Instructions to use rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4

SGLang

How to use rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4 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 "rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4" \
    --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": "rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4",
		"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 "rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4" \
        --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": "rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4 with Docker Model Runner:
```
docker model run hf.co/rene98c/Qwen3.5-397B-A17B-REAP-28-NVFP4
```

Qwen3.5-397B-A17B-REAP-28-NVFP4 / patches /qwen3_next.py.patch.md

rene98c

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8d74833 verified 5 months ago

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	# vLLM Patch: qwen3_next.py — Variable expert count per layer

	File: `vllm/model_executor/models/qwen3_next.py`
	Class: `Qwen3NextSparseMoeBlock.__init__`
	Tested on: vLLM 0.16.1rc1.dev188

	## What it does

	Reads `num_experts_per_layer` (a list) from the model config to support REAP-pruned models
	where each layer has a different number of experts. Falls back to `config.num_experts` for
	standard models.

	## Patch

	In `Qwen3NextSparseMoeBlock.__init__`, find where `self.n_routed_experts` is set
	(after `self.ep_size = ...`), and replace the fixed assignment with:

	```python
	# Support variable expert counts per layer (REAP pruned models)
	layer_idx = extract_layer_index(prefix)
	if hasattr(config, 'num_experts_per_layer') and config.num_experts_per_layer:
	num_experts = config.num_experts_per_layer[layer_idx]
	else:
	num_experts = config.num_experts
	self.n_routed_experts = num_experts
	```

	The `extract_layer_index` function is already imported from `.utils` in the stock file.