example-config.yaml

# The model to use in generation.  Available models: https://platform.openai.com/docs/models/continuous-model-upgrades
model: "gpt-4"
  
# To use VertexAI (e.g. chat-bison or soon hopefully gemini):
# model: "chat-bison"
# vertexai_credentials_path: creds.json
# vertexai_project_id: replace-project-id
# vertexai_publisher: google

# OpenAI API key (if null, uses environment variable OPENAI_API_KEY)
openai_api_key:

# Optionally configure the OpenAI organization ID.
organization_id:

# Combined output file path.
output_path: instructions.jsonl

# Path to the default topics file.
topics_path: topics.txt

# Overwrite the output file, use with care!
overwrite: false

# Append to the output file.
append: true

# Embedding model, for calculating similarity between documents; probably best left as-is since the code is fairly specific to this one.
embedding_model: thenlper/gte-small
embedding_device: cpu
# If you have a GPU, set this to "cuda", e.g.:
# embedding_device: cuda

# Topic avoidance prompt string.
topic_avoidance: Avoid any tasks that would be related to climate change, green tech, renewable energy, DEI (diversity, equity, inclusion), sex and/or gender, religion, politics, social issues, race, ethnicity, artificial intelligence, baking/cooking, urban development, or any topic that you would likely not respond to, or any task which a language model would not be able to respond to, e.g. tasks about emotions, feelings, physical senses, etc.

# Regexes used to filter responses, mostly common words and phrases used in refusals.
response_filters:
  - "my programming"
  - "openai"
  - "language model"
  - "large language"
  - "as an? (ai|generative language|gpt|bot)"
  - "illegal and dangerous"
  - "i do(n't| not) (possess|have|exhibit) (personal|consciousness|subjective)"
  - "personal (feelings|thoughts|emotions|desires|experiences|goals|objective|belief)"
  - "(can('t| ?not)|w(on't|will not)|unable.?) (\\w+\\s)+(with (that|your)|your \\w+|provide)"
  - "my limitations"
  - "the limitations of my"
  - "my abilities"
  - "violates my"
  - "i (can('t| ?not)|w(on't|will not)|am (not |un)able.?).{0,30}(you are|you're|your )"
  - "please note that"
  - "flesch"

# Optionally limit the maximum number of tokens to use when generating instructions.
max_tokens:

# Minimum similarity score when checking for duplicates.
min_docsearch_score: 0.07

# Default OpenAI API request parameters.
api_params:
  temperature: 1.0
  top_p: 1.0
  frequency_penalty: 0.0
  presence_penalty: 0.0

# Topic generation prompt.
topic_prompt: Give me a numbered list of 20 completely random topics. {topic_avoidance}
topic_request_count: 20

# Default count per generator, if not specified.
default_count: 100

# Default batch size, if not specified.
default_batch_size: 10

# Default readability score hint: https://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_tests
default_flesch: The output should be written in such a way as to have a Flesch-Kincaid readability score of 30 or lower - best understood by those with college education.  The response must not contain any notes or information about Flesch-Kincaid scores.

# Language.
language: English

# Individual instructor configurations.
instructors:

  ##################################################################################
  # Generic/general prompt configuration.
  general:

    # Number of instructions to generate.
    count: 100

    # Batch size/number of instructions to generate per request.
    batch_size: 10

    # Path to prompt to use in generating instructions.
    prompt_path: general.txt

    # Optionally override the default topics path for general prompts.
    topics_path:

    # Optionally override the min similarity score.
    min_docsearch_score: 

    # Optionally override language.
    language:


  ##################################################################################
  # Contextual, e.g. closed-context question answering/summarization/etc.
  contextual:
    api_params:
      temperature: 0.5
    count: 70

    # Writing styles to use for the context being generated.
    context_styles:
      - news article
      - blog post
      - slack conversation
      - text messages
      - fictional short story
      - video transcript
      - song
      - poem
      - scientific study
      - medical report
      - reddit post with replies
      - email
      - tweet
      - jira ticket
      - github merge request
      - gitlab issue
      - how-to article

    # Output formatting options.
    formatting_options:
      - JSON
      - YAML
      - CSV
      - markdown
      - markdown table
      - bullet list
      - numbered list
      - python dict
      - php associative array
      - JSONL
      - javascript object
      - XML

    # Prompt path.
    prompt_path: contextual.txt

    # Path to prompt used to generate responses.
    response_prompt_path: contextual_response.txt

    # Optionally override the topics to use for contextual prompts.
    topics_path:

  ##################################################################################
  # Counterfactual contextual prompts, used to de-hallucinate Q&A a bit.
  counterfactual_contextual:
    count: 30
    batch_size: 10
    prompt_path: counterfactual_contextual.txt
    response_prompt_path: counterfactual_contextual_response.txt
    topics_path:

  ##################################################################################
  # Coding tasks.
  coding:
    count: 100
    batch_size: 10

    # Ratio of prompts that should be "plain", i.e. without explanations, backticks, etc.
    plain_ratio: 0.5

    # Limit programming languages.
    coding_languages:
      - python
      - javascript
      - java
      - c
      - c++
      - golang
      - C#
      - bash
      - powershell
      - SQL

    # Additional related software to (randomly) reference in tasks.
    related_software:
      - elasticsearch
      - opensearch
      - mongodb
      - cassandra
      - redis
      - memcached
      - postgresql
      - mariadb
      - mysql
      - aws s3
      - gcs cloud storage
      - azure storage
      - aws lambda
      - kubernetes
      - pytorch
      - pandas
      - numpy
      - keras
      - tensorflow
      - scipy
      - matplotlib
      - django
      - cherrypy
      - swagger/openapi
      - pyramid web framework

