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llmsmodel-mergingnex-agiqwen

Rio de Janeiro's "homegrown" LLM appears to be a merge of an existing model

Rio-3.5-Open-397B ≈ 0.6 x Nex-N2_pro + 0.4 x Qwen · Issue #4 · nex-agi/Nex-N2

github.com

June 14, 2026

1 min read

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65/100

Summary

Rio-3.5-Open-397B is a 397B model that combines weights from Nex-N2_pro and Qwen3.5-397B-A17B in a ratio of 0.6 to 0.4. There is no evidence of independent training for this model, indicating it is a direct merge of existing models.

Key Takeaways

  • The Rio-3.5-Open-397B model is a direct element-wise merge of the Nex model and the Qwen3.5-397B-A17B base, with a ratio of approximately 0.6 Nex to 0.4 Qwen.
  • The Rio model identifies itself as "Nex, from Nex-AGI" 79% of the time when its hard-coded prompt is removed.
  • All weight tensors in the Rio model consistently reflect the 0.6/0.4 blend of Nex and Qwen across all layers and components.
  • There is no evidence of original training for the Rio model, as it replicates the backstory of Nex verbatim.
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Coverage & discussions

Hacker News · github.com

Rio de Janeiro's "homegrown" LLM appears to be a merge of an existing model ↗

Community discussions

Hacker News discussion ↗

Historical count: 5 · 5 saved in sample · Historical sample

Analysis ready

Community Sentiment

Negative

Positives

  • The concept of merging open weight models to create a potentially superior AI is intriguing, suggesting a collaborative approach to model development that could enhance performance.
  • The involvement of Rio de Janeiro's IT department in AI work, even with limited resources, indicates a growing interest in leveraging AI technologies for public benefit.

Concerns

  • Claims of improved outputs from post-training were misleading, raising serious ethical concerns about transparency and accountability in AI model development.
  • The model's actual performance is questionable, as it appears to be a weighted merge rather than a genuine fine-tune, which could mislead users about its capabilities.
  • Concerns about the misuse of taxpayer money for potentially fraudulent AI claims highlight the need for stricter oversight in public sector AI initiatives.

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