Text Generation
Transformers
Safetensors
English
grt
geometric-rotation-transformer
causal-lm
rotational
Instructions to use MatterWaveAI/grt-10m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatterWaveAI/grt-10m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MatterWaveAI/grt-10m")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MatterWaveAI/grt-10m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MatterWaveAI/grt-10m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MatterWaveAI/grt-10m" # 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-10m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MatterWaveAI/grt-10m
- SGLang
How to use MatterWaveAI/grt-10m 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-10m" \ --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-10m", "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-10m" \ --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-10m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MatterWaveAI/grt-10m with Docker Model Runner:
docker model run hf.co/MatterWaveAI/grt-10m
Update SLM-Bench Round-1 curriculum results (46.00% macro avg)
Browse files
README.md
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| Task (`SLM-Bench`) | Split (`n`) | Random | `GRT-10M` (Zero-Shot) | `PairAdam` | `SymplecticAdam` | `HuygensSymplecticAdam` | Delta (`HuygensSymplectic` vs. Zero-Shot) |
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| **BoolQ** (`google/boolq`) | `val` (`3,270`) | `50.00%` |
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| **ARC-Easy** (`allenai/ai2_arc`) | `test` (`2,376`) | `25.00%` | `26.94%` | `26.60%` | `31.73%` | **`32.
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| **ARC-Challenge** (`allenai/ai2_arc`) | `test` (`1,172`) | `25.00%` | `22.53%` | `24.91%` | `25.17%` | **`
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| **OpenBookQA** (`allenai/openbookqa`) | `test` (`500`) | `25.00%` | `30.00%` | `22.60%` | `33.40%` | **`
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| **PIQA** (`baber/piqa`) | `val` (`1,838`) | `50.00%` | **`52.18%`** | `50.82%` | **`52.18%`** | **`52.18%`** | `+0.00%` (`+2.18%` vs. random) |
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| **WinoGrande** (`allenai/winogrande`) | `val` (`1,267`) | `50.00%` | `50.51%` | `50.75%` | **`51.85%`** | **`51.85%`** | **`+1.34%`** |
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| **HellaSwag** (`Rowan/hellaswag`) | `val` (`1,000`) | `25.00%` | `25.60%` | `29.10%` | **`30.80%`** | **`30.80%`** | **`+5.20%`** |
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| **QNLI** (`nyu-mll/glue`) | `val` (`1,000`) | `50.00%` | `52.70%` | `49.70%` | `56.80%` | **`60.90%`** | **`+8.20%`** |
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| **CommonsenseQA** (`tau/commonsense_qa`) | `val` (`1,221`) | `20.00%` | `20.15%` | **`22.85%`** | **`22.85%`** | **`22.85%`** | **`+2.70%`** |
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| **GSM8K** (`openai/gsm8k`, 4-way MC / PPL) | `test` (`500`) | `25.00%` | `22.00%` (`145.9` PPL) | `21.80%` (`37.3` PPL) | `79.40%` (`43.5` PPL) | **`84.60%`** (**`30.1` PPL**) | **`+62.60%`** (`-115.8` PPL) |
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| **10-Task Macro Average** | `13,644` total | `34.50%` | `36.47%` | `36.03%` | `44.63%` | **`
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Reproduce with `grt-slm-bench`:
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| Task (`SLM-Bench`) | Split (`n`) | Random | `GRT-10M` (Zero-Shot) | `PairAdam` | `SymplecticAdam` | `HuygensSymplecticAdam` | Delta (`HuygensSymplectic` vs. Zero-Shot) |
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|---|---|---:|---:|---:|---:|---:|---:|
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| **BoolQ** (`google/boolq`) | `val` (`3,270`) | `50.00%` | `62.14%` | `61.13%` | `62.14%` | **`62.17%`** | **`+0.03%`** (`+12.17%` vs. random) |
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| **ARC-Easy** (`allenai/ai2_arc`) | `test` (`2,376`) | `25.00%` | `26.94%` | `26.60%` | `31.73%` | **`32.28%`** | **`+5.34%`** |
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| **ARC-Challenge** (`allenai/ai2_arc`) | `test` (`1,172`) | `25.00%` | `22.53%` | `24.91%` | `25.17%` | **`27.13%`** | **`+4.60%`** |
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| **OpenBookQA** (`allenai/openbookqa`) | `test` (`500`) | `25.00%` | `30.00%` | `22.60%` | `33.40%` | **`35.20%`** | **`+5.20%`** |
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| **PIQA** (`baber/piqa`) | `val` (`1,838`) | `50.00%` | **`52.18%`** | `50.82%` | **`52.18%`** | **`52.18%`** | `+0.00%` (`+2.18%` vs. random) |
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| **WinoGrande** (`allenai/winogrande`) | `val` (`1,267`) | `50.00%` | `50.51%` | `50.75%` | **`51.85%`** | **`51.85%`** | **`+1.34%`** |
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| **HellaSwag** (`Rowan/hellaswag`) | `val` (`1,000`) | `25.00%` | `25.60%` | `29.10%` | **`30.80%`** | **`30.80%`** | **`+5.20%`** |
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| **QNLI** (`nyu-mll/glue`) | `val` (`1,000`) | `50.00%` | `52.70%` | `49.70%` | `56.80%` | **`60.90%`** | **`+8.20%`** |
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| **CommonsenseQA** (`tau/commonsense_qa`) | `val` (`1,221`) | `20.00%` | `20.15%` | **`22.85%`** | **`22.85%`** | **`22.85%`** | **`+2.70%`** |
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| **GSM8K** (`openai/gsm8k`, 4-way MC / PPL) | `test` (`500`) | `25.00%` | `22.00%` (`145.9` PPL) | `21.80%` (`37.3` PPL) | `79.40%` (`43.5` PPL) | **`84.60%`** (**`30.1` PPL**) | **`+62.60%`** (`-115.8` PPL) |
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| **10-Task Macro Average** | `13,644` total | `34.50%` | `36.47%` | `36.03%` | `44.63%` | **`46.00%`** | **`+9.53%`** (`+11.50%` vs. random) |
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Reproduce with `grt-slm-bench`:
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