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

Fix model card formatting (replace raw LaTeX with clean Unicode and code spans)

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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) → **`1.34`** (`HessianPairAdam` 200-step Chat SFT; `1.37` at 100-step `PairAdam`)
- **Held-out Assistant Loss**: `5.2083` → **`0.2904`** (`HessianPairAdam`; `0.2932` w/ `CyclideAdam`, `0.2971` w/ `PairAdam`)
- **Held-out Assistant Token Top-1 Accuracy**: `24.69%` → **`100.00%`** (`HuygensAdam`; `99.88%` w/ `HessianPairAdam`, `CyclideAdam`, `PairAdam`, `HuygensSymplecticAdam`)
- **Held-out Multi-Turn ChatML Validation Loss**: `5.3480` → **`1.6645`** (`HuygensAdam`, `val_ppl = 5.28`)

---

## 8-Way Classical Geometric Optimizer Ablation (`Spark ROCm gfx1151`)

We benchmarked all 8 classical geometric, symplectic, gyroscopic, and synchronization optimizers from `transformers.models.grt.optimization_grt` on `MatterWaveAI/grt-30m-chat` (`200` ChatML SFT steps on `blocks[9:12]` + `final_norm` + `<|im_end|>` polar phase calibration from an identical cached `blocks[:9]` prefix state, `2,435,209` trainable parameters, `batch_size = 36`, `lr = 3.5e-3`, `max_angular_step = 0.08`, `seed = 42`):

| Rank | Optimizer (`optimizer_type`) | Final Train Loss | Held-Out Val Loss | Held-Out Val PPL | Assistant Token Acc | Assistant Response Margin (nats/tok) | Multi-Turn Val Loss | Multi-Turn Token Acc | Time (s) |
| :---: | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **1** | **`HessianPairAdam` (`hessian_pair_adam`)** | `0.9501` | **`0.2904`** | **`1.3370`** | `99.88%` | `1.0754` | `1.7855` | `80.26%` | `49.51s` |
| **2** | **`CyclideAdam` (`cyclide_adam`)** | `0.9506` | `0.2932` | `1.3406` | `99.88%` | `0.9446` | `1.9591` | `74.87%` | `50.19s` |
| **3** | **`PairAdam` (`pair_adam`)** | `0.9505` | `0.2971` | `1.3460` | `99.88%` | `1.0982` | `2.0593` | `73.85%` | **`48.57s`** |
| **4** | **`HuygensAdam` (`huygens_adam`)** | **`0.9498`** | `0.3107` | `1.3644` | **`100.00%`** | `1.0257` | **`1.6645`** | **`81.54%`** | `67.79s` |
| **5** | **`HuygensSymplecticAdam` (`huygens_symplectic_adam`)** | `0.9502` | `0.3361` | `1.3995` | `99.88%` | `1.0957` | `1.7592` | `80.51%` | `65.69s` |
| **6** | **`SymplecticAdam` (`symplectic_adam`)** | `0.9502` | `0.3441` | `1.4108` | `99.38%` | **`1.1464`** | `1.9487` | `75.13%` | `50.95s` |
| **7** | **`GyricAdam` (`gyric_adam`)** | `0.9503` | `0.3476` | `1.4157` | `99.75%` | `1.0779` | `1.8009` | `77.18%` | `70.50s` |
| **8** | **`PolarNaturalAdam` (`polar_natural_adam`)** | `0.9521` | `0.3883` | `1.4745` | `99.13%` | `0.9159` | `1.8642` | `76.92%` | `49.40s` |
### Key Geometric Findings on ChatML Instruction Tuning
- **Cauchy–Riemann Toroidal Curvature (`HessianPairAdam`) Achieves Lowest Conversational Validation Loss**: Using Theorem 2.1's exact scale-angle curvature identity `c_{θ_p} = -∑_{i∈{0,1}} (softplus(s_{i,p}) / σ(s_{i,p})) ∇_{s_{i,p}} L` without extra forward/backward passes, `HessianPairAdam` achieves **#1 held-out ChatML validation loss (`0.2904`) and perplexity (`1.3370`)**, followed closely by `CyclideAdam` (`0.2932` / `1.3406` PPL) and `PairAdam` (`0.2971` / `1.3460` PPL).
- **Huygens Circular Synchronization Splay Excels at Multi-Turn Generalization**: `HuygensAdam` achieves **100.00% held-out assistant token top-1 accuracy**, the lowest final training loss (`0.9498`), and **#1 multi-turn ChatML validation loss (`1.6645`, `81.54%` multi-turn token accuracy)** by preventing rotational butterfly stages from over-synchronizing onto single-turn prompt templates.
- **Symplectic Non-Squeezing Maximizes Target-vs-Distractor Contrast Margin**: `SymplecticAdam` achieves the **highest assistant response log-probability margin (`1.1464` nats/token, `+34.60` nats/sequence over cross-domain distractors)** via its Gromov–Liouville canonical area floor.

