init27

# Credit 🙏: I just used the example from langchain docs and it works quite well: https://python.langchain.com/en/latest/use_cases/question_answering.html
# Note 2: The Arxiv -> PDF logic is a bit messy, I'm sure it can be done better
# Note 3: Please install the following:

# To run:

# Save this in a `app.py`
# pip install arxiv PyPDF2 langchain chromadb

# The chat feature was shipped in H2O nightly this week, we will need to install from nightly link:

JoaoLages

Maybe you've heard about this technique but you haven't completely understood it, especially the PPO part. This explanation might help.

We will focus on text-to-text language models 📝, such as GPT-3, BLOOM, and T5. Models like BERT, which are encoder-only, are not addressed.

Reinforcement Learning from Human Feedback (RLHF) has been successfully applied in ChatGPT, hence its major increase in popularity. 📈

RLHF is especially useful in two scenarios 🌟:

• You can’t create a good loss function
  • Example: how do you calculate a metric to measure if the model’s output was funny?
• You want to train with production data, but you can’t easily label your production data

AmazingTurtle

export const DEFAULT_ENGINE = 'davinci';
export const ENGINE_LIST = ['ada', 'babbage', 'curie', 'davinci', 'davinci-instruct-beta', 'curie-instruct-beta'];
export const ORIGIN = 'https://api.openai.com';
export const DEFAULT_API_VERSION = 'v1';
export const DEFAULT_OPEN_AI_URL = `${ORIGIN}/${DEFAULT_API_VERSION}`;

yzh119

import dgl
import dgl.ops as ops
import numpy as np
import torch as th
import torch.nn as nn

class FFN(nn.Module):
    def __init__(self, d_feat, d_ffn, dropout=0.1):
        super().__init__()
        self.linear_0 = nn.Linear(d_feat, d_ffn)

Daenyth

Operation	Input	Result	Notes
map	F[A] , A => B	F[B]	Functor
apply	F[A] , F[A => B]	F[B]	Applicative
(fa, fb, ...).mapN	(F[A], F[B], ...) , (A, B, ...) => C	F[C]	Applicative
(fa, fb, ...).tupled	(F[A], F[B], ...)	F[(A, B, ...)]	Applicative
flatMap	F[A] , A => F[B]	F[B]	Monad
traverse	F[A] , A => G[B]	G[F[A]]	Traversable; fa.traverse(f) == fa.map(f).sequence; "foreach with effects"
sequence	F[G[A]]	G[F[A]]	Same as fga.traverse(identity)
attempt	F[A]	F[Either[E, A]]	Given ApplicativeError[F, E]

mike-casas

After install zsh 

- brew update
- brew install nvm
- mkdir ~/.nvm

after in your ~/.zshrc or in .bash_profile if your use bash shell: 

    export NVM_DIR=~/.nvm
    source $(brew --prefix nvm)/nvm.sh

jdegoes

Applied Functional Programming with Scala - Notes

Copyright © 2016-2018 Fantasyland Institute of Learning. All rights reserved.

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x

davidjrice

var PUNCTUATION = "" +
"’'" +       // apostrophe
"()[]{}<>" + // brackets
":" +        // colon
"," +        // comma
"‒–—―" +     // dashes
"…" +        // ellipsis
"!" +        // exclamation mark
"." +        // full stop/period
"«»" +       // guillemets

rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue: