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 β Research Dependencies | |
| # Install with: pip install -e . | |
| # ββ Symbolic Mathematics ββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| sympy>=1.12 | |
| antlr4-python3-runtime==4.11.1 # Required by sympy.parsing.latex | |
| # ββ NLP / Tokenization ββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| tokenizers>=0.15.0 | |
| transformers>=4.38.0 | |
| # ββ Numerics / Evaluation βββββββββββββββββββββββββββββββββββββββββββββββββ | |
| numpy>=1.26.0 | |
| scipy>=1.12.0 | |
| # ββ Visualisation βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| matplotlib>=3.8.0 | |
| seaborn>=0.13.0 | |
| networkx>=3.2 # AST graph visualisation | |
| # ββ Dev / Testing βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| pytest>=8.0.0 | |
| pytest-cov>=5.0.0 | |
| tqdm>=4.66.0 | |
| # ββ Notebooks βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| jupyter>=1.0.0 | |
| ipykernel>=6.29.0 | |
| # ββ Utilities βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| regex>=2023.12.25 | |