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SurweeshSP
/
mathtok

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)
Model card Files Files and versions
xet
Community

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
934 kB
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  • 1 contributor
History: 9 commits
SurweeshSP
Update README.md
c1f7e74 verified 3 days ago
  • assets
    Initial clean MathTok release 3 days ago
  • evaluation
    Initial clean MathTok release 3 days ago
  • mathtok
    Initial clean MathTok release 3 days ago
  • tests
    Initial clean MathTok release 3 days ago
  • .gitattributes
    1.62 kB
    Upload 6 files 3 days ago
  • .gitignore
    141 Bytes
    Initial clean MathTok release 3 days ago
  • MathTok Comparative Evaluation Framework.jpeg
    71.8 kB
    Upload 6 files 3 days ago
  • Mean Semantic Compression Ratio (SCR).jpeg
    87.9 kB
    Upload 6 files 3 days ago
  • Mean Semantic Density.jpeg
    87.9 kB
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  • Mean Structural Efficiency.jpeg
    86.3 kB
    Upload 6 files 3 days ago
  • README.md
    11.5 kB
    Update README.md 3 days ago
  • Semantic Compression Ratio by Category.jpeg
    99.2 kB
    Upload 6 files 3 days ago
  • Token Counts per Expression.jpeg
    117 kB
    xet
    Upload 6 files 3 days ago
  • model.md
    7.82 kB
    Initial clean MathTok release 3 days ago
  • pyproject.toml
    295 Bytes
    Initial clean MathTok release 3 days ago
  • requirements.txt
    1.74 kB
    Initial clean MathTok release 3 days ago
  • review.md
    18.7 kB
    Initial clean MathTok release 3 days ago
  • setup.py
    1.45 kB
    Initial clean MathTok release 3 days ago