How He Built The Best 7B Params LLM with Maxime Labonne #43
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Merging Models: The Path to Enhanced Performance
Merging language models (LLMs) enables reduced computational costs, requiring only a CPU instead of a GPU to combine weights, while yielding improved performance. By merging fine-tuned models, practitioners can leverage significant investments in training to create superior models without extensive resources. The community is increasingly embracing this practice, often leading to complex genealogies of models through multiple merges, akin to a family tree. However, a challenge arises with contamination from models trained on problematic datasets, which can affect reliability. Despite this, merged models may still outperform non-contaminated ones. The process resembles a 'mixers of experts' approach, focusing on intelligently combining model weights rather than simply averaging outputs. Advanced algorithms like SLURP, a form of spherical averaging, enhance this merging process, suggesting that model merging will become a major trend in 2024.
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