Instructions to use Qwen/Qwen3.5-35B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen3.5-35B-A3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Qwen/Qwen3.5-35B-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/Qwen3.5-35B-A3B")
model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3.5-35B-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?"}
        ]
    },
]
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
Local Apps Settings

vLLM

How to use Qwen/Qwen3.5-35B-A3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen3.5-35B-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/Qwen3.5-35B-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/Qwen3.5-35B-A3B

SGLang

How to use Qwen/Qwen3.5-35B-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/Qwen3.5-35B-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/Qwen3.5-35B-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/Qwen3.5-35B-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/Qwen3.5-35B-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/Qwen3.5-35B-A3B with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen3.5-35B-A3B
```

Non-deterministic outputs with identical inputs (GPTQ-Int4, vLLM 0.18, document extraction)

#65

by Starlight1997 - opened Apr 1

Discussion

Starlight1997

Apr 1

Description:

Environment

Model: Qwen3.5-35B-A3B-GPTQ-Int4 (from
Qwen/Qwen3.5-35B-A3B-GPTQ-Int4)
Inference engine: vLLM 0.18.0
GPU: NVIDIA RTX 5090 (32 GB VRAM)
OS: Ubuntu 24.04, CUDA 13.0

Problem

We use Qwen3.5-35B-A3B-GPTQ-Int4 for structured data extraction from scanned Russian accounting documents (invoices,
contracts, acts, etc.). The model receives a page image + a system prompt in Russian and must return a strict JSON with
fields like document number, date, buyer/seller names, INN, KPP, VAT amount, etc.

The issue: given the exact same inputs, the model produces different outputs across runs.

We process a batch of 37 identical PDF files with:

Same system prompt (see below)
Same user prompt
Same DPI (230) for PDF→PNG conversion
Same vLLM parameters

Yet on repeated runs, some fields change — e.g., seller name or address may differ slightly, or VAT amount
appears/disappears. This is critical for our use case where accuracy and consistency are essential for accounting
automation.

vLLM generation parameters

We explicitly set all parameters for maximum determinism:

{                                                                  
    "model": "document-parser",
    "temperature": 0.0,                                                                                                  
    "top_p": 1.0,
    "top_k": 1,                                                                                                          
    "seed": 42,                                                    
    "chat_template_kwargs": {"enable_thinking": False},
    "max_tokens": 2048                                                                                                   
}                                                                                                                        
                                                                                                                         
vLLM launch command                                                                                                      
                                                                   
python -m vllm.entrypoints.openai.api_server \
    --model Qwen3.5-35B-A3B-GPTQ-Int4 \                                                                                  
    --served-model-name document-parser \
    --port 8001 \                                                                                                        
    --dtype float16 \                                                                                                    
    --gpu-memory-utilization 0.97 \
    --max-model-len 16384 \                                                                                              
    --max-num-seqs 12 \                                                                                                  
    --trust-remote-code \
    --enable-chunked-prefill \                                                                                           
    --seed 42                                                                                                            
 
System prompt (Russian)                                                                                                  
                                                                   
The prompt instructs the model to extract structured data from Russian accounting documents and return strict JSON:      
 
Ты — экспертная система извлечения данных из российских бухгалтерских документов.                                        
                                                                                                                         
На изображении — отсканированный документ. Определи тип и извлеки реквизиты двух сторон.                                 
                                                                                                                         
Верни СТРОГО JSON:                                                                                                       
{                                                                  
    "тип_документа": "string",
    "номер_документа": "string | null",
    "дата_документа": "ДД.ММ.ГГГГ | null",
    "покупатель": "string | null",                                                                                       
    "покупатель_инн": "string | null",
    "покупатель_кпп": "string | null",                                                                                   
    "покупатель_адрес": "string | null",                           
    "продавец": "string | null",                                                                                         
    "продавец_инн": "string | null",
    "продавец_кпп": "string | null",                                                                                     
    "продавец_адрес": "string | null",                             
    "ндс": "number | null",                                                                                              
    "сумма_с_ндс": "number | null"
}                                                                                                                        
                                                                   
МАППИНГ СТОРОН:                                                                                                          
- Продавец = ПОСТАВЩИК (тот, кто отгружает товар).
- Покупатель = тот, кто получает и оплачивает товар.                                                                     
                                                                   
СУММЫ:                                                                                                                   
- НДС — итоговая сумма НДС (число).
- Сумма с НДС — итоговая сумма документа с учётом НДС.                                                                   
- Числа: десятичный разделитель — точка, без пробелов. Пример: 15000.50
                                                                                                                         
ПРАВИЛА:
1. Верни ТОЛЬКО валидный JSON.                                                                                           
2. Если данные не найдены — null.                                  
3. Даты строго ДД.ММ.ГГГГ.                                                                                               
/no_think
                                                                                                                         
          
 Question                                                                                                                 
                                                                   
1. Is fully deterministic output achievable with this model (GPTQ-Int4 quantization) given fixed seed, temperature=0,    
top_k=1?
2. Could the MoE routing introduce non-determinism even with greedy decoding? (Expert selection might vary due to        
floating-point order of operations)                                                                                      
3. Are there any recommended vLLM settings or model configurations to improve reproducibility?
4. Would the full BF16 version be more deterministic than GPTQ-Int4?                                                     
                                                                                                                         
Any guidance would be greatly appreciated. Thank you!

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