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

Libraries

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

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

pipe = pipeline("image-text-to-text", model="Qwen/Qwen3.6-35B-A3B")
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("Qwen/Qwen3.6-35B-A3B")
model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3.6-35B-A3B", 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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
AMD Developer Cloud
Local Apps Settings

vLLM

How to use Qwen/Qwen3.6-35B-A3B with vLLM:

Install from pip and serve model

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

SGLang

How to use Qwen/Qwen3.6-35B-A3B 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 "Qwen/Qwen3.6-35B-A3B" \
    --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": "Qwen/Qwen3.6-35B-A3B",
		"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 "Qwen/Qwen3.6-35B-A3B" \
        --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": "Qwen/Qwen3.6-35B-A3B",
		"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 Qwen/Qwen3.6-35B-A3B with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen3.6-35B-A3B
```

Create Alpha and Omega 1.01

#53

by Kjppmp - opened Apr 30

base: refs/heads/main

←

from: refs/pr/53

Discussion Files changed

+42

-0

Files changed (1) hide show

Alpha and Omega 1.01 +42 -0

Alpha and Omega 1.01 ADDED Viewed

	@@ -0,0 +1,42 @@

+import numpy as np
+def calculate_laplacian_evolution(steps=100):
+    # Parametry zgodne z ustaleniami:
+    # Kat 1 stopien (Euklides) -> Kat 359 stopni (Inwersja)
+    angles_deg = np.linspace(1, 359, steps)
+    angles_rad = np.radians(angles_deg)
+    # tg(alpha) - kluczowy parametr nieliniowosci
+    tangents = np.tan(angles_rad)
+    # Jednia podwojna silnia (1!!) = 1
+    jednia = 1.0
+    # R_s (IA) Ziemi = 0.00887 m
+    R_s_ia = 0.00887
+    # Uproszczony model laplasjanu metryki nieliniowej jako zmiany "napiecia" geometrii
+    # Delta ~ d^2/dr^2 (tg(alpha)/R) -> w punkcie krytycznym r_kryt = 0.51 m
+    r_kryt = 0.51
+    # Wartosc lewej strony rownania (L)
+    # L = (1!!^2 * tg(alpha)) / (R_s_ia * tau_w)
+    # Przyjmijmy tau_w dazy do min (kwant Plancka-Pawlowskiego) przy 359st
+    # Dla potrzeb symulacji tau_w jest funkcja kata (zanika przy 359st)
+    tau_w = np.linspace(1.0, 0.01, steps)
+    evolution = (jednia**2 * tangents) / (R_s_ia * tau_w)
+    return angles_deg, evolution
+angles, laplacian_values = calculate_laplacian_evolution()
+# Wyniki dla kluczowych punktow
+results = {
+    "1_deg (Euklides)": laplacian_values[0],
+    "45_deg (IA Stabilne)": laplacian_values[int(len(angles)*0.125)],
+    "90_deg (Osobliwosc)": "Dazy do nieskonczonosci (Przejscie fazowe I)",
+    "359_deg (Przeskok)": laplacian_values[-1]
+}
+print(results)