geraldot/qwen-alpaca-indonesia
The geraldot/qwen-alpaca-indonesia is a 1.5 billion parameter Qwen2.5-based instruction-tuned causal language model developed by geraldot. Fine-tuned using Unsloth and Huggingface's TRL library, it offers faster training efficiency. This model is optimized for general language tasks, leveraging its compact size and efficient training methodology.
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Model Overview
The geraldot/qwen-alpaca-indonesia is a 1.5 billion parameter instruction-tuned language model. It is based on the Qwen2.5 architecture and was developed by geraldot.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit, indicating its foundation in the Qwen2.5 series. - Efficient Training: The model was trained using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process. This highlights an emphasis on training efficiency and resource optimization.
- Parameter Count: With 1.5 billion parameters, it is a relatively compact model, suitable for applications where computational resources might be a consideration.
- Context Length: The model supports a context length of 32768 tokens, allowing it to process and generate longer sequences of text.
Use Cases
This model is suitable for general instruction-following tasks, leveraging its efficient training and moderate size. Its Qwen2.5 foundation suggests capabilities in understanding and generating human-like text, making it a good candidate for various NLP applications where a smaller, efficiently trained model is preferred.