
## Overview

This is a brand new dataset, with nothing copied from the 1.* series of airoboros, using only the June version of gpt-4.

I used the latest overhaul of the airoboros python tool to generate the data, which has several "instructions", where an instructor is a specific prompt/response generator.

The instructors include:

- agent/function style prompts, which generate a function name and args based on the provided input and available functions in either JSON or YAML format
- model/scenario/character cards, to help build random descriptive cards based on a template
- coding and scripting
- contextual q&a with the specific context obedient formatting
- chain-of-thought, i.e. for a given question, generate ~3 possible solutions, rank them, select the best
- experience, e.g. guided meditations or describing a walk through a forest
- general - completely random tasks not specifically targetting any type of task, using a random list of topics
- jokes - still horrible, but at least there are some now
- orca, i.e. "Solve [problem], provide step-by-step reasoning."
- execution planning, specifically the reWOO style, where you describe a list of available functions and it will generate a plan to make use of them
- riddles - still not great either, but present
- roleplay
- songs
- wordgames, e.g. give me a list of 28 words that start with 'cr'
- creative writing

**Is it better than 1.4?**

Not necessarily.  It has some extra functionality that didn't exist before, but if you want to be sure you don't lose much, check out m2.0, with is a merge of 1.4.1 and 2.0:
https://huggingface.co/datasets/jondurbin/airoboros-gpt4-m2.0

The main point here was to test the June version of gpt-4 against the March version (and add new prompt types).

### Category breakdown

![chart](breakdown.png)


### Configuration for airoboros

https://gist.github.com/jondurbin/65df002c16560899e05365ca6cbd43e3

### Licence and usage restrictions

The data was generated by gpt-4 via OpenAI API calls.

The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI

- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant of copyrighted or otherwise unallowable licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2

I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.

Your best bet is probably to avoid using this commercially due to the OpenAI API usage.

Either way, by using this model, you agree to completely idemnify me from any and all license related issues.

Attribution would be nice if you use some or all of the data.
license:other region:us
{"license": "other"}
TAGS
#license-other #region-us 


## Overview

This is a brand new dataset, with nothing copied from the 1.* series of airoboros, using only the June version of gpt-4.

I used the latest overhaul of the airoboros python tool to generate the data, which has several "instructions", where an instructor is a specific prompt/response generator.

The instructors include:

- agent/function style prompts, which generate a function name and args based on the provided input and available functions in either JSON or YAML format
- model/scenario/character cards, to help build random descriptive cards based on a template
- coding and scripting
- contextual q&a with the specific context obedient formatting
- chain-of-thought, i.e. for a given question, generate ~3 possible solutions, rank them, select the best
- experience, e.g. guided meditations or describing a walk through a forest
- general - completely random tasks not specifically targetting any type of task, using a random list of topics
- jokes - still horrible, but at least there are some now
- orca, i.e. "Solve [problem], provide step-by-step reasoning."
- execution planning, specifically the reWOO style, where you describe a list of available functions and it will generate a plan to make use of them
- riddles - still not great either, but present
- roleplay
- songs
- wordgames, e.g. give me a list of 28 words that start with 'cr'
- creative writing

Is it better than 1.4?

Not necessarily.  It has some extra functionality that didn't exist before, but if you want to be sure you don't lose much, check out m2.0, with is a merge of 1.4.1 and 2.0:
URL

The main point here was to test the June version of gpt-4 against the March version (and add new prompt types).

### Category breakdown

!chart


### Configuration for airoboros

URL

### Licence and usage restrictions

The data was generated by gpt-4 via OpenAI API calls.

The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI

- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant of copyrighted or otherwise unallowable licensing in the first place
- other work using the self-instruct method, e.g. the original here: URL released the data and model as apache-2

I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.

Your best bet is probably to avoid using this commercially due to the OpenAI API usage.

Either way, by using this model, you agree to completely idemnify me from any and all license related issues.

Attribution would be nice if you use some or all of the data.
## Overview

This is a brand new dataset, with nothing copied from the 1.* series of airoboros, using only the June version of gpt-4.

I used the latest overhaul of the airoboros python tool to generate the data, which has several "instructions", where an instructor is a specific prompt/response generator.

