LTS-VVE/Teuta

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:3.2BQuant:BF16Ctx Length:32kPublished:May 28, 2025License:apache-2.0Architecture:Transformer0.0K Open Weights Warm

Teuta by LTS-VVE is a 3.2 billion parameter bilingual instruction-tuned language model based on Llama-3.2-3B, designed for question answering in Albanian and English. It is fine-tuned on diverse datasets covering mathematics, philosophy, chemistry, biology, code (especially Rust), psychology, and climate science. This model excels at multilingual applications and under-resourced language support, with a strong focus on Albanian. It is particularly suited for research, educational tools, and domain-specific applications requiring instruction-following capabilities.

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Teuta: Bilingual Instruction-Tuned Language Model

Teuta, developed by LTS-VVE, is a 3.2 billion parameter instruction-tuned language model built upon the meta-llama/Llama-3.2-3B base. Its primary focus is bilingual question answering and instruction-following in both Albanian (sq) and English (en), making it particularly valuable for multilingual applications and supporting under-resourced languages.

Key Capabilities & Features

  • Bilingual Proficiency: Optimized for instruction-following and question answering in Albanian and English.
  • Diverse Domain Knowledge: Fine-tuned on a broad spectrum of subjects including mathematics, philosophy, chemistry, biology, code (with a specific emphasis on Rust), psychology, and climate science.
  • Instruction Following: Designed to handle various instructional prompts, from academic queries to more open-ended tasks.
  • Generalization: Leverages both synthetic and real datasets to enhance its ability to generalize across technical and non-technical domains.

Ideal Use Cases

  • Research: Suitable for exploring language model capabilities in bilingual contexts.
  • Educational Tools: Can be integrated into tools for learning or information retrieval in Albanian and English.
  • Domain-Specific Applications: Effective for applications requiring specialized knowledge in the aforementioned scientific and technical fields.
  • Under-resourced Language Support: Particularly strong for applications focusing on the Albanian language.

Important Considerations

  • The model's training data includes sensitive content (e.g., mental health, therapy, philosophical questions).
  • Outputs are not guaranteed to be factual or safe, requiring careful consideration for sensitive applications.

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