RickyIG/legal-qwen25-3b-sft-final
RickyIG/legal-qwen25-3b-sft-final is a 3.1 billion parameter Qwen2.5-based causal language model, fine-tuned by RickyIG. This model was optimized using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for specific applications, leveraging its Qwen2.5 architecture and efficient fine-tuning process.
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Overview
RickyIG/legal-qwen25-3b-sft-final is a 3.1 billion parameter language model, fine-tuned from unsloth/Qwen2.5-3B-Instruct-bnb-4bit. Developed by RickyIG, this model leverages the Qwen2.5 architecture and was fine-tuned for enhanced performance.
Key Characteristics
- Base Model: Fine-tuned from Qwen2.5-3B-Instruct.
- Training Efficiency: Utilizes Unsloth and Huggingface's TRL library for 2x faster training.
- Parameter Count: Features 3.1 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context length of 32768 tokens.
Potential Use Cases
This model is suitable for applications requiring a compact yet capable language model, especially where the specific fine-tuning focus (implied by 'legal' in the name, though not explicitly detailed in the provided README) aligns with the task. Its efficient training methodology suggests it could be a good candidate for further domain-specific adaptations or tasks benefiting from its Qwen2.5 foundation.