30B-Lazarus is an experimental model created by utilizing LoRAs (Learned Optimizers for Data-Efficient Learning) on language models. It involves merging models in a non-standard way, diverging from the base HuggingFace-format LLaMA model. This unconventional approach aims to explore new possibilities in language model development and optimization. By experimenting with different techniques and model architectures, 30B-Lazarus seeks to push the boundaries of what's possible in language modeling. This model represents an innovative attempt to enhance the efficiency and effectiveness of language models by leveraging novel methodologies.
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