Model Overview

The ZigZeug/Baatukaay-Qwen2.5-3B-Wolof is a 3.1 billion parameter language model developed by ZigZeug. It is a fine-tuned variant of the unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit base model, leveraging the Qwen2.5 architecture.

Key Characteristics

• Efficient Training: This model was trained with Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
• Compact Size: With 3.1 billion parameters, it offers a balance between performance and computational efficiency, making it suitable for resource-constrained environments or applications where smaller models are preferred.
• Context Length: The model supports a substantial context length of 32768 tokens, allowing it to process and generate longer sequences of text.

Use Cases

This model is suitable for applications requiring a capable language model with a focus on efficiency and faster deployment due to its optimized training. Its compact size makes it a good candidate for tasks where larger models might be overkill or too resource-intensive.