herMaster/jina-code-embeddings-1.5b-GGUF

🧠 jina-code-embeddings-1.5b — GGUF

This repository provides GGUF-format builds of
Jina AI’s jina-code-embeddings-1.5b for efficient local inference using:

• llama.cpp
• LM Studio
• Ollama
• KoboldCpp
• any GGUF-compatible runtime

These files allow you to run a state-of-the-art code embedding model locally on CPU or GPU without PyTorch.

🔹 Model files

File	Description
jina-code-embeddings-1.5b.gguf	Full precision conversion

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🔗 Original model

This is a format conversion only of the original Jina AI model:

Upstream model:
https://huggingface.co/jinaai/jina-code-embeddings-1.5b

Paper:
Efficient Code Embeddings from Code Generation Models (Kryvosheieva et al., 2025)

All model weights, training, and research belong to Jina AI.
This repository only provides GGUF format conversions by herMaster.

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🧩 What this model does

This is a code embedding model, not a chat LLM.

It generates vector embeddings for:

• Text → Code search
• Code → Code similarity
• Code → Text explanation
• Code completion retrieval
• Technical Q&A

It supports 15+ programming languages and produces 1536-dimensional embeddings (which can be truncated for smaller vectors).

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⚠️ Important: GGUF usage notes

Unlike the original Transformers version, GGUF engines do not apply instruction prefixes or pooling automatically.

To get correct embeddings you must:

1. Add the correct instruction prefix
2. Run inference
3. Use the last token embedding as the vector

Example (NL → Code)

Query:

Find the most relevant code snippet given the following query:
print hello world in python

Candidate code:

Candidate code snippet:
print("Hello world")

If you do not include the instruction text, embedding quality will be significantly worse.

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🛠 llama.cpp example (https://github.com/ggml-org/llama.cpp)

./llama-embedding \
  -m jina-code-embeddings-1.5b.gguf \
  -p "Find the most relevant code snippet given the following query:
print hello world in python"

This returns a 1536-dimension vector you can store in FAISS, Qdrant, Milvus, etc.

📜 License

This model is licensed under:

Creative Commons Attribution-NonCommercial 4.0 (CC-BY-NC-4.0)

You may:

• Use it for research
• Use it for personal projects
• Share it freely

You may not:

• Use it in commercial products
• Run it in paid APIs or SaaS
• Sell access to it

This license is inherited from the original Jina AI release.

🙏 Credits

• Model & training: Jina AI
• GGUF conversion: herMaster

All model weights, architecture, and training data belong to Jina AI. This repository only provides format-converted GGUF files for easier local inference.

If you use this model in academic or technical work, please cite the original Jina AI paper:

Efficient Code Embeddings from Code Generation Models Daria Kryvosheieva, Saba Sturua, Michael Günther, Scott Martens, Han Xiao (2025)

This ensures proper credit is given to the original authors and helps support continued research in high-quality code embeddings.