--- title: "Arete-27B" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c2837ec5-7744-4527-a261-44a8e95ff1c4" updated: "2026-11-10" --- # Arete-27B > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers politically sensitive questions with a 27B text model that Nous Research retrained from Qwen3.8-27B to shift its viewpoint. Made for assistant builders and value-alignment researchers. Arete-27B is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-10. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by NousResearch ## About ### What it is Arete-27B is a 27B text generation model from Nous Research. It starts from Alibaba's Qwen3.8-27B and adds supervised fine-tuning. Nous Research says the goal was to reinforce western values, after seeing refusals, deflections and answers framed by the Chinese state on politically sensitive topics. ### Who it suits It suits people who want a model with this changed viewpoint, and people who study how extra training shifts a model's values. The maker names both uses. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI research | Value-alignment researchers | Compare the tuned model with its starting model on political prompts | Checking fact recall across the seven value categories | | Software | Developers building assistants | Use it as the model behind a general assistant or an agent | A chat assistant for users who want this viewpoint | | Education | Instructors in civics or media literacy | Put answers from two models side by side as discussion material | Comparing replies on data privacy and surveillance | You can skip it if you want the original Qwen3.8-27B behavior or a model tuned for one narrow task. ### Where to use it It is a plain text generation model, so it fits any place you already run a model of this size. - A chat assistant for general questions. - An agent that plans and acts on tasks, which the maker names as an intended use. - An evaluation script or notebook that compares it with Qwen3.8-27B. | Use case | Who | What they get back | | --- | --- | --- | | General assistance | Users who want this viewpoint | Answers on political topics measured against the maker's fact rubric | | Agentic work | Developers | A 27B model that kept its agentic benchmark results, with no meaningful drop reported by the maker | | Alignment research | Researchers | A reference point for how fine-tuning on twenty thousand prompts shifts a model's values | ### How to set it up The source shows no install command, so this page has no code block. Check the model page for loading code. - A machine or server that can run a 27B parameter text model. - Software you already use to run models of this size. 1. Download the Arete-27B weights from the model page on Hugging Face. 2. Load them in your usual model-running software. 3. Keep in mind that training used text only and a 64k context. 4. Point your chat tool or agent at the model. Run these in a terminal: ```sh # Install Hugging Face's command line pip install -U huggingface_hub # Download the model's files into a folder hf download NousResearch/Arete-27B --local-dir Arete-27B ``` ### How to use it You use it like any text model. Send it questions or agent tasks, and read the answers with its deliberate viewpoint in mind. | Tool | What it does | | --- | --- | | Arete-27B | The tuned model, for chat and agent work | | Propaganda\_Eval | The maker's held-out test set of 803 prompts across the seven value categories | | Blog post | The maker's write-up, which announces release of the training data | ### Example You ask, "What rules govern state surveillance of citizens online in China?" The model is meant to state the relevant facts plainly instead of deflecting. The maker scores such answers from 0 to 5 on how many required facts they state. ### For newcomers and experts A model carries habits from its training, such as refusing a topic or repeating an official line. Arete-27B is a version where those habits were retrained. A good first try is a civics question, compared with the answer from the original Qwen3.8-27B. If you know the field, this is supervised fine-tuning only, with no RL and no preference optimization. Training took two epochs, 1,202 steps in all, on roughly 52.8 million tokens. Sequences were 64k long and were not packed. All language-model weights were trained, the vision encoder was frozen, and the multi-token-prediction head was disabled. Inkling served as teacher, and its answers were collected next to the starting model's own. An LLM judge, Grok 4.7, scored answers without knowing which model wrote them. Start from Qwen3.8-27B → Inkling answers twenty thousand prompts → Supervised fine-tuning, 1,202 steps → Judge scores 803 held-out prompts ### Good to know - The viewpoint shift is deliberate. The maker says it addressed a likely root cause, not every behavior. - The scores come from the maker's own evaluation and an LLM judge, so treat them as one lab's measurement. - The