stevensama73/Qwen2.5-3B-sft-think-indonesian
The stevensama73/Qwen2.5-3B-sft-think-indonesian model is a 3.1 billion parameter causal language model based on the Qwen2.5 architecture. This model is specifically fine-tuned for Indonesian language tasks, focusing on instruction following and reasoning capabilities. It is designed to process inputs up to a context length of 32768 tokens, making it suitable for applications requiring understanding and generation of Indonesian text.
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Overview
This model, stevensama73/Qwen2.5-3B-sft-think-indonesian, is a 3.1 billion parameter language model built upon the Qwen2.5 architecture. It has been specifically fine-tuned for the Indonesian language, emphasizing instruction-following and reasoning. The model supports a substantial context length of 32768 tokens, allowing it to handle extensive Indonesian text inputs and generate coherent responses.
Key Capabilities
- Indonesian Language Processing: Optimized for understanding and generating text in Indonesian.
- Instruction Following: Designed to respond effectively to user instructions and prompts.
- Reasoning: Aims to exhibit improved reasoning capabilities within the Indonesian linguistic context.
- Extended Context Window: Capable of processing long documents or conversations with its 32768-token context length.
Good for
- Applications requiring robust Indonesian language understanding.
- Chatbots or conversational AI systems interacting in Indonesian.
- Tasks involving instruction-based text generation or summarization in Indonesian.
- Research and development in Indonesian natural language processing.