{"ID":2859281,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.05788","arxiv_id":"2510.05788","title":"Mellum: Production-Grade in-IDE Contextual Code Completion with Multi-File Project Understanding","abstract":"We present the Mellum models family, open-weight code completion models designed for interactive use in JetBrains IDEs. Mellums have 4B parameters, adopt a Llama-style architecture, and are pre-trained on ~4T tokens of permissively licensed, multi-language code. Our studies show that (i) careful data curation and staged training significantly improve the model's quality, (ii) editor-critical capabilities such as context packing are necessary for high-quality suggestions, and (iii) a compact, task-focused model can meet the cost and latency constraints of interactive completion. In the paper, we describe an end-to-end industrial pipeline for producing contextualized in-editor completion: disciplined data governance, multi-stage training that includes fill-in-the-middle and project context via supervised fine-tuning, and alignment via direct preference optimization using feedback from real-world scenarios. Our quality evaluations include both large-scale offline benchmarks and online telemetry from production deployments in JetBrains IDEs. Mellums are released under the Apache-2.0 license on HuggingFace, with a public model card providing a reproducible reference for practitioners. Our experience offers a pragmatic blueprint for taking a focused, open model from a research prototype to at scale production for hundreds of thousands of users.","short_abstract":"We present the Mellum models family, open-weight code completion models designed for interactive use in JetBrains IDEs. Mellums have 4B parameters, adopt a Llama-style architecture, and are pre-trained on ~4T tokens of permissively licensed, multi-language code. Our studies show that (i) careful data curation and stage...","url_abs":"https://arxiv.org/abs/2510.05788","url_pdf":"https://arxiv.org/pdf/2510.05788v1","authors":"[\"Nikita Pavlichenko\",\"Iurii Nazarov\",\"Ivan Dolgov\",\"Ekaterina Garanina\",\"Dmitry Ustalov\",\"Ivan Bondyrev\",\"Kseniia Lysaniuk\",\"Evgeniia Vu\",\"Kirill Chekmenev\",\"Joseph Shtok\",\"Yaroslav Golubev\",\"Anton Semenkin\",\"Uladzislau Sazanovich\"]","published":"2025-10-07T11:09:11Z","proceeding":"cs.SE","tasks":"[\"cs.SE\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}