---
base_model: unknown
language:
  - code
library_name: model2vec
license: mit
model_name: potion-code-16M-v2-onnx
tags:
  - embeddings
  - static-embeddings
  - sentence-transformers
  - onnx
---

# potion-code-16M-v2-onnx

This is an ONNX export of the [minishlab/potion-code-16M-v2](https://huggingface.co/minishlab/potion-code-16M-v2) Model2Vec model, produced with the [ONNX](https://onnx.ai/) runtime. [Model2Vec](https://github.com/MinishLab/model2vec) models use static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. This ONNX export lets you run the model with `onnxruntime` or `transformers.js`, without depending on the `model2vec` package.

`model_quantized.onnx` is int8 quantized for CPU, `model.onnx` is FP32.

## Usage

### Using ONNX Runtime

```python
import onnxruntime as ort
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("potion-code-16M-v2-onnx")
session = ort.InferenceSession("onnx/model_quantized.onnx") # or onnx/model.onnx

encodings = tokenizer(["Example sentence"], padding=True, return_tensors="np")
embeddings = session.run(None, dict(encodings))[0]
```

## Overview

**potion-code-16M-v2** is a fast static code embedding model optimized for code retrieval tasks. It powers [Semble](https://github.com/MinishLab/semble), a code search library for agents. It is distilled from [nomic-ai/CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed) and trained on the [CornStack](https://huggingface.co/datasets/nomic-ai/cornstack-python-v1) code corpus using [Tokenlearn](https://github.com/MinishLab/tokenlearn) and contrastive fine-tuning.
It is the successor to [potion-code-16M](https://huggingface.co/minishlab/potion-code-16M).
It uses static embeddings, allowing text and code embeddings to be computed orders of magnitude faster than transformer-based models on both GPU and CPU.

## Model Details

| Property             | Value                                                            |
| -------------------- | ---------------------------------------------------------------- |
| Parameters           | \~16M                                                            |
| Embedding dimensions | 256                                                              |
| Vocabulary size      | \~63,500                                                         |
| Teacher model        | nomic-ai/CodeRankEmbed                                           |
| Training corpus      | CornStack (6 languages: Python, Java, JavaScript, Go, PHP, Ruby) |
| Max sequence length  | 1,000,000 tokens (static, no limit in practice)                  |

### Further details

See the original model [minishlab/potion-code-16M-v2](https://huggingface.co/minishlab/potion-code-16M-v2) for complete details.