Instructions to use MatterWaveAI/grt-30m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MatterWaveAI/grt-30m with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MatterWaveAI/grt-30m")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MatterWaveAI/grt-30m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MatterWaveAI/grt-30m with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MatterWaveAI/grt-30m:F32
# Run inference directly in the terminal:
llama cli -hf MatterWaveAI/grt-30m:F32

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MatterWaveAI/grt-30m:F32
# Run inference directly in the terminal:
llama cli -hf MatterWaveAI/grt-30m:F32

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf MatterWaveAI/grt-30m:F32
# Run inference directly in the terminal:
./build/bin/llama cli -hf MatterWaveAI/grt-30m:F32

Use Docker

docker model run hf.co/MatterWaveAI/grt-30m:F32

Llama new
LM Studio
Jan

vLLM

How to use MatterWaveAI/grt-30m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MatterWaveAI/grt-30m"
# 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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MatterWaveAI/grt-30m:F32

SGLang

How to use MatterWaveAI/grt-30m 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" \
    --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",
		"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" \
        --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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use MatterWaveAI/grt-30m with Ollama:
```
ollama run hf.co/MatterWaveAI/grt-30m:F32
```
Unsloth Desktop
Docker Model Runner
How to use MatterWaveAI/grt-30m with Docker Model Runner:
```
docker model run hf.co/MatterWaveAI/grt-30m:F32
```

Lemonade

How to use MatterWaveAI/grt-30m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MatterWaveAI/grt-30m:F32

Run and chat with the model

lemonade run user.grt-30m-F32

List all available models

lemonade list

Atomic Chat

grt-30m / eval_results.json

davidhouseholter

Update GRT-30M with 9-task SLM-Bench MC & GSM8K perplexity curriculum benchmark results (T-040)

0ae234e verified 1 day ago

Raw History Blame Contribute Delete

3.72 kB

{
  "model_id": "MatterWaveAI/grt-30m",
  "architecture": "GRTForCausalLM",
  "stored_parameters": 29305498,
  "non_embedding_parameters": 9740197,
  "dense_equiv_parameters": 55680446,
  "nn_linear_layers": 0,
  "pretraining": {
    "teacher_family": "Qwen 3 (151,669-token BPE)",
    "tokens_trained": 10789404672,
    "steps": 20580,
    "optimizer": "PairAdam",
    "val_loss": 4.4776,
    "val_ppl": 88.02,
    "val_top1_acc": 0.272,
    "wikitext2_test_ppl": 153.01,
    "wikitext2_top1_acc": 0.2571
  },
  "slm_bench": {
    "benchmark_suite": "SLM-Bench (10-Task Core Suite)",
    "evaluation_harness": "grt-slm-bench (training/slm_bench.py)",
    "hardware": "AMD Ryzen AI Max+ 395 (Radeon 8060S / ROCm gfx1151)",
    "macro_average_accuracy": {
      "random_baseline": 0.345,
      "zero_shot": 0.3723,
      "pair_adam": 0.3957,
      "symplectic_adam": 0.4768,
      "huygens_symplectic_adam": 0.4934
    },
    "tasks": {
      "boolq": {
        "split": "validation",
        "n_eval": 3270,
        "random_baseline": 0.5,
        "zero_shot_acc": 0.5141,
        "pair_adam_acc": 0.6055,
        "symplectic_adam_acc": 0.6125,
        "huygens_symplectic_adam_acc": 0.6223
      },
      "arc_easy": {
        "split": "test",
        "n_eval": 2376,
        "random_baseline": 0.25,
        "zero_shot_acc": 0.2959,
        "pair_adam_acc": 0.2896,
        "symplectic_adam_acc": 0.359,
        "huygens_symplectic_adam_acc": 0.3632
      },
      "arc_challenge": {
        "split": "test",
        "n_eval": 1172,
        "random_baseline": 0.25,
        "zero_shot_acc": 0.2082,
        "pair_adam_acc": 0.2355,
        "symplectic_adam_acc": 0.2568,
        "huygens_symplectic_adam_acc": 0.2568
      },
      "openbookqa": {
        "split": "test",
        "n_eval": 500,
        "random_baseline": 0.25,
        "zero_shot_acc": 0.282,
        "pair_adam_acc": 0.3,
        "symplectic_adam_acc": 0.304,
        "huygens_symplectic_adam_acc": 0.358
      },
      "piqa": {
        "split": "validation",
        "n_eval": 1838,
        "random_baseline": 0.5,
        "zero_shot_acc": 0.5528,
        "pair_adam_acc": 0.5408,
        "symplectic_adam_acc": 0.5528,
        "huygens_symplectic_adam_acc": 0.5528
      },
      "winogrande": {
        "split": "validation",
        "n_eval": 1267,
        "random_baseline": 0.5,
        "zero_shot_acc": 0.5028,
        "pair_adam_acc": 0.5099,
        "symplectic_adam_acc": 0.5122,
        "huygens_symplectic_adam_acc": 0.5122
      },
      "hellaswag": {
        "split": "validation",
        "n_eval": 1000,
        "random_baseline": 0.25,
        "zero_shot_acc": 0.292,
        "pair_adam_acc": 0.316,
        "symplectic_adam_acc": 0.316,
        "huygens_symplectic_adam_acc": 0.316
      },
      "qnli": {
        "split": "validation",
        "n_eval": 1000,
        "random_baseline": 0.5,
        "zero_shot_acc": 0.516,
        "pair_adam_acc": 0.54,
        "symplectic_adam_acc": 0.624,
        "huygens_symplectic_adam_acc": 0.72
      },
      "commonsenseqa": {
        "split": "validation",
        "n_eval": 1221,
        "random_baseline": 0.2,
        "zero_shot_acc": 0.217,
        "pair_adam_acc": 0.2277,
        "symplectic_adam_acc": 0.2465,
        "huygens_symplectic_adam_acc": 0.2465
      },
      "gsm8k": {
        "split": "test",
        "n_eval": 500,
        "random_baseline": 0.25,
        "zero_shot_acc": 0.342,
        "zero_shot_ppl": 27.99,
        "pair_adam_acc": 0.392,
        "pair_adam_ppl": 10.65,
        "symplectic_adam_acc": 0.984,
        "symplectic_adam_ppl": 10.98,
        "huygens_symplectic_adam_acc": 0.986,
        "huygens_symplectic_adam_ppl": 9.04
      }
    }
  }
}

