Add llmaz to Inference

Signed-off-by: kerthcet <kerthcet@gmail.com>

README.md

@@ -49,6 +49,7 @@
 | ---- | ---- | ---- | ---- | ---- | ---- |
 | **[DeepSpeed-MII](https://github.com/microsoft/DeepSpeed-MII)** | ![Stars](https://img.shields.io/github/stars/microsoft/deepspeed-mii.svg) | ![Release](https://img.shields.io/github/release/microsoft/deepspeed-mii) | ![Contributors](https://img.shields.io/github/contributors/microsoft/deepspeed-mii) | MII makes low-latency and high-throughput inference possible, powered by DeepSpeed. | |
 | **[ipex-llm](https://github.com/intel-analytics/ipex-llm)** | ![Stars](https://img.shields.io/github/stars/intel-analytics/ipex-llm.svg) | ![Release](https://img.shields.io/github/release/intel-analytics/ipex-llm) | ![Contributors](https://img.shields.io/github/contributors/intel-analytics/ipex-llm) | Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Baichuan, Mixtral, Gemma, Phi, MiniCPM, etc.) on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max); seamlessly integrate with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, GraphRAG, DeepSpeed, vLLM, FastChat, Axolotl, etc. | edge |
+| **[llmaz](https://github.com/InftyAI/llmaz)** | ![Stars](https://img.shields.io/github/stars/inftyai/llmaz.svg) | ![Release](https://img.shields.io/github/release/inftyai/llmaz) | ![Contributors](https://img.shields.io/github/contributors/inftyai/llmaz) | ☸️ Effortlessly serve state-of-the-art LLMs on Kubernetes. | |
 | **[LMDeploy](https://github.com/InternLM/lmdeploy)** | ![Stars](https://img.shields.io/github/stars/internlm/lmdeploy.svg) | ![Release](https://img.shields.io/github/release/internlm/lmdeploy) | ![Contributors](https://img.shields.io/github/contributors/internlm/lmdeploy) | LMDeploy is a toolkit for compressing, deploying, and serving LLMs. | |
 | **[llama.cpp](https://github.com/ggerganov/llama.cpp)** | ![Stars](https://img.shields.io/github/stars/ggerganov/llama.cpp.svg) | ![Release](https://img.shields.io/github/release/ggerganov/llama.cpp) | ![Contributors](https://img.shields.io/github/contributors/ggerganov/llama.cpp) | LLM inference in C/C++ | edge |
 | **[MInference](https://github.com/microsoft/minference)** | ![Stars](https://img.shields.io/github/stars/microsoft/minference.svg) | ![Release](https://img.shields.io/github/release/microsoft/minference) | ![Contributors](https://img.shields.io/github/contributors/microsoft/minference) | To speed up Long-context LLMs' inference, approximate and dynamic sparse calculate the attention, which reduces inference latency by up to 10x for pre-filling on an A100 while maintaining accuracy. | |