Llama4micro

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llama4micro is an open-source project that demonstrates running a large language model on a microcontroller. It uses the Coral Dev Board Micro to generate text based on objects detected by its camera.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is llama4micro?

llama4micro is an innovative open-source project showcasing the capability of running a "large" language model on a microcontroller, specifically the Coral Dev Board Micro. This project adapts the llama2.c implementation and tinyllamas checkpoints to fit within the microcontroller's 64MB RAM. It integrates camera image classification using the Edge TPU and a YOLOv5 model to generate prompts based on detected objects. The model then streams generated tokens to a serial port at approximately 2.5 tokens per second, demonstrating a unique application of LLMs in embedded systems. It's an exploration into making advanced AI accessible on resource-constrained hardware.

Best used for

Ideal for developers and data scientists who need to experiment with deploying large language models on resource-constrained microcontrollers, integrate camera-based object detection for AI prompting, and explore embedded AI applications. Especially valuable for prototyping edge AI solutions and educational projects in hardware-accelerated machine learning.

Common actions

deploy LLM

run AI on microcontroller

prototype edge AI

integrate camera vision

develop embedded systems

workflowsopen-sourcecollaborationgithub copilot"AI Agents"deepfakeface swappingautomated workflowlow-code/no-code

Capabilities

Key features

LLM on microcontroller
Camera-based prompting
Object detection (YOLOv5)
FreeRTOS toolchain
Serial port output
64MB RAM LLM

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What hardware is required to run llama4micro?

llama4micro is designed to run on the Coral Dev Board Micro. This board is crucial due to its 64MB of RAM and Edge TPU, which are utilized for the language model inference and camera image classification, respectively.

What is the performance of the language model on the microcontroller?

The language model inference runs on the 800 MHz Arm Cortex-M7 CPU core of the Coral Dev Board Micro. It generates tokens at a rate of approximately 2.5 tokens per second, streaming the results to the serial port.

How does llama4micro generate prompts for the language model?

The project uses the board's camera to detect objects via a compiled YOLOv5 model running on the Edge TPU. The language model then generates text based on a prompt derived from the identified object.

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