Added README.md

README.md

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+---
+license: cc-by-nc-4.0
+base_model: jinaai/jina-code-embeddings-1.5b
+tags:
+  - embeddings
+  - code
+  - gguf
+  - llama.cpp
+  - ollama
+  - vector-search
+  - retrieval
+---
+
+# 🧠 jina-code-embeddings-1.5b — Q8_0 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-Q8_0.gguf` | Full precision conversion |
+
+---
+
+## 🔗 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**.
+
+---
+
+## 🧩 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).
+
+---
+
+## ⚠️ 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:
+```markdown
+Find the most relevant code snippet given the following query:
+print hello world in python
+```
+
+Candidate code:
+
+```python
+Candidate code snippet:
+print("Hello world")
+```
+
+
+If you do **not** include the instruction text, embedding quality will be significantly worse.
+
+---
+
+## 🛠 llama.cpp example (https://github.com/ggml-org/llama.cpp)
+
+```bash
+./llama-embedding \
+  -m jina-code-embeddings-1.5b-Q8_0.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.