Messing around with fine-tuning LLMs, part 2 -- to the cloud!
Having fine-tuned a 0.5B model on my own machine, I wanted to try the same kind of tuning, but with an 8B model. My experiments suggested to me that the VRAM required for the tuning was roughly linear with two meta-parameters -- the length of the samples and the batch size -- and I'd found resources online that suggested that it was also linear with the number of parameters.
The 16x scale going from 0.5B parameters to 8B would suggest that I would need 16x24GiB to run this fine-tune, which would be 384GiB. However, the chart I'd seen before suggested I could do it with a bit more than 160GiB -- that being the number they gave for a 7B parameter model.
What I clearly needed to do was find a decent-looking cloud GPU platform where I could start with a smaller machine and easily switch over to a larger one if it wasn't sufficient. Here are my first steps, running one of my existing fine-tune notebooks on a cloud provider.
Messing around with fine-tuning LLMs
Fine-tuning an LLM is how you take a base model and turn it into something that can actually do something useful. Base models are LLMs that have been trained to learn to predict the next word on vast amounts of text, and they're really interesting to play with, but you can't really have a conversation with one. When you ask them to complete some text, they don't know whether you want to complete it as part of a novel, a technical article, or an unhinged tweetstorm. (The obvious joke about which type of people the same applies to is left as an exercise for the reader.)
Chat-like AIs like ChatGPT become possible when a base model has been fine-tuned on lots of texts representing transcriptions (real or fake) of conversations, so that they specialise in looking at texts like this:
Human: Hello!
Bot: Hello, I'm a helpful bot. What can I do for you today?
Human: What's the capital city of France?
Bot:
...and can work out that the next word should be something like "The", and then "capital", and so on to complete the sentence: "of France is Paris. Is there anything else I can help you with?"
Getting a solid intuition for how this all works felt like an interesting thing to do, and here are my lab notes on the first steps.
LLM Quantisation Weirdness
I bought myself an Nvidia RTX 3090 for Christmas to play around with local AI models. Serious work needs larger, more powerful cards, and it's easy (and not that expensive) to rent such cards by the minute from the likes of Paperspace. But the way I see it, I'm not going to be doing any serious work -- and what I really want to do is be able to run little experiments quickly and easily without worrying about spinning up a machine, getting stuff onto it, and so on.
One experiment that I tried the other day was to try to get a mental model of how model size and quantisation affect the quality of responses from LLMs. Quantisation is the process of running a model that has, say, 16 bits for each of its parameters with the parameters clipped to eight bits, four bits, or even less -- people have found that it often has a surprisingly small effect on output quality, and I wanted to play with that. Nothing serious or in-depth -- just trying stuff out with different model sizes and quantisations, and running a few prompts through them to see how the outputs differed.
I was comparing three sizes of the Code Llama HF model, with different quantisations:
- codellama/CodeLlama-7b-Instruct-hf, which has 7b parameters, in "full-fat", 8-bit and 4-bit
- codellama/CodeLlama-13b-Instruct-hf, which has 13b parameters, in 8-bit and 4-bit
- codellama/CodeLlama-34b-Instruct-hf, which has 34b parameters, in 4-bit
Code Llama is a model from Meta, and the HF version ("Human Feedback") is designed to receive questions about programming (with specific formatting), and to reply with code. I chose those particular quantisations because the 13b model wouldn't fit in the 3090's 24GiB RAM without quantisation to a least 8-bit, and the 34b model would only fit if it was 4-bit quantised.
The quality of the response to my test question was not too bad with any of these, apart from codellama/CodeLlama-34b-Instruct-hf in 4-bit, which was often (but not always) heavily glitched with missing tokens -- that is, it was worse than codellama/CodeLlama-7b-Instruct-hf in 4-bit. That surprised me!
I was expecting quantisation to worsen the results, but not to make a larger model worse than a smaller one at the same level of quantisation. I've put a repo up on GitHub to see if anyone can repro these results, and to find out if anyone has any idea why it's happening.
Giving up on the AI chatbot tutorial (for now)
I'm a big fan of learning in public, and early last year I started trying to do that by writing an AI chatbot tutorial as I learned the technology myself. But somehow it just wasn't working -- perhaps because my understanding was evolving so quickly that each time I sat down to write, I spotted dozens of errors in the previous posts, and felt I should fix those first. So I've decided to give up on that one, at least for now.
So, back to something a bit more achievable! Some lab notes will be coming on things I've been working on, including -- later on this evening -- a post about an oddity I found the other day.
In the meantime, here's a blog post I did for PythonAnywhere late last year: Five steps to create your own PythonAnywhere AI guru, on PythonAnywhere.
Building an AI chatbot for beginners: part 2
[Note that this series kind of dried up; when I started the series, I knew that I knew very little about the subject, but I was hoping to learn better by learning in public. However, as time went by it turned out that this wasn't working. There are a lot of better tutorials out there!]
