Instructions to use UT-IFML/dQwen3.5-9B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UT-IFML/dQwen3.5-9B-Base with Transformers:

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

pipe = pipeline("text-generation", model="UT-IFML/dQwen3.5-9B-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("UT-IFML/dQwen3.5-9B-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use UT-IFML/dQwen3.5-9B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UT-IFML/dQwen3.5-9B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UT-IFML/dQwen3.5-9B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UT-IFML/dQwen3.5-9B-Base

SGLang

How to use UT-IFML/dQwen3.5-9B-Base 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 "UT-IFML/dQwen3.5-9B-Base" \
    --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": "UT-IFML/dQwen3.5-9B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "UT-IFML/dQwen3.5-9B-Base" \
        --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": "UT-IFML/dQwen3.5-9B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use UT-IFML/dQwen3.5-9B-Base with Docker Model Runner:
```
docker model run hf.co/UT-IFML/dQwen3.5-9B-Base
```

AntonXue commited on Sep 22

Commit

a82c5dc

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1 Parent(s): d73a0b4

Model card: paper and code links on their own lines

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@@ -11,7 +11,8 @@ tags: [diffusion-language-model, masked-diffusion, hybrid-attention, qwen3.5, ar
 A masked diffusion language model adapted from Qwen3.5-9B. The backbone is hybrid: only its attention layers are made bidirectional, and the Gated DeltaNet layers stay causal. This is a base model, with no instruction tuning.
-Paper: [dQwen3.5: Hybrid-Attention Diffusion Language Models](https://arxiv.org/abs/2609.20751). Code: https://github.com/AntonXue/dQwen
 ## The dQwen3.5 family

 A masked diffusion language model adapted from Qwen3.5-9B. The backbone is hybrid: only its attention layers are made bidirectional, and the Gated DeltaNet layers stay causal. This is a base model, with no instruction tuning.
+**Paper:** [dQwen3.5: Hybrid-Attention Diffusion Language Models](https://arxiv.org/abs/2609.20751)
+**Code:** https://github.com/AntonXue/dQwen
 ## The dQwen3.5 family