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๐งฉ Hash sum โ b9d7b64ab0aa46cf6ba2c82f16d3d785 โ Update date: 2026-07-15 Verify Processor: high single-core performance needed for token latency RAM: 64 GB to avoid OOM crashes
๐ค Release Hash: 045f91bb4c2894ba72b8b27dbe1b04eb โข ๐ Date: 2026-07-19 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB highly recommended for 26B+ GGUF models
๐ Hash checksum: b5c94c957108685e2a24eed047164e8a โข ๐ Last updated: 2026-07-13 Verify Processor: 6-core 3.5 GHz minimum required RAM: 32 GB highly recommended for 26B+ GGUF models
๐งพ Hash-sum โ f988b3488dc1c17b83f19d7c65c5a3e4 โข ๐ Updated on: 2026-07-11 Verify Processor: 6-core 3.5 GHz minimum required RAM: 32 GB or higher for smooth 32k context
Using the Windows Package Manager is the quickest way to trigger the setup. Follow the guidelines below to continue. The script takes care of fetching
Deploying locally takes the least amount of time when executed through native OS tools. Proceed by following the technical instructions below. The download manager will
A standalone PowerShell module provides the fastest route to local installation. Kindly follow the on-screen instructions below. The setup auto-downloads all needed files (several GBs).
The most efficient approach for a local installation is leveraging Docker containers. Follow the step-by-step instructions below. The script takes care of fetching the multi-gigabyte
The fastest tactical way to launch this model locally is via a Docker image. Review and follow the instructions below. No manual effort needed; the
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