alfatih1234/pgabl-qwen25-1.5b-sft
The alfatih1234/pgabl-qwen25-1.5b-sft is a 1.5 billion parameter Qwen2.5-based causal language model developed by alfatih1234. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general instruction-following tasks, leveraging its efficient fine-tuning process for practical applications.
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Model Overview
This model, alfatih1234/pgabl-qwen25-1.5b-sft, is a 1.5 billion parameter instruction-tuned variant of the Qwen2.5 architecture. Developed by alfatih1234, it was fine-tuned from unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit.
Key Characteristics
- Base Model: Qwen2.5-1.5B-Instruct.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which is noted for enabling significantly faster training (2x faster).
- Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context length of 32768 tokens.
Potential Use Cases
This model is suitable for a variety of instruction-following tasks where a compact yet capable language model is required. Its efficient fine-tuning process suggests it could be a good candidate for applications needing rapid iteration or deployment on resource-constrained environments. It is licensed under Apache-2.0, allowing for broad use.