Qwen2idae-16x14B-v1.0

Parameter-Efficient Sparsity Crafting From Dense to Mixture-of-Experts for Instruction Tuning on General Tasks (EMNLP'24)

News

• 9/20/2024 - Our paper is accepted by EMNLP'24.
• 3/12/2024 - We release Qwen2idae-16x14B-v1.0 on 🤗 HuggingFace, which has strong performance in Math and Code with 15B activated params.
• 2/7/2024 - Serp-ai adds unsloth support for faster and memory efficient training of our Parameter-Efficient Sparsity Crafting and releases new sparsetral models based on mistral-7B.
• 1/10/2024 - Camelidae models are now available on 🤗 HuggingFace.
• 1/4/2024 - We release the paper, Parameter-Efficient Sparsity Crafting From Dense to Mixture-of-Experts for Instruction Tuning on General Tasks.
• 12/22/2023 - We release the training repo that craft the dense model with LLaMA architecture to the MoE model.

Introduction

Camelidae and Qwen2idae models are trained utilizing Parameter-Efficient Sparsity Crafting techniques

We present Parameter-Efficient Sparsity Crafting to help dense models learn knowledge from different fields (including code and math). This approach performs instruction tuning and efficiently utilizes MoE structure.

Specifically, Parameter-Efficient Sparsity Crafting utilizes parameter-efficient techniques including QLoRA and Adapter to perform Efficient Sparse Upcycling.

Model Lists

Performance

We bold the top3 scores separately for all models.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("hywu/Qwen2idae-16x14B-v1.0", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("hywu/Qwen2idae-16x14B-v1.0", device_map="auto", trust_remote_code=True).eval()

inputs = tokenizer('<|im_start|>user\nHow are you?<|im_end|>\n<|im_start|>assistant\n', return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))

Citation

@article{wu2024parameter,
  title={Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks},
  author={Wu, Haoyuan and Zheng, Haisheng and Yu, Bei},
  journal={arXiv preprint arXiv:2401.02731},
  year={2024}
}

License

The source code in this repo is licensed under the Apache 2.0 License. Qwen2idae models are developed for academic research and free commercial use, all usage must adhere to the license from Qwen1.5.