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Nomic Embed Code GGUF

Developed by nomic-ai
The Nomic code embedding model is a top-tier code retrieval tool that supports multiple programming languages and excels in code retrieval tasks.
Downloads 1,300
Release Time : 4/30/2025

Model Overview

The Nomic code embedding model is a high-performance code retrieval tool that supports multiple programming languages, including Python, Java, Ruby, PHP, JavaScript, and Go. Optimized through quantization technology, it is suitable for code retrieval and feature extraction tasks.

Model Features

High-Performance Code Retrieval
Outperforms Voyage Code 3 and OpenAI Embed 3 Large on CodeSearchNet, delivering exceptional performance.
Multilingual Support
Supports multiple programming languages, including Python, Java, Ruby, PHP, JavaScript, and Go.
Advanced Architecture
Utilizes a 7B-parameter code embedding model trained with dual consistency filtering and progressive hard negative mining.
Fully Open-Source
Publicly available model weights, training data, and evaluation code for easy research and application.

Model Capabilities

Code Retrieval
Sentence Similarity Calculation
Feature Extraction

Use Cases

Code Retrieval
Code Retrieval in RAG Applications
In Retrieval-Augmented Generation (RAG) applications, this model retrieves code snippets relevant to user queries.
Accurately retrieves code snippets related to queries, such as functions for calculating factorials.
Code Similarity Analysis
Code Similarity Comparison
Compares similarity between different code snippets for clone detection or code recommendation.
Accurately calculates similarity between code snippets, distinguishing functionally different code.
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