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

Libraries

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

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

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/Qwen3.6-35B-A3B-NVFP4-Fast"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/Qwen3.6-35B-A3B-NVFP4-Fast",
		"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/unsloth/Qwen3.6-35B-A3B-NVFP4-Fast

SGLang

How to use unsloth/Qwen3.6-35B-A3B-NVFP4-Fast 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 "unsloth/Qwen3.6-35B-A3B-NVFP4-Fast" \
    --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": "unsloth/Qwen3.6-35B-A3B-NVFP4-Fast",
		"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 "unsloth/Qwen3.6-35B-A3B-NVFP4-Fast" \
        --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": "unsloth/Qwen3.6-35B-A3B-NVFP4-Fast",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use unsloth/Qwen3.6-35B-A3B-NVFP4-Fast with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/Qwen3.6-35B-A3B-NVFP4-Fast to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/Qwen3.6-35B-A3B-NVFP4-Fast to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/Qwen3.6-35B-A3B-NVFP4-Fast to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="unsloth/Qwen3.6-35B-A3B-NVFP4-Fast",
    max_seq_length=2048,
)

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

danielhanchen commited on 6 days ago

Commit

1c3f884

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1 Parent(s): 24ccf90

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@@ -56,9 +56,10 @@ Read all benchmarks in our [NVFP4 blog](https://unsloth.ai/docs/models/qwen3.6#n
 To install vLLM in a separate venv:
 ```shell
-uv venv myenv --python 3.12
-uv pip install --python myenv/bin/python \
-    "vllm==0.24.0" "nvidia-cutlass-dsl==4.5.2" --torch-backend=auto
 ```
 Then to serve the 35B Fast variant:
 ```
@@ -78,8 +79,7 @@ Also do **NOT** use the Marlin backend since it's 2x slower - use the native vLL
 You must use the below or you will get **2x slower** inference!
 ```shell
 export CUTE_DSL_ARCH=sm_121a
-vllm serve unsloth/Qwen3.6-35B-A3B-NVFP4-Fast \
-    --moe-backend flashinfer_b12x --linear-backend flashinfer_b12x
 ```
 ## Multi-Token Prediction (MTP)

 To install vLLM in a separate venv:
 ```shell
+uv venv unsloth-nvfp4-env --python 3.13
+source unsloth-nvfp4-env/bin/activate
+uv pip install "vllm>=0.25.0" "flashinfer-python>=0.6.13" "nvidia-cutlass-dsl>=4.5.2" \
+    --torch-backend=auto
 ```
 Then to serve the 35B Fast variant:
 ```
 You must use the below or you will get **2x slower** inference!
 ```shell
 export CUTE_DSL_ARCH=sm_121a
+vllm serve unsloth/Qwen3.6-35B-A3B-NVFP4-Fast --moe-backend flashinfer_b12x
 ```
 ## Multi-Token Prediction (MTP)