基本信息
- 来源: blogs_podcasts
- 原始来源: https://aws.amazon.com/blogs/machine-learning/building-specialized-ai-without-sacrificing-intelligence-nova-forge-data-mixing-in-action
来源摘要/节选
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Large language models (LLMs) perform well on general tasks but struggle with specialized work that requires understanding proprietary data, internal processes, and industry-specific terminology. Supervised fine-tuning (SFT) adapts LLMs to these organizational contexts. SFT can be implemented through two distinct methodologies: Parameter-Efficient Fine-Tuning (PEFT), which updates only a subset of model parameters, offering faster training and lower computational costs while maintaining reasonable performance improvements; Full-rank SFT, which updates all model parameters rather than a subset and incorporates more domain knowledge than PEFT.
Full-rank SFT often faces a challenge: catastrophic forgetting .…
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