praful1/Qwen-3-0.6-nepali-qa
The praful1/Qwen-3-0.6-nepali-qa model is a 0.8 billion parameter, instruction-tuned causal language model based on Qwen/Qwen3-0.6B-Base, developed by praful1. Fine-tuned specifically for Nepali question-answering tasks, it leverages a 32768-token context length to process and generate responses in Nepali. This model is optimized for understanding and answering questions in the Nepali language, making it suitable for applications requiring localized linguistic capabilities.
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Model Overview
The praful1/Qwen-3-0.6-nepali-qa is a specialized language model fine-tuned for Nepali question-answering. It is built upon the Qwen3-0.6B-Base architecture, a 0.8 billion parameter model, and has been further trained using the TRL library.
Key Capabilities
- Nepali Question Answering: Specifically optimized to understand and generate answers for questions posed in the Nepali language.
- Base Model: Derived from the Qwen3-0.6B-Base, providing a solid foundation for language understanding.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer questions and providing more comprehensive answers.
Training Details
The model was fine-tuned on the praful1/NepaliQA-qa2 dataset, utilizing a completion-only loss strategy. The training was conducted for a limited duration on a free Colab GPU, indicating its potential for further improvement with more extensive training resources.
Use Cases
This model is particularly well-suited for:
- Developing Nepali-language chatbots or virtual assistants.
- Information retrieval systems that require answering queries in Nepali.
- Educational tools for Nepali speakers.
Due to its focused training, it excels in generating relevant and coherent responses to Nepali questions, making it a valuable asset for applications targeting the Nepali-speaking demographic.