Awesome Llmops Github

Bonisiwe Shabane

-Jan 27, 2026, 7:00 AM

An awesome & curated list of the best LLMOps tools for developers. Contributions are most welcome, please adhere to the contribution guidelines. 🔥 Just discovered: Awesome-LLMOps - The definitive resource hub for LLM developers Looking to navigate the rapidly evolving landscape of Large Language Model Operations? I've found the ultimate resource for developers, researchers, and ML practitioners. Tensorchord's Awesome-LLMOps repository offers a meticulously curated collection of the best tools, frameworks, and resources covering every aspect of the LLM lifecycle: ✅ Fine-tuning frameworks for adapting foundation models ✅ Production-ready serving infrastructure ✅... With over 4,100 stars, this open-source collection has become the go-to reference for the LLMOps community.

Check it out: https://lnkd.in/g5N9PVWC #LLMOps #MachineLearning #AI #MLOps #LLM #PromptEngineering #DevTools Stop asking AI to write your code. You're thinking too small. The latest LLMs from OpenAI, Anthropic, and Google aren't just fancy autocompletes anymore. They are agents. And you need to start treating them like one.

Last week, I had to build a full payment gateway integration. Normally, this is a 2-day job full of tedious boilerplate, security checks, and endpoint testing. This time, I tried something different. Instead of coding it line-by-line, I became the architect and delegated the execution. My prompt wasn't "write this function." It was a multi-step command: 𝐀𝐧𝐚𝐥𝐲𝐳𝐞: Read these API docs and outline the key Django models and API endpoints. 𝐒𝐜𝐚𝐟𝐟𝐨𝐥𝐝: Generate the FastAPI service with CRUD operations for those models.

𝐒𝐞𝐜𝐮𝐫𝐞: Implement the webhook signature validation using these secret keys. 𝐓𝐞𝐬𝐭: Write the initial Pytest scripts to cover success and failure cases. The AI did the heavy lifting. I did the critical thinking, reviewing, and refining. The result? A 2-day task became a 3-hour task.

This is the new leverage for senior developers. Your value is no longer in the code you can write, but in the complexity you can orchestrate. Stop being a coder. Start being an architect. What's the most complex task you've successfully delegated to an AI? 🌐 Open Source vs.

Proprietary LLMs: Who’s Driving AI’s Future? 🌐 As a Python developer and AI specialist, I’ve seen open source spark innovation from Linux to LibreOffice. Now, it’s transforming AI. Community-driven LLMs are challenging proprietary giants — but which will shape the future? Let’s break it down. 👇 ⚙️ Access & Control Open Source LLMs (e.g., Qwen, DeepSeek): Download, inspect, and customize for specialized AI tools.

Proprietary LLMs (e.g., OpenAI, Google): User-friendly APIs but limited transparency. 💰 Cost Open Source: Free, ideal for startups and researchers. Proprietary: Reliable but pricey as usage scales. 🧩 Customization Open Source: Fine-tune for your unique needs. Proprietary: “Black box” models — optimized but rigid. ⚖️ Performance Open Source: Excels in efficiency and niche applications.

Proprietary: Leads in general reasoning and polish. 🔒 Security Open Source: Community-audited for transparency. Proprietary: Relies on vendor-controlled safeguards. For example, we recently fine-tuned Ollama for a niche NLP task at my startup — something proprietary models couldn’t easily do. Which side are you on — open source or proprietary LLMs? Have you built anything cool with models like Qwen or OpenAI?

Drop a comment or DM me to swap insights — let’s shape AI’s future together! 💬 #AI #OpenSource #LLMs #Python #TechInnovation Github: https://lnkd.in/gTx6kYah live: https://lnkd.in/grAC7Zpx 🧠💬 Conversational AI That Uses Google in Real-Time? Yes, Please! I recently built a search-enabled chatbot powered by LangChain Agents + LLMs that can answer questions using live information from the web — even topics beyond the LLM's training data. 🔍 Project Goal: A Smarter, Search-Backed Chatbot Standard LLMs have a knowledge cutoff...

But what if your chatbot could search Google (or SerpAPI) for answers it doesn’t already know? ✅ That’s exactly what this project does: Ask a question → LLM decides if it knows the answer → if not, it uses a search tool to fetch real-time data → returns an informed,... Perfect for: 📰 Current Events 🔍 Niche or Long-tail Queries 🧾 Up-to-date Fact-Checking 🧠 How It Works (Agentic AI Architecture) Component Role Tools Used 🧠 LLM Brain of the chatbot, does reasoning and answering... 🛠️ Skills Demonstrated ✅ Agentic Reasoning: Creating LLMs that can use tools autonomously ✅ Real-time Grounding: Bridging static models with live internet data ✅ LangChain Proficiency: Advanced use of LangChain Agents & Tools ✅... 🔗 Explore the repo here: https://lnkd.in/gTx6kYah Would love your thoughts — and open to collaborating on building more advanced Agentic AI systems! #LangChain #AgenticAI #LLM #Chatbot #ConversationalAI #GenerativeAI #OpenAI #SearchEnabledChatbot #SerpAPI #GoogleSearch #MLOps #RealTimeAI #GitHubPortfolio #AItools #Python #GPT #DataScience #LinkedInProjects #GenAI

𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗟𝗟𝗠𝘀 𝗝𝘂𝘀𝘁 𝗚𝗼𝘁 𝗘𝗮𝘀𝗶𝗲𝗿: 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗧𝘂𝗻𝗶𝘅 Spent the weekend diving into Tunix—Google's new open-source library for LLM post-training. If you're building AI products, this is worth your attention. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝘁? Tunix is a JAX-native library that streamlines LLM post-training: supervised fine-tuning, reinforcement learning, and knowledge distillation. Built for Google Cloud TPUs, but the real value is in what it abstracts away. 𝗪𝗵𝗮𝘁 𝗜 𝘁𝗲𝘀𝘁𝗲𝗱 I was curious about non-verifiable tuning—tasks with variable outcomes rather than clear right/wrong answers.

Ran a DPO experiment on Gemma 2B: teaching it to shift customer service responses from casual to professional tone. Simple use case, but exactly the kind of alignment work that matters in production. 𝙏𝙝𝙚 𝙥𝙧𝙖𝙜𝙢𝙖𝙩𝙞𝙘 𝙩𝙖𝙠𝙚 Tunix doesn't do magic—you could build this yourself. But you're saving weeks worth of time with pre-tested implementations for loss functions, logging, multi-device orchestration, and TPU optimization. Also, you get unified API for different post-training methods, means switching between DPO, PPO, and GRPO doesn't mean rewriting your stack. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 Post-training is the new competitive moat.

Teams winning with AI aren't pre-training bigger models—they're fine-tuning faster. Tunix democratizes what used to require dedicated infrastructure teams If you're exploring post-training approaches, check out the Tunix repo: https://lnkd.in/gurWMGTN #AI #MachineLearning #LLM #PostTraining #DPO #AILeadership #OpenSource #JAX #TPU #ModelAlignment #AIStrategy #ProductionAI LLMOps is a part of MLOps practices, specialized form of MLOps that focuses on managing the entire lifecycle of large language models(LLM). Starting in 2021, as LLMs evolved rapidly and the technology matured, we began to focus on practices for managing LLMs efficiently, and LLMOps, which are adaptations of traditional MLOps practices to LLMs, began to... We welcome contributions to the Awesome LLMOps list! If you'd like to suggest an addition or make a correction, please follow these guidelines:

We appreciate your contributions and thank you for helping to make the Awesome LLMOps list even more awesome! Master LLMs through books, courses, tutorials, exercises, projects, and comprehensive guides that cover everything from foundational concepts to advanced techniques. If you are not familiar with large language models (LLMs) today, you may already be falling behind in the AI revolution. Companies are increasingly integrating LLM-based applications into their workflows. As a result, there is a high demand for LLM engineers and operations engineers who can train, fine-tune, evaluate, and deploy these language models into production. In this article, we will review 10 GitHub repositories that will help you master the tools, skills, frameworks, and theories necessary for working with large language models.

This repository is a goldmine for learning prompt engineering, one of the most critical skills for working effectively with LLMs. It provides tips, tricks, and examples to help you craft better prompts and get the most out of models like GPT-4o. This repository offers a comprehensive course on LLMs, designed for learners of all levels. It includes tutorials, projects, and hands-on exercises to help you understand and apply LLMs effectively. Production-ready platform for agentic workflow development. InfluxDB – Built for High-Performance Time Series Workloads.

InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now. A high-throughput and memory-efficient inference and serving engine for LLMs Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.

🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more. 🔗 Tooling: • Pathway GitHub • Ollama LLM Runner • Streamlit Docs Master Generative AI with 10+ Real-world Projects in 2025! In today’s world, whether you are a working professional, a student, or in the domain of research. If you didn’t know about Large Language Models (LLMs) or aren’t exploring LLM GitHub repositories, then you are already falling behind in this AI revolution. Chatbots like ChatGPT, Claude, Gemini, and others use LLMs as their backbone for performing tasks like generating content and code using simple prompting techniques and natural language.

Awesome Llmops Github

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An Awesome & Curated List Of The Best LLMOps Tools

Check It Out: Https://lnkd.in/g5N9PVWC #LLMOps #MachineLearning #AI #MLOps #LLM #PromptEngineering

Last Week, I Had To Build A Full Payment Gateway

𝐒𝐞𝐜𝐮𝐫𝐞: Implement The Webhook Signature Validation Using These Secret Keys.

This Is The New Leverage For Senior Developers. Your Value