will702/qwen25-3b-alpaca-id-sft
The will702/qwen25-3b-alpaca-id-sft is a 3.1 billion parameter Qwen2.5 model, fine-tuned by will702. This model was efficiently trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. It is designed for general language generation tasks, leveraging its Qwen2.5 architecture and 32K context length for robust performance.
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Model Overview
The will702/qwen25-3b-alpaca-id-sft is a 3.1 billion parameter language model based on the Qwen2.5 architecture, fine-tuned by will702. This model leverages the efficiency of Unsloth and Huggingface's TRL library for its training process, which allowed for a 2x faster fine-tuning compared to standard methods.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit. - Efficient Training: Utilizes Unsloth for accelerated fine-tuning, enhancing development speed.
- Context Length: Supports a context length of 32,768 tokens, suitable for processing longer inputs.
Potential Use Cases
This model is suitable for a variety of natural language processing tasks, particularly those benefiting from its Qwen2.5 foundation and efficient fine-tuning. Its 3.1 billion parameters make it a capable option for applications where a balance between performance and computational resources is desired.