simonbs

// Sample code made in response to this tweet:
// https://twitter.com/stroughtonsmith/status/1485719749673820163

final class MainView: UIView {
    private let textView: UITextView = {
        let this = UITextView()
        this.translatesAutoresizingMaskIntoConstraints = false
        this.text = "I'm a text view"
        this.font = .preferredFont(forTextStyle: .body)
        this.textColor = .label

WillyJL

from PIL import Image, ImageSequence, UnidentifiedImageError
import OpenGL.GL as gl
import pathlib
import imgui

_dummy_texture_id = None


def dummy_texture_id():
    global _dummy_texture_id

ploeber

import asyncio
import signal

import vocode
from vocode.streaming.streaming_conversation import StreamingConversation
from vocode.helpers import create_microphone_input_and_speaker_output

# Transcriber
from vocode.streaming.models.transcriber import AssemblyAITranscriberConfig
from vocode.streaming.transcriber.assemblyai_transcriber import AssemblyAITranscriber

yoavg

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

TheBloke

import time
import os
import logging
import random
from datasets import load_dataset

class QuantAutoGPTQ:
    def __init__(self, model_name_or_path, output_dir, dataset,
                 num_samples=128, trust_remote_code=False, cache_examples=True,
                 use_fast=True, use_triton=False, bits=[4], group_size=[128], damp=[0.01],

younesbelkada

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software