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Setup Guide for `llama.cpp` on Nvidia Jetson Nano 2GB

This is a full account of the steps I ran to get llama.cpp running on the Nvidia Jetson Nano 2GB. It accumulates multiple different fixes and tutorials, whose contributions are referenced at the bottom of this README.

Procedure

At a high level, the procedure to install llama.cpp on a Jetson Nano consists of 3 steps.

Compile the gcc 8.5 compiler from source.

	REM Delete eval folder with licence key and options.xml which contains a reference to it
	for %%I in ("WebStorm", "IntelliJ", "CLion", "Rider", "GoLand", "PhpStorm", "Resharper", "PyCharm") do (
	for /d %%a in ("%USERPROFILE%\.%%I*") do (
	rd /s /q "%%a/config/eval"
	del /q "%%a\config\options\other.xml"
	)
	)

	REM Delete registry key and jetbrains folder (not sure if needet but however)
	rmdir /s /q "%APPDATA%\JetBrains"

	ssh-keygen -t rsa -b 4096 -m PEM -f jwtRS256.key
	# Don't add passphrase
	openssl rsa -in jwtRS256.key -pubout -outform PEM -out jwtRS256.key.pub
	cat jwtRS256.key
	cat jwtRS256.key.pub

	4U612-DN31H-MJEJ0-0V0Z4-978HD
	5G44H-ACH50-0J4C9-1VC5P-CY0QD
	JC000-8G047-MJDF1-0H3E6-8QR5F
	JV2NU-0XL5N-0J4Q8-0T0E6-8GH56
	JZ6E0-4R1E0-HJ1Q0-1R8NH-3AK28
	0Z48K-4X29Q-MJE80-113GK-C7K3A
	HF4H0-46K80-MJ8D9-1KAZP-CKK12
	NY69H-2N05P-HJ170-1V1ZM-CG222
	4C45A-D8J0H-HJ5T0-1T25H-92A76
	0Z4H2-0WK9K-4JE10-0T9G6-9C8M4

	# Change these variables as necessary.
	MAIN_PACKAGE_PATH := ./cmd/example
	BINARY_NAME := example

	# ==================================================================================== #
	# HELPERS
	# ==================================================================================== #

	## help: print this help message
	.PHONY: help

	"""
	Simple script to plot your Discord machine-learning predicted age and gender from the data dump.

	To use, simply put this script in your activity/analytics/ folder of your Discord data dump and run it.

	You may need to install matplotlib first:
	pip install -U matplotlib

	thrown together by rebane2001
	"""

	"""Information Retrieval metrics

	Useful Resources:
	http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
	http://www.nii.ac.jp/TechReports/05-014E.pdf
	http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
	http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
	Learning to Rank for Information Retrieval (Tie-Yan Liu)
	"""
	import numpy as np

	Mix.install(
	[
	{:phoenix_playground, "~> 0.1.0"},
	{:openai, "~> 0.6.1"}
	],
	config: [
	openai: [
	api_key: System.get_env("OPENAI_API_KEY"),
	organization_key: System.get_env("OPENAI_ORGANIZATION_KEY")
	]