RickyIG/legal-qwen25-3b-sft-exp10
RickyIG/legal-qwen25-3b-sft-exp10 is a 3.1 billion parameter Qwen2.5-Instruct model, fine-tuned by RickyIG, leveraging Unsloth for accelerated training. This model is specifically optimized for legal applications, building upon the Qwen2.5 architecture with a 32768 token context length. Its primary differentiation lies in its specialized fine-tuning for legal tasks, making it suitable for processing and generating legal-specific text.
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RickyIG/legal-qwen25-3b-sft-exp10 Overview
This model is a specialized fine-tuned version of the Qwen2.5-3B-Instruct architecture, developed by RickyIG. It utilizes the unsloth/Qwen2.5-3B-Instruct-bnb-4bit as its base and was trained using Unsloth and Huggingface's TRL library, which enabled a 2x faster fine-tuning process. With 3.1 billion parameters and a 32768 token context length, it is designed for efficient performance.
Key Capabilities
- Legal Domain Specialization: Fine-tuned specifically for legal applications, suggesting enhanced performance on legal texts and queries.
- Efficient Training: Benefits from Unsloth's optimization, allowing for faster fine-tuning compared to standard methods.
- Qwen2.5 Architecture: Inherits the robust capabilities of the Qwen2.5-Instruct base model.
Good For
- Legal Text Processing: Ideal for tasks involving legal documents, research, and analysis.
- Domain-Specific Applications: Suitable for developers building applications that require a strong understanding of legal language and concepts.
- Resource-Efficient Deployment: Its 3.1 billion parameter size makes it a good candidate for deployment in environments where computational resources are a consideration, while still offering specialized capabilities.