WorldOpenTechnology/Araptor-1
WorldOpenTechnology/Araptor-1 is a 4 billion parameter Qwen3-based instruction-tuned causal language model developed by WorldOpenTechnology. This model was finetuned using Unsloth and Huggingface's TRL library, emphasizing efficient training. It is designed for general instruction-following tasks, leveraging its Qwen3 architecture for robust performance.
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WorldOpenTechnology/Araptor-1 Overview
Araptor-1 is a 4 billion parameter instruction-tuned model developed by WorldOpenTechnology. It is based on the Qwen3 architecture and was finetuned from unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit.
Key Characteristics
- Architecture: Qwen3-based, providing a strong foundation for language understanding and generation.
- Efficient Training: The model was trained using Unsloth and Huggingface's TRL library, which enabled a 2x faster finetuning process.
- Parameter Count: With 4 billion parameters, it offers a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 40960 tokens, allowing for processing longer inputs and maintaining coherence over extended conversations or documents.
Use Cases
Araptor-1 is suitable for a variety of instruction-following applications, benefiting from its efficient training and robust base model. Its large context window makes it particularly useful for tasks requiring extensive contextual understanding.