Setting up this model locally is incredibly fast if you use the native CMD prompt. Use the instructions provided below to complete the setup. The engine will automatically fetch large dependencies in the background. The installer diagnoses your environment to deploy the most compatible profile. 🔒 Hash checksum: 7dcb908dea8304fe4b81e7e9f7057491 • 📆 Last updated: 2026-06-25 Verify…
Category: LoRAs
LoRAs
Homebrew offers the quickest path to setting up this model locally. Please adhere to the deployment steps listed below. The installer automatically pulls the model (could be multiple GBs). You don’t need to tweak anything; the installer picks the highest performing setup. 📄 Hash Value: 8c27ada73b4eefb0eb3f2e7a30166970 | 📆 Update: 2026-06-28 Verify Processor: Intel i5 or…
Using Docker is the absolute quickest way to install this model on your local machine. Simply follow the directions outlined below. > The loader auto-caches the model archive (several GBs included). Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency. 🛠Hash code: b1c85977d179a0c6ea712a448a682a4d — Last modification: 2026-06-24…
For the fastest local setup of this model, Docker is the best choice. Simply follow the directions outlined below. The installer will automatically analyze your hardware and select the optimal configuration for your system. 🛠Hash code: 87d9865b53c626819737c32171f09caa — Last modification: 2026-06-26 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB highly recommended…