Instructions to use summerMC/Qwen3.5-9B-SpeedX9-GDN32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use summerMC/Qwen3.5-9B-SpeedX9-GDN32 with Transformers:

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

pipe = pipeline("text-generation", model="summerMC/Qwen3.5-9B-SpeedX9-GDN32", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("summerMC/Qwen3.5-9B-SpeedX9-GDN32", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use summerMC/Qwen3.5-9B-SpeedX9-GDN32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "summerMC/Qwen3.5-9B-SpeedX9-GDN32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "summerMC/Qwen3.5-9B-SpeedX9-GDN32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/summerMC/Qwen3.5-9B-SpeedX9-GDN32

SGLang

How to use summerMC/Qwen3.5-9B-SpeedX9-GDN32 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 "summerMC/Qwen3.5-9B-SpeedX9-GDN32" \
    --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": "summerMC/Qwen3.5-9B-SpeedX9-GDN32",
		"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 "summerMC/Qwen3.5-9B-SpeedX9-GDN32" \
        --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": "summerMC/Qwen3.5-9B-SpeedX9-GDN32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use summerMC/Qwen3.5-9B-SpeedX9-GDN32 with Docker Model Runner:
```
docker model run hf.co/summerMC/Qwen3.5-9B-SpeedX9-GDN32
```

Qwen3.5-9B-SpeedX9-GDN32 / gdn24_metadata.json

summerMC

Upload Qwen3.5 UNI MAX 9B checkpoint with benchmark results

efc38b5 verified 3 days ago

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472 Bytes

	{
	"source_model": "Qwen/Qwen3.5-9B",
	"architecture": "32x native Qwen3.5 GatedDeltaNet",
	"converted_layers": [
	3,
	7,
	11,
	15,
	19,
	23,
	27,
	31
	],
	"steps_per_converted_layer": 20,
	"seq_len": 128,
	"lr": 0.0001,
	"seed": 1234,
	"runtime_adapters": 0,
	"uni_max_runtime": 1,
	"phase_policy": "exact_bf16_prefill+autotuned_decode",
	"fusions": [
	"residual+rmsnorm",
	"swiglu"
	],
	"cuda_graph_decode_compatible": true
	}