met2-1.5B

MET2 1.5B

Introduction

MET2 1.5B is a specialized large language model fine-tuned from the Qwen2.5-1.5B base model using LoRA (Low-Rank Adaptation). It is tailored specifically for enhanced performance in coding and related tasks.

Compared to its base model, MET2 1.5B offers the following improvements for programming-focused use cases:

• Enhanced capabilities in code generation, reasoning, and bug fixing.
• A strong foundation for integration into real-world tools such as code agents.
• Maintains general-purpose language understanding while optimizing for development-oriented tasks.

Key Features

• Model Type: Causal Language Model
• Fine-Tuning Method: LoRA on pretrained Qwen2.5-1.5B with post-training
• Parameters: ~1.5 billion
• Use Case: Instruction-tuned for software development and coding assistance

For technical details on the base architecture, see the Qwen2.5 Technical Report.

You can also find and run this model via Ollama:

🔗 Run MET2 on Ollama

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Citation

If you find MET2 useful in your research or projects, please cite the Qwen2.5 base model:

@article{qwen2,
  title={Qwen2 Technical Report}, 
  author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
  journal={arXiv preprint arXiv:2407.10671},
  year={2024}
}