Local LLM on a Pi 4 controlling hardware via tool calling
SMRTR summary
A new project enables Raspberry Pi 4 users to run local language models using PrismML's ultra-efficient Bonsai models that require only 0.25-0.57GB of RAM through true 1-bit quantization, compared to traditional models needing 2-7GB. The setup creates a secure local chat server accessible via web interface from any network device, with optional hardware integration allowing the AI to control physical components like LED displays through tool calling. Performance ranges from 2-8 tokens per second depending on model size.
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