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- @manandey
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[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)
Byte-sized summaries or questions-and-answers, including math and code snippets for ML/DL/CV/NLP/GenAI/LLM interview preparation.
DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models. 🤖💤
Awesome-LLM: a curated list of Large Language Model
Explain complex systems using visuals and simple terms. Help you prepare for system design interviews.
A collection of design patterns/idioms in Python
This repository contains all the DSA problems that I have solved.
Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: …
QLoRA: Efficient Finetuning of Quantized LLMs
LLM training code for Databricks foundation models
Code and documentation to train Stanford's Alpaca models, and generate the data.
Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
A playbook for systematically maximizing the performance of deep learning models.
A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)
Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM
git extension for {collaborative, communal, continual} model development
🎢 Creating and sharing simulation environments for embodied and synthetic data research
A framework for the evaluation of autoregressive code generation language models.
Beyond the Imitation Game collaborative benchmark for measuring and extrapolating the capabilities of language models
⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x.
Google Research