Instructions to use z-lab/Qwen3-4B-PARO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use z-lab/Qwen3-4B-PARO with Transformers:

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

pipe = pipeline("text-generation", model="z-lab/Qwen3-4B-PARO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("z-lab/Qwen3-4B-PARO")
model = AutoModelForCausalLM.from_pretrained("z-lab/Qwen3-4B-PARO", device_map="auto")
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/Qwen3-4B-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/Qwen3-4B-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 Settings
LM Studio

vLLM

How to use z-lab/Qwen3-4B-PARO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "z-lab/Qwen3-4B-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/Qwen3-4B-PARO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/z-lab/Qwen3-4B-PARO

SGLang

How to use z-lab/Qwen3-4B-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/Qwen3-4B-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/Qwen3-4B-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/Qwen3-4B-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/Qwen3-4B-PARO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use z-lab/Qwen3-4B-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/Qwen3-4B-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/Qwen3-4B-PARO"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use z-lab/Qwen3-4B-PARO with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "z-lab/Qwen3-4B-PARO"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "z-lab/Qwen3-4B-PARO" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use z-lab/Qwen3-4B-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/Qwen3-4B-PARO"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "z-lab/Qwen3-4B-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/Qwen3-4B-PARO",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use z-lab/Qwen3-4B-PARO with Docker Model Runner:
```
docker model run hf.co/z-lab/Qwen3-4B-PARO
```

Hermes Agent

How to use z-lab/Qwen3-4B-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/Qwen3-4B-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/Qwen3-4B-PARO

Run Hermes

hermes

liang2kl commited on May 15

Commit

2d5d401

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1 Parent(s): 7796e2f

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@@ -52,13 +52,13 @@ python -m paroquant.cli.chat --model z-lab/Qwen3-4B-PARO
 For vLLM, you can directly use `vllm serve` to serve ParoQuant models:
 ```bash
-vllm serve $MODEL --port 8000
 ```
 For other frameworks:
 ```bash
-python -m paroquant.cli.serve --model $MODEL --port 8000
 ```
@@ -66,7 +66,7 @@ python -m paroquant.cli.serve --model $MODEL --port 8000
 ### Docker (NVIDIA GPU)
 > [!NOTE]
-> The following commands map the local cache directory to the container in order to persist kernel cache across runs. Remove `-v ...` to disable this behaviour.
 ```bash
 # Interactive chat

 For vLLM, you can directly use `vllm serve` to serve ParoQuant models:
 ```bash
+vllm serve z-lab/Qwen3-4B-PARO --port 8000
 ```
 For other frameworks:
 ```bash
+python -m paroquant.cli.serve --model z-lab/Qwen3-4B-PARO --port 8000
 ```
 ### Docker (NVIDIA GPU)
 > [!NOTE]
+> 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.
 ```bash
 # Interactive chat