QLoRA: Efficient Finetuning of Quantized LLMs
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A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
- data-drift
- jupyter-notebook
- pandas-dataframe
- machine-learning
Repo to accompany my mastering LLM engineering course
22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.
LLM Zoomcamp - a free online course about real-life applications of LLMs. In 10 weeks you will learn how to build an AI system that answers questions about your knowledge base. Register here 👇🏼
LLM-powered multiagent persona simulation for imagination enhancement and business insights.
TranslatedLarge language model knowledge sharing that everyone can understand, a must-read before LLM spring/autumn recruitment interviews, so you can talk confidently with interviewers
- llm
- interview-questions
- interview-practice
- agent
Collection of notebooks about quantitative finance, with interactive python code.
- quantitative-finance
- jupyter-notebooks
- kalman-filter
- option-pricing
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Optimize prompts, code, and more with AI-powered Reflective Optimization
- agent-skills
- coding-agent
- discovery
- distributed-evolutionary-algorithms
Implement a reasoning LLM in PyTorch from scratch, step by step
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
Notebooks for learning deep learning
Neo4j graph construction from unstructured data using LLMs
- data-import
- genai
- graph
- graph-rag
🚀 An open-source, hands-on curriculum bridging the gap from basic RL concepts to LLM alignment, RLVR, and advanced Agentic systems.
- agent
- agentic
- agentic-ai
- agentic-rl
35 production-grade agentic AI architectures (Reflexion, LATS, GraphRAG, MemGPT, Voyager, BrowserAgent, ...) — a Python library and runnable textbook with multi-provider LLM support and a 17-task benchmark leaderboard.
- agentic-ai
- ai-agents
- langchain
- langgraph
Translated"PyTorch Practical Tutorial" (Second Edition): whether you are starting from zero, working on CV, NLP, or LLM project applications, or moving on to advanced engineering deployment, it's all here. We believe that with the help of this book, readers will be able to master the use of PyTorch with ease and become an excellent deep learning engineer.
- computer-vision
- deepsort
- diffusion-models
- onnx
TranslatedNeeding only a Python foundation, build large language models from scratch; build GLM4\Llama3\RWKV6 step by step from scratch, to gain a deep understanding of how large models work
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
- agentic-ai
- agentic-rag
- agents
- rag
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
- deep-learning
- d2l
- data-science
- pytorch-implmention
TranslatedSharing AI Infra knowledge & code exercises: PyTorch, vLLM/SGLang, introductions to the slime/vime frameworks⚡️, performance acceleration🚀, large model fundamentals🧠, AI software and hardware🔧, and more
[ICLR 2023] ReAct: Synergizing Reasoning and Acting in Language Models
- decision-making
- large-language-models
- llm
- prompting
MongoDB's Generative AI Showcase: an exhaustive collection of examples and sample applications covering Retrieval-Augmented Generation (RAG), AI agents, and industry-specific use cases.