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 / bf16_every_eval_ever /aime25.json

ChibuUkachi

add bf16 every eval ever

54d4831 3 months ago

Raw

History Blame

3.84 kB

	{
	"schema_version": "0.2.2",
	"evaluation_id": "aime25\|0/Qwen/Qwen3.6-35B-A3B/1782386750.923367",
	"evaluation_timestamp": "11302276",
	"retrieved_timestamp": "1782386750.923367",
	"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": "8"
	}
	},
	"evaluation_results": [
	{
	"evaluation_name": "aime25",
	"source_data": {
	"dataset_name": "aime25",
	"source_type": "hf_dataset",
	"hf_repo": "yentinglin/aime_2025",
	"hf_split": "train"
	},
	"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": 0.9291666666666667,
	"details": {
	"seed_scores": "[0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9, 0.9333333333333333, 0.9333333333333333]",
	"evaluation_timestamps": "[11302276, 11303332, 11304489, 11305762, 11307069, 11308407, 11309761, 11311085]",
	"seed_values": "[42, 1234, 4158, 5322, 1356, 9843, 3344, 5678]"
	},
	"uncertainty": {
	"standard_error": {
	"value": 0.004166666666666666,
	"method": "across_seeds"
	},
	"num_samples": 8
	}
	},
	"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"
	}
	}
	},
	{
	"evaluation_name": "aime25",
	"source_data": {
	"dataset_name": "aime25",
	"source_type": "hf_dataset",
	"hf_repo": "yentinglin/aime_2025",
	"hf_split": "train"
	},
	"metric_config": {
	"evaluation_description": "avg@n:n=1",
	"lower_is_better": false,
	"score_type": "continuous",
	"min_score": 0.0,
	"max_score": 1.0
	},
	"score_details": {
	"score": 0.9291666666666667,
	"details": {
	"seed_scores": "[0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9, 0.9333333333333333, 0.9333333333333333]",
	"evaluation_timestamps": "[11302276, 11303332, 11304489, 11305762, 11307069, 11308407, 11309761, 11311085]",
	"seed_values": "[42, 1234, 4158, 5322, 1356, 9843, 3344, 5678]"
	},
	"uncertainty": {
	"standard_error": {
	"value": 0.004166666666666666,
	"method": "across_seeds"
	},
	"num_samples": 8
	}
	},
	"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"
	}
	}
	}
	]
	}