| import openai | |
| openai.api_key = "YOUR KEY GOES HERE" | |
| def get_completion(messages): | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo-0613", | |
| messages=messages, | |
| functions=[{ | |
| "name": "fake", |
| function mandelbrot_kernel(c, max_iter) | |
| z = c | |
| for i in 1:max_iter | |
| z = z * z + c | |
| if abs2(z) > 4 | |
| return i-1 | |
| end | |
| end | |
| return max_iter |
Not only Mojo is great for writing high-performance code, but it also allows us to leverage huge Python ecosystem of libraries and tools. With seamless Python interoperability, Mojo can use Python for what it's good at, especially GUIs, without sacrificing performance in critical code. Let's take the classic Mandelbrot set algorithm and implement it in Mojo.
We'll introduce a Complex type and use it in our implementation.
Asking GPT3.5 to refactor gpt2() into a parallel prefix over inputs with code from:
def gpt2(inputs, wte, wpe, blocks, ln_f, n_head, chunk_size):
n_chunks = len(inputs) // chunk_size
chunked_inputs = np.split(inputs, n_chunks)
embeddings = []
| I am Lieutenant Commander Data, and I am an android. | |
| I was created by Doctor Soong in the mid-2300s on Earth's moon colony. | |
| My positronic brain is a network of trillions of interconnected | |
| neurons that allow me to experience consciousness and sentience as | |
| only living beings can—and yet my mind operates at speeds far greater | |
| than those of most unenhanced organics. This makes it possible for me | |
| to perform complex analyses almost instantaneously while | |
| simultaneously running thousands of background processes without any | |
| decrease in efficiency or awareness. It also lets me communicate with |
This episode of Recsperts was transcribed with Whisper from OpenAI, an open-source neural net trained on almost 700 hours of audio. The model includes an encoder-decoder architecture by tokenizing audio into 30-second chunks, normalizing audio samples to the log-Mel scale, and passing the data into an encoder. A decoder is trained to predict the captioned text matching the encoder, and the model includes transcription, as well as timestamp-aligned transcription, and multilingual translation.
The transcription process outputs a single string file, so it's up to the end-user to parse out individual speakers, or run the model [through a sec
| \documentclass[border=8pt, multi, tikz]{standalone} | |
| \usepackage{import} | |
| \subimport{../layers/}{init} | |
| \usetikzlibrary{positioning} | |
| \usetikzlibrary{3d} %for including external image | |
| \def\ConvColor{rgb:yellow,5;red,2.5;white,5} | |
| \def\ConvReluColor{rgb:yellow,5;red,5;white,5} | |
| \def\PoolColor{rgb:red,1;black,0.3} | |
| \def\UnpoolColor{rgb:blue,2;green,1;black,0.3} |
This will help you to block, part of, the Huawei tracking and ADS using AdAway in VPN mode.
Simple add these links to the list:
"typescript" "manage and configure a list of Github repositories and their settings including branch protection rules"
$ upg
__ ______ ____ _ UPG CLI
/ / / / __ \/ __ `/ Public Beta
/ /_/ / /_/ / /_/ /
\__,_/ .___/\__, / 🇺🇸 Built by GPT Labs
| library(data.table) | |
| library(skimr) | |
| temp <- fread( | |
| "curl -s https://ftp.ncbi.nlm.nih.gov/genomes/ASSEMBLY_REPORTS/assembly_summary_genbank.txt | head -n 20", | |
| sep = "\t", | |
| header = T | |
| ) | |
| skim(temp) | |
