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- eval_results.json +5 -5
eval_results.json
CHANGED
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"zero_shot": 0.3647,
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"pair_adam": 0.3603,
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"symplectic_adam": 0.4463,
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"huygens_symplectic_adam": 0.
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},
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"tasks": {
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"boolq": {
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"zero_shot_acc": 0.6214,
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"pair_adam_acc": 0.6113,
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"symplectic_adam_acc": 0.6214,
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"huygens_symplectic_adam_acc": 0.
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},
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"arc_easy": {
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"split": "test",
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"zero_shot_acc": 0.2694,
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"pair_adam_acc": 0.266,
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"symplectic_adam_acc": 0.3173,
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"huygens_symplectic_adam_acc": 0.
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},
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"arc_challenge": {
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"split": "test",
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"zero_shot_acc": 0.2253,
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"pair_adam_acc": 0.2491,
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"symplectic_adam_acc": 0.2517,
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"huygens_symplectic_adam_acc": 0.
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},
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"openbookqa": {
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"split": "test",
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"zero_shot_acc": 0.3,
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"pair_adam_acc": 0.226,
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"symplectic_adam_acc": 0.334,
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-
"huygens_symplectic_adam_acc": 0.
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},
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"piqa": {
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"split": "validation",
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"zero_shot": 0.3647,
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"pair_adam": 0.3603,
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"symplectic_adam": 0.4463,
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"huygens_symplectic_adam": 0.46
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},
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"tasks": {
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"boolq": {
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"zero_shot_acc": 0.6214,
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"pair_adam_acc": 0.6113,
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"symplectic_adam_acc": 0.6214,
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+
"huygens_symplectic_adam_acc": 0.6217
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},
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"arc_easy": {
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"split": "test",
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"zero_shot_acc": 0.2694,
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"pair_adam_acc": 0.266,
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"symplectic_adam_acc": 0.3173,
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"huygens_symplectic_adam_acc": 0.3228
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},
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"arc_challenge": {
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"split": "test",
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"zero_shot_acc": 0.2253,
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"pair_adam_acc": 0.2491,
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"symplectic_adam_acc": 0.2517,
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"huygens_symplectic_adam_acc": 0.2713
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},
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"openbookqa": {
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"split": "test",
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"zero_shot_acc": 0.3,
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"pair_adam_acc": 0.226,
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"symplectic_adam_acc": 0.334,
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"huygens_symplectic_adam_acc": 0.352
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},
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"piqa": {
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"split": "validation",
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