cjvt/GaMS-9B-Instruct-Nemotron
cjvt/GaMS-9B-Instruct-Nemotron is a 9 billion parameter instruction-tuned language model, a variant of GaMS-9B-Instruct, developed by Timotej Petrič. It was further fine-tuned using a curated subset of the nvidia/Nemotron-Post-Training-Dataset-v1, specifically incorporating approximately 80,000 Slovenian and 20,000 English instruction-response pairs. This model is optimized for generating responses in both Slovenian and English, making it particularly suitable for multilingual applications requiring nuanced understanding and generation in these languages.
Loading preview...
GaMS-9B-Instruct-Nemotron Overview
GaMS-9B-Instruct-Nemotron is a 9 billion parameter instruction-tuned language model, building upon the GaMS-9B-Instruct base. Developed by Timotej Petrič as part of a master's thesis, this model underwent supervised fine-tuning (SFT) using a specialized dataset.
Key Capabilities
- Multilingual Instruction Following: The model was fine-tuned on a curated subset of the
nvidia/Nemotron-Post-Training-Dataset-v1, which included approximately 80,000 Slovenian instruction-response pairs and 20,000 English examples. This extensive training enables robust performance in both languages. - Contextual Understanding: The Slovenian training data involved translations with additional modifications to adjust identity and context, enhancing the model's ability to understand and generate contextually relevant responses.
Good For
- Slovenian Language Applications: Excels in tasks requiring natural language understanding and generation in Slovenian, benefiting from its specialized training data.
- Bilingual (Slovenian-English) Use Cases: Suitable for applications that need to process or generate text in both Slovenian and English, leveraging its fine-tuning on a mixed dataset.
- Research and Development: Ideal for researchers and developers exploring instruction-tuned models with a focus on less-resourced languages like Slovenian, within a bilingual context.