11. Embedding And Model Combining
Contents
11. Embedding And Model Combiningยถ
Summaryยถ
Embeddings in Large Language Models (LLMs) are high-dimensional vectors that encode semantic contexts and relationships of data tokens, facilitating nuanced comprehension by LLMs. These embeddings can be uni-modal (for single data types like text) or multi-modal (for cross-modal data interpretation, such as combining text and images). The process of combining embeddings and models involves fine-tuning pre-trained models to adapt to specific tasks, leveraging techniques like transfer learning and attention mechanisms to enhance performance and efficiency.
Key Conceptsยถ
Embeddings : Embeddings are continuous vector representations of words or tokens that capture their semantic meanings in a high-dimensional space, allowing LLMs to process discrete tokens.
Model Combining : Model combining involves integrating different models or embedding techniques, such as fine-tuning pre-trained models or using multi-modal embeddings, to enhance the performance and adaptability of LLMs.
Referencesยถ
URL Name |
URL |
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DagsHub Blog |
https://dagshub.com/blog/how-to-train-a-custom-llm-embedding-model/ |
Reddit Discussion |
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Aisera Blog |
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MongoDB Developer |
https://www.mongodb.com/developer/products/atlas/choose-embedding-model-rag/ |
Data Science Dojo |