covryzne/legal-chatbot-qwen-exp1
covryzne/legal-chatbot-qwen-exp1 is a 7.6 billion parameter Qwen2.5-based instruction-tuned causal language model developed by covryzne. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is specifically designed for legal chatbot applications, leveraging its Qwen2.5 architecture and a 32768-token context length for processing extensive legal texts.
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Model Overview
covryzne/legal-chatbot-qwen-exp1 is a 7.6 billion parameter language model, fine-tuned from unsloth/Qwen2.5-7B-Instruct-bnb-4bit. Developed by covryzne, this model leverages the Qwen2.5 architecture and boasts a substantial 32768-token context length, making it suitable for processing lengthy documents and complex queries.
Key Characteristics
- Base Model: Fine-tuned from Qwen2.5-7B-Instruct, providing a strong foundation for instruction following.
- Efficient Training: Utilizes Unsloth and Huggingface's TRL library, resulting in a 2x speedup during the fine-tuning process.
- Context Length: Features a 32768-token context window, beneficial for applications requiring extensive textual understanding.
Intended Use
This model is specifically designed and fine-tuned for legal chatbot applications. Its architecture and training methodology aim to enhance its performance in understanding and generating responses relevant to legal contexts. The efficient training process suggests a focus on practical deployment and iterative development for specialized use cases.