yusufbaykaloglu/Qwen2.5-3B-Turkish-SFT
The yusufbaykaloglu/Qwen2.5-3B-Turkish-SFT is a 3.1 billion parameter Qwen2.5 model, fine-tuned by yusufbaykaloglu. This model is specifically optimized for Turkish language tasks, leveraging Unsloth and Huggingface's TRL library for efficient training. It is designed for applications requiring a compact yet capable language model for Turkish natural language processing.
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Model Overview
The yusufbaykaloglu/Qwen2.5-3B-Turkish-SFT is a 3.1 billion parameter language model, fine-tuned by yusufbaykaloglu. It is based on the Qwen2.5 architecture and was developed using Unsloth for accelerated training and Huggingface's TRL library for fine-tuning.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/qwen2.5-3b-instruct-bnb-4bit. - Training Efficiency: Utilizes Unsloth, enabling 2x faster training compared to standard methods.
- Parameter Count: Features 3.1 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context length of 32768 tokens.
Intended Use Cases
This model is particularly suitable for applications requiring a Turkish-specific language model. Its fine-tuned nature suggests improved performance on tasks relevant to the Turkish language, making it a strong candidate for:
- Turkish text generation.
- Turkish natural language understanding tasks.
- Applications where a compact, efficiently trained model for Turkish is beneficial.