Model reference · open weights
nomic-embed-code is an open-weight embedding model from nomic-ai, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
Nomic Embed Code: A State-of-the-Art Code Retriever Blog | Technical Report | AWS SageMaker | Atlas Embedding and Unstructured Data Analytics Platform nomic-embed-code is a state-of-the-art code embedding model that excels at code retrieval tasks: - High Performance: Outperforms Voyage Code 3 and OpenAI Embed 3 Large on CodeSearchNet - Multilingual Code Support: Trained for multiple programming languages (Python, Java, Ruby, PHP, JavaScript, Go) - Advanced Architecture: 7B parameter code embedding model - Fully Open-Source: Model weights, training data, and evaluation code released Model Architecture - Total Parameters: 7B - Training Approach: Trained on the CoRNStack dataset with dual-consistency filtering and progressive hard negative mining - Supported Languages: Python, Java, Ruby, PHP, JavaScript, and Go Usage Guide Installation You can install the necessary dependencies with: Transformers SentenceTransformers CoRNStack Dataset Curation Starting with the deduplicated Stackv2, we create text-code pairs from function docstrings and respective code. We filtered out low-quality pairs where the docstring wasn't English, too short, or that contained URLs, HTML tags, or invalid characters. We additionally kept docstrings with text lengths of 256 tokens or longer to help the model learn long-range dependencies. After the initial filtering, we used dual-consistency filtering to remove potentially noisy examples. We embed each docstring and code pair and compute the similarity between each docstring and every code example. We remove pairs from the dataset if the corresponding code example is not found in the top-2 most similar examples for a given docstring. During training, we employ a novel curriculum-based hard negative mining strategy to ensure the model learns from challenging examples. We use a softmax-based sampling strategy to progressively sample hard negatives with increasing difficulty over time. Join the Nomic Community - Nomic Embed Ecosystem: https://www.nomic.ai/embed - Website: https://nomic.ai - Twitter: https://twitter.com/nomicai - Discord: https://discord.gg/myY5YDR8z8 Citation If you find the model, dataset, or training code useful, please cite o
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | nomic-ai |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 7.1B |
| Context | 32k tokens |
| Variants | 2 |
| Runs with | sentence-transformers |
| Based on | Qwen/Qwen2.5-Coder-7B-Instruct |
| Released | 2025-03-24 |
| Popularity | 385k downloads / month |
| Likes | 124 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
Using it via the API
Once AxForge deploys nomic-embed-code for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (nomic-embed-code below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"nomic-embed-code","input":"text to embed"}'
Licence
Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗
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