Model Overview

The damianGil/Qwen2.5-1.5B-KTO-Finetuning is a 1.5 billion parameter language model based on the Qwen2.5 architecture. It was developed by damianGil and fine-tuned from the unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit base model.

Key Characteristics

• Architecture: Qwen2.5-based, a causal language model known for its performance.
• Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
• Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
• Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs.

Intended Use Cases

This model is suitable for various instruction-following tasks, benefiting from its efficient fine-tuning and the robust Qwen2.5 base. Its optimized training process makes it a good candidate for applications requiring a capable yet relatively compact language model.