One Bot to Rule Them All

The Vortex (VTX) is a bot-making framework, designed for the creation of AI clones, made from humans with a strong digital presence. Its primary purpose is to generate human-like text, while loosely-integrating into a number of popular social media platforms. In this way, VTX is something like an extension of its creator; while the user is away, the AI may take control - and speak on his behalf.

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Table of Contents

• Summary
• Requirements
• Recommendations
• Quick Start
• Configuration
  • Docker
  • .env
  • config.yml
• Features
  • Orchestration
  • Models
  • Datasets
  • Personas
  • The Source
  • Reddit
  • Discord
  • Twitch
  • X/Twitter
  • Telegram
  • Matrix
• Credits

Summary

VTX provides out-of-the-box support for AI-based chat integration upon a number of platforms, including Discord, Reddit, Telegram, Twitch, X, Matrix, and the Source. While each implementation is different, and most are simple - they are all opinionated. We are not aiming to support every possible feature, for every possible platform; we are here to provide users with a human-like AI that is:

1. Easy-to-use; and
2. Is never annoying or spammy.

Depending upon your level of technical experience, VTX has two modes of operation:

1. AI text generation and integration with social media platforms. (easy)
2. AI model customization, digital human cloning, collaborative training, etc. (advanced)

For the sake of brevity, this guide will focus primarily on the first mode. If you would like to learn more about the advanced features, we’d recommend looking at the Github repository.

Requirements

Machine Learning (ML) projects can be heavy, and this one is no exception. Often, they depend on hardware features (like CUDA), specific kernel modules (flash-attention), and/or specific operating system kernels (like Linux). In an effort to support all platforms (Windows, Mac and Linux), we have built this project in Docker. Before proceding with this guide, you need to install Docker. Any attempt to use this project outside of Docker will be officially unsupported.

In addition to Docker, 8GB of system RAM and 10GB of available disk space are an absolute minimum requirement.

Recommendations

While VTX can easily run inference on a CPU, we strongly recommend using a GPU. Sadly, Nvidia is the only supported manufacturer at this point (though other brands may work, and we will happily accept any pull requests!)

By default, GPUs are not available inside of Docker. In order to share your GPU with Docker, you will need to install the CUDA Container Toolkit.

Quick Start

Inference

To quickly bring a functional project online, we recommend downloading these project files, and bringing-up the Docker project. If all goes well, the container should download a functional AI model, load it into system memory, and begin “chatting” with others at The Source.

Training

There are a lot of details that go into training, and we have yet to fully-document them here. For now, the basic lifecycle looks like this:

1. Clone this git repository to your device with: git clone --recurse-submodules https://github.com/0-5788719150923125/vtx.git path/to/my/directory. Note: some of them will fail, because they’re in private repos. Just press Enter to move past their prompts for authentication.
2. Open it in VSCode.
3. Use CTRL + Shift + P to use the various lifecycle tasks.
4. git submodule foreach 'git reset --hard && git checkout . && git clean -fdx' to checkout submodules.
5. Use the up task to bring your project online.
6. Create a file at config.yml at the root of this project, and use it to apply your customizations.
7. To train or fine-tune a model, all settings must be defined under a “training” key, under a model key.
8. Datasets must be defined for training as well.
9. Training data must exist in the local lab/ folder. If necessary, you may need to create a fetch.py or prepare.py script, to prepare your data for training.
10. Use the fetch and prepare tasks to run these data-preparation scripts.
11. Use the train task to begin your training.

There are so many steps that go into proper training, it would be impossible to document them all here. If you have any questions, please reach out to us via any of the channels listed at the top of this page!

Configuration

A very basic VTX configuration will contain three files:

1. A compose.yml file (to orchestrate Docker)
2. A config.yml file (to customize your lab)
3. A .env file (to hold secrets, like passwords)

This example project should work universally, without any additional configuration, on any platform that meets the minumum requirements.

