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
File size: 4,162 Bytes
edede4c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | """
Integration tests for the end-to-end MathTok Pipeline.
"""
import pytest
from mathtok.pipeline import MathTokPipeline, TokenizedOutput
@pytest.fixture(scope="module")
def pipeline():
return MathTokPipeline(include_metadata=True)
class TestBasicEncode:
def test_returns_output(self, pipeline):
out = pipeline.encode("x^2 + 1")
assert isinstance(out, TokenizedOutput)
def test_tokens_nonempty(self, pipeline):
out = pipeline.encode("sin(x)")
assert len(out.tokens) > 0
def test_input_ids_match_tokens(self, pipeline):
out = pipeline.encode("x^2 + 2*x + 1")
assert len(out.tokens) == len(out.input_ids)
def test_ids_are_integers(self, pipeline):
out = pipeline.encode("x + 1")
assert all(isinstance(i, int) for i in out.input_ids)
def test_no_negative_ids(self, pipeline):
out = pipeline.encode("x + 1")
# All IDs should be non-negative (UNK=1 is minimum valid)
assert all(i >= 0 for i in out.input_ids)
class TestMathSpans:
def test_math_start_end_tokens(self, pipeline):
out = pipeline.encode("x^2")
assert "[MATH_START]" in out.tokens
assert "[MATH_END]" in out.tokens
def test_sexp_nonempty(self, pipeline):
out = pipeline.encode("x^2 + 1")
assert len(out.sexp) > 0
def test_sexp_contains_op(self, pipeline):
out = pipeline.encode("x^2")
assert "OP_POW" in out.sexp
def test_canon_results(self, pipeline):
# Use a simple ASCII expression guaranteed to parse successfully
out = pipeline.encode("x^2 + 1")
assert len(out.canon_results) >= 1
assert out.canon_results[0].success
class TestMixedInput:
def test_mixed_latex(self, pipeline):
out = pipeline.encode("The result is $x^2 + 1$.")
assert len(out.tokens) > 0
def test_mixed_ascii(self, pipeline):
out = pipeline.encode("Compute sin(x) for x = pi.")
assert len(out.tokens) > 0
def test_multiple_math_spans(self, pipeline):
out = pipeline.encode("If $a > 0$ and $b < 0$ then $a + b$ can be zero.")
# Should have at least some math tokens
math_toks = [t for t in out.tokens if t.startswith("OP_") or t.startswith("VAR_")]
assert len(math_toks) > 0
class TestMetadata:
def test_metadata_present(self, pipeline):
out = pipeline.encode("x + 1")
assert len(out.metadata) > 0
def test_metadata_positions_sequential(self, pipeline):
out = pipeline.encode("x^2 + 1")
positions = [m.position for m in out.metadata]
assert positions == sorted(positions)
def test_metadata_categories(self, pipeline):
out = pipeline.encode("x + 1")
categories = {m.token_category for m in out.metadata}
assert "operator" in categories or "variable" in categories or "constant" in categories
def test_tree_position_keys(self, pipeline):
out = pipeline.encode("x + 1")
keys = [m.tree_position_key for m in out.metadata if m.node_id >= 0]
assert len(keys) > 0
assert all(isinstance(k, str) for k in keys)
class TestEncodeMathOnly:
def test_encode_math_only(self, pipeline):
out = pipeline.encode_math_only("x^2 + 2*x + 1")
assert len(out.tokens) > 0
assert "OP_ADD" in out.tokens or "OP_POW" in out.tokens
def test_encode_batch(self, pipeline):
exprs = ["x + 1", "sin(x)", "x^2"]
outs = pipeline.encode_batch(exprs)
assert len(outs) == 3
assert all(len(o.tokens) > 0 for o in outs)
class TestHFTokenizer:
def test_hf_tokenizer_callable(self, pipeline):
hf_tok = pipeline.get_hf_tokenizer()
result = hf_tok("x^2 + 1")
assert "input_ids" in result
assert len(result["input_ids"]) == 1
def test_hf_tokenizer_encode(self, pipeline):
hf_tok = pipeline.get_hf_tokenizer()
ids = hf_tok.encode("sin(x)")
assert isinstance(ids, list)
assert len(ids) > 0
def test_hf_vocab_size(self, pipeline):
hf_tok = pipeline.get_hf_tokenizer()
assert len(hf_tok) > 100
|