---

## 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)
8. **Rotational Text-to-Speech (`GRTTTS`)**: [`MatterWaveAI/grt-30m-tts`](https://huggingface.co/MatterWaveAI/grt-30m-tts)
9. **Multimodal Vision-Audio-Language-Speech (`GRTOmni`)**: [`MatterWaveAI/grt-30m-omni`](https://huggingface.co/MatterWaveAI/grt-30m-omni)
- **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) → `1.34` (`HessianPairAdam` 200-step Chat SFT; `1.37` at 100-step `PairAdam`)
	- Held-out Assistant Loss: `5.2083` → `0.2904` (`HessianPairAdam`; `0.2932` w/ `CyclideAdam`, `0.2971` w/ `PairAdam`)
	- Held-out Assistant Token Top-1 Accuracy: `24.69%` → `100.00%` (`HuygensAdam`; `99.88%` w/ `HessianPairAdam`, `CyclideAdam`, `PairAdam`, `HuygensSymplecticAdam`)
	- Held-out Multi-Turn ChatML Validation Loss: `5.3480` → `1.6645` (`HuygensAdam`, `val_ppl = 5.28`)

	---

	## 8-Way Classical Geometric Optimizer Ablation (`Spark ROCm gfx1151`)

	We benchmarked all 8 classical geometric, symplectic, gyroscopic, and synchronization optimizers from `transformers.models.grt.optimization_grt` on `MatterWaveAI/grt-30m-chat` (`200` ChatML SFT steps on `blocks[9:12]` + `final_norm` + `<\|im_end\|>` polar phase calibration from an identical cached `blocks[:9]` prefix state, `2,435,209` trainable parameters, `batch_size = 36`, `lr = 3.5e-3`, `max_angular_step = 0.08`, `seed = 42`):

	\| Rank \| Optimizer (`optimizer_type`) \| Final Train Loss \| Held-Out Val Loss \| Held-Out Val PPL \| Assistant Token Acc \| Assistant Response Margin (nats/tok) \| Multi-Turn Val Loss \| Multi-Turn Token Acc \| Time (s) \|
	\| :---: \| :--- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \|
	\| 1 \| `HessianPairAdam` (`hessian_pair_adam`) \| `0.9501` \| `0.2904` \| `1.3370` \| `99.88%` \| `1.0754` \| `1.7855` \| `80.26%` \| `49.51s` \|
	\| 2 \| `CyclideAdam` (`cyclide_adam`) \| `0.9506` \| `0.2932` \| `1.3406` \| `99.88%` \| `0.9446` \| `1.9591` \| `74.87%` \| `50.19s` \|
	\| 3 \| `PairAdam` (`pair_adam`) \| `0.9505` \| `0.2971` \| `1.3460` \| `99.88%` \| `1.0982` \| `2.0593` \| `73.85%` \| `48.57s` \|
	\| 4 \| `HuygensAdam` (`huygens_adam`) \| `0.9498` \| `0.3107` \| `1.3644` \| `100.00%` \| `1.0257` \| `1.6645` \| `81.54%` \| `67.79s` \|
	\| 5 \| `HuygensSymplecticAdam` (`huygens_symplectic_adam`) \| `0.9502` \| `0.3361` \| `1.3995` \| `99.88%` \| `1.0957` \| `1.7592` \| `80.51%` \| `65.69s` \|
	\| 6 \| `SymplecticAdam` (`symplectic_adam`) \| `0.9502` \| `0.3441` \| `1.4108` \| `99.38%` \| `1.1464` \| `1.9487` \| `75.13%` \| `50.95s` \|
	\| 7 \| `GyricAdam` (`gyric_adam`) \| `0.9503` \| `0.3476` \| `1.4157` \| `99.75%` \| `1.0779` \| `1.8009` \| `77.18%` \| `70.50s` \|
	\| 8 \| `PolarNaturalAdam` (`polar_natural_adam`) \| `0.9521` \| `0.3883` \| `1.4745` \| `99.13%` \| `0.9159` \| `1.8642` \| `76.92%` \| `49.40s` \|

	### Key Geometric Findings on ChatML Instruction Tuning
	- Cauchy–Riemann Toroidal Curvature (`HessianPairAdam`) Achieves Lowest Conversational Validation Loss: Using Theorem 2.1's exact scale-angle curvature identity `c_{θ_p} = -∑_{i∈{0,1}} (softplus(s_{i,p}) / σ(s_{i,p})) ∇_{s_{i,p}} L` without extra forward/backward passes, `HessianPairAdam` achieves #1 held-out ChatML validation loss (`0.2904`) and perplexity (`1.3370`), followed closely by `CyclideAdam` (`0.2932` / `1.3406` PPL) and `PairAdam` (`0.2971` / `1.3460` PPL).
	- Huygens Circular Synchronization Splay Excels at Multi-Turn Generalization: `HuygensAdam` achieves 100.00% held-out assistant token top-1 accuracy, the lowest final training loss (`0.9498`), and #1 multi-turn ChatML validation loss (`1.6645`, `81.54%` multi-turn token accuracy) by preventing rotational butterfly stages from over-synchronizing onto single-turn prompt templates.
	- Symplectic Non-Squeezing Maximizes Target-vs-Distractor Contrast Margin: `SymplecticAdam` achieves the highest assistant response log-probability margin (`1.1464` nats/token, `+34.60` nats/sequence over cross-domain distractors) via its Gromov–Liouville canonical area floor.

	---

	## 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)
	8. Rotational Text-to-Speech (`GRTTTS`): [`MatterWaveAI/grt-30m-tts`](https://huggingface.co/MatterWaveAI/grt-30m-tts)
	9. Multimodal Vision-Audio-Language-Speech (`GRTOmni`): [`MatterWaveAI/grt-30m-omni`](https://huggingface.co/MatterWaveAI/grt-30m-omni)

	- 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}
	}
	```