hanifnurfai/Qwen2.5-1.5B-Indo-Instruct
hanifnurfai/Qwen2.5-1.5B-Indo-Instruct is a 1.5 billion parameter instruction-tuned causal language model developed by hanifnurfai, fine-tuned from unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. With a 32768 token context length, it is optimized for instruction-following tasks.
Loading preview...
Model Overview
hanifnurfai/Qwen2.5-1.5B-Indo-Instruct is a 1.5 billion parameter instruction-tuned language model developed by hanifnurfai. It is fine-tuned from the unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit base model, leveraging the Qwen2.5 architecture.
Key Characteristics
- Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence over extended interactions.
- Training Efficiency: The model was fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process. This indicates an optimized and efficient fine-tuning methodology.
Intended Use Cases
This model is designed for instruction-following tasks, making it suitable for applications requiring the model to adhere to specific prompts and generate relevant responses. Its efficient training and moderate parameter count suggest it could be a good candidate for deployment in environments where resource optimization is important, while still providing robust language understanding and generation capabilities.