Instructions to use ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast with Transformers:

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

pipe = pipeline("text-generation", model="ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast")
model = AutoModelForCausalLM.from_pretrained("ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast

SGLang

How to use ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast 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 "ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast" \
    --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": "ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast",
		"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 "ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast" \
        --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": "ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast with Docker Model Runner:
```
docker model run hf.co/ArliAI/Qwen3-30B-A3B-ArliAI-RpR-v4-Fast
```

EXL3 - please, Sir, or anyone else

by sphiratrioth666 - opened Aug 13, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-0

sphiratrioth666

Aug 13, 2025

No description provided.

sphiratrioth666 changed pull request status to open Aug 13, 2025

imoc

Aug 13, 2025

? What?
I recommend llama.cpp/GGUF for this as exl3 is much much slower on MoE but uses less vram but Q5~Q6 is basically lossless.
If you do want you can just create yourself it's relatively fast and easy. I forgot how much vram needed though, should be less than 16G.

sphiratrioth666

Aug 13, 2025

EXL2/3 is always faster than GGUF on my RTX 5090 and it used to be the same for 4090 and 4080. With RTX5000, there's a much bigger difference but it's still faster for every single model I run. The base Qwen that this stands on as well :-P

imoc

Aug 14, 2025

With a 5090 you can easily create the quant super fast yourself, tried it?

sphiratrioth666

Aug 14, 2025

Nope. I used to always download them. I can try doing it, I guess...

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