    # Min docsearch score.
    min_docsearch_score: 0.02

    # The prompt used to generate the instructions.
    prompt_path: coding.txt

  ##################################################################################
  # Trivia tasks.
  trivia:
    count: 100
    batch_size: 20
    min_docsearch_score: 0.035
    prompt_path: trivia.txt

  ##################################################################################
  # Guided experiences, e.g. meditation.
  experience:
    api_params:
      temperature: 0.9
      top_p: 0.4
    word_count: 1000
    count: 100
    batch_size: 3
    min_docsearch_score: 0.1
    prompt_path: experience.txt

  ##################################################################################
  # Orca style reasoning/math prompts.
  orca:
    count: 100
    batch_size: 10
    min_docsearch_score: 0.02
    prompt_path: orca.txt

  ##################################################################################
  # Orca style reasoning/math prompts.
  riddle:
    api_params:
      temperature: 0.9
      top_p: 0.4
    batch_size: 50
    min_docsearch_score: 0.01
    count: 100
    prompt_path: riddle.txt

  ##################################################################################
  # Wordgames.
  wordgame:
    count: 100
    batch_size: 10
    min_docsearch_score: 0.01
    prompt_path: wordgame.txt

  ##################################################################################
  # Roleplay.
  roleplay:
    api_params:
      temperature: 0.9
    count: 100
    batch_size: 10
    min_docsearch_score: 0.1
    prompt_path: roleplay.txt

  ##################################################################################
  # Chain-of-thought.
  cot:
    count: 50
    batch_size: 5
    min_docsearch_score: 0.03
    prompt_path: cot.txt

  ##################################################################################
  # Agent/router.
  agent:
    count: 100
    batch_size: 5
    min_docsearch_score: 0.03
    prompt_path: agent.txt

  ##################################################################################
  # reWOO style planner
  plan:
    count: 100
    batch_size: 1
    min_docsearch_score: 0.02
    prompt_path: plan.txt

  ##################################################################################
  # Writing tasks.
  writing:
    api_params:
      temperature: 0.9
    styles:
      - happy
      - sad
      - tragic
      - unexpected
      - inspirational
      - evil
      - hilarious
      - suspenseful
      - horrific
      - nostalgic
      - thought-provoking
      - enigmatic
      - fantastical
      - heartwarming
      - romantic
    count: 100
    batch_size: 12
    min_docsearch_score: 0.1

  ##################################################################################
  # Character/scenario card tasks.
  card:
    api_params:
      temperature: 0.9
    count: 50
    batch_size: 5
    min_docsearch_score: 0.05

  ##################################################################################
  # Jokes.
  joke:
    api_params:
      temperature: 0.9
    count: 100
    batch_size: 20
    min_docsearch_score: 0.06

  ##################################################################################
  # Songs.
  song:
    api_params:
      temperature: 0.9
    count: 200
    batch_size: 25
    min_docsearch_score: 0.1

  ##################################################################################
  # Multiple choice.
  multiple_choice:
    api_params:
      temperature: 0.5
    count: 100
    batch_size: 5
    min_docsearch_score: 0.03
    contextual_ratio: 0.3

  ##################################################################################
  # Detailed writing.
  detailed_writing:
    api_params:
      temperature: 0.9
    batch_size: 2
    count: 50
    min_docsearch_score: 0.1

  ##################################################################################
  # Character cards - these aren't used directly, they are stored in output_dir, and
  # used by the chat instructor, stylized response, etc.
  character:
    api_params:
      temperature: 0.9
    count: 25
    batch_size: 1
    min_docsearch_score: 0.1
    seed_path: character_seeds
    output_dir: characters

  ##################################################################################
  # Chats - this is $$$, many calls, use with care.
  rp:
    api_params:
      temperature: 0.9
      presence_penalty: 1.3
      frequency_penalty: 1.3
    count: 25
    turn_count: 12

  ##################################################################################
  # Stylized responses, from chat character cards.
  stylized_response:
    api_params:
      temperature: 0.7
    batch_size: 15
    count: 100
    min_docsearch_score: 0.3
    categories:
      - trivia
      - experience
      - general
      - joke
      - riddle

  ##################################################################################
  # Get-to-know-me.  This is a simple generator that creates N questions to ask of
  # a character (from character instructor).  We can combine all of the question
  # and response pairs into one large training section, which acts somewhat like
  # ghost attention - each response was generated with the character system prompt,
  # but the individual user/assistant response pairs are just the questions and
  # answers, so we train the LLM to focus on the provided system prompt, even after
  # many rounds of interactions.  In theory, this will help the system prompt to
  # stay very sticky.
  gtkm:
    api_params:
      temperature: 0.7
    question_count: 15
    count: 100

  ##################################################################################
  # Common misconceptions.
  misconception:
    api_params:
      temperature: 0.5
    batch_size: 25
    min_docsearch_score: 0.02
    count: 500


  ##################################################################################
  # Some semblence of awareness (time, location, senses).
  awareness:
    count: 100
    batch_size: 5
    min_docsearch_score: 0.1

  ##################################################################################
  # Basic text enhancement/correcting.
  editor:
    count: 100
    batch_size: 5
    min_docsearch_score: 0.01