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license: apache-2.0 |
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pipeline_tag: text-generation |
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# 🧨 FLAME-MoE |
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This repository contains the model described in [FLAME-MoE: A Transparent End-to-End Research Platform for Mixture-of-Experts Language Models](https://huggingface.co/papers/2505.20225). |
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**FLAME-MoE** is a fully open Mixture-of-Experts (MoE) language model suite developed by Carnegie Mellon University. It provides a transparent and reproducible research platform for investigating expert routing, model scaling, and training dynamics in sparse architectures. The suite includes seven decoder-only transformer models ranging from 38M to 1.7B active parameters and reflects production-grade MoE setups with 64 experts per MoE layer, top-8 routing, and shared experts. |
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## 🔍 Model Summary |
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| Model Name | Active / Total Params | Layers | MoE Experts (Total/Active/Shared) | Training FLOPs | Tokens Trained | |
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| -------------------- | --------------------- | ------ | --------------------------------- | -------------- | -------------- | |
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| FLAME-MoE-38M-100M | 38M / 100M | 9 | 64 / 8 / 2 | 1.0e18 | 4.4B | |
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| FLAME-MoE-98M-349M | 98M / 349M | 9 | 64 / 8 / 2 | 3.0e18 | 5.0B | |
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| FLAME-MoE-115M-459M | 115M / 459M | 12 | 64 / 8 / 2 | 6.0e18 | 8.7B | |
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| FLAME-MoE-290M-1.3B | 290M / 1.3B | 9 | 64 / 8 / 2 | 2.0e19 | 11.4B | |
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| FLAME-MoE-419M-2.2B | 419M / 2.2B | 15 | 64 / 8 / 2 | 3.0e19 | 11.9B | |
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| FLAME-MoE-721M-3.8B | 721M / 3.8B | 12 | 64 / 8 / 2 | 8.0e19 | 18.4B | |
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| FLAME-MoE-1.7B-10.3B | 1.7B / 10.3B | 18 | 64 / 8 / 2 | 2.4e20 | 23.1B | |
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## 📖 Training Details |
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* **Framework**: Megatron-LM with Expert Parallelism (EP=8), Pipeline Parallelism (PP=1) |
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* **Data**: Pretrained on DataComp-LM (DCLM) |
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* **Batch Size**: 1024 |
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* **Sequence Length**: 2048 |
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* **Optimizer**: Adam |
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* **Scheduler**: WSD (Warmup + Decay) |
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* **Learning Rate**: Max 3e-4, Min 3e-5 |
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* **Checkpoints**: 10 saved per model across training |
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* **Hardware**: 32× NVIDIA H100 GPUs |
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## 🛠 Intended Use |
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FLAME-MoE is developed for **research purposes only**. It supports academic study in: |
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* Sparse model training dynamics |
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* Expert routing behavior and specialization |
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* Scaling laws and compute-optimal design |
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* Benchmarking and reproducibility in MoE LLMs |
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It is not intended for commercial deployment or instruction-tuned downstream tasks. |
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## 📂 Access |
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All models, training scripts, logs, routing traces, and evaluation pipelines are available at: |
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🔗 [https://github.com/cmu-flame/FLAME-MoE](https://github.com/cmu-flame/FLAME-MoE) |
