Model release

Rnj-1 Instruct

Rnj-1 Instruct is an open-weight model release in the Rnj family from Essential AI1, version rnj-1-instruct, released Dec 2025. Its weights can be downloaded by the public1 under the Apache License 2.01 2.

The instruction-tuned version of Essential AI's Rnj-1, an 8.3B-parameter dense model trained from scratch. The base model was pretrained on 8.4T tokens at an 8K context, extended to a 32K context in a 380B-token mid-training stage, and then given a 150B-token supervised fine-tuning stage to produce this model.1

Model hub: Model card (Hugging Face) (external site: huggingface.co)
Model hub: Rnj-1 base model card (external site: huggingface.co)
Model hub: GGUF build for llama.cpp (external site: huggingface.co)
Release notes: Rnj-1 announcement (external site: essential.ai)
License: LICENSE (external site: huggingface.co)

On AI on Hugging Face: EssentialAI/rnj-1-instruct, EssentialAI/rnj-1-instruct-GGUF, EssentialAI/rnj-1-instruct-evals

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

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 2
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

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 Instruct
Item	Status	Notes and evidence
WeightsCan the general public download the model parameters for this release?	Public	Base and instruction-tuned weights are both published, plus an official GGUF build.1
Inference codeIs code for running the model published?	Public	The card documents use with Transformers 4.51.2 or later, vLLM (with the hermes tool-call parser), SGLang, and llama.cpp via the GGUF build.1
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.	Unknown	The card says the model was trained on online web data and gives token counts per stage, but does not name the datasets.1
Training recipeAre the training configuration and procedure documented in enough detail to follow?	Partial	The card lists the stage token budgets, context lengths, the Muon optimizer, the learning-rate schedule, and batch sizes; the fine-tuning data is not described.1
Evaluation materialsPublic = evaluation code or prompts that let others re-run the evaluations are published. Partial = results only.	Public	The card reports evaluation results, and Essential AI publishes the model's generations for its evaluations as Hugging Face datasets (for example rnj-1-instruct-evals).1 3

What it is useful for

The model card describes it for code generation across programming languages, agentic coding in frameworks such as mini-SWE-agent and Cline, tool calling, fill-in-the-middle code infilling, and math and science questions. It notes the model is mainly a coding and STEM model rather than one tuned for factual recall.1

Run and use notes

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

The card recommends always using a system prompt and temperatures from 0 to 0.2, and warns that without them the model can truncate outputs or write code for non-code tasks. It documents extending the context to 128K with YaRN RoPE scaling by editing config.json and reports some regressions at that length, particularly on some science and performance evaluations.1

Organization context

Essential AI

Provenance and derivatives

Fine-tuned by Essential AI from its own Rnj-1 base model, which it trained from scratch. The card says the architecture is similar to Gemma 3 but uses only global attention, with YaRN for long-context extension.1

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

Other releases in the Rnj family

Rnj-1.5 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.
Legacy data assessment (v0.1): 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 from scratch and published by Essential AI, which gives its location as San Francisco, CA; the Essential-Web paper lists the same affiliation.1 4 5

Assessed Sep 29, 2026

Sources

1.
EssentialAI/rnj-1-instruct model card (external site: huggingface.co)
Essential AI · Model card · published Dec 8, 2025 · accessed Sep 29, 2026
2.
EssentialAI/rnj-1-instruct LICENSE (external site: huggingface.co)
Essential AI · License · accessed Sep 29, 2026
3.
EssentialAI/rnj-1-instruct-evals dataset (external site: huggingface.co)
Essential AI · Dataset card · accessed Sep 29, 2026
4.
About | Essential AI (external site: essential.ai)
Essential AI · Official page · accessed Sep 29, 2026
5.
Essential-Web v1.0: 24T tokens of organized web data (arXiv 2506.14111v2) (external site: arxiv.org)
Essential AI · Paper · published Jun 19, 2025 · accessed Sep 29, 2026

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Cite this page

APA style

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

BibTeX

@misc{usasi_rnj_1_instruct,
  author = {{USASI}},
  title = {Rnj-1 Instruct},
  year = {2026},
  month = sep,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/rnj-1-instruct/}},
  note = {United States of America Superintelligence. Last updated 2026-09-29}
}

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