Text Generation
Transformers
Safetensors
grt
geometric-rotation-transformer
mixture-of-rotations
mor
Mixture of Experts
rotational-transformer
givens-rotations
clifford-algebra
matterwave
Instructions to use MatterWaveAI/grt-30m-mor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatterWaveAI/grt-30m-mor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MatterWaveAI/grt-30m-mor")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MatterWaveAI/grt-30m-mor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MatterWaveAI/grt-30m-mor with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MatterWaveAI/grt-30m-mor" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MatterWaveAI/grt-30m-mor", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MatterWaveAI/grt-30m-mor
- SGLang
How to use MatterWaveAI/grt-30m-mor 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-mor" \ --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": "MatterWaveAI/grt-30m-mor", "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 "MatterWaveAI/grt-30m-mor" \ --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": "MatterWaveAI/grt-30m-mor", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MatterWaveAI/grt-30m-mor with Docker Model Runner:
docker model run hf.co/MatterWaveAI/grt-30m-mor
Update README with GRT-30M suite, GRT_Whitepaper.pdf, GNU Free-as-in-Freedom statement, and matterwave.ai citation
Browse files
README.md
CHANGED
|
@@ -98,20 +98,17 @@ print(outputs[0].outputs[0].text)
|
|
| 98 |
|
| 99 |
---
|
| 100 |
|
| 101 |
-
##
|
| 102 |
-
|
| 103 |
-
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
|
| 107 |
-
|
| 108 |
-
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
|
| 112 |
-
- [`fermionic_grt.pdf`](https://huggingface.co/MatterWaveAI/grt-30m/resolve/main/papers/fermionic_grt.pdf) — *FermionicGRT: Real-Only Clifford-Algebra Transformers with Matchgate Simulation*
|
| 113 |
-
- [`prism_grt.pdf`](https://huggingface.co/MatterWaveAI/grt-30m/resolve/main/papers/prism_grt.pdf) — *PRISM: Probe-Reservoir Injection and State Modulation for Self-Editing Rotational Transformers*
|
| 114 |
-
- [`rotation_machine.pdf`](https://huggingface.co/MatterWaveAI/grt-30m/resolve/main/papers/rotation_machine.pdf) — *Rotation Machine: Language Modeling via Recursive Application of a Single Computational Rule*
|
| 115 |
|
| 116 |
---
|
| 117 |
|
|
|
|
| 98 |
|
| 99 |
---
|
| 100 |
|
| 101 |
+
## Complete `MatterWaveAI/grt-30m*` Research Suite on Hugging Face
|
| 102 |
+
|
| 103 |
+
1. **Base Causal LM**: [`MatterWaveAI/grt-30m`](https://huggingface.co/MatterWaveAI/grt-30m)
|
| 104 |
+
2. **Chat Fine-Tune**: [`MatterWaveAI/grt-30m-chat`](https://huggingface.co/MatterWaveAI/grt-30m-chat)
|
| 105 |
+
3. **4-Expert Mixture-of-Rotations (`MoR`)**: [`MatterWaveAI/grt-30m-mor`](https://huggingface.co/MatterWaveAI/grt-30m-mor)
|
| 106 |
+
4. **Circular-Pooled Sentence Embedding**: [`MatterWaveAI/grt-30m-embedding`](https://huggingface.co/MatterWaveAI/grt-30m-embedding)
|
| 107 |
+
5. **Cross-Encoder Reranker**: [`MatterWaveAI/grt-30m-reranker`](https://huggingface.co/MatterWaveAI/grt-30m-reranker)
|
| 108 |
+
6. **Vision-Language (`GRTVL`)**: [`MatterWaveAI/grt-30m-vl`](https://huggingface.co/MatterWaveAI/grt-30m-vl)
|
| 109 |
+
7. **Canonical Tokenizer & Polar Embedding Bank**: [`MatterWaveAI/grt-30m-tokenizer`](https://huggingface.co/MatterWaveAI/grt-30m-tokenizer)
|
| 110 |
+
|
| 111 |
+
- **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)
|
|
|
|
|
|
|
|
|
|
| 112 |
|
| 113 |
---
|
| 114 |
|