Gabriel2502/Qwen2.5-0.5B-Indo-SFT
Gabriel2502/Qwen2.5-0.5B-Indo-SFT is a 0.5 billion parameter Qwen2.5 model developed by Gabriel2502, fine-tuned for instruction following. This model was trained using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general instruction-tuned tasks, leveraging its compact size and efficient training methodology.
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Model Overview
Gabriel2502/Qwen2.5-0.5B-Indo-SFT is a 0.5 billion parameter instruction-tuned language model based on the Qwen2.5 architecture. Developed by Gabriel2502, this model was fine-tuned from unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit.
Key Characteristics
- Architecture: Qwen2.5 base model.
- Parameter Count: 0.5 billion parameters, making it a compact and efficient model.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- License: Distributed under the Apache-2.0 license.
Use Cases
This model is suitable for applications requiring a small, efficient instruction-following language model. Its compact size and optimized training make it a good candidate for:
- Resource-constrained environments: Deployments where computational resources are limited.
- Rapid prototyping: Quickly testing and iterating on instruction-tuned tasks.
- General instruction following: Handling a variety of prompts and generating coherent responses based on instructions.