I only wanted to generate the multiple choices but these also generated:
{"instruction": "Analyze different genres of literature and categorize the following books accordingly: \"Pride and Prejudice\" by Jane Austen, \"1984\" by George Orwell, \"To Kill a Mockingbird\" by Harper Lee, and \"The Lord of the Rings\" by J.R.R. Tolkien. Provide a bulleted list indicating the genre for each book.", "response": "- \"Pride and Prejudice\" by Jane Austen: Romance, Classic Literature\n- \"1984\" by George Orwell: Dystopian Fiction, Political Fiction\n- \"To Kill a Mockingbird\" by Harper Lee: Coming-of-Age, Southern Gothic\n- \"The Lord of the Rings\" by J.R.R. Tolkien: Fantasy, Epic Literature", "category": "general"}
and config is
# The model to use in generation. Available models: https://platform.openai.com/docs/models/continuous-model-upgrades
model: "gpt-3.5-turbo"
# 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: false
# 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: 0.35
# Default OpenAI API request parameters.
api_params:
temperature: 0.7
top_p: 0.5
frequency_penalty: 0.0
presence_penalty: 2
# 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:
# OpenAI settings.
api_params:
temperature: 0.7
top_p: 0.5
frequency_penalty: 0.0
presence_penalty: 2
# 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:
# api_params: {}
# 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: 0.04
# # The prompt used to generate the instructions.
# prompt_path: coding.txt
##################################################################################
# # Trivia tasks.
# trivia:
# count: 100
# batch_size: 20
# min_docsearch_score: 0.05
# prompt_path: trivia.txt
# ##################################################################################
# # Guided experiences, e.g. meditation.
# experience:
# api_params:
# temperature: 0.9
# top_p: 0.4
# count: 100
# min_docsearch_score: 0.15
# prompt_path: experience.txt
# ##################################################################################
# # Orca style reasoning/math prompts.
# orca:
# count: 100
# batch_size: 10
# min_docsearch_score: 0.05
# 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: 0.03
# prompt_path: wordgame.txt
# ##################################################################################
# # Roleplay.
# roleplay:
# api_params:
# temperature: 0.9
# count: 100
# batch_size: 10
# min_docsearch_score: 0.15
# prompt_path: roleplay.txt
# ##################################################################################
# # Chain-of-thought.
# cot:
# count: 50
# batch_size: 5
# min_docsearch_score: 0.05
# 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: 0.03
# 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: 0.35
# ##################################################################################
# # 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: 0.1
# ##################################################################################
# # Songs.
# song:
# api_params:
# temperature: 0.9
# count: 200
# batch_size: 25
# min_docsearch_score: 0.25
##################################################################################
# Multiple choice.
multiple_choice:
api_params:
temperature: 0.3
count: 20
batch_size: 5
min_docsearch_score: 0.1
contextual_ratio: 0.2
# ##################################################################################
# # Detailed writing.
# detailed_writing:
# api_params:
# temperature: 1.0
# batch_size: 4
# count: 100
# min_docsearch_score: 0.1
I only wanted to generate the multiple choices but these also generated:
and config is