In this blog, I explored Autogen's Agent AutoBuild and experimented with the Mixtral 8x7B model. I configured Autogen, envisioning a software academy project for coding novices. Through code snippets, I showcased AutoBuild's multi-agent system creation and tailored a task that wrote a General Paper article on art and courage. The Mixtral 8x7B model in LM Studio brought excitement but posed challenges with duplicate content. Check out the blog for a firsthand look at the dynamic interplay between Autogen and cutting-edge AI, complete with code snippets and images.

In this post, I explored enhancing Autogen's capabilities by enabling seamless Java code execution. Drawing inspiration from 0xlws' fork supporting JavaScript, I embarked on modifying Autogen to robustly support Java. I detailed the setup process, including installing Java on Windows Subsystem for Linux (WSL) and modifying key files. The post includes code snippets showcasing the changes, recompilation steps, and instructions for generating Java code. I extended functionality to additional test cases, seamlessly switching between Java and Python code execution. Docker integration for Java code execution was also optimized, showcasing Autogen's versatility and robust development experience.

In my recent blog, I demonstrated setting up a multi-agent conservation using Autogen. Employing agents "for_motion" and "against_motion," each engaged in a dynamic debate, facilitated by a neutral party. The debate evolved through multiple rounds, with each agent providing substantiated arguments, exceeding 300 words per response. The facilitator ensured debate guidelines were adhered to. The messages were then passed to an assistant tasked with synthesizing a comprehensive article. Utilizing Autogen's versatility, I showcased the system in action, debating different questions and generating diverse articles. The blog includes a full script in app.py and snapshots of the entire debate session.

I explored AutoGen, an innovative framework on GitHub, enabling the development of Large Language Model (LLM) applications. Collaborating with LM Studio, I set up a local LLM application, showcasing the step-by-step process. Installing LM Studio involved configuring context length, enabling GPU acceleration, and setting CPU threads. The integration process showcased a seamless environment for running local LLMs. Additionally, I explored the AutoGen setup, including installing Anaconda and creating a virtual environment. With the provided guidelines, I executed the app.py script, generating a stock price comparison chart through AutoGen's dynamic conversation.

I delve into the transformative realm of MLC LLM, an advanced universal deployment solution for extensive language models. My post guides you personally through the setup, emphasizing critical components like TVM and Conda. I demonstrate the process, including TVM installation via pip, Conda setup on WSL, and Vulkan SDK installation for optimal performance. Navigating the MLC Chat exploration, I detail creating a Conda environment and running MLC LLM's CLI version, offering a glimpse into its potential through a sample question. With MLC LLM and MLC Chat at your fingertips, the world of machine learning and language understanding unfolds boundless possibilities. 🚀🧠

Embarking on my journey with vLLM, I explore its potential for streamlined Large Language Model (LLM) inference and deployment. The blog details my personal experience setting up vLLM on a Windows Subsystem for Linux (WSL) instance running Ubuntu 22.04. I meticulously guide through installing WSL, NVIDIA GPU drivers, CUDA Toolkit, and Docker for efficient utilization. Delving into vLLM setup within the NVIDIA PyTorch Docker image, I navigate through the installation process and launch the API server. The blog provides insights into querying the model and creating a Docker image snapshot, offering a comprehensive guide to efficient language model serving.