Ask HN: What do you do with Local LLMs?

The article discusses the use of Local LLMs (Language Learning Models) and their potential applications. The author mentions that while they find these models useful, they are slow and difficult to output in a consistent format. They question the practicality of Local LLMs, especially when compared to the speed and consistency of GPT 3.5. They also ask for potential use cases for Local LLMs, aside from handling sensitive documents.

One user suggests using Local LLMs as an offline search engine to avoid online distractions. Another user shares their experience of running models from HuggingFace on their computer, highlighting the performance difference between CPU and GPU offloading. They also discuss the cost-effectiveness of using Local LLMs, comparing the costs of running a program on Azure or AWS to running a local model on their computer.

Key takeaways:

Local LLMs are considered good but slow and hard to output in a consistent format.
OpenAI has made it less necessary to use local LLMs by offering fine-tuning of GPT 3.5 and 4.
Running models from HuggingFace on personal computers can be a fun and resourceful experience.
Using a local model can be cost-effective compared to cloud-based solutions, despite the longer processing time.

Ask HN: What do you do with Local LLMs?

Key takeaways:

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