PLM-Team/PLM-1.8B-Instruct-id-gguf

🖲️ PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing

👉 Project PLM Website

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The PLM (Peripheral Language Model) series introduces a novel model architecture to peripheral computing by delivering powerful language capabilities within the constraints of resource-limited devices. Through modeling and system co-design strategy, PLM optimizes model performance and fits edge system requirements, PLM employs Multi-head Latent Attention and squared ReLU activation to achieve sparsity, significantly reducing memory footprint and computational demands. Coupled with a meticulously crafted training regimen using curated datasets and a Warmup-Stable-Decay-Constant learning rate scheduler, PLM demonstrates superior performance compared to existing small language models, all while maintaining the lowest activated parameters, making it ideally suited for deployment on diverse peripheral platforms like mobile phones and Raspberry Pis.

Here we present the static quants for identified model of PLM-1.8B-Instruct

Usage (llama.cpp)

The original contribution to the llama.cpp framwork is Si1w/llama.cpp. Here is the usage:

git clone https://github.com/Si1w/llama.cpp.git
cd llama.cpp
pip install -r requirements.txt

Then, we can build with CPU of GPU (e.g. Orin). The build is based on cmake.

• For CPU

cmake -B build
cmake --build build --config Release

• For GPU

cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release

Don't forget to download the GGUF files of the PLM. We use the quantization methods in llama.cpp to generate the quantized PLM.

huggingface-cli download --resume-download PLM-Team/PLM-1.8B-Instruct-gguf --local-dir PLM-Team/PLM-1.8B-Instruct-gguf

After build the llama.cpp, we can use llama-cli script to launch the PLM.

./build/bin/llama-cli -m ./PLM-Team/PLM-1.8B-Instruct-gguf/PLM-1.8B-Instruct-Q8_0.gguf -cnv -p "hello!" -n 128

Citation

If you find Project PLM helpful for your research or applications, please cite as follows:

@misc{deng2025plmefficientperipherallanguage,
      title={PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing}, 
      author={Cheng Deng and Luoyang Sun and Jiwen Jiang and Yongcheng Zeng and Xinjian Wu and Wenxin Zhao and Qingfa Xiao and Jiachuan Wang and Lei Chen and Lionel M. Ni and Haifeng Zhang and Jun Wang},
      year={2025},
      eprint={2503.12167},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.12167}, 
}