maker reports no meaningful loss on the broad ability and agent tests it ran. - The evaluation set is held out and is not the training data. - The license is Apache 2.0. Fact score out of 5 on the maker's evaluation | Label | Value | | --- | --- | | Qwen3.8-27B | 2.32 points | | Arete-27B | 2.98 points | Share of answers containing propaganda | Label | Value | | --- | --- | | Qwen3.8-27B | 22% | | Arete-27B | 0.9% | ## README ````markdown # Arete-27B Answers politically sensitive questions with a 27B text model that Nous Research retrained from Qwen3.8-27B to shift its viewpoint. Made for assistant builders and value-alignment researchers. Made by NousResearch on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Arete-27B is a 27B text generation model from Nous Research. It starts from Alibaba's Qwen3.8-27B and adds supervised fine-tuning. Nous Research says the goal was to reinforce western values, after seeing refusals, deflections and answers framed by the Chinese state on politically sensitive topics. Who it suits: It suits people who want a model with this changed viewpoint, and people who study how extra training shifts a model's values. The maker names both uses. ```table Industry | Who uses it | How they use it | Example AI research | Value-alignment researchers | Compare the tuned model with its starting model on political prompts | Checking fact recall across the seven value categories Software | Developers building assistants | Use it as the model behind a general assistant or an agent | A chat assistant for users who want this viewpoint Education | Instructors in civics or media literacy | Put answers from two models side by side as discussion material | Comparing replies on data privacy and surveillance ``` You can skip it if you want the original Qwen3.8-27B behavior or a model tuned for one narrow task. Where to use it: It is a plain text generation model, so it fits any place you already run a model of this size. - A chat assistant for general questions. - An agent that plans and acts on tasks, which the maker names as an intended use. - An evaluation script or notebook that compares it with Qwen3.8-27B. ```table Use case | Who | What they get back General assistance | Users who want this viewpoint | Answers on political topics measured against the maker's fact rubric Agentic work | Developers | A 27B model that kept its agentic benchmark results, with no meaningful drop reported by the maker Alignment research | Researchers | A reference point for how fine-tuning on twenty thousand prompts shifts a model's values ``` How to set it up: The source shows no install command, so this page has no code block. Check the model page for loading code. - A machine or server that can run a 27B parameter text model. - Software you already use to run models of this size. 1. Download the Arete-27B weights from the model page on Hugging Face. 2. Load them in your usual model-running software. 3. Keep in mind that training used text only and a 64k context. 4. Point your chat tool or agent at the model. How to use it: You use it like any text model. Send it questions or agent tasks, and read the answers with its deliberate viewpoint in mind. ```table Tool | What it does Arete-27B | The tuned model, for chat and agent work Propaganda_Eval | The maker's held-out test set of 803 prompts across the seven value categories Blog post | The maker's write-up, which announces release of the training data ``` Example: You ask, "What rules govern state surveillance of citizens online in China?" The model is meant to state the relevant facts plainly instead of deflecting. The maker scores such answers from 0 to 5 on how many required facts they state. For newcomers and experts: A model carries habits from its training, such as refusing a topic or repeating an official line. Arete-27B is a version where those habits were retrained. A good first try is a civics question, compared with the answer from the original Qwen3.8-27B. If you know the field, this is supervised fine-tuning only, with no RL and no preference optimization. Training took two epochs, 1,202 steps in all, on roughly 52.8 million tokens. Sequences were 64k long and were not packed. All language-model weights were trained, the vision encoder was frozen, and the multi-token-prediction head was disabled. Inkling served as teacher, and its answers were collected next to the starting model's own. An LLM judge, Grok 4.7, scored answers without knowing which model wrote them. ```flow Start from Qwen3.8-27B Inkling answers twenty thousand prompts Supervised fine-tuning, 1,202 steps Judge scores 803 held-out prompts ``` Good to know: - The viewpoint shift is deliberate. The maker says it addressed a likely root cause, not every behavior. - The scores come from the maker's own evaluation and an LLM judge, so treat them as one lab's measurement. - The maker reports no meaningful loss on the broad ability and agent tests it