update readme.md.

README.md

@@ -2,7 +2,7 @@
 
 # Distributed Llama
 
-[![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/b4rtaz/distributed-llama/.github%2Fworkflows%2Fmain.yml?style=flat-square)](https://github.com/b4rtaz/distributed-llama/actions) [![License: MIT](https://img.shields.io/github/license/mashape/apistatus.svg?style=flat-square)](/LICENSE) [![Discord](https://discordapp.com/api/guilds/1245814812353495070/widget.png?style=shield)](https://discord.com/widget?id=1245814812353495070&theme=dark)
+[![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/b4rtaz/distributed-llama/.github%2Fworkflows%2Fmain.yml?style=flat-square)](https://github.com/b4rtaz/distributed-llama/actions) [![License: MIT](https://img.shields.io/github/license/mashape/apistatus.svg?style=flat-square)](/LICENSE) [![Support this project](https://img.shields.io/github/sponsors/b4rtaz?style=flat-square&label=support%20this%20project&color=green)](https://github.com/sponsors/b4rtaz) [![Discord](https://discordapp.com/api/guilds/1245814812353495070/widget.png?style=shield)](https://discord.com/widget?id=1245814812353495070&theme=dark)
 
 Tensor parallelism is all you need. Run LLMs on weak devices or make powerful devices even more powerful by distributing the workload and dividing the RAM usage. This project proves that it's possible split the workload of LLMs across multiple devices and achieve a significant speedup. Distributed Llama allows you to run huge LLMs in-house. The project uses TCP sockets to synchronize the state. You can easily configure your AI cluster by using a home router.
 
@@ -25,7 +25,7 @@ Python 3 and C++ compiler required. The command will download the model and the
 
 ### 🛠️ Convert Model Manually
 
-Supported architectures: Llama, Mixtral, Grok
+Supported architectures: Llama, Mixtral
 
 * [How to Convert Llama 2, Llama 3, Llama 3.1](./docs/LLAMA.md)
 * [How to Convert Hugging Face Model](./docs/HUGGINGFACE.md)