cmu-flame/FLAME-MoE
Official repository for FLAME-MoE: A Transparent End-to-End Research Platform for Mixture-of-Experts Language Models
This platform helps AI researchers develop and test Mixture-of-Experts (MoE) language models. It takes in raw textual data and configuration settings to produce trained MoE models and evaluation metrics. AI researchers and machine learning engineers focused on advanced language model architectures would use this.
No commits in the last 6 months.
Use this if you are an AI researcher building, training, and evaluating Mixture-of-Experts language models and need a robust, transparent framework for your experiments.
Not ideal if you are looking to simply use a pre-trained language model or fine-tune an existing model without delving into MoE architecture research.
Stars
33
Forks
7
Language
Jupyter Notebook
License
—
Category
Last pushed
Sep 19, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/cmu-flame/FLAME-MoE"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
EfficientMoE/MoE-Infinity
PyTorch library for cost-effective, fast and easy serving of MoE models.
raymin0223/mixture_of_recursions
Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation...
AviSoori1x/makeMoE
From scratch implementation of a sparse mixture of experts language model inspired by Andrej...
thu-nics/MoA
[CoLM'25] The official implementation of the paper
jaisidhsingh/pytorch-mixtures
One-stop solutions for Mixture of Expert modules in PyTorch.