# PLM

## Overview The PLM model was proposed in [PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing](https://arxiv.org/abs/2503.12167) by PLM-Team. ### Summary 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. ## Usage tips Ensure your Transformers library version is up-to-date. PLM requires Transformers>=4.51.3 for full support. `PLM-1.8B-Instruct` can be found on the [Huggingface Hub](https://huggingface.co/PLM-Team/PLM-1.8B-Instruct) In the following, we demonstrate how to use it for inference ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("PLM-Team/PLM-1.8B-Instruct") model = AutoModelForCausalLM.from_pretrained("PLM-Team/PLM-1.8B-Instruct", torch_dtype=torch.bfloat16) # Input text input_text = "Tell me something about reinforcement learning." inputs = tokenizer(input_text, return_tensors="pt") # Completion output = model.generate(inputs["input_ids"], max_new_tokens=100) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` ## PLMConfig [[autodoc]] PLMConfig ## PLMModel [[autodoc]] PLMModel - forward ## PLMForCausalLM [[autodoc]] PLMForCausalLM - forward ## PLMForSequenceClassification [[autodoc]] PLMForSequenceClassification - forward ## PLMForTokenClassification [[autodoc]] PLMForTokenClassification - forward