The instructors include:

- agent/function style prompts, which generate a function name and args based on the provided input and available functions in either JSON or YAML format
- model/scenario/character cards, to help build random descriptive cards based on a template
- coding and scripting
- contextual q&a with the specific context obedient formatting
- chain-of-thought, i.e. for a given question, generate ~3 possible solutions, rank them, select the best
- experience, e.g. guided meditations or describing a walk through a forest
- general - completely random tasks not specifically targetting any type of task, using a random list of topics
- jokes - still horrible, but at least there are some now
- orca, i.e. "Solve [problem], provide step-by-step reasoning."
- execution planning, specifically the reWOO style, where you describe a list of available functions and it will generate a plan to make use of them
- riddles - still not great either, but present
- roleplay
- songs
- wordgames, e.g. give me a list of 28 words that start with 'cr'
- creative writing

Is it better than 1.4?

Not necessarily.  It has some extra functionality that didn't exist before, but if you want to be sure you don't lose much, check out m2.0, with is a merge of 1.4.1 and 2.0:
URL

The main point here was to test the June version of gpt-4 against the March version (and add new prompt types). ### Category breakdown

!chart ### Configuration for airoboros

URL ### Licence and usage restrictions

The data was generated by gpt-4 via OpenAI API calls.

The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI

- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant of copyrighted or otherwise unallowable licensing in the first place
- other work using the self-instruct method, e.g. the original here: URL released the data and model as apache-2

I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.

Your best bet is probably to avoid using this commercially due to the OpenAI API usage.

Either way, by using this model, you agree to completely idemnify me from any and all license related issues.

Attribution would be nice if you use some or all of the data.
TAGS
#license-other #region-us 
 ## Overview

This is a brand new dataset, with nothing copied from the 1.* series of airoboros, using only the June version of gpt-4.

I used the latest overhaul of the airoboros python tool to generate the data, which has several "instructions", where an instructor is a specific prompt/response generator.

The instructors include:

- agent/function style prompts, which generate a function name and args based on the provided input and available functions in either JSON or YAML format
- model/scenario/character cards, to help build random descriptive cards based on a template
- coding and scripting
- contextual q&a with the specific context obedient formatting
- chain-of-thought, i.e. for a given question, generate ~3 possible solutions, rank them, select the best
- experience, e.g. guided meditations or describing a walk through a forest
- general - completely random tasks not specifically targetting any type of task, using a random list of topics
- jokes - still horrible, but at least there are some now
- orca, i.e. "Solve [problem], provide step-by-step reasoning."
- execution planning, specifically the reWOO style, where you describe a list of available functions and it will generate a plan to make use of them
- riddles - still not great either, but present
- roleplay
- songs
- wordgames, e.g. give me a list of 28 words that start with 'cr'
- creative writing

Is it better than 1.4?

Not necessarily.  It has some extra functionality that didn't exist before, but if you want to be sure you don't lose much, check out m2.0, with is a merge of 1.4.1 and 2.0:
URL

The main point here was to test the June version of gpt-4 against the March version (and add new prompt types). ### Category breakdown

!chart ### Configuration for airoboros

URL ### Licence and usage restrictions

The data was generated by gpt-4 via OpenAI API calls.

The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI

- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant of copyrighted or otherwise unallowable licensing in the first place
- other work using the self-instruct method, e.g. the original here: URL released the data and model as apache-2

I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.

Your best bet is probably to avoid using this commercially due to the OpenAI API usage.

Either way, by using this model, you agree to completely idemnify me from any and all license related issues.

Attribution would be nice if you use some or all of the data.
passage: TAGS
#license-other #region-us 
## Overview

This is a brand new dataset, with nothing copied from the 1.* series of airoboros, using only the June version of gpt-4.

I used the latest overhaul of the airoboros python tool to generate the data, which has several "instructions", where an instructor is a specific prompt/response generator.

The instructors include:

- agent/function style prompts, which generate a function name and args based on the provided input and available functions in either JSON or YAML format
- model/scenario/character cards, to help build random descriptive cards based on a template
- coding and scripting
- contextual q&a with the specific context obedient formatting
- chain-of-thought, i.e. for a given question, generate ~3 possible solutions, rank them, select the best
- experience, e.g. guided meditations or describing a walk through a forest
- general - completely random tasks not specifically targetting any type of task, using a random list of topics
- jokes - still horrible, but at least there are some now
- orca, i.e. "Solve [problem], provide step-by-step reasoning."
- execution planning, specifically the reWOO style, where you describe a list of available functions and it will generate a plan to make use of them
- riddles - still not great either, but present
- roleplay
- songs
- wordgames, e.g. give me a list of 28 words that start with 'cr'
- creative writing

Is it better than 1.4?

Not necessarily.  It has some extra functionality that didn't exist before, but if you want to be sure you don't lose much, check out m2.0, with is a merge of 1.4.1 and 2.0:
URL

The main point here was to test the June version of gpt-4 against the March version (and add new prompt types).### Category breakdown

!chart### Configuration for airoboros

URL