jakeju92

# train_grpo.py
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer

# Load and prep dataset

jakeju92

conda create -n textgen python=3.10.9
conda activate textgen
install pytorch: pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
pip install -r requirements.txt
python server.py
# download model
# refresh model list
# load model

jakeju92

Prerequisites

You need to install the pv library first for visualize progress bar.

In Debian system, just run this command: sudo apt-get install pv

Compress

tar cf - <files> -P | pv -s $(du -sb <files> | awk '{print $1}') | gzip > <some .tar.gz file>

jakeju92

# "#################################################"
# Dockerfile to build a GitHub Pages Jekyll site
#   - Ubuntu 22.04
#   - Ruby 3.1.2
#   - Jekyll 3.9.3
#   - GitHub Pages 288
#
#   This code is from the following Gist:
#   https://gist.github.com/BillRaymond/db761d6b53dc4a237b095819d33c7332#file-post-run-txt
#

jakeju92

Here's a simple implementation of bilinear interpolation on tensors using PyTorch.

I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).

For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).

In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle

jakeju92

# CMake version requirement
cmake_minimum_required(VERSION 3.8.0)

project(MY_PROJ VERSION 1.0.0 LANGUAGES CXX CUDA)

# find_package()

set(INCUDE_DIR "${PROJECT_SOURCE_DIR}/include")
set(LIBRARY_DIR "${PROJECT_SOURCE_DIR}/lib")

jakeju92

PowerCfg /SETACVALUEINDEX SCHEME_CURRENT SUB_PROCESSOR IDLEDISABLE 000
PowerCfg /SETACTIVE SCHEME_CURRENT