--- datasets: - bigcode/commitpackft language: - en base_model: - Qwen/Qwen2.5-Coder-1.5B-Instruct license: apache-2.0 --- # Hi, I’m Seniru Epasinghe šŸ‘‹ I’m an AI undergraduate and an AI enthusiast, working on machine learning projects and open-source contributions. I enjoy exploring AI pipelines, natural language processing, and building tools that make development easier. --- ## 🌐 Connect with me [![Hugging Face](https://img.shields.io/badge/Hugging%20Face-seniruk-orange?logo=huggingface&logoColor=white)](https://huggingface.co/seniruk)    [![Medium](https://img.shields.io/badge/Medium-seniruk_epasinghe-black?logo=medium&logoColor=white)](https://medium.com/@senirukepasinghe)    [![LinkedIn](https://img.shields.io/badge/LinkedIn-seniru_epasinghe-blue?logo=linkedin&logoColor=white)](https://www.linkedin.com/in/seniru-epasinghe-b34b86232/)    [![GitHub](https://img.shields.io/badge/GitHub-seth2k2-181717?logo=github&logoColor=white)](https://github.com/seth2k2) --- - **Developed by:** seniruk - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-Coder-1.5B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [](https://github.com/unslothai/unsloth) --- base_model: unsloth/Qwen2.5-Coder-1.5B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- --- datasets: - bigcode/commitpackft --- # Purpose Used for generating high quality commit messages for a given git difference ### Model Description Generated by fine tuning Qwen2.5-Coder-1.5B-Instruct on bigcode/commitpackft dataset for 2 epochs Trained on a total of 277 Languages Achieved a final training loss in the range of 1- 1.7 (due to data set not containing equal data rows for each language) For common languages(python, java ,javascripts,c etc) loss went for a minimum of 1.0335 ## Environmental Impact - **Hardware Type:** geforce RTX 4060 TI - 16GB] - **Hours used:** 10 Hours - **Cloud Provider:** local ### Results ![Logo](./image1.png) ![Logo](./image2.png) ### Inference input format (If using API mostly) ``` <|im_start|>system You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|> <|im_start|>user {instructions} {git_diff}<|im_end|> <|im_start|>assistant ``` And the model will predict the rest of the content -> {assistant output}<|im_end|>