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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⧉In you guys' experience, which is "better" (faster, higher quality, etc) : One shotting with the larger slower more intelligent model, OR Research + plan with slow smart model, implement with fast dumber model, audit/revise and fix implementation with larger smarter model? I like the second one because often times the the audit step tends to find gaps beyond just the original plan, and even beyond the spec, but it does take more steps and is slower overall. I guess one shot (no formal research and plan step) + audit could also be an option. Or one shot with the smaller dumber model then audit with the larger slower model. >tl;dr : What's your vibecoding workflow with local models
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