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.
At a high level, the procedure to install llama.cpp on a Jetson Nano consists of 3 steps.
gcc 8.5 compiler from source.
| /** | |
| *Submitted for verification at Etherscan.io on 2021-04-22 | |
| */ | |
| // File: @openzeppelin/contracts/utils/Context.sol | |
| // SPDX-License-Identifier: MIT | |
| pragma solidity >=0.6.0 <0.8.0; |
| import numpy as np | |
| from tensorflow import keras | |
| from tensorflow.keras.models import Sequential | |
| from tensorflow.keras.layers import Dense | |
| from tensorflow.keras import backend as K | |
| def cosine_decay_with_warmup(global_step, | |
| learning_rate_base, | |
| total_steps, |
| from tensorflow.python.layers import base | |
| import tensorflow as tf | |
| class AddCoords(base.Layer): | |
| """Add coords to a tensor""" | |
| def __init__(self, x_dim=64, y_dim=64, with_r=False): | |
| super(AddCoords, self).__init__() | |
| self.x_dim = x_dim | |
| self.y_dim = y_dim |
| # Redis Cheatsheet | |
| # All the commands you need to know | |
| redis-server /path/redis.conf # start redis with the related configuration file | |
| redis-cli # opens a redis prompt | |
| # Strings. |
| """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 |
| def format_time(secs): | |
| return "%d:%02d" % (secs / 60, secs % 60) | |
| def invert(arr): | |
| """ | |
| Make a dictionary that with the array elements as keys and | |
| their positions positions as values. | |
| >>> invert([3, 1, 3, 6]) |