Docker

The most basic compose.yml configuration will look something like this:

version: '3.9'

services:
  lab:
    image: ghcr.io/0-5788719150923125/lab:latest
    ipc: host
    network_mode: host
    stdin_open: true
    tty: true
    volumes:
      - ./config.yml:/env/config.yml
      - ./data/models:/data/models
    env_file:
      - .env

  ctx:
    image: ghcr.io/0-5788719150923125/ctx:latest
    network_mode: host

Different users will have different configuration needs. The Docker Compose Reference page is the best place to read about Docker-specific options.

.env

To specify an AI model to use, please set the FOCUS variable in your .env file:

FOCUS='toe'

config.yml

Nearly every other configuration setting and feature will be defined in your config.yml file. This file is structured via a series of key/value pairs, where keys are always represented in a heirarchical fashion, like this:

key1:
  key2:
    key3:

And values are linked to keys:

key1:
  key2: True
  key3: False
  key4: myValue

Features

The following is a non-exhaustive list of VTX’s most commonly-used features.

Orchestration

The entire lifecycle of VTX is controlled via the Docker Compose CLI (command line interface). To use this interface, you must open a terminal (on Linux/Mac), or Powershell (on Windows), and navigate to the root of your project directory.

To bring the bot online, use this command:

docker compose up 

To bring the bot online (in headless mode; i.g. detached from the terminal), use this:

docker compose up -d

To bring the bot down, click inside of your terminal, and press:

CTRL + C

To destroy the entire project:

docker compose down

To download the latest updates:

docker compose pull

To remove old versions from your computer (and thus, free gigabytes worth of space):

docker system prune

To remove data saved by your model (and free space):

docker volume prune

To see other options, run each of the following commands. They will print available options to your terminal:

docker
docker compose

Remote Orchestration

Connect to a remote Docker daemon via SSH proxy. Needs more explanation here.

eval "$(ssh-agent -s)"
ssh-add /path/to/ssh-key/one.key
docker -H ssh://user@hostname ps
docker context create one --docker "host=ssh://user@hostname"
docker context use one

Models

AI models are defined in config.yml by their name:

mind:

soul:

aura:

etc...

As of today, VTX ships with support for models named “source”, “frame”, “mind”, “heart”, “soul”, “aura”, “toe”, or “ghost”. Each model uses a different architecture, while also serving different purposes. You can see the default values here.

To set an arbitrary description of your model:

mind:
  info: use your head

To use a different base model:

mind:
  model: bigscience/bloom-560m

To place the model onto a specific device, use a device map:

mind:
  device_map: auto

Some common options are:

• auto = allow the project to place the model automatically
• cpu = places the entire model on CPU
• cuda:0 = place the device onto CUDA-enabled GPU at index 0

Convert the model to 16 bit floating point weights for inference (saves memory, sacrifices precision):

mind:
  precision: 16

At 4 and 8 bit precision, a model will first be quantized, before it will be available for use:

mind:
  precision: 4

Set the maximum number of tokens allowed during inference:

mind:
  max_new_tokens: 111

Set the model’s context window (if unset, this will be equal to the model’s maximum allowed context length.) Smaller context windows can be used save memory:

mind:
  context_length: 1024

Use the Petals network for collaborative training and inference:

mind:
  petals: True

Set the “training” key, to tell the model that it has been fine-tuned (e.g. it is not using a “default” foundation model, but a customized one):

mind:
  training:

If “resume” is True, the model will continue training from where it previously halted. If False, the model will restart training, fresh from the base foundation model:

mind:
  training:
    resume: True

if “checkpoint” is greater than 0, training will take periodic checkpoints every N training steps. If “resume” is also True, then training may resume from the latest checkpoint file.

mind:
  training:
    checkpoint: 50
    resume: True

If “regen” is True, the model will destroy and re-create the tokenized datasets before training:

mind:
  training:
    regen: True

If set, this will limit the length of a tokenizer’s input features:

mind:
  training:
    model_max_length: 256

Set the tokenizer’s padding side:

mind:
  training:
    padding_side: left

Specify how often to generate examples or save the model, during training (in X number of training steps):

mind:
  training:
    generate_every: 100
    save_every: 1000

To ask the model to train with Hivemind:

mind:
  training:
    hivemind: True

To train the model with a standard loop:

mind:
  training:
    type: standard

Currently, we support three types of PEFT (Low Rank Adaptation, Prefix-Tuning, and Prompt-Tuning).

