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---
title: README
emoji: 🌍
colorFrom: red
colorTo: gray
sdk: static
pinned: false
---
# LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training
📢 **A SMALLER AFFORDABLE MoE MODEL FOR EVERYONE!!**
LLaMA-MoE is a series of open-sourced Mixture-of-Expert (MoE) models based on [LLaMA](https://github.com/facebookresearch/llama) and [SlimPajama](https://www.cerebras.net/blog/slimpajama-a-627b-token-cleaned-and-deduplicated-version-of-redpajama).
We build LLaMA-MoE with the following two steps:
1. Partition LLaMA's FFNs into sparse experts and insert top-K gate for each layer of experts.
2. Continually pre-train the initialized MoE model with an optimized data sampling weights from [Sheared LLaMA](https://arxiv.org/abs/2310.06694) and filtered datasets from [SlimPajama](https://www.cerebras.net/blog/slimpajama-a-627b-token-cleaned-and-deduplicated-version-of-redpajama).
The number of activated model parameters is only 3.0~3.5B, which is friendly for deployment and research usage. Please refer to our [technical report](https://github.com/pjlab-sys4nlp/llama-moe/blob/main/docs/LLaMA_MoE.pdf) for more details.
| Model | \#Activated Experts | \#Experts | \#Activated Params | Links |
| :------------------------ | :-----------------: | :-------: | :----------------: | :-----------------------------------------------------------------------: |
| **LLaMA-MoE-3.0B** | 2 | 16 | 3.0B | [[🤗 HF Weights]](https://huggingface.co./llama-moe/LLaMA-MoE-v1-3_0B-2_16) |
| **LLaMA-MoE-3.5B (4/16)** | 4 | 16 | 3.5B | [[🤗 HF Weights]](https://huggingface.co./llama-moe/LLaMA-MoE-v1-3_5B-4_16) |
| **LLaMA-MoE-3.5B (2/8)** | 2 | 8 | 3.5B | [[🤗 HF Weights]](https://huggingface.co./llama-moe/LLaMA-MoE-v1-3_5B-2_8) |
| Model | Average | SciQ | PIQA | WinoGrande | ARC-e | ARC-c (25) | HellaSwag (10) | LogiQA | BoolQ (32) | LAMBADA | NQ (32) | MMLU (5) |
| :------------------------------------------------------------------------------------ | :------: | :------: | :------: | :--------: | :------: | :--------: | :------------: | :------: | :--------: | :------: | :------: | :-------: |
| [OPT-2.7B](https://huggingface.co./facebook/opt-2.7b) | 50.3 | 78.9 | 74.8 | 60.8 | 54.4 | 34.0 | 61.4 | 25.8 | 63.3 | 63.6 | 10.7 | 25.8 |
| [Pythia-2.8B](https://huggingface.co./EleutherAI/pythia-2.8b) | 51.5 | 83.2 | 73.6 | 59.6 | 58.8 | 36.7 | 60.7 | 28.1 | 65.9 | 64.6 | 8.7 | 26.8 |
| [INCITE-BASE-3B](https://huggingface.co./togethercomputer/RedPajama-INCITE-Base-3B-v1) | 53.7 | 85.6 | 73.9 | 63.5 | 61.7 | 40.3 | 64.7 | 27.5 | 65.8 | 65.4 | 15.2 | 27.2 |
| [Open-LLaMA-3B-v2](https://huggingface.co./openlm-research/open_llama_3b_v2) | 55.6 | 88.0 | 77.9 | 63.1 | 63.3 | 40.1 | 71.4 | 28.1 | 69.2 | 67.4 | 16.0 | 26.8 |
| [Sheared-LLaMA-2.7B](https://huggingface.co./princeton-nlp/Sheared-LLaMA-2.7B) | 56.4 | 87.5 | 76.9 | 65.0 | 63.3 | 41.6 | 71.0 | 28.3 | 73.6 | 68.3 | 17.6 | **27.3** |
| **LLaMA-MoE-3.0B** | 55.5 | 84.2 | 77.5 | 63.6 | 60.2 | 40.9 | 70.8 | **30.6** | 71.9 | 66.6 | 17.0 | 26.8 |
| **LLaMA-MoE-3.5B (4/16)** | **57.7** | 87.6 | **77.9** | 65.5 | **65.6** | **44.2** | **73.3** | 29.7 | **75.0** | **69.5** | **20.3** | 26.8 |
| **LLaMA-MoE-3.5B (2/8)** | 57.6 | **88.4** | 77.6 | **66.7** | 65.3 | 43.1 | **73.3** | 29.6 | 73.9 | 69.4 | 19.8 | 27.0 |
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