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Grok-1 is currently designed with the following specifications: You can download the weights using a torrent client and this magnet link: or directly using HuggingFace 🤗 Hub: The code and associated Grok-1 weights in this release are licensed under the Apache 2.0 license.
xAI has made Grok-1's model weights and architecture openly accessible on a GitHub repository for developers. To configure the model, simply follow the instructions provided in the repository. Additionally, for publishing the model, detailed guidelines are available on HuggingFace, courtesy of xAI.
Compact client (~5MB) on Linux/Windows/MacOS, download it now; Fully compatible with self-deployed llms, recommended for use with RWKV-Runner or LocalAI; Privacy first, all data stored locally in the browser; Markdown support: LaTex, mermaid, code highlight, etc. Responsive design, dark mode and PWA
With Grok's capabilities and its open-source nature (Grok-1 is on Apache 2.0 license), developers are likely to: Build AI applications faster : Quick start models and efficient downloading capabilities allow developers to get started faster on their projects.
17 Μαρ 2024 · To get started with using the model, follow the instructions at github.com/xai-org/grok. Model Details. Base model trained on a large amount of text data, not fine-tuned for any particular task. 314B parameter Mixture-of-Experts model with 25% of the weights active on a given token.
19 Μαρ 2024 · xAI has openly released Grok-1’s model weights and architecture on a GitHub repository which developers can access. You can set up the model by following the instructions mentioned in the repository.
15 Απρ 2024 · Start by cloning the Grok-1 repository from GitHub: git clone https://github.com/xai-org/grok-1.git cd grok-1 Step 2: Install Dependencies. Install the required Python...