	{
	"model_id": "MatterWaveAI/grt-30m",
	"architecture": "GRTForCausalLM",
	"stored_parameters": 29305498,
	"non_embedding_parameters": 9740197,
	"dense_equiv_parameters": 55680446,
	"nn_linear_layers": 0,
	"pretraining": {
	"teacher_family": "Qwen 3 (151,669-token BPE)",
	"tokens_trained": 10789404672,
	"steps": 20580,
	"optimizer": "PairAdam",
	"val_loss": 4.4776,
	"val_ppl": 88.02,
	"val_top1_acc": 0.272,
	"wikitext2_test_ppl": 153.01,
	"wikitext2_top1_acc": 0.2571
	},
	"slm_bench": {
	"benchmark_suite": "SLM-Bench (10-Task Core Suite)",
	"evaluation_harness": "grt-slm-bench (training/slm_bench.py)",
	"hardware": "AMD Ryzen AI Max+ 395 (Radeon 8060S / ROCm gfx1151)",
	"macro_average_accuracy": {
	"random_baseline": 0.345,
	"zero_shot": 0.3723,
	"pair_adam": 0.3957,
	"symplectic_adam": 0.4768,
	"huygens_symplectic_adam": 0.4934
	},
	"tasks": {
	"boolq": {
	"split": "validation",
	"n_eval": 3270,
	"random_baseline": 0.5,
	"zero_shot_acc": 0.5141,
	"pair_adam_acc": 0.6055,
	"symplectic_adam_acc": 0.6125,
	"huygens_symplectic_adam_acc": 0.6223
	},
	"arc_easy": {
	"split": "test",
	"n_eval": 2376,
	"random_baseline": 0.25,
	"zero_shot_acc": 0.2959,
	"pair_adam_acc": 0.2896,
	"symplectic_adam_acc": 0.359,
	"huygens_symplectic_adam_acc": 0.3632
	},
	"arc_challenge": {
	"split": "test",
	"n_eval": 1172,
	"random_baseline": 0.25,
	"zero_shot_acc": 0.2082,
	"pair_adam_acc": 0.2355,
	"symplectic_adam_acc": 0.2568,
	"huygens_symplectic_adam_acc": 0.2568
	},
	"openbookqa": {
	"split": "test",
	"n_eval": 500,
	"random_baseline": 0.25,
	"zero_shot_acc": 0.282,
	"pair_adam_acc": 0.3,
	"symplectic_adam_acc": 0.304,
	"huygens_symplectic_adam_acc": 0.358
	},
	"piqa": {
	"split": "validation",
	"n_eval": 1838,
	"random_baseline": 0.5,
	"zero_shot_acc": 0.5528,
	"pair_adam_acc": 0.5408,
	"symplectic_adam_acc": 0.5528,
	"huygens_symplectic_adam_acc": 0.5528
	},
	"winogrande": {
	"split": "validation",
	"n_eval": 1267,
	"random_baseline": 0.5,
	"zero_shot_acc": 0.5028,
	"pair_adam_acc": 0.5099,
	"symplectic_adam_acc": 0.5122,
	"huygens_symplectic_adam_acc": 0.5122
	},
	"hellaswag": {
	"split": "validation",
	"n_eval": 1000,
	"random_baseline": 0.25,
	"zero_shot_acc": 0.292,
	"pair_adam_acc": 0.316,
	"symplectic_adam_acc": 0.316,
	"huygens_symplectic_adam_acc": 0.316
	},
	"qnli": {
	"split": "validation",
	"n_eval": 1000,
	"random_baseline": 0.5,
	"zero_shot_acc": 0.516,
	"pair_adam_acc": 0.54,
	"symplectic_adam_acc": 0.624,
	"huygens_symplectic_adam_acc": 0.72
	},
	"commonsenseqa": {
	"split": "validation",
	"n_eval": 1221,
	"random_baseline": 0.2,
	"zero_shot_acc": 0.217,
	"pair_adam_acc": 0.2277,
	"symplectic_adam_acc": 0.2465,
	"huygens_symplectic_adam_acc": 0.2465
	},
	"gsm8k": {
	"split": "test",
	"n_eval": 500,
	"random_baseline": 0.25,
	"zero_shot_acc": 0.342,
	"zero_shot_ppl": 27.99,
	"pair_adam_acc": 0.392,
	"pair_adam_ppl": 10.65,
	"symplectic_adam_acc": 0.984,
	"symplectic_adam_ppl": 10.98,
	"huygens_symplectic_adam_acc": 0.986,
	"huygens_symplectic_adam_ppl": 9.04
	}
	}
	}
	}