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 / README.md

davidhouseholter

Update README: remove GNU Free Software Statement, keep Apache 2.0 and matterwave.ai citation

4e03af2 verified about 19 hours ago

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---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
  - grt
  - chat
  - conversational
  - rotational-transformer
  - givens-rotations
  - clifford-algebra
  - matterwave
base_model: MatterWaveAI/grt-30m
---

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

**GRT-30M-Chat** is the instruction and multi-turn conversational fine-tune of [`MatterWaveAI/grt-30m`](https://huggingface.co/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`)

```python
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

1. **Base Causal LM**: [`MatterWaveAI/grt-30m`](https://huggingface.co/MatterWaveAI/grt-30m)
2. **Chat Fine-Tune**: [`MatterWaveAI/grt-30m-chat`](https://huggingface.co/MatterWaveAI/grt-30m-chat)
3. **4-Expert Mixture-of-Rotations (`MoR`)**: [`MatterWaveAI/grt-30m-mor`](https://huggingface.co/MatterWaveAI/grt-30m-mor)
4. **Circular-Pooled Sentence Embedding**: [`MatterWaveAI/grt-30m-embedding`](https://huggingface.co/MatterWaveAI/grt-30m-embedding)
5. **Cross-Encoder Reranker**: [`MatterWaveAI/grt-30m-reranker`](https://huggingface.co/MatterWaveAI/grt-30m-reranker)
6. **Vision-Language (`GRTVL`)**: [`MatterWaveAI/grt-30m-vl`](https://huggingface.co/MatterWaveAI/grt-30m-vl)
7. **Canonical Tokenizer & Polar Embedding Bank**: [`MatterWaveAI/grt-30m-tokenizer`](https://huggingface.co/MatterWaveAI/grt-30m-tokenizer)

- **Whitepaper**: [**The Geometric Rotation Transformer (GRT): A Purely Rotational Foundation Architecture (`GRT_Whitepaper.pdf`)**](https://huggingface.co/MatterWaveAI/grt-30m/resolve/main/GRT_Whitepaper.pdf)

---

## License

Apache 2.0

---

## Citation

```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}
}
```

	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- grt
	- chat
	- conversational
	- rotational-transformer
	- givens-rotations
	- clifford-algebra
	- matterwave
	base_model: MatterWaveAI/grt-30m
	---

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

	GRT-30M-Chat is the instruction and multi-turn conversational fine-tune of [`MatterWaveAI/grt-30m`](https://huggingface.co/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`)

	```python
	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

	1. Base Causal LM: [`MatterWaveAI/grt-30m`](https://huggingface.co/MatterWaveAI/grt-30m)
	2. Chat Fine-Tune: [`MatterWaveAI/grt-30m-chat`](https://huggingface.co/MatterWaveAI/grt-30m-chat)
	3. 4-Expert Mixture-of-Rotations (`MoR`): [`MatterWaveAI/grt-30m-mor`](https://huggingface.co/MatterWaveAI/grt-30m-mor)
	4. Circular-Pooled Sentence Embedding: [`MatterWaveAI/grt-30m-embedding`](https://huggingface.co/MatterWaveAI/grt-30m-embedding)
	5. Cross-Encoder Reranker: [`MatterWaveAI/grt-30m-reranker`](https://huggingface.co/MatterWaveAI/grt-30m-reranker)
	6. Vision-Language (`GRTVL`): [`MatterWaveAI/grt-30m-vl`](https://huggingface.co/MatterWaveAI/grt-30m-vl)
	7. Canonical Tokenizer & Polar Embedding Bank: [`MatterWaveAI/grt-30m-tokenizer`](https://huggingface.co/MatterWaveAI/grt-30m-tokenizer)

	- Whitepaper: [The Geometric Rotation Transformer (GRT): A Purely Rotational Foundation Architecture (`GRT_Whitepaper.pdf`)](https://huggingface.co/MatterWaveAI/grt-30m/resolve/main/GRT_Whitepaper.pdf)

	---

	## License

	Apache 2.0

	---

	## Citation

	```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}
	}
	```