A practical guide to run lightweight LLMs using python
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Please enjoy the read: Free link!
With the rise of multimodal Large Language Models (LLMs), we can now interact with them in more ways than just typing text, like using audio inputs. OpenAI has recently released a voice feature for ChatGPT, allowing one to talk directly with the chat platform. This opens up a myriad of novel opportunities and applications built around it.
As machine learning and data science practitioners, it’s an exciting time to be involved. Using OpenAI’s realtime speech to speech APIs, you can create a voice assistant powered by these multi-modal LLMs. However, if you are interested in the open-source libraries, you can build a voice assistant as well, completely in a local environment and without subscriptions to proprietary APIs!
Why local voice assistant?
- Data privacy
- No API calls limit
- Fine-tuning models
First, I am sure most people who use mainstream generative AI chatbots are aware of the data that was transmitted through their servers. A lot of people may be concerned about the data privacy issue…