Text Generation
Transformers
Safetensors
English
grt
geometric-rotation-transformer
causal-lm
rotational
Instructions to use MatterWaveAI/grt-10m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatterWaveAI/grt-10m-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MatterWaveAI/grt-10m-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MatterWaveAI/grt-10m-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MatterWaveAI/grt-10m-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MatterWaveAI/grt-10m-base" # 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-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MatterWaveAI/grt-10m-base
- SGLang
How to use MatterWaveAI/grt-10m-base 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-base" \ --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-base", "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-base" \ --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-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MatterWaveAI/grt-10m-base with Docker Model Runner:
docker model run hf.co/MatterWaveAI/grt-10m-base
Download eval_results.json from MatterWaveAI/grt-10m-base: direct link, hf CLI and curl.
- Browser
- Download file 2.48 kB
-
https://huggingface.co/MatterWaveAI/grt-10m-base/resolve/main/eval_results.json
- Command line
-
hf download hf://MatterWaveAI/grt-10m-base/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/MatterWaveAI/grt-10m-base/resolve/main/eval_results.json
2.48 kB
| { | |
| "model_id": "MatterWaveAI/grt-10m-base", | |
| "architecture": "GRTForCausalLM", | |
| "stored_parameters": 8684515, | |
| "non_embedding_parameters": 2234515, | |
| "dense_equiv_parameters": 17518848, | |
| "nn_linear_layers": 0, | |
| "pretraining": { | |
| "corpus": "nvidia/Nemotron-Pretraining-Dataset-sample", | |
| "steps": 336000, | |
| "optimizer": "PairAdam", | |
| "train_loss": 2.144, | |
| "val_loss": 4.033, | |
| "val_ppl": 56.4, | |
| "val_top1_acc": 0.4 | |
| }, | |
| "slm_bench_zero_shot": { | |
| "benchmark_suite": "SLM-Bench (9 Multiple-Choice Tasks + GSM8K Perplexity)", | |
| "evaluation_harness": "grt-slm-bench (training/slm_bench.py)", | |
| "mc_macro_average_accuracy": 0.3808, | |
| "random_macro_average": 0.3556, | |
| "tasks": { | |
| "boolq": { | |
| "split": "validation", | |
| "n_eval": 3270, | |
| "random": 0.5, | |
| "zero_shot_acc": 0.6214, | |
| "pair_adam_acc": 0.6113 | |
| }, | |
| "qnli": { | |
| "split": "validation", | |
| "n_eval": 1000, | |
| "random": 0.5, | |
| "zero_shot_acc": 0.527, | |
| "pair_adam_acc": 0.497 | |
| }, | |
| "piqa": { | |
| "split": "validation", | |
| "n_eval": 1838, | |
| "random": 0.5, | |
| "zero_shot_acc": 0.5218, | |
| "pair_adam_acc": 0.5082 | |
| }, | |
| "winogrande": { | |
| "split": "validation", | |
| "n_eval": 1267, | |
| "random": 0.5, | |
| "zero_shot_acc": 0.5051, | |
| "pair_adam_acc": 0.5075 | |
| }, | |
| "openbookqa": { | |
| "split": "test", | |
| "n_eval": 500, | |
| "random": 0.25, | |
| "zero_shot_acc": 0.3, | |
| "pair_adam_acc": 0.226 | |
| }, | |
| "arc_easy": { | |
| "split": "test", | |
| "n_eval": 2376, | |
| "random": 0.25, | |
| "zero_shot_acc": 0.2694, | |
| "pair_adam_acc": 0.266 | |
| }, | |
| "hellaswag": { | |
| "split": "validation", | |
| "n_eval": 1000, | |
| "random": 0.25, | |
| "zero_shot_acc": 0.256, | |
| "pair_adam_acc": 0.291 | |
| }, | |
| "arc_challenge": { | |
| "split": "test", | |
| "n_eval": 1172, | |
| "random": 0.25, | |
| "zero_shot_acc": 0.2253, | |
| "pair_adam_acc": 0.2491 | |
| }, | |
| "commonsenseqa": { | |
| "split": "validation", | |
| "n_eval": 1221, | |
| "random": 0.2, | |
| "zero_shot_acc": 0.2015, | |
| "pair_adam_acc": 0.2285 | |
| }, | |
| "gsm8k": { | |
| "split": "test", | |
| "n_eval": 500, | |
| "zero_shot_ppl": 145.85, | |
| "pair_adam_ppl": 37.27 | |
| } | |
| } | |
| } | |
| } | |