Fish-like autosuggestions for zsh

holehe allows you to check if the mail is used on different sites like twitter, instagram and will retrieve information on sites with the forgotten password function.

PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis

A curated list of reinforcement learning with human feedback resources (continually updated)

LLM training in simple, raw C/CUDA

SQL databases in Python, designed for simplicity, compatibility, and robustness.

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

🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.

Make huge neural nets fit in memory

In this implementation, using the Flan T5 large language model, we performed the Text Classification task on the IMDB dataset and obtained a very good accuracy of 93%.

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

QLoRA: Efficient Finetuning of Quantized LLMs

Melusine is a high-level library for emails classification and feature extraction "dédiée aux courriels français".

A quick guide (especially) for trending instruction finetuning datasets

A scikit-learn-compatible module for comparing imputation methods.

All Algorithms implemented in Python

A french sequence to sequence pretrained model

Get up and running with Llama 3.2, Mistral, Gemma 2, and other large language models.

Uniform Manifold Approximation and Projection

Typer, build great CLIs. Easy to code. Based on Python type hints.

Full stack, modern web application template. Using FastAPI, React, SQLModel, PostgreSQL, Docker, GitHub Actions, automatic HTTPS and more.

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

🦀 Small exercises to get you used to reading and writing Rust code!

Code repository for supporting the paper "Atlas Few-shot Learning with Retrieval Augmented Language Models",(https//arxiv.org/abs/2208.03299)

This repo contains the code for our paper "The Effect of Using Masked Language Models in Random Textual Data Augmentation".

Commented templates for CVs, homework, lecture notes, presentations, research papers, and essays, with commands for math/statistics symbols