---
license: apache-2.0
language:
- code
library_name: peft
tags:
- code-search
- text-embeddings
- decoder-only
- supervised-contrastive-learning
- codegemma
- llm2vec
---

## 📖 Are Decoder-Only Large Language Models the Silver Bullet for Code Search?

This model is an official artifact from our research paper: **"[Are Decoder-Only Large Language Models the Silver Bullet for Code Search?](https://arxiv.org/abs/2410.22240)"**.

In this work, we conduct a large-scale systematic evaluation of decoder-only Large Language Models for the task of code search and present a set of effective fine-tuning and optimization strategies.

For complete details on all our experiments, to reproduce the full training/evaluation pipeline, or to use other models from the paper, please visit our official GitHub repository:

➡️ **[GitHub: Georgepitt/DecoderLLMs-CodeSearch](https://github.com/Georgepitt/DecoderLLMs-CodeSearch)**

---

# Model Card: DCS-CodeGemma-7b-it-SupCon-CSN

## 📜 Model Description

This is a PEFT adapter for the **`google/codegemma-7b-it`** model, fine-tuned for the task of **Code Search** as part of the research mentioned above.

The model was trained using the **Supervised Contrastive Learning** method proposed in the [llm2vec](https://github.com/McGill-NLP/llm2vec) framework, designed to generate high-quality vector embeddings for code snippets.

## 🔬 Model Performance & Reproducibility

The table below provides details about this model, its corresponding results in our paper, and how to reproduce the evaluation.

| Attribute                  | Details                                                                                                                         |
| :------------------------- | :------------------------------------------------------------------------------------------------------------------------------ |
| **Base Model** | `google/codegemma-7b-it`                                                                                                           |
| **Fine-tuning Method** | Supervised Contrastive Learning via `llm2vec`                                                                                   |                                                                      |
| **Evaluation Script** | [CSN_Test_Finetuning_Decoder_Model.py](https://github.com/Georgepitt/DecoderLLMs-CodeSearch/blob/main/Fine-tuning/CSN_Test_Finetuning_Decoder_Model.py),<br>[CoSQA_Plus_Test_Finetuning_Decoder_Model.py](https://github.com/ChenyxEugene/DecoderLLMs-CodeSearch/blob/main/Fine-tuning/CoSQA_Plus_Test_Finetuning_Decoder_Model.py) |
| **Prerequisite Model** | This model must be loaded on top of an MNTP pre-trained model.                        |

---

## 🚀 How to Use (with `llm2vec`)

For best results, we strongly recommend using the official `llm2vec` wrapper to load and use this model.

**1. Install Dependencies**
```bash
pip install llm2vec transformers torch peft accelerate
```

**2. Example Usage**

> **Important**: The `llm2vec` supervised contrastive (SupCon) models are fine-tuned on top of **MNTP (Masked Next Token Prediction)** models. Therefore, loading requires first merging the MNTP weights before loading the SupCon adapter.

```python
import torch
from transformers import AutoTokenizer, AutoModel, AutoConfig
from peft import PeftModel
from llm2vec import LLM2Vec

# --- 1. Define Model IDs ---
base_model_id = "google/codegemma-7b-it" 
mntp_model_id = "SYSUSELab/DCS-CodeGemma-7B-It-MNTP" 
supcon_model_id = "SYSUSELab/DCS-CodeGemma-7B-It-SupCon-E5" 

# --- 2. Load Base Model and MNTP Adapter ---
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
config = AutoConfig.from_pretrained(base_model_id, trust_remote_code=True)
model = AutoModel.from_pretrained(
    base_model_id,
    trust_remote_code=True,
    config=config,
    torch_dtype=torch.bfloat16,
    device_map="cuda" if torch.cuda.is_available() else "cpu",
)
model = PeftModel.from_pretrained(model, mntp_model_id)
model = model.merge_and_unload()

# --- 3. Load the Supervised (this model) Adapter on top of the MNTP-merged model ---
model = PeftModel.from_pretrained(model, supcon_model_id)

# --- 4. Use the LLM2Vec Wrapper for Encoding ---
l2v = LLM2Vec(model, tokenizer, pooling_mode="mean", max_length=512)

queries = ["how to read a file in Python?"]
code_snippets = ["with open('file.txt', 'r') as f:\n    content = f.read()"]
query_embeddings = l2v.encode(queries)
code_embeddings = l2v.encode(code_snippets)

print("Query Embedding Shape:", query_embeddings.shape)
# This usage example is adapted from the official llm2vec repository. Credits to the original authors.
```

---

## 📄 Citation

If you use our model or work in your research, please cite our paper. As our method is built upon `llm2vec`, please also cite their foundational work.

**Our Paper:**
* **Paper Link:** [Are Decoder-Only Large Language Models the Silver Bullet for Code Search?](https://arxiv.org/abs/2410.22240)
* **GitHub:** [https://github.com/Georgepitt/DecoderLLMs-CodeSearch](https://github.com/Georgepitt/DecoderLLMs-CodeSearch)
* **BibTeX:**
    ```bibtex
    @article{chen2024decoder,
      title={Are Decoder-Only Large Language Models the Silver Bullet for Code Search?},
      author={Chen, Yuxuan and Liu, Mingwei and Ou, Guangsheng and Li, Anji and Dai, Dekun and Wang, Yanlin and Zheng, Zibin},
      journal={arXiv preprint arXiv:2410.22240},
      year={2024}
    }
    ```

**llm2vec (Foundational Work):**
* **Paper Link:** [LLM2Vec: Large Language Models Are Good Contextual Text Encoders](https://arxiv.org/abs/2404.05961)
* **GitHub:** [https://github.com/McGill-NLP/llm2vec](https://github.com/McGill-NLP/llm2vec)
* **BibTeX:**
    ```bibtex
    @article{vaishaal2024llm2vec,
        title={LLM2Vec: Large Language Models Are Good Contextual Text Encoders},
        author={Vaishaal, Shankar and Bansal, Mohit and Arora, Simran},
        journal={arXiv preprint arXiv:2404.05961},
        year={2024}
    }
    ```