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Nanbeige releases Nanbeige4.3-33B, an Apache 2.0 teacher model for smaller agents [N]

Reddit r/MachineLearning ↗ · 2026-10-25

Nanbeige released Nanbeige4.3-33B, an Apache 2.0 teacher model with a 512K context window intended to train smaller agent models like an upcoming 3B variant, reporting strong Terminal-Bench 2.1 results but mixed performance on SWE-Bench Pro.

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#apache-license

EmbeddingGemma 2

Simon Willison's Blog ↗ · 2026-10-06 Cached

Simon Willison comments on Google's release of EmbeddingGemma 2 under the Apache 2.0 license, arguing that open weights are essential for embedding models so users can avoid being locked into proprietary hosted services that could later be discontinued.

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#apache-license

Granite 4.2 LLMs: How They're Built

Hugging Face Blog ↗ · 2026-08-25 Cached

Granite 4.2 is IBM's new family of reasoning LLMs available in 3B, 8B, and 30B sizes, featuring thinking modes, tool calling, and trained with a multi-stage reinforcement learning pipeline under the Apache 2.0 license.

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#apache-license

@AdinaYakup: More players are joining the open source summer RedNote just released dots3-note preview The first open weight model in…

X AI KOLs Following ↗ · 2026-08-14 Cached

RedNote has released dots3-note preview, the first open-weight model in the dots3 family, featuring 280B parameters with 16B active, multi-modal understanding (text, image, video, audio), 512K context length, and Apache 2.0 license, with strong agent capabilities.

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#apache-license

@WolframRvnwlf: Super excited to see a new release from one of my favorite model makers! Command-R was my main model for quite a while …

X AI KOLs Following ↗ · 2026-05-20 Cached

Cohere announces Command A+, their most powerful open-source LLM yet, optimized for efficiency and released under the Apache license.

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Gemma 4: Byte for byte, the most capable open models

Google DeepMind Blog ↗ · 2026-04-02 Cached

Google DeepMind introduces Gemma 4, its most capable family of open models to date, designed for advanced reasoning and agentic workflows with high intelligence-per-parameter efficiency across multiple sizes.

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