ZigZeug/Baatukaay-Qwen2.5-3B-Wolof
The ZigZeug/Baatukaay-Qwen2.5-3B-Wolof is a 3.1 billion parameter causal language model developed by ZigZeug, fine-tuned from unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is specifically optimized for tasks requiring a compact yet capable model, leveraging its Qwen2.5 architecture and a 32768 token context length.
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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.