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10:["$","p",null,{"children":"Every Zen model consists of multiple expert subnetworks, each trained to excel at a specific class of tasks:"}]
11:["$","ul",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"Reasoning experts"}]," — deep chain-of-thought, math, formal logic"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Code experts"}]," — multi-language generation, debugging, architecture"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Language experts"}]," — translation, writing, summarization"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Vision experts"}]," — multimodal understanding, image description"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Domain experts"}]," — medicine, science, law, finance"]}],"\n"]}]
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13:["$","p",null,{"children":"A learned router determines which experts to activate for each input. This sparse activation means a 744B parameter model might only use 40B active parameters for a given request — achieving near-dense quality at a fraction of the compute cost."}]
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15:["$","p",null,{"children":"The secret sauce is how we initialize and train our experts. Rather than random initialization, we distill from the best publicly available models in each domain. A code expert might be distilled from multiple specialized coding models. A reasoning expert incorporates the thinking patterns from frontier reasoning systems."}]
16:["$","p",null,{"children":"The result: our 40B active parameter model has internalized capabilities that would take a 400B+ parameter generalist model to achieve."}]
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18:["$","p",null,{"children":"The AI field benefits enormously from open research. When we release Zen model weights under the Zen Open License, we're enabling:"}]
19:["$","ul",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"Independent safety research"}]," — external auditing of model behavior"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Custom fine-tuning"}]," — organizations adapting models to their specific domains"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Reproducibility"}]," — the scientific community can verify our claims"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Competition"}]," — other labs can identify what we got right (and wrong)"]}],"\n"]}]
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1b:["$","p",null,{"children":"Zen 5 represents our most sophisticated MoDE implementation yet. Looking forward, we're exploring continuous learning, collaborative distillation, and native 3D, audio generation, and video understanding experts."}]
1c:["$","p",null,{"children":"The open frontier is just getting started."}]
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