added info that Ollama also supports embedding and reranking models

community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md

@@ -41,7 +41,7 @@ Ollama provides a comprehensive set of libraries and tools to facilitate the dep
 VLLM is an optimized inference engine designed for high-throughput token generation and efficient memory utilization, making it suitable for large-scale AI deployments. Ollama is a lightweight and intuitive framework that facilitates the execution of open-source LLMs on local on-prem hardware. In terms of popularity, the [vLLM](https://github.com/vllm-project/vllm) GitHub repository has 35K stars, while [Ollama](https://github.com/ollama/ollama) has 114K stars.
 
 #### Key Features of Ollama
-- **Extensive Model Support**: Ollama supports a variety of open-source language models, including state-of-the-art models that are continuously updated.
+- **Extensive Model Support**: Ollama supports a variety of open-source large and small language models, including state-of-the-art models that are continuously updated. Additionally, it also hosts embedding and re-ranking models that are critical for RAG applications.
 - **Ease of Integration**: The libraries and tools provided by Ollama are designed to integrate smoothly with existing systems, reducing the complexity of adding new models to the workflow.
 - **Scalability**: Ollama's tools are built to handle different scales of deployment, from small-scale local setups to larger, more complex environments.
 - **Community and Documentation**: Ollama has a strong community and extensive documentation, providing support and resources for developers to effectively use and troubleshoot the tools.