# GPTeacher
A collection of modular datasets generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer

The General-Instruct used many of the same seed prompts as alpaca, but also had specific examples of things we didnt see much in with alpaca. Such as Chain of Thought Reasoning, Logic Puzzles, Wordplay, Role Playing (lightly), and was asked to include reasoning behind and thought steps where appropriate in example responses, among other things. 
The General-Instruct dataset is about 20,000 examples with just deduplication.

Still cleaning the codegen instruct dataset, will be up when its cleaned.

Each dataset is split into 5 separate datasets, based on similarity scored cleaning. Simple dedupe only, and then range of <60% to <90% similarity cleaned sets for each.

They are all made to be compliant with Alpaca's dataset format, i.e. each has an instruction, input, and output field, should make it easier to use the same fine tune script and process as alpaca has.

Documentation on the toolformers section coming soon, we generated a dataset to use a set of predefined tools, including search, python, terminal/shell, wikipedia, wolfram, and others. More info on prompt format for inference soon..

region:us
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TAGS
#region-us 

# GPTeacher
A collection of modular datasets generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer

The General-Instruct used many of the same seed prompts as alpaca, but also had specific examples of things we didnt see much in with alpaca. Such as Chain of Thought Reasoning, Logic Puzzles, Wordplay, Role Playing (lightly), and was asked to include reasoning behind and thought steps where appropriate in example responses, among other things. 
The General-Instruct dataset is about 20,000 examples with just deduplication.

Still cleaning the codegen instruct dataset, will be up when its cleaned.

Each dataset is split into 5 separate datasets, based on similarity scored cleaning. Simple dedupe only, and then range of <60% to <90% similarity cleaned sets for each.

They are all made to be compliant with Alpaca's dataset format, i.e. each has an instruction, input, and output field, should make it easier to use the same fine tune script and process as alpaca has.

Documentation on the toolformers section coming soon, we generated a dataset to use a set of predefined tools, including search, python, terminal/shell, wikipedia, wolfram, and others. More info on prompt format for inference soon..

# GPTeacher
A collection of modular datasets generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer

The General-Instruct used many of the same seed prompts as alpaca, but also had specific examples of things we didnt see much in with alpaca. Such as Chain of Thought Reasoning, Logic Puzzles, Wordplay, Role Playing (lightly), and was asked to include reasoning behind and thought steps where appropriate in example responses, among other things. 
The General-Instruct dataset is about 20,000 examples with just deduplication.

Still cleaning the codegen instruct dataset, will be up when its cleaned.

Each dataset is split into 5 separate datasets, based on similarity scored cleaning. Simple dedupe only, and then range of <60% to <90% similarity cleaned sets for each.

They are all made to be compliant with Alpaca's dataset format, i.e. each has an instruction, input, and output field, should make it easier to use the same fine tune script and process as alpaca has.

Documentation on the toolformers section coming soon, we generated a dataset to use a set of predefined tools, including search, python, terminal/shell, wikipedia, wolfram, and others. More info on prompt format for inference soon..
TAGS
#region-us 
 # GPTeacher
A collection of modular datasets generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer

The General-Instruct used many of the same seed prompts as alpaca, but also had specific examples of things we didnt see much in with alpaca. Such as Chain of Thought Reasoning, Logic Puzzles, Wordplay, Role Playing (lightly), and was asked to include reasoning behind and thought steps where appropriate in example responses, among other things. 
The General-Instruct dataset is about 20,000 examples with just deduplication.

Still cleaning the codegen instruct dataset, will be up when its cleaned.

Each dataset is split into 5 separate datasets, based on similarity scored cleaning. Simple dedupe only, and then range of <60% to <90% similarity cleaned sets for each.

They are all made to be compliant with Alpaca's dataset format, i.e. each has an instruction, input, and output field, should make it easier to use the same fine tune script and process as alpaca has.

Documentation on the toolformers section coming soon, we generated a dataset to use a set of predefined tools, including search, python, terminal/shell, wikipedia, wolfram, and others. More info on prompt format for inference soon..
passage: TAGS
#region-us 
# GPTeacher
A collection of modular datasets generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer

The General-Instruct used many of the same seed prompts as alpaca, but also had specific examples of things we didnt see much in with alpaca. Such as Chain of Thought Reasoning, Logic Puzzles, Wordplay, Role Playing (lightly), and was asked to include reasoning behind and thought steps where appropriate in example responses, among other things. 
The General-Instruct dataset is about 20,000 examples with just deduplication.

Still cleaning the codegen instruct dataset, will be up when its cleaned.

Each dataset is split into 5 separate datasets, based on similarity scored cleaning. Simple dedupe only, and then range of <60% to <90% similarity cleaned sets for each.

They are all made to be compliant with Alpaca's dataset format, i.e. each has an instruction, input, and output field, should make it easier to use the same fine tune script and process as alpaca has.

Documentation on the toolformers section coming soon, we generated a dataset to use a set of predefined tools, including search, python, terminal/shell, wikipedia, wolfram, and others. More info on prompt format for inference soon..