Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.
Avoid being a link dump. Try to provide only valuable well tuned information.
Neural network links before starting with transformers.
# Based on younesbelkada/finetune_llama_v2.py | |
# Install the following libraries: | |
# pip install accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 scipy | |
from dataclasses import dataclass, field | |
from typing import Optional | |
import torch | |
from datasets import load_dataset | |
from transformers import ( |
# 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 |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
from peft import PeftModel | |
import torch | |
import os | |
import argparse | |
def get_args(): | |
parser = argparse.ArgumentParser() | |
parser.add_argument("--base_model_name_or_path", type=str) |
If you, like me, resent every dollar spent on commercial PDF tools,
you might want to know how to change the text content of a PDF without
having to pay for Adobe Acrobat or another PDF tool. I didn't see an
obvious open-source tool that lets you dig into PDF internals, but I
did discover a few useful facts about how PDFs are structured that
I think may prove useful to others (or myself) in the future. They
are recorded here. They are surely not universally applicable --
the PDF standard is truly Byzantine -- but they worked for my case.