CYFRAGOVPL/PLLuM-12B-chat-2512

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:12BQuant:FP8Ctx Length:32kPublished:Feb 2, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Warm

CYFRAGOVPL/PLLuM-12B-chat-2512 is a 12 billion parameter large language model developed by the PLLuM consortium and continued by HIVE AI, specialized in Polish with additional English data. Built on the Mistral-Nemo-Base-2407 architecture, it is fine-tuned with extensive Polish instruction datasets and preference learning for dialogue and general-purpose scenarios. This model excels in generating contextually coherent text and assisting with tasks like question answering and summarization, particularly for Polish public administration applications.

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PLLuM-12B-chat-2512: Polish-centric LLM

CYFRAGOVPL/PLLuM-12B-chat-2512 is a 12 billion parameter model from the PLLuM family, developed by the PLLuM consortium and later HIVE AI. It is specialized for the Polish language, incorporating English data for broader generalization, and is based on the Mistral-Nemo-Base-2407 architecture. This chat-optimized variant has been aligned to human preferences, making it safer and more efficient for dialogue and general-purpose use.

Key Capabilities

  • Extensive Polish Data Collection: Trained on large-scale, high-quality Polish and English text data, rigorously cleaned and deduplicated.
  • Organic Instruction Dataset: Fine-tuned with approximately 70k manually curated Polish "organic instructions" and additional programmatic and synthetic instructions.
  • Polish Preference Corpus: Aligned using ~60k manually annotated preference pairs to ensure balanced, safe, and contextually appropriate responses.
  • State-of-the-Art Polish Performance: Achieves top scores on custom benchmarks relevant to Polish public administration and state-of-the-art results in broader Polish-language tasks.
  • RAG Optimization: Specifically trained to perform well in Retrieval-Augmented Generation (RAG) settings, with a dedicated prompt format for document-based question answering.

Good For

  • General Language Tasks: Text generation, summarization, extraction, and question answering in Polish.
  • Domain-Specific Assistants: Particularly effective for applications within Polish public administration, legal, and bureaucratic contexts.
  • Research & Development: Serving as a foundational building block for AI applications requiring strong Polish language capabilities.