Here’s the translation into American English:
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A new approach to optimizing artificial intelligence models has been introduced with the Low Rank Adaptation (LoRA) technique, which allows for fine-tuning Microsoft Windows’ Phi Silica model. This tool is particularly useful for improving the model’s performance in specific situations, resulting in more accurate and adaptable outcomes for users’ needs.
The implementation process for LoRA involves training an adapter that is then applied during the inference phase, leading to a significant improvement in the model’s accuracy. However, to achieve the best results, users must meet certain prerequisites, such as having a clear use case, defining evaluation criteria, and having tested the Phi Silica APIs without satisfactory outcomes.
The first step in training a LoRA adapter is creating a dataset. This phase involves splitting the information into two files in JSON format, where each line represents a message in the interaction between a user and an assistant. The quality and diversity of the data are crucial, and it’s recommended to collect at least a few thousand examples for the training file.
The use of the AI Toolkit for Visual Studio Code further facilitates this process. Users should download the extension, select the Phi Silica model, and generate the appropriate project for performing the fine-tuning. Training the adapter may take between 45 and 60 minutes and concludes with the option to download the optimized LoRA adapter.
Once trained, the adapter can be applied during the inference phase. Using the AI Dev Gallery application, users can experiment with local models and observe how the LoRA adapter impacts the generated responses.
It’s essential to consider the risks and limitations associated with fine-tuning. Aspects such as data quality, model robustness, the possibility of information regurgitation, and transparency in results must be carefully considered. While the proper implementation of LoRA can lead to effective model optimization, it is always crucial to review the results to ensure that the model’s output is relevant and accurate.
Additionally, it should be noted that the features of Phi Silica are not available in China, which limits its application in that territory.
Referrer: MiMub in Spanish
