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Your Own Private AI-daho: Using custom Local LLMs from the privacy of your own computer
Discover the benefits of using custom Local Language Models (LLMs) on your own computer, including more compute cycles, lower costs, and ethics considerations.
- More compute cycles are available with local LLMs, allowing for more creativity and economic value.
- LLMs are heavy and take up a lot of memory, but running them locally can be cheaper than cloud-based solutions.
- Quantization is a technique to reduce the memory requirements of LLMs, making them more suitable for local use.
- GGUF (Google’s Global Unified Format) is a standard for structuring information about LLMs, making them easier to work with and integrate.
- Local AI apps can be fun and exciting, but it’s important to consider the ethics and implications of using these models.
- LLMs can be fine-tuned for specific jobs, making them more useful and effective.
- There is a growing ecosystem of open-source LLMs, including Mistral, which is a European model vendor.
- Phi2 is another open-source LLM that can be used for tasks such as generating code.
- Local AI can be more permissive than cloud-based solutions, making it easier to integrate into projects.
- There are many opportunities for innovation and commercialization in the local AI space, and it’s an exciting area to watch.