tayaee/Qwen2.5-1.5B-Instruct-ko-Reasoning-alpha
tayaee/Qwen2.5-1.5B-Instruct-ko-Reasoning-alpha is a 1.5 billion parameter Qwen2.5-based model, fine-tuned via Supervised Fine-Tuning (SFT) as a personal educational exercise. This model is specifically intended for studying SFT pipelines and comparing fine-tuned model behavior against its base, rather than for practical application. It serves as a practice model for understanding training methodologies and is not designed for production use cases.
Loading preview...
Overview
This model, tayaee/Qwen2.5-1.5B-Instruct-ko-Reasoning-alpha, is a 1.5 billion parameter language model built upon the Qwen/Qwen2.5-1.5B base. It was developed as a practice and educational exercise using Supervised Fine-Tuning (SFT) to explore the fine-tuning process.
Key Characteristics
- Base Model: Qwen/Qwen2.5-1.5B
- Training Method: Supervised Fine-Tuning (SFT)
- Purpose: Strictly for learning and experimentation; not for production use.
- Expected Output: Users should anticipate poor or inconsistent outputs, as its primary goal is to demonstrate the SFT pipeline rather than to be a functional assistant.
Intended Use Cases
This model is specifically designed for:
- Studying the impact of SFT on a smaller base model.
- Comparing the behavioral differences between an SFT-tuned model and its original base model.
- Experimenting with various training data, hyperparameters, and model merging techniques.
Limitations
It is crucial to understand that this model is not suitable for any real-world tasks or deployment. Its performance is not optimized for practical applications, and it should only be used in an academic or experimental context. The license is Apache 2.0, inherited from the base Qwen2.5 model.