"LLM from Zero to Hero: An End-to-End Large Language Model Journey from Data to Application!"

🧠 Learn LLM Observability step-by-step using FOSS tools. From zero visibility to full monitoring with Langtrace, OpenTelemetry, and Jaeger. Demo from FOSS United Coimbatore Meetup.

Convert GitHub repositories to MCP servers automatically. Extract tools from OpenAPI, GraphQL & REST APIs for Claude Desktop, Cursor, Windsurf, Cline & VS Code. AI-powered code generation creates type-safe TypeScript/Python MCP servers. Zero config setup - just paste a repo URL. Built for AI assistants & LLM tool integration.

Conference talk: from zero to your first LLM application

Prompt Security is a blazing-fast, zero-config Go tool that automatically detects and filters sensitive data from your clipboard—before it can leak to large language models ( LLMs) or online tools.

The repository contains data for LLM training and evaluation, LLM models, pre-training, fine-tuning, inference, deployment, applications, and a number of popular repositories about LLM, which can be called as 'Build Your Own LLM from Zero to One'.

Complete roadmap to become an AI Security Engineer from zero to advanced — covering Python, ML, Deep Learning, LLM Engineering, RAG Security, Intrusion Detection, Anomaly Detection, and a full Master Project (AI-Powered Security Analyst).

⚡ From Zero to Monitoring LLMs in 5 minutes ⚡

Welcome to From Zero to LLM, a journey into the fascinating world of Large Language Models (LLMs). This repository serves as a personal learning diary, showcasing hands-on experiments, tutorials, and projects related to LLMs, including fine-tuning, prompt engineering, and real-world applications.

a self-trained 0.5B llm in one Nvdia 4090 from zero to one

Official implementation of "From Generator to Embedder: Harnessing Innate Abilities of Multimodal LLMs via Building Zero-Shot Discriminative Embedding Model."

Extract structured knowledge from group chats using any LLM. Members, facts, decisions, tasks — searchable in SQLite. Built for OpenClaw agents. Zero dependencies to start.

Welcome to GenAI_DayZero, a hands-on journey into Generative AI and Large Language Models (LLMs). This repository showcases my learning and experiments, from zero knowledge to advanced AI tools. I’ll be sharing code, insights, and resources to explore the exciting possibilities of GenAI. Let’s learn and build together!

Veridion is a zero-trust AI firewall and model integrity platform designed to protect large language model (LLM) systems from sophisticated threats including prompt injection, dataset poisoning, jailbreaking, and unauthorized data exfiltration.

This repo is dedicated to an analysis of the LLMs performance as text classifiers on five German datasets from social media covering 13 tasks. We experiment with zero- and few-shot classification approaches as well as with fine-tuning using various LLMs

Used to record my experience of deploying LLM from zero

Practice in understanding and coding LLMS from scratch, following https://karpathy.ai/zero-to-hero.html

This repository contains code to evaluate the performance of LLMs under zero-shot and few-shot settings for extracting feature-sentiment pairs from app reviews.

LLM knowledge base + zero-to-hero course for authoring production-quality AI agent skills (SKILL.md). 30+ wiki articles compiled from 18 repos, 700K+ stars combined.

A comprehensive tutorial teaching Retrieval-Augmented Generation (RAG) from zero to production. Includes 6 progressive lessons, working code examples, reusable utilities, and hands-on demos. Learn embeddings, vector databases, LLMs, and advanced techniques to build your own RAG systems.

ShadowMixer is an open-source AI privacy mixer and zero-trust gateway. Inspired by Tor and CoinJoin, it uses Fragmented Obfuscation to decouple intelligence from identity, downgrading LLM providers into blind compute vendors. Protect your intent and business logic through semantic multi-tenant anonymity.

This repository documents my personal journey in creating a Large Language Model from scratch.

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A comprehensive guide to fine-tuning Large Language Models (LLMs) from scratch.

Course from zero to agents using LLMs

Code to reproduce experiments from the upcoming paper "Zero-shot Topical Text Classification with LLMs - an Experimental Study" (EMNLP, 2023).

This project evaluates multiple approaches to sentiment classification on financial text data, ranging from traditional ML models to zero-shot large language models (LLMs).

Enterprise-grade LLM evaluation framework | Multi-model benchmarking, honest dashboards, system profiling | Academic metrics: MMLU, TruthfulQA, HellaSwag | Zero fake data | PyPI: llm-benchmark-toolkit | Blog: https://dev.to/nahuelgiudizi/building-an-honest-llm-evaluation-framework-from-fake-metrics-to-real-benchmarks-2b90

The easiest way to protect your Next.js content from AI crawlers. Shield specific UI components or entire routes from LLM scrapers with zero hydration mismatches and built-in fallback support.

A tiny app using a fine-tuned LLM model for zero-shot classification (NLI) to extract skills from your resume, backed up by a shiny minimalist Streamlit UI.