rafiqiraihan/qwen-rag-indonesia
rafiqiraihan/qwen-rag-indonesia is a 1.5 billion parameter Qwen2.5 instruction-tuned causal language model developed by rafiqiraihan. Fine-tuned using Unsloth and Huggingface's TRL library, this model is optimized for specific RAG (Retrieval Augmented Generation) applications. It leverages a 32768-token context length, making it suitable for tasks requiring extensive contextual understanding and generation.
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Overview
rafiqiraihan/qwen-rag-indonesia is a 1.5 billion parameter instruction-tuned model based on the Qwen2.5 architecture. Developed by rafiqiraihan, this model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed to handle tasks requiring a substantial context window, supporting up to 32768 tokens.
Key Capabilities
- Efficient Fine-tuning: Leverages Unsloth for accelerated training, making it a cost-effective option for specific applications.
- Large Context Window: Supports a 32768-token context length, beneficial for processing and generating long-form content or complex queries.
- Qwen2.5 Base: Built upon the robust Qwen2.5 architecture, providing a strong foundation for language understanding and generation.
Good For
- Retrieval Augmented Generation (RAG): Specifically fine-tuned for RAG applications, suggesting its strength in tasks that combine information retrieval with text generation.
- Applications requiring extensive context: Its large context window makes it suitable for summarizing long documents, detailed question answering, or conversational AI where historical context is crucial.