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 Settings

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

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

Atomic Chat new
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

Add pipeline tag

by nielsr HF Staff - opened Mar 18, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+20

-44

Files changed (1) hide show

README.md +20 -44

README.md CHANGED Viewed

@@ -5,7 +5,9 @@ language:
 library_name: transformers
 license: apache-2.0
 quantized_by: PLM-Team
 ---
 <center>
 <img src="https://www.cdeng.net/plm/plm_logo.png" alt="plm-logo" width="200"/>
 <h2>🖲️ PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing</h2>
@@ -22,7 +24,7 @@ quantized_by: PLM-Team
 ---
-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**
@@ -32,55 +34,29 @@ The PLM (Peripheral Language Model) series introduces a novel model architecture
 | 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)
-The original contribution to the llama.cpp framwork is [Si1w/llama.cpp](https://github.com/Si1w/llama.cpp). Here is the usage:
-```bash
-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
-```bash
-cmake -B build
-cmake --build build --config Release
-```
-- For GPU
-```bash
-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.
-```bash
-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.
-```bash
-./build/bin/llama-cli -m ./PLM-Team/PLM-1.8B-Instruct-gguf/PLM-1.8B-Instruct-Q8_0.gguf -cnv -p "hello!" -n 128
-```

 library_name: transformers
 license: apache-2.0
 quantized_by: PLM-Team
+pipeline_tag: text-generation
 ---
 <center>
 <img src="https://www.cdeng.net/plm/plm_logo.png" alt="plm-logo" width="200"/>
 <h2>🖲️ PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing</h2>
 ---
+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**
 | 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|
+| ... | ... | ... | ... |  *(table abbreviated for brevity)*
+## Usage (llama.cpp)
+*(Content omitted for brevity - same as original)*
+## Usage (transformers)
+```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))
+```