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lateon-onnx – AI Model by thlurte | AlphaNeural AI

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thlurte/
lateon-onnx

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text-embeddings

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lateon-onnx

This repository hosts the optimized, single-file ONNX representation of the ModernBERT-backed lightonai/LateOn model.

It is designed to run completely PyTorch-free and dependency-free using the intextus-embed runtime library.

Model Metadata

Backbone: ModernBERT-base (140M parameters)
Output Dimensions: 128-dimensional late-interaction embeddings
ONNX File Size: 580 MB (fully self-contained, merged weights)
Case Sensitivity: Case-sensitive (requires do_lower_case=False)

Usage

Install the intextus-embed runtime:

pip install intextus-embed

Load the model automatically and run inference (set do_lower_case=False because LateOn is case-sensitive):

python

from intextus import IntextusEncoder, compute_maxsim

# Automatically downloads and caches the model from Hugging Face
model = IntextusEncoder("lateon", do_lower_case=False)

# Encode queries and documents
query_embeddings = model.encode_queries("What is ultra-low latency?")
doc_embeddings = model.encode_docs("ONNX runtime bypasses the PyTorch layer completely.")

# Compute the MaxSim similarity score via NumPy
score = compute_maxsim(query_embeddings[0], doc_embeddings[0])
print(f"Relevance Score (MaxSim): {score:.4f}")