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

File size: 2,217 Bytes

54d4831

{
  "schema_version": "0.2.2",
  "evaluation_id": "math_500|0/Qwen/Qwen3.6-35B-A3B/1782386797.165193",
  "evaluation_timestamp": "11267660",
  "retrieved_timestamp": "1782386797.165193",
  "source_metadata": {
    "source_name": "lighteval",
    "source_type": "evaluation_run",
    "source_organization_name": "RedHatAI",
    "evaluator_relationship": "third_party"
  },
  "eval_library": {
    "name": "lighteval",
    "version": "6f0f351abfd7c06004bf0dcdaeab14c8f289b86c"
  },
  "model_info": {
    "name": "Qwen/Qwen3.6-35B-A3B",
    "id": "Qwen/Qwen3.6-35B-A3B",
    "developer": "Qwen",
    "inference_engine": {
      "name": "vllm"
    },
    "additional_details": {
      "provider": "hosted_vllm",
      "base_url": "http://0.0.0.0:8700/v1",
      "concurrent_requests": "16",
      "verbose": "False",
      "api_max_retry": "8",
      "api_retry_sleep": "1.0",
      "api_retry_multiplier": "2.0",
      "timeout": "1200.0",
      "num_seeds_merged": "3"
    }
  },
  "evaluation_results": [
    {
      "evaluation_name": "math_500",
      "source_data": {
        "dataset_name": "math_500",
        "source_type": "hf_dataset",
        "hf_repo": "HuggingFaceH4/MATH-500",
        "hf_split": "test"
      },
      "metric_config": {
        "evaluation_description": "pass@k:k=1&n=1",
        "lower_is_better": false,
        "score_type": "continuous",
        "min_score": 0.0,
        "max_score": 1.0
      },
      "score_details": {
        "score": 0.848,
        "details": {
          "seed_scores": "[0.846, 0.85, 0.848]",
          "evaluation_timestamps": "[11267660, 11274842, 11281738]",
          "seed_values": "[42, 1234, 4158]"
        },
        "uncertainty": {
          "standard_error": {
            "value": 0.0011547005383792527,
            "method": "across_seeds"
          },
          "num_samples": 3
        }
      },
      "generation_config": {
        "generation_args": {
          "temperature": 1.0,
          "top_p": 0.95,
          "top_k": 20.0,
          "max_tokens": 64000,
          "max_attempts": 1
        },
        "additional_details": {
          "seed": "42",
          "min_p": "0.0",
          "num_fewshot": "0"
        }
      }
    }
  ]
}