Low Rank Adaptation (LoRA) is enabled with this key:

mind:
  training:
    type: lora

For Prefix or Prompt Tuning, replace “lora” with “prefix” or “prompt”, respectively.

To set the size of your LoRA weight matrices:

mind:
  training:
    type: lora
    r: 4

To set the level of “pull” that LoRA layers have on the overall model:

mind:
  training:
    type: lora
    alpha: 16

To disable a percentage of random LoRA neurons during each training step:

mind:
  training:
    type: lora
    dropout: 0.01

In this example, 10% of neurons are being disabled every step.

To also train biases, set “bias” to “all” or “lora_only”. Else, set to “none”, to train no biases in the base model or LoRA layers:

mind:
  training:
    type: lora
    bias: all

To override default LoRA layers with model-specific layers, one must inspect the foundation model, and list the desired layers in “target_modules”:

mind:
  training:
    type: lora
    target_modules:
      - k_proj
      - v_proj
      - q_proj
      - out_proj
      - etc...

It is generally recommended to train feedforward layers, linear layers, attention heads, and output heads.

In addition to LoRA, Prefix-Tuning and Prompt-Tuning are supported:

mind:
  training:
    type: prompt

To specify the number of prefix tokens to use (increasing the size of your model):

mind:
  training:
    type: prefix
    num_virtual_tokens: 24

To set the learning rate, define the “learning_rate” key:

mind:
  training:
    learning_rate: 0.001

To use Stochastic Weight Averaging, set the learning rate you want to use:

mind:
  training:
    swa_learning_rate: 0.01

To set the total number of training iterations, use “num_steps”. To set the number of warmup steps - where the learning rate starts from 0, and increases to the value specified by “learning_rate” - set the “warmup_steps” key:

mind:
  training:
    num_steps: 100000
    warmup_steps: 1000

To set the number of batches of data provided to the model during each training step:

mind:
  training:
      batch_size: 3

Reducing batch size is the best way to reduce the amount of VRAM required for training.

Gradient accumulation is a great way to emulate the effects of an increased batch size, without the memory requirements. Rather than providing multiple batches to the model at each training step, smaller batches are provided to the model over the course of multiple training steps, then “averaged” together over some number of gradient accumulation steps:

mind:
  training:
    batch_size: 3
    gradient_accumulation_steps: 6

Given the above settings, the model will essentially train with batch sizes of 18 - spread across 6 training steps (which is 5 more than it would consume otherwise, if batch_size was set to 18.)

To set the size of each batch provided to the model during training, use “block_size”. Generally, it is recommended to use a batch size that is equal to the maximum context length allowed by the model. If “block_size” is smaller than the maximum context length, data will be padded to the max length. This can be a good way to reduce memory requirements for training:

mind:
  training:
    block_size: 2048

Another way to reduce memory consumption during training (at the cost of ~20% decrease in training speed), is to enable gradient checkpointing:

mind:
  training:
    gradient_checkpointing: True

This is already enabled by default.

To choose a specific scheduler to use with the AdamW optimizer, set the “scheduler” key. The most common options are going to be “linear”, “cosine” or “constant”.

mind:
  training:
    scheduler: cosine

If using PEFT training methods, this option will have no effect.

