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activation-functions-search

alpacare

autoawq

AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. Documentation:

autoawq_kernels

autogptq

An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.

awq-test

axolotl

Go ahead and axolotl questions

cnn-from-scratch

Building A CNN From Scratch in NumPy

colossalai

Making large AI models cheaper, faster and more accessible

covid-19-detection

dgq

Official Code For Dual Grained Quantization: Efficient Fine-Grained Quantization for LLM

flask-stripe-mysql-bootstrapped

Flask template with microservices architecture. Fully integrated with Stripe 🚀

gpu_kernels

gridsearchcv-with-keras-sklearn

iris-deployment

linear-regression-from-scratch

llm-awq

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

llm-foundry

LLM training code for MosaicML foundation models

logistic-regression-from-scratch

milespergallonprediction

model-stacking

Model stacking example on toy dataset using XGBoost, LightGBM and more, combined with mlxtend model stacking.

nested-cross-validation

Nested cross-validation for unbiased predictions. Can be used with Scikit-Learn, XGBoost, Keras and LightGBM, or any other estimator that implements the scikit-learn interface.

neural-network-from-scratch

NumPy - PyTorch - TensorFlow (+Keras)

prediction_intervals_lightgbm

self-rag

This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi.

smoothquant

[ICML 2023] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

statistical_significance_testing

text-generation-webui

A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.

torch-int

This repository contains integer operators on GPUs for PyTorch.

tour-of-tensorflow-2.0

Exploring operations and custom training and testing loops in TensorFlow 2.0