𓁟 host1.bitchan.one

+⧖/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
+⧖/lmg/ - a general dedicated to the discussion and development of local language models. News >(10/29) Mistral Large 4 (Le Chonk) 1T-A52B released: https://mistral.ai/news/mistral-large-4-oss >(10/27) NVidia releases Nemotron 3.5 Transcribe 30B-A4B: https://hf.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B >(10/26) XHToken releases Spark-X2.6-1.7B: https://hf.co/XHToken/Spark-X2.6-1.7B >(10/26) LiquidAI releases LFM3 1.7B and 2B: https://hf.co/collections/LiquidAI/lfm3 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 >(10/29) Mistral Large 4 (Le Chonk) 1T-A52B released: https://mistral.ai/news/mistral-large-4-oss >(10/27) NVidia releases Nemotron 3.5 Transcribe 30B-A4B: https://hf.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B >(10/26) XHToken releases Spark-X2.6-1.7B: https://hf.co/XHToken/Spark-X2.6-1.7B >(10/26) LiquidAI releases LFM3 1.7B and 2B: https://hf.co/collections/LiquidAI/lfm3 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
𓀐 @f?7+G7*y: I saw an anon mention a QAT quant of gemma4-31b that was from google themselves, yet I dont see any quants on their HF. Any anons have suggestions for cope quants of 31b? want to try fitting one entirely into 16gb VRAM

unsloth's goofs are genuinely better in this case, the google goofs are not proper Q4_0 across the board, the embeddings are in Q6_K in theirs for e.g and various testing showed that the qat does behave slightly better if it's actually being run at q4 beside being faster and a bit smaller to boot https://huggingface.co/unsloth/gemma-4-31B-it-qat-GGUF it's not the first time google is being retarded while publishing goofs either, these were the only good goofs for gemma 3: https://huggingface.co/stduhpf/google-gemma-3-27b-it-qat-q4_0-gguf-small

⧖expiring <1d0/1 kept1w ago

𓀐 @4hct?gtn

⏱ 1w 👍 0

⬆ upvote 0