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                    "openjev\n        Can we run something like Jev in your browser?\n          GitHub repo \u2197\n        \n      \n\n      \n        A live, local experiment\n        \n        A local model can either read probabilities for your allowed options without decoding them, or write the same kind of distribution token by token. Pick a size, run both on your own GPU, and measure the difference.\n        browser onlyno backendyour timings1.56 GB model\n        \n        There is no waitlist! Just try it out \u2193\n        MiniCPM5 2B is selected by default. On a phone or smaller device, switch to Qwen3 0.6B in the model box if needed.\n      \n\n      \n        \n          00 / setupLoad the model once\n          Model\n              \n            \n            \n          \n        \n        Larger model. Loading may be slower or may not fit on some low-end devices.\n        \n          Model performancehigher is better\n          \n          Native BF16 \u00b7 TypeSafe: same 102-row subset \u00b7 Jev: published result \u00b7 browser builds are quantized\n        \n        {{ text }}\n        \n          download / cache\u2014starts only when you click load\n          model load\u2014download and prepare\n          warmup\u2014compile passes for both methods\n        \n        Weights come from Hugging Face and remain in your browser cache. Inputs never leave this page. First load can take several minutes depending on the selected model, network and GPU.\n      \n\n      \n        01 / decisionGive it a real choice\n\n        Try an example\n          \n          \n        \n\n        \n        Both paths receive the same decision. One reads option probabilities directly; the other asks the model to write its option probabilities as JSON text.\n      \n\n      \n        your decisionstate + question + options\n        \n        same local modelMiniCPM5 \u00b7 2B\n        \n        \n          read logitsA\u2026T probabilities\n          write tokens{options + probabilities}\n        \n      \n\n      \n        \n          02A / direct readoutChoice probabilitiesno decoding\n          Read the model\u2019s choice logits and normalize only across the options you supplied.\n          waiting for a run\n          \n            total\u2014\n            input\u2014\n            output1 readout\n          \n        \n\n        \n          02B / generationJSON probabilitiestoken by token\n          Ask the model to estimate the same displayed-option distribution and write it as JSON. Watch every token arrive.\n          waiting for a run\n          \n            first token\u2014\n            total\u2014\n            input\u2014\n            output\u2014\n          \n        \n      \n\n      \n        measured wall-time ratiorun it on your GPU\n        The methods run sequentially on the same loaded model so they do not contend for one GPU. Direct runs first, then generation.\n      \n\n      \n        What these numbers do\u2014and do not\u2014mean\n        \n          Conditional probabilities. Direct scores are a softmax over only the displayed option tokens. They are not calibrated confidence and do not include every answer the model might prefer.\n          Local model tiers. The phone model trades accuracy for size. MiniCPM is the desktop default. The 4B option needs substantially more memory. None is claimed to match Jev.\n          Real local timing. Setup, warmup, prompt preparation, direct execution, first generated token and generation completion are timed with performance.now(). No canned results appear.\n          Quantized weights. The demo uses pinned GGUF builds through wllama. Quantization can change both quality and speed."
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