LLaMA-MoE-v2-3.8B (1+1/7) SFT
[💻 Code] | [📃 Technical Report]
LLaMA-MoE-v2 is a series of open-sourced Mixture-of-Expert (MoE) models based on LLaMA3. We build LLaMA-MoE-v2 with the following two steps:
- Partition LLaMA's FFN layers or Attention layers into sparse experts and insert top-K gate for each layer of experts.
- Supervised fine-tuning the constructed MoE models using open-source data with a two-stage training.
| Model | #Activated Experts | #Experts | #Activated Params | SFT Model |
|---|---|---|---|---|
| LLaMA-MLP-MoE (2/8) | 2 | 8 | 3.8B | 🤗 SFT |
| LLaMA-MLP-MoE (1+1/7) | 2 | 8 | 3.8B | 🤗 SFT |
🚀 QuickStart
# python>=3.10
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_dir = "llama-moe/LLaMA-MoE-v2-3_8B-residual-sft"
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_dir, torch_dtype=torch.bfloat16, trust_remote_code=True)
model.eval()
model.cuda()
input_text = "Could you recommend me some mystery novels?"
input_text = f"<|start_header_id|>user<|end_header_id|>\n\n{input_text}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
inputs = tokenizer(input_text, return_tensors="pt")
input_ids = inputs["input_ids"].cuda()
pred = model.generate(input_ids, max_length=200, temperature=1.0, do_sample=True, use_cache=True)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
"""
I'd be delighted to recommend some mystery novels to you! Here are a few suggestions across various sub-genres:
**Classic Whodunit**
1. "And Then There Were None" by Agatha Christie - A timeless tale of ten strangers who are invited to an isolated island, only to be killed off one by one.
2. "The Murder on the Orient Express" by Agatha Christie - A classic whodunit set on a luxurious train traveling from Istanbul to Paris, where a famous author goes missing.
3. "The Devil in the White City" by Erik Larson - A non-fiction book that combines historical events with a mystery, exploring the 1893 World's Columbian Exposition in Chicago and the serial killer H.H. Holmes.
**Modern Whodunits**
1. "Gone Girl" by Gillian Flynn - A twisty, psychological thriller about a couple whose seemingly perfect ...
"""
📊 Performance
| Model | #Training Tokens | MMLU(5) | GSM8k(8) | HumanEval(pass@10) | IFEval | BoolQ(32) | SciQ | PIQA | ARC-c(25) | TruthfulQA | HellaSwag(10) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| LLaMA3-8B | 15T | 67.2 | 76.5 | 71.4 | 76.5 | 83.0 | 93.2 | 78.5 | 61.9 | 51.7 | 78.8 |
| INCITE-3B | 1T | 25.1 | 2.1 | 6.92 | 30.1 | 66.5 | 94.7 | 74.4 | 40.2 | 36.4 | 65.6 |
| Sheared-LLaMA-2.7B | 50B | 28.2 | 1.9 | 3.2 | 28.8 | 67.6 | 75.8 | 41.1 | 47.6 | 71.2 | 39.0 |
| Gemma-2-2b | 2T | 53.0 | 26.3 | 46.1 | 34.9 | 72.3 | 75.8 | 67.5 | 52.6 | 50.8 | 69.0 |
| Salamandra-2b | 7.8T | 25.1 | 1.90 | 5.82 | 27.7 | 68.0 | 89.8 | 74.7 | 46.3 | 43.4 | 62.3 |
| SmolLM2-1.7B | 11T | 50.4 | 38.5 | 39.1 | 29.0 | 68.2 | 84.3 | 76.0 | 53.2 | 39.9 | 72.6 |
| OpenMoE-3B-9B | 1T | 26.5 | 1.36 | 1.01 | 31.2 | 61.7 | 68.4 | 65.7 | 33.3 | 40.5 | 56.5 |
| LLaMA-MoE-3B-7B | 200B | 28.2 | 4.62 | 12.0 | 28.1 | 68.1 | 88.8 | 77.9 | 44.0 | 33.3 | 73.2 |
| OLMoE-1B-7B | 1T | 53.8 | 40.9 | 40.5 | 35.5 | 80.9 | 94.9 | 80.1 | 55.6 | 43.3 | 79.6 |
| MLP-MoE (8top2) | 7B | 40.6 | 53.1 | 53.5 | 32.7 | 74.6 | 90.6 | 69.3 | 42.8 | 45.6 | 59.0 |
| MLP-MoE (8top2) | 8.4B | 41.0 | 59.6 | 57.1 | 31.7 | 74.5 | 90.2 | 69.5 | 43.3 | 46.9 | 58.1 |
| MLP-MoE (1+7top1) | 7B | 42.7 | 55.0 | 51.2 | 36.0 | 76.9 | 88.8 | 67.9 | 40.2 | 46.9 | 53.7 |
📃 Citation
@misc{llama-moe-v2,
title={LLaMA-MoE v2: Exploring Sparsity of LLaMA from Perspective of Mixture-of-Experts with Post-Training},
author={Xiaoye Qu, Daize Dong, Xuyang Hu, Tong Zhu, Weigao Sun, Yu Cheng},
year={2024},
month={Nov},
url={https://arxiv.org/abs/2411.15708}
}
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