yeonhyung/day1-train-model
The yeonhyung/day1-train-model is a 0.5 billion parameter instruction-tuned causal language model, finetuned by yeonhyung from unsloth/Qwen2.5-0.5B-Instruct-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. It is designed for general instruction-following tasks, leveraging its efficient training methodology.
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Model Overview
The yeonhyung/day1-train-model is a 0.5 billion parameter instruction-tuned language model developed by yeonhyung. It is finetuned from the unsloth/Qwen2.5-0.5B-Instruct-unsloth-bnb-4bit base model, utilizing the Unsloth library in conjunction with Huggingface's TRL library.
Key Characteristics
- Base Model: Finetuned from Qwen2.5-0.5B-Instruct.
- Parameter Count: 0.5 billion parameters.
- Training Efficiency: Achieved 2x faster training speeds due to the use of Unsloth.
- Context Length: Supports a context length of 32768 tokens.
Use Cases
This model is suitable for general instruction-following tasks where a smaller, efficiently trained model is beneficial. Its optimized training process makes it a good candidate for applications requiring rapid iteration or deployment on resource-constrained environments.