Text Generation
Transformers
English
custom
tokenizer
symbolic-ai
mathematics
llm
reasoning
ast
compiler
nlp
deep-learning
machine-learning
mathematical-reasoning
symbolic-reasoning
tokenization
parser
artificial-intelligence
Eval Results (legacy)
Instructions to use SurweeshSP/mathtok with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SurweeshSP/mathtok with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SurweeshSP/mathtok")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SurweeshSP/mathtok", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SurweeshSP/mathtok with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SurweeshSP/mathtok" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SurweeshSP/mathtok", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SurweeshSP/mathtok
- SGLang
How to use SurweeshSP/mathtok 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 "SurweeshSP/mathtok" \ --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": "SurweeshSP/mathtok", "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 "SurweeshSP/mathtok" \ --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": "SurweeshSP/mathtok", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SurweeshSP/mathtok with Docker Model Runner:
docker model run hf.co/SurweeshSP/mathtok
| """ | |
| MathTok setup — installable as: pip install -e . | |
| """ | |
| from setuptools import setup, find_packages | |
| from pathlib import Path | |
| long_description = (Path(__file__).parent / "README.md").read_text(encoding="utf-8") | |
| setup( | |
| name="mathtok", | |
| version="0.1.0", | |
| description=( | |
| "A Hybrid Canonicalized AST-Based Tokenization Framework " | |
| "for Mathematical Language Modeling" | |
| ), | |
| long_description=long_description, | |
| long_description_content_type="text/markdown", | |
| author="Surweesh SP", | |
| python_requires=">=3.10", | |
| packages=find_packages(exclude=["tests*", "notebooks*", "paper*"]), | |
| install_requires=[ | |
| "sympy>=1.12", | |
| "antlr4-python3-runtime==4.11.1", | |
| "tokenizers>=0.15.0", | |
| "transformers>=4.38.0", | |
| "numpy>=1.26.0", | |
| "regex>=2023.12.25", | |
| "tqdm>=4.66.0", | |
| ], | |
| extras_require={ | |
| "eval": ["scipy>=1.12.0", "matplotlib>=3.8.0", "seaborn>=0.13.0", "networkx>=3.2"], | |
| "dev": ["pytest>=8.0.0", "pytest-cov>=5.0.0", "jupyter>=1.0.0"], | |
| }, | |
| classifiers=[ | |
| "Development Status :: 3 - Alpha", | |
| "Intended Audience :: Science/Research", | |
| "Topic :: Scientific/Engineering :: Artificial Intelligence", | |
| "License :: OSI Approved :: MIT License", | |
| "Programming Language :: Python :: 3.10", | |
| ], | |
| entry_points={ | |
| "console_scripts": [ | |
| "mathtok=mathtok.pipeline:cli", | |
| ] | |
| }, | |
| ) | |