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
```

GRT-30M-Chat (`MatterWaveAI/grt-30m-chat`)

GRT-30M-Chat is the instruction and multi-turn conversational fine-tune of MatterWaveAI/grt-30m (29,305,498 parameters, zero nn.Linear layers), formatted with the standard ChatML (<|im_start|> / <|im_end|>) template.

SFT Alignment Metrics

Held-out Conversational Perplexity: 182.79 (base) → 1.37 (Chat SFT)
Held-out Assistant Loss: 5.2083 → 0.3129

Quickstart (`transformers`)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "MatterWaveAI/grt-30m-chat"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float32)

messages = [
    {"role": "user", "content": "What is GRT-30M?"}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)

out_ids = model.generate(**inputs, max_new_tokens=64, do_sample=False)
print(tokenizer.decode(out_ids[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Complete `MatterWaveAI/grt-30m*` Research Suite on Hugging Face

Base Causal LM: MatterWaveAI/grt-30m
Chat Fine-Tune: MatterWaveAI/grt-30m-chat
4-Expert Mixture-of-Rotations (MoR): MatterWaveAI/grt-30m-mor
Circular-Pooled Sentence Embedding: MatterWaveAI/grt-30m-embedding
Cross-Encoder Reranker: MatterWaveAI/grt-30m-reranker
Vision-Language (GRTVL): MatterWaveAI/grt-30m-vl
Canonical Tokenizer & Polar Embedding Bank: MatterWaveAI/grt-30m-tokenizer

Whitepaper: The Geometric Rotation Transformer (GRT): A Purely Rotational Foundation Architecture (GRT_Whitepaper.pdf)

License

Apache 2.0

Citation

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

Downloads last month: -

Safetensors

Model size

29.8M params

Tensor type

I64

F32

Model tree for MatterWaveAI/grt-30m-chat

Base model

MatterWaveAI/grt-30m

Finetuned

(5)

this model

GRT-30M-Chat (MatterWaveAI/grt-30m-chat)