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

File size: 926 Bytes

af91ceb

{
  "angle_init_scale": 0.1,
  "architectures": [
    "GRTForCausalLM"
  ],
  "attention_dropout": 0.0,
  "bos_token_id": 151643,
  "coupling_init_mode": "diagonal",
  "coupling_init_scale": 0.01,
  "dtype": "float32",
  "eos_token_id": 151643,
  "ffn_n_rotations": 21,
  "hidden_size": 256,
  "initializer_range": 0.02,
  "intermediate_size": 1024,
  "learnable_residual_gates": true,
  "learnable_rope": true,
  "log_magnitude_init": 0.0,
  "max_position_embeddings": 2048,
  "model_type": "grt",
  "n_mixer_rotations": 1,
  "n_qkv_rotations": 11,
  "num_attention_heads": 4,
  "num_hidden_layers": 12,
  "output_scale_init": 30.0,
  "pad_token_id": 151643,
  "peri_ln": false,
  "phase_init_scale": 0.1,
  "pos_freq_base": 10000.0,
  "residual_gate_mode": "bounded_depth",
  "residual_norm_mode": "none",
  "tie_word_embeddings": true,
  "transformers_version": "5.21.0.dev0",
  "use_cache": true,
  "vocab_size": 151669
}