Text Generation
Transformers
Safetensors
grt
code
python
tool-calling
function-calling
rotational-transformer
givens-rotations
clifford-algebra
matterwave
Instructions to use MatterWaveAI/grt-30m-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatterWaveAI/grt-30m-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MatterWaveAI/grt-30m-code")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MatterWaveAI/grt-30m-code", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MatterWaveAI/grt-30m-code with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MatterWaveAI/grt-30m-code" # 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-code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MatterWaveAI/grt-30m-code
- SGLang
How to use MatterWaveAI/grt-30m-code 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-code" \ --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-code", "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-code" \ --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-code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MatterWaveAI/grt-30m-code with Docker Model Runner:
docker model run hf.co/MatterWaveAI/grt-30m-code
Release MatterWaveAI/grt-30m-code (zero nn.Linear, 8-way classical geometric optimizer ablation)
6f6a759 verified Download model.safetensors from MatterWaveAI/grt-30m-code: direct link, hf CLI and curl.
- Browser
- Download file 122 MB
-
https://huggingface.co/MatterWaveAI/grt-30m-code/resolve/main/model.safetensors
- Command line
-
hf download hf://MatterWaveAI/grt-30m-code/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/MatterWaveAI/grt-30m-code/resolve/main/model.safetensors
122 MB
- Xet hash:
- 9e930a4a30f07499b5a716f03937e1db61f47e4f205c7a0904a5f8697c37abe9
- Size of remote file:
- 122 MB
- SHA256:
- 11ac67b25baec1f95808d8093ecc27ba4e0b3742318ca60d971448cc46d714d1
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