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LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders - GitHub LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders It consists of 3 simple steps: 1) enabling bidirectional attention, 2) training with masked next token prediction, and 3) unsupervised contrastive learning
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders In this work, we introduce LLM2Vec, a simple unsupervised approach that can transform any decoder-only LLM into a strong text encoder LLM2Vec consists of three simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning
llm2vec · PyPI LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders It consists of 3 simple steps: 1) enabling bidirectional attention, 2) training with masked next token prediction, and 3) unsupervised contrastive learning
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders It consists of 3 simple steps: 1) enabling bidirectional attention, 2) training with masked next token prediction, and 3) unsupervised contrastive learning
llm2vec README. md at main · McGill-NLP llm2vec · GitHub LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders It consists of 3 simple steps: 1) enabling bidirectional attention, 2) training with masked next token prediction, and 3) unsupervised contrastive learning