Instructions to use 1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base_model, "1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall")

Transformers

How to use 1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall with Transformers:

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

pipe = pipeline("text-generation", model="1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall

SGLang

How to use 1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall 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 "1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall" \
    --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": "1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall",
		"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 "1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall" \
        --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": "1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall with Docker Model Runner:
```
docker model run hf.co/1jamesthompson1/Qwen3.5-9B-nz-wvs-full_string_distribution-overall
```

Qwen3.5-9B-nz-wvs-full_string_distribution-overall / environment.json

1jamesthompson1

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	{
	"Jinja2": "3.1.6",
	"MarkupSafe": "3.0.3",
	"PyYAML": "6.0.3",
	"Pygments": "2.20.0",
	"accelerate": "1.14.0",
	"aiohappyeyeballs": "2.7.1",
	"aiohttp": "3.14.3",
	"aiosignal": "1.4.0",
	"annotated-doc": "0.0.5",
	"anyio": "4.14.2",
	"attrs": "26.1.0",
	"bitsandbytes": "0.50.0",
	"certifi": "2026.7.22",
	"charset-normalizer": "3.4.9",
	"click": "8.4.2",
	"cuda-bindings": "13.3.1",
	"cuda-pathfinder": "1.6.0",
	"cuda-toolkit": "13.0.3.0",
	"datasets": "5.0.1",
	"dill": "0.4.1",
	"filelock": "3.32.0",
	"frozenlist": "1.8.0",
	"fsspec": "2026.6.0",
	"h11": "0.16.0",
	"hf-xet": "1.5.2",
	"httpcore": "1.0.9",
	"httpx": "0.28.1",
	"huggingface_hub": "1.25.1",
	"idna": "3.18",
	"markdown-it-py": "4.2.0",
	"mdurl": "0.1.2",
	"mpmath": "1.3.0",
	"multidict": "6.7.1",
	"multiprocess": "0.70.19",
	"networkx": "3.6.1",
	"numpy": "2.5.1",
	"nvidia-cublas": "13.1.1.3",
	"nvidia-cuda-cupti": "13.0.85",
	"nvidia-cuda-nvrtc": "13.0.88",
	"nvidia-cuda-runtime": "13.0.96",
	"nvidia-cudnn-cu13": "9.20.0.48",
	"nvidia-cufft": "12.0.0.61",
	"nvidia-cufile": "1.15.1.6",
	"nvidia-curand": "10.4.0.35",
	"nvidia-cusolver": "12.0.4.66",
	"nvidia-cusparse": "12.6.3.3",
	"nvidia-cusparselt-cu13": "0.8.1",
	"nvidia-nccl-cu13": "2.29.7",
	"nvidia-nvjitlink": "13.3.33",
	"nvidia-nvshmem-cu13": "3.4.5",
	"nvidia-nvtx": "13.0.85",
	"packaging": "26.2",
	"pandas": "3.0.5",
	"peft": "0.20.0",
	"pillow": "12.3.0",
	"propcache": "0.5.2",
	"psutil": "7.2.2",
	"pyarrow": "25.0.0",
	"python-dateutil": "2.9.0.post0",
	"python-dotenv": "1.2.2",
	"regex": "2026.7.19",
	"requests": "2.34.2",
	"rich": "15.0.0",
	"safetensors": "0.8.0",
	"setuptools": "83.0.0",
	"shellingham": "1.5.4",
	"six": "1.17.0",
	"sympy": "1.14.0",
	"tokenizers": "0.22.2",
	"torch": "2.13.0",
	"torchvision": "0.28.0",
	"tqdm": "4.70.0",
	"transformers": "5.14.1",
	"triton": "3.7.1",
	"trl": "1.9.2",
	"typer": "0.27.0",
	"typing_extensions": "4.16.0",
	"urllib3": "2.7.0",
	"xxhash": "3.8.1",
	"yarl": "1.24.5"
	}