Instructions to use PLM-Team/PLM-1.8B-Instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PLM-Team/PLM-1.8B-Instruct-gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="PLM-Team/PLM-1.8B-Instruct-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("PLM-Team/PLM-1.8B-Instruct-gguf", dtype="auto")

llama-cpp-python

How to use PLM-Team/PLM-1.8B-Instruct-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="PLM-Team/PLM-1.8B-Instruct-gguf",
	filename="PLM-1.8B-Instruct-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use PLM-Team/PLM-1.8B-Instruct-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf PLM-Team/PLM-1.8B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf PLM-Team/PLM-1.8B-Instruct-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf PLM-Team/PLM-1.8B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf PLM-Team/PLM-1.8B-Instruct-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf PLM-Team/PLM-1.8B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf PLM-Team/PLM-1.8B-Instruct-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf PLM-Team/PLM-1.8B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PLM-Team/PLM-1.8B-Instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/PLM-Team/PLM-1.8B-Instruct-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use PLM-Team/PLM-1.8B-Instruct-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PLM-Team/PLM-1.8B-Instruct-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PLM-Team/PLM-1.8B-Instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PLM-Team/PLM-1.8B-Instruct-gguf:Q4_K_M

SGLang

How to use PLM-Team/PLM-1.8B-Instruct-gguf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "PLM-Team/PLM-1.8B-Instruct-gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PLM-Team/PLM-1.8B-Instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "PLM-Team/PLM-1.8B-Instruct-gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PLM-Team/PLM-1.8B-Instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use PLM-Team/PLM-1.8B-Instruct-gguf with Ollama:
```
ollama run hf.co/PLM-Team/PLM-1.8B-Instruct-gguf:Q4_K_M
```

Unsloth Studio new

How to use PLM-Team/PLM-1.8B-Instruct-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for PLM-Team/PLM-1.8B-Instruct-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for PLM-Team/PLM-1.8B-Instruct-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for PLM-Team/PLM-1.8B-Instruct-gguf to start chatting

Docker Model Runner
How to use PLM-Team/PLM-1.8B-Instruct-gguf with Docker Model Runner:
```
docker model run hf.co/PLM-Team/PLM-1.8B-Instruct-gguf:Q4_K_M
```

Lemonade

How to use PLM-Team/PLM-1.8B-Instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PLM-Team/PLM-1.8B-Instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.PLM-1.8B-Instruct-gguf-Q4_K_M

List all available models

lemonade list

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

👉 Project PLM Website

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 of https://huggingface.co/PLM-Team/PLM-1.8B-Instruct

Provided Quants

Link	Type	Size/GB	Notes
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-F16.gguf	F16	3.66GB	Recommanded
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q2_K.gguf	Q2_K	827 MB
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q3_K_L.gguf	Q3_K_L	1.09 GB
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q3_K_M.gguf	Q3_K_M	1.01 GB
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q3_K_S.gguf	Q3_K_S	912 MB
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q4_0.gguf	Q4_0	1.11 GB
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q4_1.gguf	Q4_1	1.21 GB
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q4_K_M.gguf	Q4_K_M	1.18 GB	Recommanded
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q4_K_S.gguf	Q4_K_S	1.12 GB
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q5_0.gguf	Q5_0	1.3 GB
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q5_1.gguf	Q5_1	1.4 GB
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q5_K_M.gguf	Q5_K_M	1.34 GB
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q5_K_S.gguf	Q5_K_S	1.3 GB
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q6_K.gguf	Q6_K	1.5 GB
https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q8_0.gguf	Q8_0	1.95 GB	Recommanded

Usage (llama.cpp)

Now llama.cpp supports our model. Here is the usage:

git clone https://github.com/Si1w/llama.cpp.git
cd llama.cpp

If you want to convert the orginal model into gguf form by yourself, you can

pip install -r requirements.txt
python convert_hf_to_ggyf.py [model] --outtype {f32,f16,bf16,q8_0,tq1_0,tq2_0,auto}

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}, 
}

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Paper for PLM-Team/PLM-1.8B-Instruct-gguf

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

Paper • 2503.12167 • Published Mar 15, 2025 • 1