Welcome to the second part of my tutorial on how to build a chatbot using OpenAI's interface to their Large Language Models (LLMs)! You can read the introduction here, and the first part here. As a reminder, I'm writing this not because I'm an expert, but because I'm learning how to do it myself, and writing about it helps me learn faster. Caveat lector :-)
In this post, we'll give the bot some memory of the conversation so far.
At the end of the first part, we had a program that would accept input from a user, combine it with some static text to make a prompt that an LLM would complete in the character of a chatbot (stopping at the point that the chatbot should stop, and not trying to carry on the conversation), then send it to OpenAI's API specifying an LLM model, and print out the result.
Building an AI chatbot for beginners: part 1
[Note that this series kind of dried up; when I started the series, I knew that I knew very little about the subject, but I was hoping to learn better by learning in public. However, as time went by it turned out that this wasn't working. There are a lot of better tutorials out there!]
Welcome to the first part of my tutorial on how to build a chatbot using OpenAI's interface to their Large Language Models (LLMs)! You can read the introduction here.
If you're reading this and want to get the best out of it, I strongly recommend that you run the code on your own machine as you go along: trust me, it will stick in your mind much better if you do that.
The goal in this post is to write a basic bot script that accepts user input, and just bounces it off an OpenAI LLM to generate a response.
Building an AI chatbot for beginners: part 0
[Note that this series kind of dried up; when I started the series, I knew that I knew very little about the subject, but I was hoping to learn better by learning in public. However, as time went by it turned out that this wasn't working. There are a lot of better tutorials out there!]
Like a lot of people, I've been blown away by the capabilities of Large Language Model (LLM) based systems over the last few months. I'm using ChatGPT regularly for all kinds of things, from generating basic code to debugging errors to writing emails.
I wanted to understand more about how these tools worked, and feel strongly that there's no better way to learn something than by doing it. Building an LLM is, at least right now, super-expensive -- in the millions of dollars (although maybe that will be coming down fast?). It also requires a lot of deep knowledge to get to something interesting. Perhaps something to try in the future, but not right now.
However, using LLMs to create something interesting -- that's much easier, especially because OpenAI have a powerful API, which provides ways to do all kinds of stuff. Most relevantly, they provide access to a Completion API. That, as I understand it, is the lowest-level way of interacting with an LLM, so building something out of it is probably the best bang for the buck for learning.
Over the last few weeks I've put together a bunch of things I found interesting, and have learned a lot. But it occurred to me that an even better way to learn stuff than by building it is to build it, and then explain it to someone else, even if that person is an abstract persona for "someone out there on the Internet". So: time for a LLM chatbot tutorial!
Python code to generate Let's Encrypt certificates
I spent today writing some Python code to request certificates from Let's Encrypt. I couldn't find much in the way of simple sample code out there, so I thought it would be worth sharing some. It uses the acme Python package, which is part of the certbot client script.
It's worth noting that none of this is useful stuff if you just want to get a Let's Encrypt certificate for your website; scripts like certbot and dehydrated are what you need for that. This code and the explanation below are for people who are building their own systems to manage Let's Encrypt certs (perhaps for a number of websites) or who want a reasonably simple example showing a little more of what happens under the hood.
Creating a time series from existing data in pandas
pandas is a high-performance library for data analysis in Python. It's generally excellent, but if you're a beginner or you use it rarely, it can be tricky to find out how to do quite simple things -- the code to do what you want is likely to be very clear once you work it out, but working it out can be relatively hard.
A case in point, which I'm posting here largely so that I can find it again next
time I need to do the same thing... I had a list start_times of dictionaries,
each of which had (amongst other properties) a timestamp and a value. I wanted
to create a pandas time series object to represent those values.
The code to do that is this:
import pandas as pd
series = pd.Series(
[cs["value"] for cs in start_times],
index=pd.DatetimeIndex([cs["timestamp"] for cs in start_times])
)
Perfectly clear once you see it, but it did take upwards of 40 Google searches and help from two colleagues with a reasonable amount of pandas experience to work out what it should be.
Parsing website SSL certificates in Python
A kindly PythonAnywhere user dropped us a line today to point out that StartCom and WoSign's SSL certificates are no longer going to be supported in Chrome, Firefox and Safari. I wanted to email all of our customers who were using certificates provided by those organisations.
We have all of the domains we host stored in a database, and it was surprisingly hard to find out how I could take a PEM-formatted certificate (the normal base-64 encoded stuff surrounded by "BEGIN CERTIFICATE" and "END CERTIFICATE") in a string and find out who issued it.
After much googling, I finally found the right search terms to get to this Stack Overflow post by mhawke, so here's my adaptation of the code:
from OpenSSL import crypto
for domain in domains:
cert = crypto.load_certificate(crypto.FILETYPE_PEM, domain.cert)
issuer = cert.get_issuer().CN
if issuer is None:
# This happened with a Cloudflare-issued cert
continue
if "startcom" in issuer.lower() or "wosign" in issuer.lower():
# send the user an email