Instructions to use z-lab/Llama-3.1-8B-Instruct-PARO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use z-lab/Llama-3.1-8B-Instruct-PARO with Transformers:

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

pipe = pipeline("text-generation", model="z-lab/Llama-3.1-8B-Instruct-PARO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("z-lab/Llama-3.1-8B-Instruct-PARO")
model = AutoModelForCausalLM.from_pretrained("z-lab/Llama-3.1-8B-Instruct-PARO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use z-lab/Llama-3.1-8B-Instruct-PARO with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("z-lab/Llama-3.1-8B-Instruct-PARO")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use z-lab/Llama-3.1-8B-Instruct-PARO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "z-lab/Llama-3.1-8B-Instruct-PARO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "z-lab/Llama-3.1-8B-Instruct-PARO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/z-lab/Llama-3.1-8B-Instruct-PARO

SGLang

How to use z-lab/Llama-3.1-8B-Instruct-PARO 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 "z-lab/Llama-3.1-8B-Instruct-PARO" \
    --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": "z-lab/Llama-3.1-8B-Instruct-PARO",
		"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 "z-lab/Llama-3.1-8B-Instruct-PARO" \
        --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": "z-lab/Llama-3.1-8B-Instruct-PARO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Pi new

How to use z-lab/Llama-3.1-8B-Instruct-PARO with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "z-lab/Llama-3.1-8B-Instruct-PARO"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "z-lab/Llama-3.1-8B-Instruct-PARO"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use z-lab/Llama-3.1-8B-Instruct-PARO with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "z-lab/Llama-3.1-8B-Instruct-PARO"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default z-lab/Llama-3.1-8B-Instruct-PARO

Run Hermes

hermes

MLX LM

How to use z-lab/Llama-3.1-8B-Instruct-PARO with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "z-lab/Llama-3.1-8B-Instruct-PARO"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "z-lab/Llama-3.1-8B-Instruct-PARO"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "z-lab/Llama-3.1-8B-Instruct-PARO",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use z-lab/Llama-3.1-8B-Instruct-PARO with Docker Model Runner:
```
docker model run hf.co/z-lab/Llama-3.1-8B-Instruct-PARO
```

z-lab/Llama-3.1-8B-Instruct-PARO

Pairwise Rotation Quantization for Efficient Reasoning LLM Inference

ParoQuant is the state-of-the-art INT4 quantization for LLMs. It closes the accuracy gap with FP16 while running at near-AWQ speed. Supports NVIDIA GPUs (vLLM, Transformers) and Apple Silicon (MLX). For more information, see https://github.com/z-lab/paroquant.

z-lab/Llama-3.1-8B-Instruct-PARO is a 4-bit meta-llama/Llama-3.1-8B-Instruct quantized with ParoQuant. Check out other ParoQuant models from the Hugging Face collection.

Quick Start

Installation

# NVIDIA GPU (CUDA 12.9)
pip install "paroquant[vllm]"

# NVIDIA GPU (CUDA 13.0)
pip install "paroquant[vllm]" "vllm==0.19.1" \
  --extra-index-url https://wheels.vllm.ai/0.19.1/cu130 \
  --extra-index-url https://download.pytorch.org/whl/cu130

# Apple Silicon
pip install "paroquant[mlx]"

Interactive Chat

python -m paroquant.cli.chat --model z-lab/Llama-3.1-8B-Instruct-PARO

OpenAI-Compatible API Server

For vLLM, you can directly use vllm serve to serve ParoQuant models:

vllm serve z-lab/Llama-3.1-8B-Instruct-PARO --port 8000

For other frameworks:

python -m paroquant.cli.serve --model z-lab/Llama-3.1-8B-Instruct-PARO --port 8000

Docker (NVIDIA GPU)

The following commands map the local cache directory to the container in order to persist kernel cache across runs. Remove -v ... to disable this behavior.

# Interactive chat
docker run --pull=always --rm -it --gpus all --ipc=host \
  -v $HOME/.cache/paroquant:/root/.cache/paroquant \
  ghcr.io/z-lab/paroquant:chat --model z-lab/Llama-3.1-8B-Instruct-PARO

# API server (port 8000)
docker run --pull=always --rm -it --gpus all --ipc=host -p 8000:8000 \
  -v $HOME/.cache/paroquant:/root/.cache/paroquant \
  ghcr.io/z-lab/paroquant:serve --model z-lab/Llama-3.1-8B-Instruct-PARO

Citation

@inproceedings{liang2026paroquant,
  title     = {{ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning LLM Inference}},
  author    = {Liang, Yesheng and Chen, Haisheng and Zhang, Zihan and Han, Song and Liu, Zhijian},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2026}
}