What are the properties of file system UX and how we implemented it in postgres.
Before we begin, here's the table SQL
CREATE TABLE documents (
id text NOT NULL,
collection_id text NULL,
{ | |
"\u0905": "<i>a</i>, the first letter of the alphabet; the first short vowel inherent in consonants. ; <i>a</i>, ind. an interjection of pity (Ah!). ; <i>a</i> (before a vowel <i>an</i>), a prefix corresponding to Gr. [greek] Lat. in, Goth. and Germ. un, Eng. in or un, and having a negative or privative or depreciative sense; e. g. <i>eka</i> one, <i>an-eka</i> not one; <i>anta</i> end, <i>an-anta</i> endless; <i>pa\u015byat</i> seeing, <i>a-pa\u015byat</i> not seeing. Sometimes this prefix denotes comparison. It is occasionally an expletive. ; <i>a</i>, the base of some pronouns and pronom. forms; (substituted for <i>idam</i>, in <i>asya, atra</i>, &c.) ; <i>a</i>, the augment prefixed to the root in the formation of the imperfect, aorist, and conditional tenses, by some considered as connected with 3. <i>a</i>, and by others as connected with 4. <i>a</i>. ; <i>a, as</i>, m., N. of Vi\u1e63\u1e47u (especially as the first of the three sounds in the sacred syllable <i>om</i>), also of Brahm\u0101, \u0 |
Hi there, thank you for your interest in NimbleBox.
We built ChatNBX and ChainFury
We are looking for talented coders who sit at the intersection of ML and SDE. There are three positions:
(ENG-01) The first engineering blog.
ChainFury started as a weekend hackathon but since then has developed into a much bigger project (dare I say, one of the last systems). The core idea behind it being the rapid development (with chains), deployment (with embeddable chatbot UI) and gathering feedback for the performance. Initially it was built with langflow as inspiration which was in turn built on top of langchain.
Chandrani's written a great starting blog on ChainFury.
// This is a copy of saturn/runner.py translated to Go | |
package main | |
import ( | |
"encoding/json" | |
"fmt" | |
"io/ioutil" | |
"os" |
import os | |
import sys | |
import json | |
import logging | |
import subprocess | |
from pathlib import Path | |
try: | |
import tensorflow as tf | |
except: |
#!/usr/bin/env python | |
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved. | |
# Licensed under the Apache License, Version 2.0 | |
# Modifications copyright Yash Bonde (C) 2021 Nimblebox.ai, Inc. | |
# This file is peak Google! <3 | |
# How far can you push Python before it's just too hard? | |
from typing import Any, Dict, Iterable, List, Tuple, Optional |
# wrapper for using GPT generation first-class | |
# MIT - License, 2021, Yash Bonde | |
import os | |
import torch | |
import pickle | |
import hashlib | |
import warnings | |
import numpy as np | |
from time import time |
In this quick script we are trying to solve sharding problem: | |
often in very large datasets there is no way to tokenize everything and store | |
them. Considering the CLM datasets we have a fixed dataset where each row | |
has dynamic number of tokens. A dummy looks like follows: | |
j n sequence (w/o EOT = 42) | |
[0] [15] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], | |
[1] [13] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], | |
[2] [11] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], | |
[3] [13] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], |