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license: apache-2.0
pipeline_tag: text-generation
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# 🧨 FLAME-MoE
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).
**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
| Model Name | Active / Total Params | Layers | MoE Experts (Total/Active/Shared) | Training FLOPs | Tokens Trained |
| -------------------- | --------------------- | ------ | --------------------------------- | -------------- | -------------- |
| FLAME-MoE-38M-100M | 38M / 100M | 9 | 64 / 8 / 2 | 1.0e18 | 4.4B |
| FLAME-MoE-98M-349M | 98M / 349M | 9 | 64 / 8 / 2 | 3.0e18 | 5.0B |
| FLAME-MoE-115M-459M | 115M / 459M | 12 | 64 / 8 / 2 | 6.0e18 | 8.7B |
| FLAME-MoE-290M-1.3B | 290M / 1.3B | 9 | 64 / 8 / 2 | 2.0e19 | 11.4B |
| FLAME-MoE-419M-2.2B | 419M / 2.2B | 15 | 64 / 8 / 2 | 3.0e19 | 11.9B |
| FLAME-MoE-721M-3.8B | 721M / 3.8B | 12 | 64 / 8 / 2 | 8.0e19 | 18.4B |
| FLAME-MoE-1.7B-10.3B | 1.7B / 10.3B | 18 | 64 / 8 / 2 | 2.4e20 | 23.1B |
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## 📖 Training Details
* **Framework**: Megatron-LM with Expert Parallelism (EP=8), Pipeline Parallelism (PP=1)
* **Data**: Pretrained on DataComp-LM (DCLM)
* **Batch Size**: 1024
* **Sequence Length**: 2048
* **Optimizer**: Adam
* **Scheduler**: WSD (Warmup + Decay)
* **Learning Rate**: Max 3e-4, Min 3e-5
* **Checkpoints**: 10 saved per model across training
* **Hardware**: 32× NVIDIA H100 GPUs
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## 🛠 Intended Use
FLAME-MoE is developed for **research purposes only**. It supports academic study in:
* Sparse model training dynamics
* Expert routing behavior and specialization
* Scaling laws and compute-optimal design
* Benchmarking and reproducibility in MoE LLMs
It is not intended for commercial deployment or instruction-tuned downstream tasks.
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## 📂 Access
All models, training scripts, logs, routing traces, and evaluation pipelines are available at:
🔗 [https://github.com/cmu-flame/FLAME-MoE](https://github.com/cmu-flame/FLAME-MoE) |