ponpay21/qwen2.5-3b-legal-merged
The ponpay21/qwen2.5-3b-legal-merged is a 3.1 billion parameter Qwen2.5-based causal language model, fine-tuned by ponpay21. This model was optimized for faster training using Unsloth and Huggingface's TRL library, making it efficient for specific domain applications. Its architecture is derived from unsloth/Qwen2.5-3B-Instruct-bnb-4bit, suggesting a focus on instruction-following tasks within its specialized domain.
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Model Overview
The ponpay21/qwen2.5-3b-legal-merged is a 3.1 billion parameter language model, fine-tuned by ponpay21. It is based on the Qwen2.5 architecture, specifically finetuned from unsloth/Qwen2.5-3B-Instruct-bnb-4bit.
Key Characteristics
- Architecture: Qwen2.5-based, a causal language model.
- Parameter Count: 3.1 billion parameters, offering a balance between performance and computational efficiency.
- Training Optimization: This model was trained with Unsloth and Huggingface's TRL library, enabling significantly faster fine-tuning.
- Context Length: Supports a context length of 32768 tokens.
Intended Use Cases
This model is suitable for applications requiring a compact yet capable language model, particularly where rapid fine-tuning and deployment are beneficial. Its origin from an instruction-tuned base suggests proficiency in following directives, making it potentially useful for specialized instruction-based tasks within its fine-tuned domain.