---
language:
- code
license: mit
library_name: model2vec
tags:
- model2vec
- embeddings
- code
- retrieval
- static-embeddings
datasets:
- minishlab/tokenlearn-cornstack-queries-coderankembed
- minishlab/tokenlearn-cornstack-docs-coderankembed
- nomic-ai/cornstack-python-v1
- nomic-ai/cornstack-java-v1
- nomic-ai/cornstack-php-v1
- nomic-ai/cornstack-go-v1
- nomic-ai/cornstack-javascript-v1
- nomic-ai/cornstack-ruby-v1
---

# potion-code-16M-v2 Model Card

## 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.

## Installation

```bash
pip install model2vec
```

## Usage

```python
from model2vec import StaticModel

model = StaticModel.from_pretrained("minishlab/potion-code-16M-v2")

# Embed natural language queries
query_embeddings = model.encode(["How to read a file in Python?"])

# Embed code documents
code_embeddings = model.encode(["def read_file(path):\n    with open(path) as f:\n        return f.read()"])
```

## How it works

potion-code-16M-v2 is created using the following pipeline:

1. **Vocabulary mining**: code-specific tokens are mined from CornStack and added to the base CodeRankEmbed tokenizer (43k extra tokens → ~63.5k total)
2. **Distillation**: the extended vocabulary is distilled from CodeRankEmbed using Model2Vec (256-dimensional embeddings, PCA)
3. **Tokenlearn**: the distilled model is fine-tuned on 1.2 million (query, document) pairs from CornStack using cosine similarity loss
4. **Contrastive fine-tuning**: the model is further fine-tuned using MultipleNegativesRankingLoss on 1.2 million CornStack query-document pairs

## Results

Results on the [CoIR benchmark](https://github.com/CoIR-team/coir) on [MTEB](https://github.com/embeddings-benchmark/mteb) (NDCG@10, `mteb>=2.10`):

| Model | Params | AVG | AppsRetrieval | COIRCodeSearchNet | CodeFeedbackMT | CodeFeedbackST | CodeSearchNetCC | CodeTransContest | CodeTransDL | CosQA | StackOverflow | Text2SQL |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CodeRankEmbed | 137M | 59.14 | 23.46 | 94.70 | 42.61 | 78.11 | 76.39 | 66.43 | 34.84 | 35.92 | 80.53 | 58.37 |
| **potion-code-16M-v2 + BM25 (hybrid)** | **16M** | **43.36** | **6.08** | **47.71** | **45.38** | **61.10** | **51.68** | **53.80** | **33.42** | **21.39** | **66.73** | **46.29** |
| BM25 | — | 42.31 | 4.76 | 40.86 | 59.19 | 68.15 | 53.97 | 47.78 | 34.42 | 18.75 | 70.26 | 24.94 |
| **potion-code-16M-v2** | **16M** | **39.08** | **5.19** | **46.37** | **38.02** | **53.22** | **43.66** | **43.66** | **32.64** | **24.36** | **59.57** | **44.07** |
| potion-code-16M | 16M | 37.05 | 3.97 | 42.99 | 36.26 | 50.27 | 43.40 | 39.76 | 31.72 | 21.37 | 57.47 | 43.34 |
| potion-retrieval-32M | 32M | 32.10 | 4.22 | 31.80 | 36.71 | 45.11 | 38.64 | 29.97 | 32.62 | 8.70 | 56.26 | 36.93 |
| potion-base-32M | 32M | 31.42 | 3.37 | 29.58 | 34.77 | 42.69 | 37.88 | 28.51 | 30.55 | 14.61 | 53.36 | 38.88 |

CoIR covers a broad range of code retrieval scenarios. For the use case of finding code given a natural language query, **CosQA** and **CodeFeedback (ST/MT)** are the most relevant tasks. Others are less so: **COIRCodeSearchNetRetrieval** retrieves text given a code query (the reverse direction), and the **CodeTransOcean** tasks target cross-language code translation.
The hybrid row combines dense retrieval with BM25 using Reciprocal Rank Fusion (k=60).

## 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) |

## Additional Resources

- [Semble repository](https://github.com/MinishLab/semble)
- [Model2Vec repository](https://github.com/MinishLab/model2vec)
- [Tokenlearn repository](https://github.com/MinishLab/tokenlearn)
- [Tokenlearn document dataset](https://huggingface.co/minishlab/tokenlearn-cornstack-docs-coderankembed-v2)
- [Tokenlearn query dataset](https://huggingface.co/minishlab/tokenlearn-cornstack-queries-coderankembed-v2)
- [CornStack dataset](https://huggingface.co/datasets/nomic-ai/cornstack-python-v1)
- [CoIR benchmark](https://github.com/CoIR-team/coir)

## Citation

```bibtex
@software{minishlab2024model2vec,
  author       = {Stephan Tulkens and {van Dongen}, Thomas},
  title        = {Model2Vec: Fast State-of-the-Art Static Embeddings},
  year         = {2024},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.17270888},
  url          = {https://github.com/MinishLab/model2vec},
  license      = {MIT}
}
```