From 9aa6b93c72722cbf214651da3fb51e36563ee305 Mon Sep 17 00:00:00 2001 From: Michael Peter Christen Date: Tue, 11 Aug 2026 22:59:23 +0200 Subject: [PATCH] modified model list --- htroot/LLMSelection_p.html | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/htroot/LLMSelection_p.html b/htroot/LLMSelection_p.html index 334d062bf..8d983fda6 100644 --- a/htroot/LLMSelection_p.html +++ b/htroot/LLMSelection_p.html @@ -98,20 +98,17 @@ let persistedModelCapabilities = {}; const RECOMMENDED_MODELS = [ - ["qwen2.5:1.5b-instruct-q4_K_M", " 0.88", "1GB", "Good small model for 2GB RAM", "Alibaba", "apache-2.0"], - ["llama3.2:3b-instruct-q4_K_M", " 0.66", "2GB", "A good 3B model", "Meta", "llama3.2"], ["qwen3:4b-instruct-2507-q4_K_M", " 7.70", "3GB", "a good 4B model", "Alibaba", "apache-2.0"], - ["hf.co/janhq/Jan-v3-4B-base-instruct-gguf:Q4_K_M", " 8.19", "3GB", "a brilliant 4B model, post-trained with large teacher from qwen3:4b", "jan.ai", "apache-2.0"], - ["hf.co/unsloth/medgemma-4b-it-GGUF:Q4_K_M", " 0.60", "4GB", "Medical Knowledge and Vision", "Google", "health-ai-developer-foundations"], - ["frob/qwen3.5-instruct:4b", " 8.52", "4GB", "best model for its size, non-thinking version", "Alibaba", "apache-2.0"], - ["olmo-3:7b-instruct-q4_K_M", " 2.22", "5GB", "open and accessible training data, open-source training code", "allenai.org", "apache-2.0"], - ["qwen3.5:9b-q4_K_M", "18.37", "7GB", "multimodal, outstanding for its size, long-context 256K Tokens, strong instruction following model", "Alibaba", "apache-2.0"], - ["gemma4:12b-it-qat", "47.09", "8GB", "Very mighty, very small model from Google", "Google", "apache-2.0"], - ["ministral-3:14b-instruct-2512-q4_K_M", " 8.55", "10GB", "European flagship model, strong multilangual, vision, agentic", "mistral.ai", "apache-2.0"], + ["frob/qwen3.5-instruct:4b", " 8.52", "4GB", "good model for its size, non-thinking version", "Alibaba", "apache-2.0"], + ["hf.co/InternScience/Agents-A1-4B-Q8_0-GGUF:Q8_0", "24.34", "6GB", "fine-tuned version of qwen3.5-instruct:4b; extreme benchmark gains using teacher model", "Intern Science, Shanghai Artificial Intelligence Laboratory", "apache-2.0"], + //["qwen3.5:9b-q4_K_M", "18.37", "7GB", "multimodal, outstanding for its size, long-context 256K Tokens, strong instruction following model", "Alibaba", "apache-2.0"], + //["gemma4:12b-it-qat", "47.09", "8GB", "Very mighty, very small model from Google", "Google", "apache-2.0"], + ["ministral-3:14b-instruct-2512-q4_K_M", " 8.55", "10GB", "European model, strong multilangual, has vision, is agentic", "mistral.ai", "apache-2.0"], ["hf.co/mradermacher/Ling-mini-2.0-GGUF:Q4_K_M", "20.00", "10GB", "very fast 16B MoE model with 1.4B activated parameters per expert", "InclusionAI", "mit"], ["olmo-3.1:32b-instruct-q4_K_M", "10.25", "19GB", "open and accessible training data, open-source training code", "allenai.org", "apache-2.0"], - ["qwen3.6:27b-mtp-q4_K_M", "70.84", "17GB", "Exceptional good 27B model with vision, ranking above GPT-4-turbo, GPT-4.1-nano, GPT-o1, GPT-4o-mini", "Alibaba", "apache-2.0"], - ["qwen3.6:35b-a3b-mtp-q4_K_M", "63.03", "22GB", "Almost as good as the 27B model, but much faster", "Alibaba", "apache-2.0"] + ["qwen3.6:27b-mtp-q4_K_M", "49.22", "17GB", "Exceptional good 27B model with vision, ranking above GPT-4-turbo, GPT-4.1-nano, GPT-o1, GPT-4o-mini", "Alibaba", "apache-2.0"], + ["muse-glimmer:30b-q4_K_M-dflash", "55.55", "21GB", "Agent-first model from Meta, tuned for tool use, long tasks, and failure recovery", "Meta", "apache-2.0"], + ["qwen3.6:35b-a3b-mtp-q4_K_M", "45.91", "22GB", "Almost as good as the 27B model, but much faster", "Alibaba", "apache-2.0"] ]; const MODEL_TABLE_HEADERS = ["Model", "Ranking", "Size", "Description", "Provider", "License", "Actions"];