Text Generation
Transformers
Safetensors
hybrid
ssm
state-space-model
linear-attention
gated-deltanet
priming
long-context
instruction-tuned
Instructions to use amazon/GDN-primed-HQwen3-32B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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")# Load model directly from transformers import AutoModel model = AutoModel.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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "amazon/GDN-primed-HQwen3-32B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "amazon/GDN-primed-HQwen3-32B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "amazon/GDN-primed-HQwen3-32B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - 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
- Xet hash:
- 77d3dd51f3f5517fbf87496d26657aa94bd3fd6d67fcb5a8c428903123e57aaa
- Size of remote file:
- 4.93 GB
- SHA256:
- 791b33062349ca24ec9a3b494eafd1c51d8476dee5f5138c4610a1a6cc9708fd
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