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README.md

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----
-license: apache-2.0
----
+---
+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`                                                                                   |
+| **Corresponds to Paper** | Section IV, Table VI                                                                           |
+| **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 copy.py](https://github.com/Georgepitt/DecoderLLMs-CodeSearch/blob/main/Fine-tuning/CoSQA_Plus_Test_Finetuning_Decoder_Model%20copy.py) |
+| **Prerequisite Model** | This model must be loaded on top of an MNTP pre-trained model. E.g., `[SYSUSELab/DCS-CodeGemma-7b-It-MNTP]`                           |
+
+---
+
+## 🚀 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-CSN]" 
+
+# --- 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}
+    }
+    ```

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "GemmaBiModel",
+    "parent_library": "llm2vec.models.bidirectional_gemma"
+  },
+  "base_model_name_or_path": "google/codegemma-7b-it",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "gate_proj",
+    "q_proj",
+    "down_proj",
+    "up_proj",
+    "v_proj",
+    "o_proj"
+  ],
+  "task_type": null,
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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