Instructions to use briscoooe/tiny-cube-dagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use briscoooe/tiny-cube-dagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="briscoooe/tiny-cube-dagger")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("briscoooe/tiny-cube-dagger") model = AutoModelForCausalLM.from_pretrained("briscoooe/tiny-cube-dagger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use briscoooe/tiny-cube-dagger with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "briscoooe/tiny-cube-dagger" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "briscoooe/tiny-cube-dagger", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/briscoooe/tiny-cube-dagger
- SGLang
How to use briscoooe/tiny-cube-dagger 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 "briscoooe/tiny-cube-dagger" \ --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": "briscoooe/tiny-cube-dagger", "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 "briscoooe/tiny-cube-dagger" \ --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": "briscoooe/tiny-cube-dagger", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use briscoooe/tiny-cube-dagger with Docker Model Runner:
docker model run hf.co/briscoooe/tiny-cube-dagger
| """ | |
| Regression test for prompt parsing. | |
| Worth having as a standing test rather than a one-off check, because a wrong | |
| parse is invisible: it still yields a syntactically valid 54-character state, so | |
| the model is simply asked to solve a different cube and the damage shows up only | |
| as an unexplained low benchmark score. The fixture is generated by the real | |
| renderer (scripts/print-prompt-pairs.ts), so it also fails if the prompt layout | |
| ever changes. | |
| python -m pytest test_serve.py # or: python test_serve.py | |
| """ | |
| import json | |
| from pathlib import Path | |
| from serve import extract_state | |
| FIXTURE = Path(__file__).parent / "fixtures" / "prompt_pairs.json" | |
| def load(): | |
| return json.loads(FIXTURE.read_text()) | |
| def test_extracts_state_from_real_prompts(): | |
| for case in load(): | |
| got = extract_state(case["prompt"]) | |
| assert got == case["facelets"], ( | |
| f"depth {case['depth']} index {case['index']}:\n" | |
| f" want {case['facelets']}\n got {got}") | |
| def test_rejects_prose_without_a_net(): | |
| """The prompt's prose alone contains plenty of face letters -- naming the | |
| colours up front and the move vocabulary at the end. Parsing must fail loudly | |
| on text with no diagram rather than assembling a state out of that prose.""" | |
| prose = "Use U, D, L, R, F, or B. U=White (Up), R=Red (Right), F=Green (Front)." | |
| try: | |
| extract_state(prose) | |
| except ValueError: | |
| return | |
| raise AssertionError("expected ValueError on prompt with no net diagram") | |
| if __name__ == "__main__": | |
| test_extracts_state_from_real_prompts() | |
| test_rejects_prose_without_a_net() | |
| print(f"ok: parsed all {len(load())} fixture prompts; rejected prose-only input") | |