jjhyscrt/day1-train-model
The jjhyscrt/day1-train-model is a 0.5 billion parameter Qwen2.5-based instruction-tuned causal language model, developed by jjhyscrt. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging its efficient training methodology.
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Overview
The jjhyscrt/day1-train-model is a 0.5 billion parameter instruction-tuned language model based on the Qwen2.5 architecture. Developed by jjhyscrt, this model was fine-tuned from unsloth/Qwen2.5-0.5B-Instruct-unsloth-bnb-4bit.
Key Characteristics
- Efficient Training: The model was trained significantly faster (2x) by utilizing Unsloth and Huggingface's TRL library.
- Parameter Count: With 0.5 billion parameters, it offers a compact size suitable for various applications.
- Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs.
Use Cases
This model is suitable for general instruction-following tasks where a smaller, efficiently trained model is beneficial. Its Qwen2.5 base and instruction-tuning make it adaptable for conversational AI, text generation, and other NLP applications requiring adherence to given instructions.