blackkira12/PGABL-Muhammad-Fadhil-Abdul-Baatsith-SFT
The blackkira12/PGABL-Muhammad-Fadhil-Abdul-Baatsith-SFT is a 0.5 billion parameter Qwen2.5-based causal language model, developed by blackkira12. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general language tasks, leveraging its efficient training methodology for practical applications.
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Model Overview
The blackkira12/PGABL-Muhammad-Fadhil-Abdul-Baatsith-SFT is a 0.5 billion parameter language model based on the Qwen2.5 architecture. Developed by blackkira12, this model was fine-tuned from unsloth/qwen2.5-0.5b-unsloth-bnb-4bit.
Key Characteristics
- Architecture: Qwen2.5-based causal language model.
- Parameter Count: 0.5 billion parameters.
- Context Length: Supports a context length of 32768 tokens.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
Use Cases
This model is suitable for various general language understanding and generation tasks, particularly where a smaller, efficiently trained model with a substantial context window is beneficial. Its optimized training process makes it a practical choice for applications requiring rapid deployment and iteration.