galnoel/qwen2.5-7b-instruct-legal-chatbot-sft
galnoel/qwen2.5-7b-instruct-legal-chatbot-sft is a 7.6 billion parameter instruction-tuned Qwen2.5 model developed by galnoel, fine-tuned from unsloth/Qwen2.5-7B-Instruct-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling faster training. It is specifically designed and optimized for legal chatbot applications, leveraging its 32768 token context length for processing extensive legal texts.
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Model Overview
This model, galnoel/qwen2.5-7b-instruct-legal-chatbot-sft, is a 7.6 billion parameter instruction-tuned variant of the Qwen2.5 architecture. Developed by galnoel, it was fine-tuned from unsloth/Qwen2.5-7B-Instruct-bnb-4bit.
Key Characteristics
- Architecture: Based on the Qwen2.5 model family.
- Parameter Count: Features 7.6 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, crucial for processing lengthy documents and complex queries.
- Training Efficiency: The model was trained significantly faster using the Unsloth library in conjunction with Huggingface's TRL library.
Primary Use Case
This model is specifically fine-tuned and optimized for legal chatbot applications. Its instruction-tuned nature and large context window make it well-suited for tasks requiring understanding, generation, and interaction within the legal domain, such as answering legal questions, summarizing legal documents, or assisting with legal research.