The fastest way to get this model running locally is via Optional Features. Review and follow the instructions below. The setup auto-downloads all needed files (several GBs). There is no... read more →
Homebrew offers the quickest path to setting up this model locally. Refer to the instructions below to proceed. The setup auto-streams the model assets (expect a multi-GB download). The smart... read more →
Using a native PowerShell script is the absolute quickest way to install this model. Follow the guidelines below to continue. 1-click setup: the app automatically fetches the large weight files.... read more →
The most efficient approach for a local installation is leveraging Docker containers. Kindly follow the on-screen instructions below. The script takes care of fetching the multi-gigabyte model weights. The setup... read more →
Deploying locally takes the least amount of time when executed through native OS tools. Go through the configuration rules shown below. The engine will automatically fetch large dependencies in the... read more →
To get this model running locally in no time, utilize the built-in WSL tools. Make sure to follow the instructions below. The client handles the setup, pulling gigabytes of data... read more →
For the fastest local setup of this model, enabling Windows Features is best. Please follow the instructions listed below to get started. The client handles the setup, pulling gigabytes of... read more →
If you need a near-instant local setup, just fetch files via a basic curl request. Execute the commands and steps outlined below. The installer automatically pulls the model (could be... read more →
The most efficient approach for a local installation is leveraging Docker containers. Follow the guidelines below to continue. Be patient as the system self-retrieves massive model weights dynamically. The installer... read more →
The shortest path to running this model is by activating Hyper-V features. Execute the commands and steps outlined below. The framework seamlessly downloads the massive neural network binaries. An automated... read more →