<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom"><title>Ju Lin's AI Weblog: Models</title><subtitle>An independent research notebook on AI engineering, agents, models and the systems around them.</subtitle><id>https://julin.ai/atom/tags/models/index.xml</id><link rel="self" type="application/atom+xml" href="https://julin.ai/atom/tags/models/index.xml"/><link rel="alternate" type="text/html" href="https://julin.ai/tags/models/"/><author><name>Ju Lin</name></author><updated>2026-10-22T00:00:00+13:00</updated><entry><title>Flint: A Small MoE Model</title><id>https://julin.ai/2026/10/22/flint-moe-model/</id><link rel="alternate" type="text/html" href="https://julin.ai/2026/10/22/flint-moe-model/"/><published>2026-10-22T00:00:00+13:00</published><updated>2026-10-22T00:00:00+13:00</updated><category term="models"/><category term="moe"/><category term="swe-bench"/><content type="html">&lt;p&gt;&lt;a href="https://huggingface.co/RecursiveSI/flint"&gt;Flint&lt;/a&gt; is a new MoE model: 39B parameters, 2.5B active per token. Architecture is 2 dense layers followed by 47 MoE layers.&lt;/p&gt;
&lt;p&gt;Benchmarks: 54.9% on Terminal-Bench 2.1, 89.7% on SWE-bench Pro v2, 74.2% on SWE-bench Verified.&lt;/p&gt;
&lt;p&gt;Trained from scratch (tokenizer → pretrain → midtrain → SFT → RL), giving more flexibility for fine-tuning downstream. The team built the complete training pipeline in 4 months.&lt;/p&gt;</content></entry></feed>