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README.md

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+---
+license: mit
+tags:
+- colbert
+- onnx
+- zero-pytorch
+- modernbert
+- text-embeddings
+pipeline_tag: feature-extraction
+library_name: generic
+---
+
+# lateon-onnx
+
+This repository hosts the optimized, single-file **ONNX representation** of the ModernBERT-backed [lightonai/LateOn](https://huggingface.co/lightonai/LateOn) model.
+
+It is designed to run completely PyTorch-free and dependency-free using the [intextus-embed](https://github.com/Intextus/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:
+```bash
+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}")
+```