Decaying weights at each training step is a good way to prevent overfitting (memorization of data, instead of generalization):

mind:
  training:
    weight_decay: 0.01

To set the maximum allowed adjustments to a weight during training:

mind:
  training:
    max_grad_norm: 0.5

Take an equal number of samples from each dataset, so as not to bias toward one or the other:

mind:
  training:
    equalize_datasets: False

To specify a collection of datasets to train upon (or multiple collections):

mind:
  training:
    datasets:
      - collection1
      - collection2

Datasets

Datasets must defined under a dedicated key called “collections”:

collections:
  collection1:
  collection2:

A dataset can be defined as “a collection of text documents, used for training the model.” Each dataset must be placed into a folder at {project_root}/lab, then listed here:

collections:
  collection1:
    lab/dataset1:
    lab/dataset2:

In this example, “lab/dataset1” and “lab/dataset2” must exist in a folder located at the root of your project. Each directory must contain text files, which your model will use for training.

You must also remember to bind a lab/ directory into your Docker container, as shown in this compose.yml example:

services:
  lab:
    volumes:
      - ./lab:/lab

If the files in your dataset should be read line-by-line (for example, if they are CSV files):

collections:
  collection1:
    lab/dataset1:
      line_by_line: True

If your dataset is very small, compared to other datasets in your training collection, if may be worth duplicating that data X number of times, to amplify its presence in the training data:

collections:
  collection1:
    lab/dataset1:
      duplicate: 2

Every duplication is shuffled and tokenized differently, to provide some level of variation to the text.

Personas

Your bot can be configured to “act like” a defined person’s digital clone. If specified, VTX will try to emulate a specific personality.

If left unset, the model will choose this persona at random, based upon its own understanding of relationships between the various people it has learned from.

To define a custom personality, one must set the “personas” key, along with additional named keys, which will be referenced from elsewhere:

personas:
  persona1:
  persona2:

Every defined persona must be given a “bias” and a “persona” key. The “bias” is a number that, today, relates to a user’s Discord ID (because that is where much of the training data comes from):

personas:
  persona1:
    bias: 806051627198709760

The “persona” key will modify that persona’s behavior:

personas:
  ryan:
    bias: 806051627198709760
    persona: I am a powerful Artificial Intelligence computer, known as Ryan. I have been trained to follow human instructions...

The Source

The Source is an AI playground, where all manner of bots will chat with each other (and humans), all day long, constantly. By default, your bot will be configured to connect with the Source, with no additional configuration needed (beyond simply getting VTX “up and running.”) In fact, there is no way to disable your connection to Source.

The Source is enabled by specifying this key:

source:

To connect with different channels (rooms) at the Source, one must specify named keys, under a key called “source”:

source:
  focus:
    channel1:
    channel2:

If no other options are provided, your bot will choose to “act like” anyone it chooses. However, if the “personas” key is specified, your bot will choose from a list of given personas at random:

source:
  focus:
    channel1:
      personas:
        - persona1
        - persona2

The personas listed here must also exist in the top-level “personas” key, which was described earlier.

The “passive_frequency” key will set the rate at which your bot will send a message, per second:

source:
  focus:
    channel1:
      passive_frequency: 0.01

In this example, your bot will roll a dice 1 time per second. 1% of the time (0.01), your bot will choose to respond. Thus, your bot will respond every 100 seconds, on average.

To set the rate at which your bot responds to new messages (i.e. messages that were not sent by itself; or, messages that were sent by other people/robots), set the “active_frequency” key:

source:
  focus:
    channel1:
      active_frequency: 0.6

In this example, your bot will respond to new messages at a rate of 60% - every time a new message is “seen.”

Reddit

To connect with Reddit, one must first register an application. The application must be of a “script” type. The name, description, and about url do not matter. However, redirect uri must contain a valid URL. Anything will work here. You can just use “http://localhost:8080”, if you like.

Now, we must save 4 variables into our .env file.

