CYFRAGOVPL/Llama-PLLuM-8B-chat-2412

TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Feb 7, 2025License:llama3.1Architecture:Transformer0.0K Cold

The CYFRAGOVPL/Llama-PLLuM-8B-chat-2412 is an 8 billion parameter Llama 3.1-based large language model developed by CYFRAGOVPL, specialized in Polish and other Slavic/Baltic languages, with a 32768 token context length. It is fine-tuned for chat applications, leveraging extensive Polish instruction datasets and a unique Polish preference corpus for improved safety and contextual coherence. This model excels in general language tasks and is particularly effective for domain-specific applications in Polish public administration.

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PLLuM: A Family of Polish Large Language Models

CYFRAGOVPL/Llama-PLLuM-8B-chat-2412 is an 8 billion parameter model from the PLLuM family, built upon Llama 3.1 and specialized for Polish and other Slavic/Baltic languages. It incorporates additional English data for broader generalization. Developed through a collaboration led by Politechnika Wrocławska, this model is designed for chat-based interactions and general-purpose scenarios.

Key Capabilities

  • Extensive Polish Data: Pretrained on up to 150 billion tokens of Polish text, with 28 billion tokens available for fully open-source commercial use.
  • Organic Instruction Tuning: Refined using the largest Polish collection of manually created "organic instructions" (~40k prompt-response pairs, including ~3.5k multi-turn dialogues) to mitigate negative linguistic transfer.
  • Polish Preference Corpus: Features the first Polish-language preference corpus, manually assessed for correctness, balance, and safety, especially for controversial topics.
  • State-of-the-Art Polish Performance: Achieves top scores in custom benchmarks for Polish public administration tasks and state-of-the-art results in broader Polish-language tasks.
  • Retrieval Augmented Generation (RAG) Support: Trained to perform well in RAG settings, providing structured responses with document citations.

Good For

  • General Language Tasks: Text generation, summarization, and question answering in Polish.
  • Domain-Specific Assistants: Particularly effective for applications in Polish public administration, legal, and bureaucratic contexts requiring domain-aware retrieval.
  • Research & Development: Serves as a foundational model for AI applications requiring strong command of the Polish language.

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