Instructions to use amazon/GDN-primed-HQwen3-32B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amazon/GDN-primed-HQwen3-32B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="amazon/GDN-primed-HQwen3-32B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("amazon/GDN-primed-HQwen3-32B-Instruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use amazon/GDN-primed-HQwen3-32B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amazon/GDN-primed-HQwen3-32B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amazon/GDN-primed-HQwen3-32B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amazon/GDN-primed-HQwen3-32B-Instruct

SGLang

How to use amazon/GDN-primed-HQwen3-32B-Instruct 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 "amazon/GDN-primed-HQwen3-32B-Instruct" \
    --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": "amazon/GDN-primed-HQwen3-32B-Instruct",
		"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 "amazon/GDN-primed-HQwen3-32B-Instruct" \
        --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": "amazon/GDN-primed-HQwen3-32B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amazon/GDN-primed-HQwen3-32B-Instruct with Docker Model Runner:
```
docker model run hf.co/amazon/GDN-primed-HQwen3-32B-Instruct
```

GDN-primed-HQwen3-32B-Instruct / model-00014-of-00015.safetensors

zancato

Super-squash branch 'main' using huggingface_hub

6a5ae5d about 2 months ago

download

history blame contribute delete

3.58 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 0408fdafbc34c2e29fa0c945564da3b531942ea47469f311bcefcbf61a551b56
Size of remote file:: 3.58 GB
SHA256:: ca3a7deb164b3ffb43c9c0aac61a94935803bebcfdcb8cef9a69f68e9371e20c

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.