You can run this program on any LMC emulator, such as http://peterhigginson.co.uk/LMC/
LMC, which stands for Little Man Computer is a model of a computer, used to teach students how CPUs work. Read More.
You can run this program on any LMC emulator, such as http://peterhigginson.co.uk/LMC/
LMC, which stands for Little Man Computer is a model of a computer, used to teach students how CPUs work. Read More.
Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggerganov/llama.cpp#5962
In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.
See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix
[loggers] | |
keys=root,app,uvicorn | |
[handlers] | |
keys=console,file,uvicorn | |
[formatters] | |
keys=console,file | |
[logger_root] |
FILE SPACING: | |
# double space a file | |
sed G | |
# double space a file which already has blank lines in it. Output file | |
# should contain no more than one blank line between lines of text. | |
sed '/^$/d;G' |
const RANDOM_MEMO_SETTINGS = { | |
// Amount of memos to cache | |
memoAmount: 100, | |
// Kinds of memos to cache: PUBLIC = visible to everyone, PROTECTED = logged in users, PRIVATE = only the creator | |
memoKinds: ["PUBLIC", "PROTECTED", "PRIVATE"], | |
// Time in minutes to cache the memos | |
memoCacheTimeMinutes: 60, | |
// Username of the memo creator to filter the memos | |
memoCreatorUsername: "", | |
// Button text |
;; eglot-codelens.el --- Add support for codelenses to eglot -*- lexical-binding: t -*- | |
;;; Commentary: | |
;;; Code: | |
;;; Extending eglot to support lenses | |
;;;; Findings | |
;; Lenses often support the option to be used as a code action | |
;; some servers rely on custom code actions implemented by the client | |
;; - [[https://github.com/emacs-lsp/lsp-mode/issues/2250]] mentions this |
In Germany, more and more state agencies allow free access to high resolution elevation models. However, these are often released as xyz tables, which are not easily used in GIS environments. A standard method to convert this format to raster formats (eg. GeoTiff) is the GDAL function gdal_translate [1, 2]. However, converting 1M lines takes dozens of seconds and is not trivial to parallize.
import numpy as np | |
import cv2 | |
img = cv2.imread('baseball.png') | |
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
ret, thresh = cv2.threshold(imgray, 127, 255, 0) | |
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) | |
print("Number of contours = " + str(len(contours))) | |
print(contours[0]) |
# all imports | |
from IPython.display import Javascript | |
from google.colab import output | |
from base64 import b64decode | |
from io import BytesIO | |
!pip -q install pydub | |
from pydub import AudioSegment | |
RECORD = """ | |
const sleep = time => new Promise(resolve => setTimeout(resolve, time)) |