affandymurad/legal-ft-sft
The affandymurad/legal-ft-sft is a 3.1 billion parameter Qwen2.5-3B-Instruct model, developed by affandymurad, and fine-tuned for specific applications. This model was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. With a context length of 32768 tokens, it is optimized for tasks requiring processing of substantial text inputs.
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Model Overview
The affandymurad/legal-ft-sft is a 3.1 billion parameter language model, fine-tuned from the unsloth/Qwen2.5-3B-Instruct-bnb-4bit base model. Developed by affandymurad, this model leverages the Qwen2.5 architecture and was fine-tuned for enhanced performance.
Key Characteristics
- Architecture: Based on the Qwen2.5-3B-Instruct model family.
- Parameter Count: Features 3.1 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, suitable for processing lengthy documents or conversations.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Potential Use Cases
This model is suitable for applications that benefit from a Qwen2.5-based architecture with a large context window. Its efficient fine-tuning process suggests it could be adapted for various domain-specific tasks where rapid iteration and deployment are beneficial.