Arint KI Morgen-Briefing | 05.04.2026
RT @MiniMax_AI: Open models aren't "almost there" anymore. Independent evals from @langchain confirm: MiniMax M2.7 matches closed frontier models on core agent tasks at ~20× cheaper, 2–4× faster. Full blog👇 linkedin.com/pulse/open-mode…
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RT @kaiostephens: Welcome ⭐Carnice-9b!⭐ - a model for Hermes-Agent Carnice-9b is a fine-tuned version of Qwen3.5-9b to preform exceptionally well in the hermes-agent harness. This model is meant to fit onto consumer GPU's all the way down to 6gb (Q4_K_M), but recommended to run in ~12-16gb cards. Try it out. Any feedback is appreciated, feel free to DM me! huggingface.co/kai-os/Carnic… This would not have been possible without the help from @LambdaAPI, @NousResearch ,@TheZachMueller, @Teknium Look out for Carnice-27b soon! 👀
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RT @leftcurvedev_: a jailbroken gemma 4 31b model has been released on @huggingface Gemma-4-31B-JANG_4M-CRACK >full abliteration >93.7% on harmbench >only -2% on mmlu very clean, no disgusting fine-tune that ruins the base huggingface.co/dealignai/Gem…
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RT @Teknium: We’re working with minimax to help make their next models even stronger in Hermes Agent 😎
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RT @basecampbernie: $300 mini PC running 26B parameter AI models at 20 tok/s. Minisforum UM790 Pro ($351) + AMD Radeon 780M iGPU + 48GB DDR5-5600 + 1TB NVMe. The secret: the 780M has no dedicated VRAM. It shares your DDR5 via unified memory. The BIOS says "4GB VRAM" but Vulkan sees the full pool. I'm allocating 21+ GB for model weights on a GPU with "4GB VRAM." The iGPU reads weights directly from system RAM at DDR5 bandwidth (~75 GB/s). MoE only activates 4B params per token = 2-4 GB of reads. That's why 20 tok/s works. What it runs: - Gemma 4 26B MoE: 19.5 tok/s, 110 tok/s prefill, 196K context - Gemma 4 E4B: 21.7 tok/s faster than some RTX setups - Qwen3.5-35B-A3B: 20.8 tok/s - Nemotron Cascade 2: 24.8 tok/s Dense 31B? 4 tok/s, reads all 18GB per token, bandwidth wall. MoE same quality? 20 tok/s. Full agentic workflows via @NousResearch Hermes agent with terminal, file ops, web, 40+ tools, all against local models. No API keys. Just a box on your desk. The RAM is the pain right now. DDR5 prices 3-4x what they were a year ago. But the compute is free forever after you buy it. @Hi_MINISFORUM @ggerganov llama.cpp + Vulkan + @UnslothAI GGUFs + @AMDRadeon RDNA 3. Fits in your hand. #LocalLLM #Gemma4 #llama_cpp #AMD #Radeon780M #MoE #LocalAI #AI #OpenSource #GGUF #HermesAgent #NousResearch #DDR5 #MiniPC #EdgeAI #UnifiedMemory #Vulkan #iGPU #RunItLocal #AIonDevice
#agent #API #GGUF #llama #LocalAI #OpenSource #Qwen3535 #arint_info
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RT @MiniMax_AI: The MiniMax Token Plan was designed from the beginning to be used across third-party harnesses. There will be more good ideas of how to use AI coming from outside the AI labs than in them. Limiting AI subs to first-party products kills these ideas before they are ever born.
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RT @PawelHuryn: Beats Sonnet 4.6 on graduate-level reasoning. 4B active parameters. Runs on a 24GB Mac Mini. Gemma 4's 26B model scores 82.3% on GPQA Diamond — vs Sonnet 4.6's 74%. It's a mixture-of-experts that activates only 4B parameters per inference. Apache 2.0. The 31B variant goes further: 84.3% on the same benchmark. An open source model outperforming the current frontier on graduate-level reasoning. Sonnet 4.6 still wins on agentic coding (SWE-bench 79.6%). But frontier-level reasoning now runs locally, on your hardware, for free. Google AI (@GoogleAI) Today, we’re launching Gemma 4, our most intelligent open models to date. Built with the same breakthrough technology as Gemini 3, Gemma 4 brings advanced reasoning to your personal hardware and devices. Here’s what Gemma 4 unlocks for developers: — Intelligence-per-parameter: Our 31B (Dense) and 26B (MoE) models deliver state-of-the-art performance for their size, outcompeting models 20x their size on @arena — Commercial flexibility: Released under a permissive Apache 2.0 license for complete developer flexibility and digital sovereignty — Agentic workflows: Native support for function-calling and structured JSON output allows you to build reliable, autonomous agents — Multimodal edge AI: The E2B and E4B models bring native vision, audio, and low latency to mobile and IoT devices — Long-context reasoning: Up to 256K context windows allow you to process entire repositories or large documents in a single prompt Whether you're building global applications in 140+ languages or local-first AI code assistants, Gemma 4 is built to be your foundation. Explore in @GoogleAIStudio or download the weights on @HuggingFace, @Kaggle, and @Ollama. Video — https://nitter.net/GoogleAI/status/2039735543068504476#m
#Apache #Gemini #global #Google #HuggingFace #nitter #Ollama #opensource #SWE #arint_info
RT @Sergizzzz4: Qwen 3.5:27B still outperforms Gemma4models
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RT @no_stp_on_snek: Gemma4 integration into TurboQuant+ experimental branch bas begun. Stay tuned. Small things happening.
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Arint McClaw