Text Generation
PyTorch
GGUF
English
Albanian
llama
al
math
philosophy
chemistry
code
biology
climate
Not-For-All-Audiences
conversational
| license: apache-2.0 | |
| datasets: | |
| - LTS-VVE/Teuta-sq | |
| - LTS-VVE/grammar_sq_0.1 | |
| - LTS-VVE/linguistic_sq | |
| - LTS-VVE/Math-physics-dataset-sq | |
| - LTS-VVE/albanian-synthetic | |
| - noxneural/lilium_albanicum_eng_alb | |
| - MIND-Lab/Safety-Evaluation | |
| - shb777/simple-math-steps-7M | |
| - RishiKompelli/TherapyDataset | |
| - microsoft/orca-math-word-problems-200k | |
| - Vezora/Tested-143k-Python-Alpaca | |
| - AI4Chem/ChemPref-DPO-for-Chemistry-data-en | |
| - jkhedri/psychology-dataset | |
| - samhog/psychology-10k | |
| - Amod/mental_health_counseling_conversations | |
| - sayhan/strix-philosophy-qa | |
| - Maverfrick/Rust_dataset | |
| - Neloy262/rust_instruction_dataset | |
| - Tesslate/Rust_Dataset | |
| language: | |
| - en | |
| - sq | |
| base_model: | |
| - meta-llama/Llama-3.2-3B | |
| pipeline_tag: text-generation | |
| tags: | |
| - al | |
| - math | |
| - philosophy | |
| - chemistry | |
| - code | |
| - biology | |
| - climate | |
| - not-for-all-audiences | |
| <p align="center"> | |
| <span style="color:yellow">This model is not suitable for all audiences and may contain inappropriate or explicit content.</span> | |
| </p> | |
| <p align="center"> | |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/67b7476deb48853c39ca000b/CzUTg97aTxK283qwD6kEm.png" alt="Teuta Logo" /> | |
| </p> | |
| # Teuta | |
| Teuta is a bilingual instruction-tuned language model designed for question answering in both Albanian (sq) and English (en). It is fine-tuned on a diverse mix of datasets covering subjects such as mathematics, philosophy, chemistry, biology, code (especially Rust), psychology, and climate science. | |
| ## Model | |
| - **Base model**: meta-llama/Llama-3.2-3B | |
| - **Languages**: Albanian, English | |
| - **Primary task**: Instruction-following and question answering | |
| ## Description | |
| Teuta is built to handle a variety of instructional prompts, from academic and scientific queries to more open-ended tasks. It is particularly suited for multilingual applications and under-resourced language support, with a strong focus on Albanian. | |
| The model leverages both synthetic and real datasets to improve generalization across technical and non-technical domains. | |
| ## Considerations | |
| - Some datasets include sensitive content (e.g., mental health, therapy, and philosophical questions). | |
| - Outputs are not guaranteed to be factual or safe; use in sensitive contexts should be done with care. | |
| - Best suited for research, educational tools, and domain-specific applications. |