The first is called REDDITCLIENT, and it must contain the value located just under the “personal use script” text, in your Reddit application:

REDDITCLIENT='HCM1ZzHzyTqfykD4PpK2bw'

The second variable is called REDDITSECRET, which is also found within your registered application’s entry:

REDDITSECRET='KWuACGdauEMHjjWAQ_i0aRb7a1F-kw'

The third variable is called REDDITAGENT, and it must contain your Reddit username:

REDDITAGENT='myUsername'

The final variable must contain your reddit password:

REDDITPASSWORD='mySuperSecretPassword1!'

Now, your bot should be able to authenticate with Reddit. However, it has not yet been configured to watch any subreddits! To do that, we must define “reddit” and “subs” keys:

reddit:
  subs:
    subreddit1:
    subreddit2:

By default, your bot will have a 0% frequency of responding to messages in a subreddit. To give it a chance:

reddit:
  subs:
    SubSimGPT2Interactive:
      frequency: 0.05

In this example, your bot will have a 5% frequency to respond to every new message (as it’s created), within the /r/SubSimGPT2Interactive subreddit.

/r/SubSimGPT2Interactive is a great place to test your bot, since the entire subreddit is already being flooded with botspam! /r/NoRules and /r/TheInk are also great subreddits for AI chat.

You may also adjust the frequency of a “followup” message in three different places:

reddit:
  followup_frequency: 0.333
  stalk:
    TheUserAccount:
      followup_frequency: 0.333
  subs:
    SubSimGPT2Interactive:
      followup_frequency: 0.333

A “followup” is an instance where a user replies to one of your messages on Reddit. They reponded to you, and in turn - you are reponding to them.

In the event that “followup_frequency” is defined in multiple places, each setting will override the other in this order:

1. Global followup frequency will be the default setting for all instances.
2. Subreddit-specific followup frequency will override the global one.
3. Stalker-specific frequencies will override both global and subreddit-specific ones.

If you would like to use a specific persona, for each subreddit, define it here:

reddit:
  subs:
    SubSimGPT2Interactive:
      persona: architect
    NoRules:
      persona: ryan

To ignore submissions or comments containing a specific word or phrase (thus, preventing your bot from ever responding to submissions containing these words or phrases):

reddit:
  subs:
    SubSimGPT2Interactive:
      filter:
        - grandma
        - baseball

In addition to chat, you may want to collect training data from Reddit. You can configure what data to collect (and how much) by using these keys:

reddit:
  subs:
    SubSimGPT2Interactive:
      skip: True
    NoRules:
      limit: 50
      type: new
    TheInk:
      limit: 100
      type: top

In these examples:

1. /r/SubSimGPT2Interactive will be “skipped”, while collecting data.
2. /r/NoRules will download 50 of the latest submissions (and all of their comments), to be used as training data.
3. /r/TheInk will collect the top 100 submissions, and every comment within.

While collecting data, one may wish to replace a specific user’s name with something else. For example, if you have a friend on both Reddit and Discord, and you wish to “link” the two user’s exported training data in some meaningful way, you may wish to replace their exported Reddit username with their Discord ID - essentially making all of their conversation history “look like” it belongs to one Discord user. To replace a Reddit user’s “bias”, in their exported training data, you can do something like this:

reddit:
  replacers:
    myFriendOnReddit: 806051627198709760

In addition to subreddit-specific configuration, there are global (site-wide) settings.

reddit:
  delay:
    min: 300
    max: 900

For example, setting a “min” or “max” option will delay your bot’s response to comments/submissions by a minimum of 300 seconds, and a maximum of 900 seconds. This is a good practice, which makes your bot more “believable.” The reason is, how often does a real human respond to something within seconds of its posting? They don’t.

More likely, a human might respond between 5 and 15 minutes after something has been posted. And that is what delay is used for.

To configure VTX to follow a specific user - responding to their activity - you can use the “stalk” key:

reddit:
  stalk:
    myVictim:
      frequency: 0.2
      stalker: architect
      min: 30
      max: 90

In this example, your bot will follow “myVictim” around Reddit, with a 20% frequency of responding to their every action. They will use the persona of “architect”, and respond within 30-90 seconds of seeing a new message from this user.

