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+⧖/lmg/ - a general dedicated to the discussion and development of local language models. News >Claude BANS recomp! >https://www.youtube.com/watch?v=GlRVbIdc_Mk News Archive: https://rentry.org/lmg-news-archive Links: https://rentry.org/LocalModelsLinks Official /lmg/ card: https://files.catbox.moe/cbclyf.png Getting Started https://rentry.org/lmg-lazy-getting-started-guide https://rentry.org/lmg-build-guides https://rentry.org/IsolatedLinuxWebService https://rentry.org/recommended-models https://rentry.org/samplers https://rentry.org/MikupadIntroGuide Further Learning https://rentry.org/machine-learning-roadmap https://rentry.org/llm-training https://rentry.org/LocalModelsPapers Benchmarks LiveBench: https://livebench.ai Programming: https://swe-rebench.com Agentic Coding: https://deepswe.datacurve.ai Context Length: https://github.com/RecapAnon/NoLiMa GPUs: https://github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inference Tools GGUF VRAM Calculator: https://hf.co/spaces/NyxKrage/LLM-Model-VRAM-Calculator Sampler Visualizer: https://artefact2.github.io/llm-sampling Token Speed Visualizer: https://shir-man.com/tokens-per-second Text Gen. UI, Inference Engines https://github.com/lmg-anon/mikupad https://github.com/oobabooga/text-generation-webui https://github.com/LostRuins/koboldcpp https://github.com/ggerganov/llama.cpp https://github.com/theroyallab/tabbyAPI https://github.com/vllm-project/vllm https://rentry.org/custom-uis
/lmg/ - a general dedicated to the discussion and development of local language models. News >Claude BANS recomp! >https://www.youtube.com/watch?v=GlRVbIdc_Mk⧉ News Archive: https://rentry.org/lmg-news-archive⧉ Links: https://rentry.org/LocalModelsLinks⧉ Official /lmg/ card: https://files.catbox.moe/cbclyf.png⧉ Getting Started https://rentry.org/lmg-lazy-getting-started-guide⧉ https://rentry.org/lmg-build-guides⧉ https://rentry.org/IsolatedLinuxWebService⧉ https://rentry.org/recommended-models⧉ https://rentry.org/samplers⧉ https://rentry.org/MikupadIntroGuide⧉ Further Learning https://rentry.org/machine-learning-roadmap⧉ https://rentry.org/llm-training⧉ https://rentry.org/LocalModelsPapers⧉ Benchmarks LiveBench: https://livebench.ai⧉ Programming: https://swe-rebench.com⧉ Agentic Coding: https://deepswe.datacurve.ai⧉ Context Length: https://github.com/RecapAnon/NoLiMa⧉ GPUs: https://github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inference⧉ Tools GGUF VRAM Calculator: https://hf.co/spaces/NyxKrage/LLM-Model-VRAM-Calculator⧉ Sampler Visualizer: https://artefact2.github.io/llm-sampling⧉ Token Speed Visualizer: https://shir-man.com/tokens-per-second⧉ Text Gen. UI, Inference Engines https://github.com/lmg-anon/mikupad⧉ https://github.com/oobabooga/text-generation-webui⧉ https://github.com/LostRuins/koboldcpp⧉ https://github.com/ggerganov/llama.cpp⧉ https://github.com/theroyallab/tabbyAPI⧉ https://github.com/vllm-project/vllm⧉ https://rentry.org/custom-uis⧉Are any of the people who wanted the NUMA fork around? v0.1 is (finally) out. Sorry it took so long. https://github.com/nathanmp/delphi.cpp⧉ Has a bunch of stuff in it - somewhat improved NUMA support, some of ik_llama's tensors, an expert cache, support for DeepSeek's CED architecture, Unsloth at home. Still working on documentation and some other things. Let me know if you have any feature requests, if there are any bugs, what your numbers are like, whatever. Getting around 500 t/s prefill and 45 t/s decode on DeepSeek V4.1F at IQ4_KS on 2 EPYC 7532s, 16x32GB DDR4-3200, and 2 R9700s.
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