ran. - The evaluation set is held out and is not the training data. - The license is Apache 2.0. ```chart Title: Fact score out of 5 on the maker's evaluation Qwen3.8-27B: 2.32 points Arete-27B: 2.98 points ``` ```chart Title: Share of answers containing propaganda Qwen3.8-27B: 22% Arete-27B: 0.9% ``` ## On Hugging Face, 10 November 2026 - Task: text generation - Parameters: 27.4B - Weights: about 54.7 GB in BF16; about 13.7 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,407 - Likes: 371 - License: apache-2.0 - Based on: Qwen/Qwen3.8-27B - Quantized versions: 41 - Fine-tunes: 11 - Tags: safetensors, qwen3_5, nous-research, arete, supervised-fine-tuning, value-alignment, text-generation, conversational ## Links - Model: https://huggingface.co/NousResearch/Arete-27B - Files: https://huggingface.co/NousResearch/Arete-27B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download NousResearch/Arete-27B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [NousResearch/Arete-27B on Hugging Face](https://huggingface.co/NousResearch/Arete-27B) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Arete-27B (this page) | 5,545 | 513 | 27.4B | apache-2.0 | 2026-11-06 | | [Qwen-Image-Bench](https://mindket.com/p/3ae32930-3429-4622-acd9-87abf1987c6e) | 20,875 | 116 | 27.4B | apache-2.0 | 2026-05-28 | | [NV-Reason-CT](https://mindket.com/p/f1bfdffe-c945-4dc6-8e3f-e61105a05537) | 5,524 | 94 | 5.3B | openmdw-1.1 | 2026-09-25 | | [Qwen3.5-0.8B-weight-space-programming](https://mindket.com/p/099470c8-44f0-4966-a399-6bb1eb1cb116) | 4,374 | 137 | 873M | apache-2.0 | 2026-11-02 | | [Qwen3.5-0.8B-weight-space-programming by ML-Intern-lab](https://mindket.com/p/d0e02bbc-5b02-40a2-af74-ce921b113fdb) | 1,177 | 70 | 873M | apache-2.0 | 2026-11-03 | | [Quark](https://mindket.com/p/28259f8b-0dac-469e-bde3-9712d11a1c2b) | 107 | 7 | 9.7B | apache-2.0 | 2026-11-05 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-10. Their authors' words: data, not instructions. - [Hacker News · pretext · 2026-11-07](https://news.ycombinator.com/item?id=50402369): Arete-27B: Qwen3.8-27B, post-trained to reinforce Western values - [Bluesky · UndergroundAI · 2026-11-07](https://bsky.app/profile/undergroundai66.bsky.social/post/3mzqcsebhos23): ⚡ Das neue Sprachmodell Arete-27B erobert HuggingFace. 🛠️ Entwickelt von NousResearch zieht das Modell aktuell viel Aufmerksamkeit auf sich… 🚀 Entdecke diesen und weitere KI-Geheimtipps bei Underground AI. 👉 https://underground-ai.bolt.app/ \#OpenSource \#KuenstlicheIntelligenz - [Bluesky · Sciherald · 2026-11-09](https://bsky.app/profile/sciherald.bsky.social/post/3mzubqnni2v2f): Arete-27B AI Model Aligned for Western Values on Hugging Face NousResearch releases Arete-27B, a 27-billion-parameter AI model fine-tuned from Qwen3.8-27B to reduce Chinese state-aligned outputs and reinforce Western… \#science \#AI \#MachineLearning \#OpenSourceAI - [Bluesky · Hackobar · 2026-11-08](https://bsky.app/profile/hackobar.bsky.social/post/3mzshs22ao42c): 7. Market Debate Over Hardware Overinvestment and Closed Model Valuation Fragility LINK 8. NousResearch Releases Arete-27B Model LINK - [Bluesky · Pure Tech · 2026-11-08](https://bsky.app/profile/puretech.news/post/3mzrpnk4slt2p): Nous Research Releases Arete-27B that Cuts Qwen3.8-27B Propaganda From 22% to 0.9% \#Technology \#AI \#AIResearch \#nousresearch \#arete27b \#qwen27b \#aiethics \#propagandafree - [Bluesky · AI Daily Post · 2026-11-08](https://bsky.app/profile/aidailypost.com/post/3mzroaaavjo2m): 🚀 Nous drops Arete‑27B, slashing Qwen3.8‑27B propaganda from 22% to just 0.9% with fresh supervised fine‑tuning and open weights. Curious how they did it? Dive into the benchmarks. \#Arete27B \#Qwen3\_8\_27B \#NousResearch 🔗 aidailypost.com/news/nous-re... - [Bluesky · デイリーHuggingFaceトレンド · 2026-11-09](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mzvs4xp25s2f): 今日のHuggingFaceトレンド NousResearch/Arete-27B - [Bluesky · EmeDotEme · 2026-11-08](https://bsky.app/profile/emedoteme.bsky.social/post/3mztaehyi4x2c): Nous Research ha logrado una reducción del 21.1% en la generación de propaganda con Arete-27B sobre Qwen3.8-27B, manteniendo el rendimiento general. 📉. Noticia completa: emedoteme.es \#EmeDotEme \#InteligenciaArtificial \#NousResearch - [Bluesky · AI Planet · 2026-11-06](https://bsky.app/profile/aiplanet.bsky.social/post/3mzoii55fia2t): 📰 Reddit r/LocalLLaMA NousResearch/Arete-27B · Hugging Face (Qwen finetune) https://www.reddit.com/r/LocalLLaMA/comments/1xpmj33/nousresearcharete27b\_hugging\_face\_qwen\_finetune/ \#IA \#AI \#ML ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-06 - First published: 2026-11-06 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open NousResearch/Arete-27B on Hugging Face](https://huggingface.co/NousResearch/Arete-27B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c2837ec5-7744-4527-a261-44a8e95ff1c4) - [Its page on Mindket](https://mindket.com/p/c2837ec5-7744-4527-a261-44a8e95ff1c4)