rnlkav/legal-ft
The rnlkav/legal-ft is a 7.6 billion parameter Qwen2.5-7B-Instruct model, fine-tuned by rnlkav. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster fine-tuning. It is designed for instruction-following tasks, leveraging its Qwen2.5 base for general language understanding and generation.
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Model Overview
The rnlkav/legal-ft is a 7.6 billion parameter instruction-tuned language model, developed by rnlkav. It is fine-tuned from the unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit base model, leveraging the Qwen2.5 architecture known for its strong general language capabilities. The fine-tuning process utilized Unsloth and Huggingface's TRL library, which significantly accelerated the training, achieving a 2x speed improvement.
Key Characteristics
- Base Model: Qwen2.5-7B-Instruct, providing a robust foundation for various NLP tasks.
- Parameter Count: 7.6 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context length of 32768 tokens, suitable for processing longer inputs.
- Training Efficiency: Fine-tuned with Unsloth, resulting in faster training times.
Potential Use Cases
This model is well-suited for general instruction-following applications where a Qwen2.5-based model is desired, particularly in scenarios benefiting from efficient fine-tuning.