Instructions to use MatterWaveAI/grt-30m-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MatterWaveAI/grt-30m-chat with Transformers:

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

pipe = pipeline("text-generation", model="MatterWaveAI/grt-30m-chat")
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("MatterWaveAI/grt-30m-chat", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MatterWaveAI/grt-30m-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MatterWaveAI/grt-30m-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MatterWaveAI/grt-30m-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MatterWaveAI/grt-30m-chat

SGLang

How to use MatterWaveAI/grt-30m-chat 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 "MatterWaveAI/grt-30m-chat" \
    --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": "MatterWaveAI/grt-30m-chat",
		"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 "MatterWaveAI/grt-30m-chat" \
        --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": "MatterWaveAI/grt-30m-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MatterWaveAI/grt-30m-chat with Docker Model Runner:
```
docker model run hf.co/MatterWaveAI/grt-30m-chat
```

davidhouseholter commited on about 21 hours ago

Commit

5ced7ab

verified ·

1 Parent(s): 77ce685

Update README with GRT-30M suite, GRT_Whitepaper.pdf, GNU Free-as-in-Freedom statement, and matterwave.ai citation

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README.md +1 -1

README.md CHANGED Viewed

@@ -76,7 +76,7 @@ All Geometric Rotation Transformer (**GRT**) model weights, polar tokenizers, tr
 ```bibtex
 @techreport{householter2026grt,
   title       = {The Geometric Rotation Transformer: A Purely Rotational Foundation Architecture},
-  author      = {Householter, David and {The MatterWave AI Team}},
   institution = {MatterWave AI},
   year        = {2026},
   url         = {https://matterwave.ai}

 ```bibtex
 @techreport{householter2026grt,
   title       = {The Geometric Rotation Transformer: A Purely Rotational Foundation Architecture},
+  author      = {Householter, David},
   institution = {MatterWave AI},
   year        = {2026},
   url         = {https://matterwave.ai}