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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⧉I read the paper and if their numbers are true it seems like the next step for memory in LLMs. It's much more context efficient than previous memory systems. The catch is that the memory system itself is its own transformer, similar to a LoRA, so it will probably require more compute to run alongside models. Might not be worth it for local unless you already have the hardware to spare.
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