Text Generation
Transformers
Safetensors
GGUF
English
grt
geometric-rotation-transformer
causal-lm
rotational
knowledge-distillation
qwen3
Instructions to use MatterWaveAI/grt-30m-curriculum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatterWaveAI/grt-30m-curriculum with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MatterWaveAI/grt-30m-curriculum")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MatterWaveAI/grt-30m-curriculum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MatterWaveAI/grt-30m-curriculum with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MatterWaveAI/grt-30m-curriculum" # 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-curriculum", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MatterWaveAI/grt-30m-curriculum
- SGLang
How to use MatterWaveAI/grt-30m-curriculum 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-curriculum" \ --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-curriculum", "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-curriculum" \ --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-curriculum", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MatterWaveAI/grt-30m-curriculum with Docker Model Runner:
docker model run hf.co/MatterWaveAI/grt-30m-curriculum
Initial release of MatterWaveAI/grt-30m-curriculum (weights and SLM-Bench curriculum evaluation)
feacfb0 Download tokenizer.json from MatterWaveAI/grt-30m-curriculum: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/MatterWaveAI/grt-30m-curriculum/resolve/main/tokenizer.json
- Command line
-
hf download hf://MatterWaveAI/grt-30m-curriculum/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/MatterWaveAI/grt-30m-curriculum/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- dde3c5a4d468da6fe61f11ffa8f04af2d101f8f4a0fe118c37bd31bb8c3017b6
- Size of remote file:
- 11.4 MB
- SHA256:
- be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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