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 generation_config.json from MatterWaveAI/grt-30m-code: direct link, hf CLI and curl.
- Browser
- Download file 246 Bytes
-
https://huggingface.co/MatterWaveAI/grt-30m-code/resolve/main/generation_config.json
- Command line
-
hf download hf://MatterWaveAI/grt-30m-code/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/MatterWaveAI/grt-30m-code/resolve/main/generation_config.json
246 Bytes
| { | |
| "_from_model_config": true, | |
| "bos_token_id": 151643, | |
| "eos_token_id": 151643, | |
| "output_attentions": false, | |
| "output_hidden_states": false, | |
| "pad_token_id": 151643, | |
| "transformers_version": "5.23.0.dev0", | |
| "use_cache": true | |
| } | |