L

llm-training

https://github.com/InternLM/xtuner An efficient, flexible and full-featured toolkit for fine-tuning LLM (InternLM2, Llama3, Phi3, Qwen, Mistral, ...)

🔧🔗https://github.com/pytorch/torchtune PyTorch native post-training library

🕸️🔗pytorch.org/torchtune/main/

https://github.com/QuixiAI/spectrum This repository contains the implementation of Spectrum, as detailed in the paper Spectrum: Targeted Training on Signal to Noise Ratio.

LLM training in simple, raw C/CUDA LLMs in simple, pure C/CUDA with no need for 245MB of PyTorch or 107MB of cPython. Current focus is on pretraining, in particular reproducing the GPT-2 and GPT-3 miniseries, along with a parallel PyTorch ref

🔧🔗https://github.com/nbursa/user-sentiment-model The primary goal of this project is to train a machine learning model designed to analyze the sentiment of user tweets, aiming to support a broader a

The Arcee client for executing domain-adpated language model routines

https://github.com/coqui-ai/snakepit 🐍 Coqui's machine learning job scheduler

https://github.com/lm-sys/llm-decontaminator Code for the paper "Rethinking Benchmark and Contamination for Language Models with Rephrased Samples"