Realization path for real-time knowledge evolution
KBLaM supports dynamic updating of the knowledge base without retraining the base model by separating the knowledge storage layer from the model parameter layer. In terms of technical implementation, users only need to modify the knowledge file in JSON format and regenerate the vector embedding, and then complete the knowledge refresh of the model through the integrate.py script. For example, in a test case, updating the New Crown Pneumonia treatment guidelines from the 7th to the 9th edition took only 8 minutes (using A100 GPUs). This feature is particularly suited to medical and legal fields that require frequent updates of expertise, avoiding the cost of hundreds of GPUs per update of traditional fine-tuning solutions.
This answer comes from the articleKBLaM: An Open Source Enhanced Tool for Embedding External Knowledge in Large ModelsThe
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