potion-code-16M-v2

Model overview

**potion-code-16M-v2** is a fast static code embedding model optimized for code retrieval tasks. It powers Semble, a code search library for agents. It is distilled from nomic-ai/CodeRankEmbed and trained on the CornStack code corpus using Tokenlearn and contrastive fine-tuning. It is the successor to 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. 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 on the CoIR benchmark on MTEB (NDCG@10, `mteb>=2.10`):

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。