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@_lewtun

Today we're releasing Carbon-X, a 14B model that brings DNA and natural language together for the first time 🧬 1. Beats every open language model we tested across DNA benchmarks. Within a point of GPT-6.1 Sol at >1,000× lower inference cost. 2. Mid-trained on 300B tokens of interleaved DNA and text, followed by SFT (without distillation!) and RL. 3. A new hybrid tokenizer encodes DNA using ~3× fewer tokens than BPE. 4. Gives other models a DNA specialist subagent: Kimi K3 gains 11.1 points when it consults Carbon-X. 5. Runs locally on a laptop with llama.cpp. Apache 2.0. Your DNA stays on your hardware. Model: https://t.co/7wBE7WUVp1 Blog: https://t.co/A3OjalTpIJ

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