license: apache-2.0
language:
- en
tags:
- MoE
LLaMA-MoE-v1-3.5B (2/8)
[💻 Code] | [📜 Technical Report]
👋 Very nice to meet you here~
❤️ This repo contains the model LLaMA-MoE-v1-3.5B (2/8), which activates 2 out of 8 experts (3.5B parameters).
This model is NOT fine-tuned by instruction pairs, so it may not be good enough to act like a chatbot.
📢 LLaMA-MoE is a series of Mixture-of-Expert (MoE) models based on LLaMA-2. You can find the code for training this model at this repo.
💎 This series of models are obtained by partitioning original LLaMA FFNs into experts and further continual pre-training. The total model size is only 6.7B parameters, which is very convenient for deployment and research usage. More details could be found at our technical report.
🚀 QuickStart
# python>=3.10
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_dir = "llama-moe/LLaMA-MoE-v1-3_5B-2_8"
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.to("cuda:0")
input_text = "Suzhou is famous of"
inputs = tokenizer(input_text, return_tensors="pt")
inputs = inputs.to("cuda:0")
pred = model.generate(**inputs, max_length=50, temperature=0.0)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
# Suzhou is famous of its beautiful gardens. The most famous one is the Humble Administrator's Garden. It is a classical Chinese garden with a history of more than 600 years. The garden is divided into three
📊 Performance
| Model | #Activated Experts | #Experts | #Activated Params | Links |
|---|---|---|---|---|
| LLaMA-MoE-3.0B | 2 | 16 | 3.0B | [🤗 HF Weights] |
| LLaMA-MoE-3.5B (4/16) | 4 | 16 | 3.5B | [🤗 HF Weights] |
| LLaMA-MoE-3.5B (2/8) | 2 | 8 | 3.5B | [🤗 HF Weights] |
| Model | SciQ | PIQA | WinoGrande | ARC-e | ARC-c (25) | HellaSwag (10) | LogiQA | BoolQ (32) | LAMBADA | NQ (32) | MMLU (5) | Average |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OPT-2.7B | 78.9 | 74.8 | 60.8 | 54.4 | 34.0 | 61.4 | 25.8 | 63.3 | 63.6 | 10.7 | 25.8 | 50.3 |
| Pythia-2.8B | 83.2 | 73.6 | 59.6 | 58.8 | 36.7 | 60.7 | 28.1 | 65.9 | 64.6 | 8.7 | 26.8 | 51.5 |
| INCITE-BASE-3B | 85.6 | 73.9 | 63.5 | 61.7 | 40.3 | 64.7 | 27.5 | 65.8 | 65.4 | 15.2 | 27.2 | 53.7 |
| Open-LLaMA-3B-v2 | 88.0 | 77.9 | 63.1 | 63.3 | 40.1 | 71.4 | 28.1 | 69.2 | 67.4 | 16.0 | 26.8 | 55.6 |
| Sheared-LLaMA-2.7B | 87.5 | 76.9 | 65.0 | 63.3 | 41.6 | 71.0 | 28.3 | 73.6 | 68.3 | 17.6 | 27.3 | 56.4 |
| LLaMA-MoE-3.0B | 84.2 | 77.5 | 63.6 | 60.2 | 40.9 | 70.8 | 30.6 | 71.9 | 66.6 | 17.0 | 26.8 | 55.5 |
| LLaMA-MoE-3.5B (4/16) | 87.6 | 77.9 | 65.5 | 65.6 | 44.2 | 73.3 | 29.7 | 75.0 | 69.5 | 20.3 | 26.8 | 57.7 |
| LLaMA-MoE-3.5B (2/8) | 88.4 | 77.6 | 66.7 | 65.3 | 43.1 | 73.3 | 29.6 | 73.9 | 69.4 | 19.8 | 27.0 | 57.6 |
📖 Details
Training Data: 200B tokens from SlimPajama with the same data sampling weights as Sheared LLaMA.
📃 Citation
@article{llama-moe,
title={LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training},
author={Tong Zhu and Xiaoye Qu and Daize Dong and Jiacheng Ruan and Jingqi Tong and Conghui He and Yu Cheng},
journal={arXiv preprint arXiv:2406.16554},
year={2024},
url={https://arxiv.org/abs/2406.16554},
}