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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- code
+library_name: peft
+tags:
+- llm2vec
+- mntp
+- decoder-only
+- pre-training
+- codegemma
+---
+
+## 📖 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: CodeGemma-7B - MNTP Pre-trained Model
+
+### 📜 Model Description
+
+This is a PEFT adapter for the **`bigcode/starcoder2-7b`** model, pre-trained with the **Masked Next Token Prediction (MNTP)** objective from the [llm2vec](https://github.com/McGill-NLP/llm2vec) framework.
+
+**Important Note on its Role**:
+This model is **not intended for direct downstream task evaluation**. Instead, it serves as a crucial **foundational prerequisite** for our supervised fine-tuned (SupCon) models. The MNTP pre-training enables the decoder-only model to learn bidirectional representations, which is an essential step before applying supervised contrastive learning.
+
+### 🚀 How to Use
+
+#### Standalone Use (for Base Embeddings)
+
+You can also use this MNTP model by itself to generate text or code embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel, AutoConfig
+from peft import PeftModel
+from llm2vec import LLM2Vec
+
+base_model_id = "bigcode/starcoder2-7b"
+mntp_model_id = "SYSUSELab/DCS-StarCoder2-7B-It-MNTP"
+
+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="auto")
+model = PeftModel.from_pretrained(model, mntp_model_id)
+
+l2v = LLM2Vec(model, tokenizer, pooling_mode="mean", max_length=512)
+embeddings = l2v.encode(["def hello_world():\n    print('Hello, World!')"])
+print("Embedding from MNTP model:", embeddings.shape)
+```
+
+### ⚙️ Training Methodology
+
+This model was pre-trained using the **MNTP** objective as described in the `llm2vec` paper. If you wish to train your own MNTP model from scratch, please refer to the instructions in the `Fine-tuning/Fine-tuning_method/MNTP/` directory of our GitHub repository.
+
+### 📄 Citation
+
+If you use this model, please cite both our paper and the foundational work of `llm2vec`.
+
+```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}
+}
+
+@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": "Starcoder2BiModel",
+    "parent_library": "llm2vec.models.bidirectional_starcoder2"
+  },
+  "base_model_name_or_path": "bigcode/starcoder2-7b",
+  "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": [
+    "q_proj",
+    "k_proj",
+    "up_proj",
+    "gate_proj",
+    "o_proj",
+    "down_proj",
+    "v_proj"
+  ],
+  "task_type": null,
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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special_tokens_map.json

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tokenizer_config.json

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+}