Maria Khalusova's starred repositories
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Web-Dev-For-Beginners
24 Lessons, 12 Weeks, Get Started as a Web Developer
tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
LLMs-from-scratch
Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step
recommenders
Best Practices on Recommendation Systems
ml-engineering
Machine Learning Engineering Open Book
Transformers-Tutorials
This repository contains demos I made with the Transformers library by HuggingFace.
unstructured
Open source libraries and APIs to build custom preprocessing pipelines for labeling, training, or production machine learning pipelines.
alignment-handbook
Robust recipes to align language models with human and AI preferences
llm-twin-course
🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴
WeightWatcher
The WeightWatcher tool for predicting the accuracy of Deep Neural Networks
awesome-local-ai
An awesome repository of local AI tools
computer-vision-course
This repo is the homebase of a community driven course on Computer Vision with Neural Networks. Feel free to join us on the Hugging Face discord: hf.co/join/discord
actions-comment-pull-request
GitHub action to comment pull request
audio-transformers-course
The Hugging Face Course on Transformers for Audio
ollama-reply
open-source browser extension that leverages the power of the AI to generate engaging replies for social media growth.
awesome-osml-for-devs
List of resources, libraries and more for developers who would like to build with open-source machine learning off-the-shelf
Advanced_RAG
Advanced Retrieval-Augmented Generation (RAG) through practical notebooks, using the power of the Langchain, OpenAI GPTs ,META LLAMA3 ,Agents.
dspy-gradio-rag
RAG example using DSPy, Gradio, FastAPI
hugging-face-qa-bot
Open source Hugging Face Question Answering Bot to aid users in developing and troubleshooting ML solutions.