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Rune 26B-A4B v3 is an open-weight multimodal decision model developed by Invergent and distributed through Surogate, designed for fast classification, scoring, and probability estimation. Based on Gemma 4 26B-A4B, it contains 26.5B parameters with eight of 128 experts activated per token. The model accepts text, structured data, and images within a 262K-token context window and supports three decision formats: Choice for categorical selection, Noul for binary judgments, and Score for ordinal evaluation. It returns probability distributions in a single forward pass, with optional reasoning for uncertain questions. ...

...You supply the model file — nothing is sent to any server. At a glance Downloads 0 Views 5 Rating No ratings yet License Apache-2.0 Built by Python (tkinter + CLI), llama-cpp-python Branch main Updated Sep 4, 2026 Screenshots Releases All artifacts are signed — verify your download . localllm-studio 1.0.5 v1.0.5 Aug 20, 2026 Windows: double-click the -Setup.exe to install. Authenticode-signed with the Dosvak LLC EV code-signing certificate (Sectigo Public Code Signi Built entirely by AI. Source: https://github.com/quickpod/localllm-studio

Muse Glimmer-30B is Meta Superintelligence Lab’s open-weight multimodal model built specifically for autonomous agentic tasks on consumer hardware. Distilled from the larger Muse Spark, it combines multi-step reasoning, reliable tool use, coding, failure recovery, and image understanding in a dense 29.6B-parameter architecture with a dedicated 1.8B-parameter perception encoder. It supports more than 100 languages and a 131K+ token context window, allowing agents to maintain coherent plans...

Mellum-4b-base is JetBrains’ first open-source large language model designed and optimized for code-related tasks. Built with 4 billion parameters and a LLaMA-style architecture, it was trained on over 4.2 trillion tokens across multiple programming languages, including datasets such as The Stack, StarCoder, and CommitPack. With a context window of 8,192 tokens, it excels at code completion, fill-in-the-middle tasks, and intelligent code suggestions for professional developer tools and IDEs. ...

...It contains 78B total parameters while activating only 3.46B per token, providing substantial model capacity with relatively low inference compute. Its 50-layer architecture uses 384 experts per layer, with six routed experts and one shared expert active during processing, alongside a 4:1 combination of sliding-window and grouped-query attention. Kolibri supports explicit reasoning with configurable low, medium, and high effort levels, as well as structured tool calling for agentic workflows. Its native 262K-token context can scale to a validated 1M tokens, enabling long-document processing, RAG, research, and extended coding tasks. ...