LLaMA-MoE-v2-3_8B-2_8-sft

LLaMA-MoE-v2-3.8B (2/8) 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:

1. Partition LLaMA's FFN layers or Attention layers into sparse experts and insert top-K gate for each layer of experts.
2. Supervised fine-tuning the constructed MoE models using open-source data with a two-stage training.

🚀 QuickStart

# python>=3.10

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_dir = "llama-moe/LLaMA-MoE-v2-3_8B-2_8-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

📃 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}
}