BigCode
bigcode-9f5405e1·6 events·first seen 28d agoAliases: BigCode
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Large-scale Near-deduplication Behind BigCode
This Hugging Face blog post details the near-deduplication pipeline developed for the BigCode project, which processes large-scale source code datasets used to train code language models. The post covers the technical methodology for identifying and removing near-duplicate documents at scale, including hashing techniques and distributed processing approaches. Deduplication is a critical preprocessing step that affects training data quality and model generalization.
BigCodeArena: Judging code generations end to end with code executions
BigCodeArena is a new evaluation framework for code generation models that uses end-to-end code execution to judge outputs rather than relying on static metrics or human preference ratings alone. The approach aims to provide more reliable and objective assessments of coding model capabilities by running generated code and evaluating actual execution results. This addresses known limitations of LLM-as-judge and human annotation methods for code evaluation benchmarks.
StarCoder2 and The Stack v2
Hugging Face and BigCode released StarCoder2, a new family of open code language models trained on The Stack v2, a significantly expanded code dataset. The release includes multiple model sizes and represents a major update to the BigCode open-weights code model lineage. The Stack v2 is a new large-scale permissively licensed code dataset used for training.
StarCoder: A State-of-the-Art LLM for Code
Hugging Face and ServiceNow released StarCoder, a large language model for code trained on permissively licensed data from The Stack dataset. The model targets code generation, completion, and understanding tasks and is positioned as an open-weights alternative to proprietary code models. The release includes model weights, training details, and an associated technical report.
StarCoder2-Instruct: Fully Transparent and Permissive Self-Alignment for Code Generation
Hugging Face introduces StarCoder2-Instruct, a code generation model fine-tuned via a self-alignment approach that requires no human-annotated instruction data. The method uses the base model itself to generate synthetic instruction-response pairs, which are then filtered and used for supervised fine-tuning. The model and all training data, pipelines, and evaluation code are released under permissive licenses, making it one of the more transparent instruction-tuned code models available.
Creating a Coding Assistant with StarCoder
This Hugging Face blog post describes the process of building StarChat-Alpha, a conversational coding assistant fine-tuned from the StarCoder large language model. The post covers the instruction-tuning methodology used to adapt StarCoder for chat-style interactions, including dataset preparation and training details. It represents an early example of open-weights coding LLMs being adapted into assistant-style deployments.