Instructions to use RedHatAI/Qwen3.6-35B-A3B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Qwen3.6-35B-A3B-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="RedHatAI/Qwen3.6-35B-A3B-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("RedHatAI/Qwen3.6-35B-A3B-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("RedHatAI/Qwen3.6-35B-A3B-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 RedHatAI/Qwen3.6-35B-A3B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Qwen3.6-35B-A3B-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": "RedHatAI/Qwen3.6-35B-A3B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/Qwen3.6-35B-A3B-NVFP4

SGLang

How to use RedHatAI/Qwen3.6-35B-A3B-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 "RedHatAI/Qwen3.6-35B-A3B-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": "RedHatAI/Qwen3.6-35B-A3B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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/Qwen3.6-35B-A3B-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": "RedHatAI/Qwen3.6-35B-A3B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/Qwen3.6-35B-A3B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Qwen3.6-35B-A3B-NVFP4
```

Qwen3.6-35B-A3B-NVFP4 / score /data_overall.csv

Chibu Ukachi

add raw scores

9b56e81 2 months ago

Raw

History Blame

986 Bytes

Rank,Overall Acc,Model,Model Link,Total Cost ($),Latency Mean (s),Latency Standard Deviation (s),Latency 95th Percentile (s),Non-Live AST Acc,Non-Live Simple AST,Non-Live Multiple AST,Non-Live Parallel AST,Non-Live Parallel Multiple AST,Live Acc,Live Simple AST,Live Multiple AST,Live Parallel AST,Live Parallel Multiple AST,Multi Turn Acc,Multi Turn Base,Multi Turn Miss Func,Multi Turn Miss Param,Multi Turn Long Context,Web Search Acc,Web Search Base,Web Search No Snippet,Memory Acc,Memory KV,Memory Vector,Memory Recursive Summarization,Relevance Detection,Irrelevance Detection,Format Sensitivity Max Delta,Format Sensitivity Standard Deviation,Organization,License

1,56.10%,RedHatAI/Qwen3.6-35B-A3B-NVFP4 (FC),https://huggingface.co/RedHatAI/Qwen3.6-35B-A3B-NVFP4,14.97,13.46,70.98,24.44,39.02%,45.58%,36.50%,53.50%,20.50%,67.88%,57.36%,70.66%,75.00%,54.17%,58.13%,69.00%,53.00%,48.00%,62.50%,47.00%,58.00%,36.00%,51.61%,39.35%,58.71%,56.77%,93.75%,82.51%,N/A,N/A,Qwen,apache-2.0