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@nickbudi
nickbudi / README.md
Last active August 15, 2024 19:54
Budi's Counter-Strike: Global Offensive config

Budi's CS:GO Config

This is my constantly updated CS:GO autoexec config. Changelogs can be found under revisions here

Put autoexec.cfg in ...\Steam\steamapps\common\Counter-Strike Global Offensive\csgo\cfg or take what you want from it and add to your autoexec config!

After the Wild West Simulator 2015 update, video.txt needs to be put in ...\Steam\userdata\<Steam3 ID>\730\local\cfg

Launch Options

@MaxHalford
MaxHalford / fit.py
Created May 18, 2017 15:35
Keras fit/predict scikit-learn pipeline
import os
from keras import backend as K
from keras import callbacks
from keras import layers
from keras import models
from keras.wrappers.scikit_learn import KerasClassifier
import pandas as pd
import tensorflow as tf
from sklearn import metrics
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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

@willccbb
willccbb / grpo_demo.py
Last active May 14, 2025 09:41
GRPO Llama-1B
# train_grpo.py
#
# See https://github.com/willccbb/verifiers for ongoing developments
#
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer

Cursor's Memory Bank

I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read ALL memory bank files at the start of EVERY task - this is not optional.

Memory Bank Structure

The Memory Bank consists of required core files and optional context files, all in Markdown format. Files build upon each other in a clear hierarchy:

flowchart TD