Sample code and notebooks for Generative AI on Google Cloud, with Gemini on Vertex AI

Unlock the potential of finetuning Large Language Models (LLMs). Learn from industry expert, and discover when to apply finetuning, data preparation techniques, and how to effectively train and eva…

Unlock the power of vector databases with the "Vector Databases: from Embeddings to Applications" course! A journey that will equip you with essential skills to leverage vector databases for variou…

Embark on the "Reinforcement Learning from Human Feedback" course and align Large Language Models (LLMs) with human values.

Dive into the world of text embeddings. This course will guide you through leveraging text embeddings to enhance various natural language processing (NLP) tasks.

Leverage vector databases to swiftly construct a diverse range of applications through "Building Applications with Vector Databases" course!

Learn linear quantization techniques using the Quanto library and downcasting methods with the Transformers library to compress and optimize generative AI models effectively.

Explore Mistral AI's extensive collection of models. Learn to select, prompt, and integrate Mistral's open-source and commercial models for tasks like classification, coding, and Retrieval Augmente…

Dive into advanced quantization techniques. Learn to implement and customize linear quantization functions, measure quantization error, and compress model weights using PyTorch for efficient and ac…

This repos contains notebooks for the Advanced Solutions Lab: ML Immersion

Jupyter notebooks for the Natural Language Processing with Transformers book

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI

🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

Train transformer language models with reinforcement learning.

🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Public repo for HF blog posts

GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.

Robust recipes to align language models with human and AI preferences

"Probabilistic Machine Learning" - a book series by Kevin Murphy

Python code for "Probabilistic Machine learning" book by Kevin Murphy

A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.

Virtual whiteboard for sketching hand-drawn like diagrams

Code for A Programmer's Introduction to Mathematics

Pen and paper exercises in machine learning

The SDK for Jetpac's iOS Deep Belief image recognition framework