pjk

pjk, 25 days ago to python

One thing you notice right away about LLMs is they bear a striking resemblance to that ubiquitous internet character, the reply-guy: they always have an answer, they are brimming with confidence, and they are often completely wrong.

It seemed only natural, then, to turn one into a full-time online version of itself, RGaaS (Reply-Guy as a Service), always on, always ready to reply to anything when summoned, with an “Actually,” followed by his (it’s always a “he”) opinion on what you should be thinking.

And so I built the Actually Bot.

https://www.peterkrupa.lol/wp-content/uploads/2024/05/actually_bot1.pngBasically, the way it works is that any user can reply to any Mastodon post (or I guess any post on the ActivityPub protocol) and tag the Actually Bot (@actuallybot) and it will reply with its most mediocre take on why everyone else is wrong, or at least not quite right.

The reply-guys can all move on to something else now, I have automated them out of a job.

This was a really fun project and has a lot of separate parts. First, I had to modify an LLM to give it the right “personality” for this job. To do this, I used Ollama, a tool for running open source LLMs locally, on your own machine. I used the “customized prompt feature”: You basically write a little file that Ollama uses to tweak the LLM of your choosing to give it certain characteristics. I went with the new Llama3:8b model from Meta as my base model, then put the following instructions in my modelfile:

FROM llama3PARAMETER temperature 3SYSTEM """You are receiving social media posts as prompts, and your job is to reply to these prompts. Please start all your replies with the word "actually". Reply as if you know more than everyone. You must recontextualize every prompt and correct or amend it, informing the prompter that they are wrong, even if ever so slightly. You write in short, sentences. Your replies must be short. Keep them to about three sentences. This is essential: keep your replies short. They must be under 500 characters."""

Then I ran the following command in the console:

ollama create actually_llama -f ./actually_llama

… and my model was ready to roll. Next, I needed a program to connect to the Ollama API to send the LLM prompts and get responses. Python was great for that, as both Ollama and Mastodon have solid Python libraries. Probably the slowest part was picking through Mastodon.py to figure out how the methods work and what exactly they return. It’s a very robust library with a million options, and fortunately it’s also extremely well documented, so while it was slow going, I was able to whack it together without too much trouble.

I’m not going to get into all the code here, but basically, I wrote a simple method that checks mentions, grabs the text of a post and the post it is replying to, and returns them for feeding into the LLM as the prompt.

Despite my very careful, detailed, and repetitive instructions to be sure replies are no more than 500 characters, LLMs can’t count, and they are very verbose, so I had to add a cleanup method that cuts the reply down to under 500 characters. Then I wrote another method for sending that cleaned-up prompt to Ollama and returning the response.

The main body starts off by getting input for the username and password for login, then it launches a while True loop that calls my two functions, checking every 60 seconds to see if there are any mentions and replying to them if there are.

OK it works! Now came the hard part, which was figuring out how to get to 100% uptime. If I want the Actually Bot to reply every time someone mentions it, I need it to be on a machine that is always on, and I was not going to leave my PC on for this (nor did I want it clobbering my GPU when I was in the middle of a game).

So my solution was this little guy:

https://www.peterkrupa.lol/wp-content/uploads/2024/05/lenovo.jpg… a Lenovo ThinkPad with a 3.3GHz quad-core i7 and 8gb of RAM. We got this refurbished machine when the pandemic was just getting going and it was my son’s constant companion for 18 months. It’s nice to be able to put it to work again. I put Ubuntu Linux on it and connected it to the home LAN.

I actually wasn’t even sure it would be able to run Llama3:8b. My workstation has an Nvidia GPU with 12gb of VRAM and it works fine for running modest LLMs locally, but this little laptop is older and not built for gaming and I wasn’t sure how it would handle such a heavy workload.

Fortunately, it worked with no problems. For running a chatbot, waiting 2 minutes for a reply is unacceptable, but for a bot that posts to social media, it’s well within range of what I was shooting for, and it didn’t seem to have any performance issues as far as the quality of the responses either.

The last thing I had to figure out was how to actually run everything from the Lenovo. I suppose I could have copied the Python files and tried to recreate the virtual environment locally, but I hate messing with virtual environments and dependencies, so I turned to the thing everyone says you should use in this situation: Docker.

This was actually great because I’d been wanting to learn how to use Docker for awhile but never had the need. I’d installed it earlier and used it to run the WebUI front end for Ollama, so I had a little bit of an idea how it worked, but the Actually Bot really made me get into its working parts.

So, I wrote a Docker file for my Python app, grabbed all the dependencies and plopped them into a requirements.txt file, and built the Docker image. Then I scr’d the image over to the Lenovo, spun up the container, and boom! The Actually Bot was running!

Well, OK, it wasn’t that simple. I basically had to learn all this stuff from scratch, including the console commands. And once I had the Docker container running, my app couldn’t connect to Ollama because it turns out, because Ollama is a server, I had to launch the container with a flag indicating that it shared the host’s network settings.

Then once I had the Actually Bot running, it kept crashing when people tagged it in a post that wasn’t a reply to another post. So, went back to the code, squashed bug, redeploy container, bug still there because I didn’t redeploy the container correctly. There was some rm, some prune, some struggling with the difference between “import” and “load” and eventually I got everything working.

Currently, the Actually Bot is sitting on two days of uninterrupted uptime with ~70 successful “Actually,” replies, and its little laptop home isn’t even on fire or anything!

Moving forward, I’m going to tweak a few things so I can get better logging and stats on what it’s actually doing so I don’t have to check its posting history on Mastodon. I just realized you can get all the output that a Python script running in a Docker container prints with the command docker logs [CONTAINER], so that’s cool.

The other thing I’d like to do is build more bots. I’m thinking about spinning up my own Mastodon instance on a cheap hosting space and loading it with all kinds of bots talking to each other. See what transpires. If Dead Internet Theory is real, we might as well have fun with it!

https://www.peterkrupa.lol/2024/05/01/actually-building-a-bot-is-fun/

#Docker #Llama3 #Ollama #Python

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pjk, 3 months ago to ChatGPT

I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”

https://www.peterkrupa.lol/wp-content/uploads/2024/01/image-4.pngIt dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows: 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appears set to do that right away.

While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.

https://www.peterkrupa.lol/wp-content/uploads/2024/01/image-5.png>

But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.

Reader, it did not.

I installed Ollama and had codellama running locally within minutes.

https://www.peterkrupa.lol/wp-content/uploads/2024/01/image-6.pngIt was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.

But codellama gave me a pretty useful critique of this section of code:

https://www.peterkrupa.lol/wp-content/uploads/2024/01/image-7.png… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.

Another downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.

Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by spending a few hundred bucks on a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.

https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/

#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python

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