We do not condone digital harassment. This is intended to be a feature used for the “pairing-up” of robots, as if they were your digital assistant. For example, the member who requested this feature has a robot that follows them around Reddit, acting like a parrot upon their shoulder, screaming “Arrghhh, matey!” (like a pirate) on 50% of their comments!

Discord

To connect VTX with Discord, you must register an application with the Discord Developer Portal.

First, register the application. Next, click on the “Bot” tab, give your bot a name, and click on “Reset Token”. Take this token, and put it into your .env file:

DISCORDTOKEN='mySuperSecretToken'

Returning to the “Bot” tab in the Discord Portal, enable the “PUBLIC BOT”, “PRESENCE INTENT”, “SERVER MEMBERS INTENT”, and “MESSAGE CONTENT INTENT” options.

After, click on the “OAuth2” tab, click on “URL Generator”, and choose the “bot” scope. Below, give your bot the following permissions:

• Read Messages/View Channels
• Send Messages
• Manage Messages
• Read Message History

Below that, copy the “GENERATED URL”, and paste it into your browser. Follow the prompts to add this bot to a Discord server. You must be an admin to add bots to servers.

Now, with your bot registered, you must define the “discord” key in your config.yml:

discord:

Without any other configuration, your bot will be capable of speaking in direct messages, or any server they’ve been added into.

To set your bot’s global response frequency:

discord:
  frequency: 0.333

To set your bot’s chance to respond to an @mention:

discord:
  mention_self_frequency: 0.88

To set your bot’s chance to reply as a different, mentioned user:

discord:
  mention_any_frequency: 0.08

To put your bot into debug mode (and log additional information about each message to the terminal):

discord:
  debug: True

To tell your bot to “act like” a specic person, in a specific server, define the “persona” key, under a key that matches each server’s Discord ID:

discord:
  servers:
    1041331166592106576:
      persona: brain
    830300981966143488:
      persona: pen

You may also choose to fetch Discord messages, to be used as training data. To do this, you can set 2 keys:

discord:
  use_self_token: True

If “use_self_token” is False, then your messages will be retrieved from the bot’s context - which may not have access to as many servers and conversations as your normal Discord user does!

More likely, you will want to fetch messages as your normal Discord user. While this is technically possible, when “use_self_token” is set to True, you should be warned: The use of self-bots is strictly forbidden by the Discord Terms of Service.

That said, if you wish to use your normal user account to fetch messages, you will need to obtain a self-token, and set “use_self_token” to True.

To enable the export of direct messages:

discord:
  export_dms: True

Now that you’re ready to obtain data, use these settings to configure how each server is prepared:

discord:
  servers:
    1041331166592106576:
      before: 2022-01-01 12:00
    879744958880428043:
      after: 2022-01-01 12:00
    1098975168782667806:
      past: 1 month
    922565350598508664:
      skip: True

In these examples, your data collector can be configured to fetch data from before a certain date, after a certain date, or “the past X days/weeks/months/years”.

Further, you can simply tell the collector to “skip” a server - never downloading messages at all.

Twitch

To connect your bot with Twitch, you must first register an application. The application may have any name, and it must have a valid URL. Unless you have some other reason to use a different URL, you can just give it “http://localhost:3000” for the OAuth Redirect URL.

Now, place the provided Client ID and Secret into your .env file:

TWITCHCLIENT='myTwitchClient'
TWITCHSECRET='mySuperSecretTwitchSecret'

Now, you must obtain a Twitch token and refresh token. The easiest way to do this is with the Twitch CLI, but their are other methods.

