| ---
|
| license: apache-2.0
|
| base_model: Alibaba-NLP/gte-modernbert-base
|
| library_name: onnxruntime
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| pipeline_tag: sentence-similarity
|
| tags:
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| - onnx
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| - sentence-similarity
|
| - embeddings
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| - feature-extraction
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| - code-search
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| - ziv
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| - code-aware
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| language:
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| - en
|
| ---
|
|
|
| # Ziv Embedder — Code Aware (ONNX)
|
|
|
| This is an ONNX export of **[Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base)**, prepared for **[Ziv](https://github.com/Muhd-Uwais/ziv)** — a local semantic code search engine for Python repositories.
|
|
|
| Ziv uses this embedder to improve code-aware search quality in version **0.4.0**. The model is optimized for local inference with **onnxruntime**, making it lightweight, fast, and practical for offline developer workflows.
|
|
|
| ## Why this model?
|
|
|
| Ziv needs embeddings that work well for code search and code understanding while staying fully local. This model is designed to support that goal with:
|
|
|
| - **Code-aware semantic search**
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| - **Fast local inference**
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| - **No cloud dependency**
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| - **No API keys**
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| - **ONNX runtime compatibility**
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|
|
| Compared to a standard Python-based embedding stack, this setup is easier to ship and more efficient to run inside a local developer tool.
|
|
|
| ## Usage with Ziv
|
|
|
| ```bash
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| ziv init --model code
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| ziv start
|
| ```
|
|
|
| ## Model details
|
|
|
| | Property | Value |
|
| |---|---|
|
| | Base model | Alibaba-NLP/gte-modernbert-base |
|
| | Model type | Text embedding |
|
| | Embedding dimension | 768 |
|
| | Max sequence length | 8192 |
|
| | Runtime | onnxruntime |
|
| | Primary use | Semantic code search / code understanding |
|
|
|
| ## Files
|
|
|
| | File | Description |
|
| |---|---|
|
| | `model.onnx` | ONNX model weights and graph |
|
| | `tokenizer.json` | Tokenizer vocabulary and rules |
|
| | `tokenizer_config.json` | Tokenizer settings |
|
| | `config.json` | Model architecture config |
|
| | `1_Pooling/config.json` | Pooling configuration |
|
|
|
| ## Relation to the original model
|
|
|
| This model is based on **[Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base)**, developed by **Tongyi Lab, Alibaba Group**.
|
|
|
| This repository does **not** claim ownership of the original model weights or training recipe. It provides an ONNX-exported runtime version tailored for Ziv and local inference.
|
|
|
| The original model and its concepts should be credited to:
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| - **Tongyi Lab, Alibaba Group**
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| - **The gte-modernbert model authors**
|
| - **The broader Sentence Transformers ecosystem**
|
|
|
| ## License
|
|
|
| This model is released under the **Apache 2.0 License**, consistent with the upstream model license.
|
|
|
| Original model: **[Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base)**
|
|
|
| ## Citation
|
|
|
| If you use this model or the upstream base model in your work, please cite the original paper:
|
|
|
| ```bibtex
|
| @inproceedings{zhang2024mgte,
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| title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
|
| author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
|
| booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track},
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| pages={1393--1412},
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| year={2024}
|
| }
|
|
|
| @article{li2023towards,
|
| title={Towards general text embeddings with multi-stage contrastive learning},
|
| author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
|
| journal={arXiv preprint arXiv:2308.03281},
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| year={2023}
|
| }
|
| ``` |
