Text Generation
Transformers
Safetensors
GGUF
English
grt
geometric-rotation-transformer
causal-lm
rotational
knowledge-distillation
qwen3
Instructions to use MatterWaveAI/grt-30m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 3,722 Bytes
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}
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