One of Unsloth's core technological innovations is its unique dynamic 4-bit quantization technology. This technology realizes significant performance improvement during training by intelligently adjusting the quantization accuracy of model parameters. Specifically, the model accuracy can be improved to a level close to full-precision training without increasing the use of more than 10% of video memory.
The implementation of this technique relies heavily on Unsloth's optimized underlying computational architecture. It is able to dynamically sense differences in the sensitivity of the parameters of each layer, retaining higher accuracy for important parameters and stronger compression for less important ones. This differentiated treatment allows the model to maintain high efficiency without sacrificing critical inference capabilities.
In practice, users can enable this feature by simply setting quantization="dynamic_4bit" in the training parameters. Test data shows that LoRA adapters trained with this technique achieve performance close to the original model in several benchmarks, while training speed and memory usage are optimized significantly.
This answer comes from the articleUnsloth: an open source tool for efficiently fine-tuning and training large language modelsThe
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