huggingface.co/LiquidAI/LFM2… [Translated from EN to English]
→ View original post on X — @maximelabonne, 2026-02-24 17:48 UTC
OPEN SOURCE
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Unfortunate timing: 24B-A2B release coincides with Qwen3.5 launch
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Releasing a 24B-A2B model on the same day as Qwen3.5-35B-A3B is NOT great timing 🥲 Qwen (@Alibaba_Qwen) 🚀 Introducing the Qwen 3.5 Medium Model Series Qwen3.5-Flash · Qwen3.5-35B-A3B · Qwen3.5-122B-A10B · Qwen3.5-27B ✨ More intelligence, less compute. • Qwen3.5-35B-A3B now surpasses Qwen3-235B-A22B-2507 and Qwen3-VL-235B-A22B — a reminder that better architecture, data quality, and RL can move intelligence forward, not just bigger parameter counts. • Qwen3.5-122B-A10B and 27B continue narrowing the gap between medium-sized and frontier models — especially in more complex agent scenarios. • Qwen3.5-Flash is the hosted production version aligned with 35B-A3B, featuring: – 1M context length by default – Official built-in tools 🔗 Hugging Face: huggingface.co/collections/Q… 🔗 ModelScope: modelscope.cn/collections/Qw… 🔗 Qwen3.5-Flash API: modelstudio.console.alibabac… Try in Qwen Chat 👇 Flash: chat.qwen.ai/?models=qwen3.5… 27B: chat.qwen.ai/?models=qwen3.5… 35B-A3B: chat.qwen.ai/?models=qwen3.5… 122B-A10B: chat.qwen.ai/?models=qwen3.5… Would love to hear what you build with it. — https://nitter.net/Alibaba_Qwen/status/2026339351530188939#m
→ View original post on X — @maximelabonne, 2026-02-24 17:01 UTC
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LFM2-24B-A2B: Liquid AI’s Fastest 24B MoE Model Released
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Meet LFM2-24B-A2B, @liquidai's largest model. 24B MoE, 2B active. Blazing fast even on CPU. Available now in LM Studio 👾💧 lmstudio.ai/models/lfm2-24b-…
→ View original post on X — @maximelabonne, 2026-02-24 15:44 UTC
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Liquid AI releases LFM2-24B-A2B model for on-device inference
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ollama run lfm2:24b-a2b .@liquidai's latest on-device model is here! It's the largest LFM2 model yet, and is designed to run fast on device, and fits on devices with 32GB of unified memory. Liquid AI (@liquidai) Today, we release our largest LFM2 model: LFM2-24B-A2B 🐘 > 24B total parameters > 2.3B active per token > Built on our hybrid, hardware-aware LFM2 architecture It combines LFM2’s fast, memory-efficient design with a Mixture of Experts setup, so only 2.3B parameters activate each run. The result: best-in-class efficiency, fast edge inference, and predictable log-linear scaling all in a 32GB, 2B-active MoE footprint. 🧵 — https://nitter.net/liquidai/status/2026301771539202269#m
→ View original post on X — @maximelabonne, 2026-02-24 14:36 UTC
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Liquid AI Releases Largest LFM2-24B-A2B Model with Fast Inference
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Early checkpoint of our biggest LFM2 model to date 🎉 It shows good scaling and extremely fast inference vs. gpt-oss-20b and Qwen3-30B-A3B We'll release an LFM2.5 version with more pre-training and RL in a few months Liquid AI (@liquidai) Today, we release our largest LFM2 model: LFM2-24B-A2B 🐘 > 24B total parameters > 2.3B active per token > Built on our hybrid, hardware-aware LFM2 architecture It combines LFM2’s fast, memory-efficient design with a Mixture of Experts setup, so only 2.3B parameters activate each run. The result: best-in-class efficiency, fast edge inference, and predictable log-linear scaling all in a 32GB, 2B-active MoE footprint. 🧵 — https://nitter.net/liquidai/status/2026301771539202269#m
→ View original post on X — @maximelabonne, 2026-02-24 14:31 UTC
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AI mimics devs but lacks real expertise
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When you say "act as a senior developer" the model doesn't think like one. It writes like one. Big difference. It pattern-matches to how developers sound in training data. Not how they actually solve problems. You get confident-sounding output. Not expert-level thinking.
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NanoClaw: Open-source Apple-based AI assistant
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Someone just built a Claude AI assistant that runs entirely inside Apple containers. It's called NanoClaw and it connects to WhatsApp, has memory, scheduled jobs, and runs on Anthropic's Agent SDK. 12K stars in days. 100% Opensource.
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29k GitHub Contributions: Building and Shipping Consistently
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29,023 contributions in the last year. Building quietly. Shipping consistently.
Compounding daily. How did I do? GitHub: https://
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Scikit-Learn Classification Techniques for Machine Learning
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Scikit-Learn for Classification in Machine Learning. @kdnuggets #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #Linux #Programming #Coding #100DaysofCode https://
geni.us/Scikit-Classif -
AI Ultimate Guide: Machine Learning, Data Science and Development Tools
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Artificial Intelligence. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/AI-Ultimate