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 / benchmark_results.json

summerMC

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

efc38b5 verified 9 days ago

Raw History Blame

2.69 kB

	{
	"model": "/content/qwen35_uni_max_work/qwen35-unimax-engine-9b-v2_1",
	"source_model": "Qwen/Qwen3.5-9B",
	"environment": {
	"gpu": "NVIDIA RTX PRO 6000 Blackwell Server Edition",
	"vram_gib": 94.971,
	"peak_allocated_vram_gib": 70.141,
	"torch": "2.11.0+cu130",
	"cuda": "13.0",
	"cuda_capability": [
	12,
	0
	],
	"nvidia_driver": "580.82.07",
	"python": "3.13.15"
	},
	"benchmark": {
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	"decode_steps": 64,
	"warmup": 2,
	"runs": 5,
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	"quant_modes": [
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	"quality_steps": 64,
	"quality_context": 64,
	"min_agreement": 1.0,
	"wall_time_seconds": 90.61699619499996
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	"safe": [
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	"agreement": 0.1875,
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	},
	{
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	"block": 2,
	"reason": "not faster than current 79.45 tok/s"
	}
	]
	}
	}