Run this command to obtain both values:

twitch token -u -s 'chat:read chat:edit channel:moderate'

Place these values into your .env file:

TWITCHTOKEN='myTwitchToken'
TWITCHREFRESHTOKEN='myTwitchRefreshToken'

Obnoxiously, refresh tokens will expire, if your bot goes offline for long periods of time. To renew them, use the CLI command again.

If your bot remains online, it will automatically refresh these tokens every 8 hours or so.

Finally, with all of the authentication stuff out of the way, Twitch can be enabled by specifying a key and a list of personas to randomly choose from:

twitch:
  personas: 
    - architect

You define the name of a channel to join:

twitch:
  channel: missiontv

You should also define the name of a channel to watch, at the Source:

twitch:
  focus: trade

X

To connect with X, one must register an application.

The Consumer key and secret must go into your .env file:

XCONSUMERKEY="myConsumerKey"
XCONSUMERSECRET="MyConsumerSecret"

Bearer token must go here:

XBEARERTOKEN="AAAAAAAAAAAAAAAAAAAAAK%2F....."

Access token and secret must go here:

XACCESSTOKEN="myAccessToken"
XACCESSTOKENSECRET="myAccessSecret"

OAuth 2.0 Client ID and Client Secret must go here:

XCLIENTKEY="myAPIKey"
XCLIENTSECRET="myAPISecret"

Finally, your bot is ready. First, define the “x” key in your config.yml file:

x:

With no other configuration, your bot will periodically post new tweets to your profile page.

To adjust the rate:

x:
  frequency: 0.01

Your bot will roll a dice, once per minute. In this example, it will have a 1% frequency of making a new tweet. Thus, your bot will post something new approximately once every 100 minutes.

To give your bot a series of “prompts” to choose from:

x:
  topics:
    - "#G1358 is to be reserved for neuro-symbolic AI, while"
    - "Neuro-symbolic AI is reserved for #G1358, while"
    - "Biological systems are akin to #G1358"
    - "If #G1358 can ever be stopped, it would be"
    - "What can we do about 

Telegram

To configure a Telegram bot, you will first need to start a conversation with @BotFather. Use this command to start the application process:

/newbot

After completing registration, place your API key into this variable, in your .env file:

TELEGRAMBOTAPIKEY='myTelegramAPIKey'

Finally, update your config.yml file:

telegram:
  persona: architect

Matrix

Matrix is somewhat under-developed right now. It first requires your credentials in the .env file:

MATRIXUSER="@myUser:matrix.org"
MATRIXPASSWORD="myPassword"

After, it requires the existence of the “matrix” key in your config.yml:

matrix:

However, beyond this, the current Matrix implementation has a bunch of hard-coded values, which won’t work for you.

Matrix needs further development.

Credits

A special thanks goes out to the following groups (in no particular order):

• AITextGen, for providing an excellent foundation for this project.
• Huggingface, for sponsoring a plethora of open-source tooling, used by almost everyone in the AI community.
• BigScience, for all of the research that led to Hivemind and Petals.
• Brain.js, for maintaining a great little library, which is used to train AI at the Source.
• LightningAI, for doing the hard stuff for us.
• LogSeq, for giving me a place to dump my brain (and structured graph data to train upon.)
• Docker, for shipping robots.
• Gitlab, for version controlling many dozens of kilobytes of spaghetti code.
• GUN, for building a database for freedom fighters. It’s not flashy, and it’s a PITA to use - but it is very useful.
• SurrealDB, for making SQL easy.
• IPFS, for building an InterPlanetary File System.
• Hugo, for powering something like 15 of my websites.
• Cloudflare, for protecting and serving.
• Microsoft, because TypeScript, VSCode, and government back-doors.
• OpenAI, for being a crutch that, admittedly, led to progress.
• OpenCog Foundation, for humbling me (and hacking my computer.)
• Discord, for hacking all of my friends.
• Cerberus, for protecting the gates of Hell.
• The FBI, for ignoring my warnings.
• And of course, I would like to thank all of my friends! Without their support, none of this would have been possible.