Instructions to use RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16")
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 RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16

SGLang

How to use RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16 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 "RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16" \
    --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": "RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16",
		"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 "RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16" \
        --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": "RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16
```

Tokenizer you are loading with an incorrect regex pattern

by traphix - opened Dec 14, 2025

Discussion

traphix

Dec 14, 2025

•

edited Dec 21, 2025

2 * L40s

vllm == 0.13.0rc2.dev6+g434ac76a7

params

python3 -m vllm.entrypoints.openai.api_server \
    --served-model-name qwen3-next-80b-a3b-instruct \
    --model /data/model-cache/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16 \
    --tensor-parallel-size 2 \
    --enable-expert-parallel \
    --enable-auto-tool-choice \
    --tool-call-parser hermes

Tokenizer error

(EngineCore_DP0 pid=325) The tokenizer you are loading from '/data/model-cache/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue.

traphix

Jan 21

How to fix?

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