent and mini-swe-agent, and tasks that need retrieval over long inputs. The card says the model was optimized for long-context comprehension rather than long-context generation.1

Organization context

Essential AI

Provenance and derivatives

Built by Essential AI from its Rnj-1 base model, which it trained from scratch. The card says the synthetic software-engineering trajectories used in training were generated by three teacher models, which it does not identify.1 2

Derived from: Rnj-1 (base) (external site: huggingface.co) — Essential AI base model; no separate catalog record.

Other releases in the Rnj family

Rnj-1 InstructModel-disclosure tier (USASI rubric v0.2): Open-weight

Rnj 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

Trained and published by Essential AI from its own Rnj-1 base model. Essential AI gives its location as San Francisco, CA.1 3

Assessed Sep 29, 2026

Sources

1.
EssentialAI/rnj-1.5-instruct model card (external site: huggingface.co)
Essential AI · Model card · published 2026 · accessed Sep 29, 2026
2.
EssentialAI/rnj-1 model card (external site: huggingface.co)
Essential AI · Model card · accessed Sep 29, 2026
3.
About | Essential AI (external site: essential.ai)
Essential AI · Official page · accessed Sep 29, 2026

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APA style

USASI. (2026, September 29). Rnj-1.5 Instruct. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/rnj-1-5-instruct/

BibTeX

@misc{usasi_rnj_1_5_instruct,
  author = {{USASI}},
  title = {Rnj-1.5 Instruct},
  year = {2026},
  month = sep,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/rnj-1-5-instruct/}},
  note = {United States of America Superintelligence. Last updated 2026-09-29}
}

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