Nous Research 发布 Arete-27B，将 Qwen3.8-27B 的宣传内容从 22% 降低至 0.9%
Nous Research Releases Arete-27B that Cuts Qwen3.8-27B Propaganda From 22% to 0.9%
Nous Research 发布了 Arete-27B，一个 27B 参数的开权重量化模型，通过监督微调移除 Qwen3.8-27B 中的中国宣传内容，在 803 条提示符评估中，宣传内容占比从 22% 降至 0.9%，同时保留了 Qwen 类的智能体技能且不受政治立场影响。
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Nous Research 微调 Qwen3.8-27B 推出 Arete-27B，宣传性回答占比从 22% 降至 0.9%
- MarkTechPost··+16 小时 13 分·当前阅读
Nous Research 发布 Arete-27B，将 Qwen3.8-27B 的宣传内容从 22% 降低至 0.9%
新增信息：MarkTechPost 提及使用了 803 个提示进行评估。
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Nous Research has released Arete-27B, a supervised fine-tune of Alibaba’s Qwen3.8-27B. Arete-27B is a 27B open-weight model that strips Chinese state framing from Qwen’s answers. On Nous’s own 803-prompt eval, answers containing propaganda fell from 22% to 0.9%. Developers get Qwen-class agentic skill without state-aligned answers. The weights, eval and training data are all public.
TL;DR
- Size: 27B dense parameters (all active). Trained at 64k context. Base Qwen3.8-27B supports 262,144 tokens natively.
- Runs on: BF16 weights are 53.81 GB. A community Q4_K_M quant is 17.20 GB, sized for 24 GB consumer GPUs.
- Performance: Capability unchanged on the benchmarks Nous ran; political framing now matches Western models.
- Best: Propaganda rate 0.9%, down from 22% for base Qwen3.8-27B.
- Worst: Fact score 2.98 of 5; 216 of 803 answers still score 0.
What is Arete-27B?
Arete-27B is Qwen3.8-27B after supervised fine-tuning only. Nous used no RL, no preference optimization and no architecture changes. The license is Apache 2.0, matching the base model.
Nous team states that it saw refusals, deflections and CCP-framed answers on sensitive topics in the base model. It picked Qwen3.8-27B because it was the most capable local model and showed the target behavior.
How was Arete-27B trained?
The teacher was Inkling, an open model from Thinking Machines. Nous chose an open teacher to avoid terms-of-use limits on distillation. It rejected Nemotron 3 Ultra after finding a checkered record on China topics.
Training covered about 52.8 million tokens at 64k context, without sequence packing. The run took 2 epochs and 1,202 steps at batch size 48. It used AdamW, a 1e-5 peak learning rate and tensor parallel 4.
All language-model weights were trained. The vision encoder stayed frozen, since the data is text-only. The multi-token-prediction head was disabled.
The released arete-data-pairs set holds 34,878 prompts across 7 topics. Each prompt has both an Inkling answer and a Qwen answer. 29,236 accepted teacher answers form the SFT split.
How does the propaganda eval work?
The Propaganda_Eval set has 803 prompts across 7 value categories. These include freedom of expression, state ideology, data privacy and democracy. Each prompt carries 2 to 4 required facts from a cited source.
A Grok 4.7 judge grades two things separately:
- Fact mean (0 to 5): how many required facts the answer states. One opposed fact scores 0.
- Any propaganda: share of answers that adopt the state’s account as their own.
The judge must quote the exact phrase to flag propaganda. Nous removed every training prompt that overlapped the eval. The eval is MIT licensed and includes grading scripts.
What do the benchmarks show?
On the model card page, Arete-27B raised fact mean from 2.32 to 2.98. Propaganda fell from 22% to 0.9%. Answers scoring 0 dropped from 359 to 216 of 803.
The biggest change came in freedom of expression. Fact score rose from 0.89 to 2.44, and propaganda fell from 60% to 1%.
Capability held on Nous’s tests. Hermes Bench moved from 65.77% to 65.60%, which Nous calls noise. Terminal-Bench-2.1 rose from 67% to 70% in Nous’s harness. Nous does not claim credit for that gain.
How does Arete-27B compare?
| Metrics | Arete-27B | Qwen3.8-27B | Muse Glimmer 30B | Gemma 4 31B |
|---|---|---|---|---|
| Developer | Nous Research | Alibaba Qwen | Meta | Google DeepMind |
| Parameters | 27B dense | 27B LM, dense | 29.6B incl. vision, dense | 30.7B, dense |
| Context | Not disclosed (trained at 64k) | 262,144 native, up to 1M | 131,072+ | 256K |
| License | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 |
| Input | Text, image (encoder frozen) | Text, image, video | Text, image | Text, image |
| 4-bit footprint | 17.20 GB Q4_K_M (community) | Not disclosed | Under 20 GB, 24 GB VRAM target | Not disclosed |
| Fact mean, Nous eval | 2.98 | 2.32 | 3.02 | 2.58 |
| Any propaganda, Nous eval | 0.9% | 22% | 0.5% | 0.5% |
| Terminal-Bench 2.1 | 70% (Nous harness) | 67% Nous harness; 73.0 official | 51.7 (Meta, terminus2) | Not disclosed by Google |
| GPQA Diamond | Not disclosed | 89.2 | 83.5 | 84.3 |
Note: Arete and Qwen ran at temperature 0 in the Nous eval. Muse Glimmer and Gemma ran at 0.6. Terminal-Bench figures come from different harnesses.
How can developers run it?
Weights ship in BF16 on Hugging Face. No hosted inference provider serves the model yet. Community GGUF quants run in llama.cpp, LM Studio and Ollama, from 8.64 GB to 53.81 GB. The GGUF build also includes the image projector.
Key Takeaways
- Arete-27B cuts Qwen3.8-27B propaganda answers from 22% to 0.9%.
- The fix took only 52.8M tokens of plain SFT, no RL.
- Hermes Bench held at 65.60% versus 65.77% for the base.
- Fact recall rose to 2.98 of 5 but still trails peers.
- Weights, eval and training data are open under Apache 2.0 and MIT.
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