airoboros 2.0.21

Updated and improved implementation of the self-instruct system.

airoboros: using large language models to fine-tune large language models

This is my take on implementing the Self-Instruct paper. The approach is quite heavily modified, and does not use any human-generated seeds.

This updated implementation supports either the /v1/completions endpoint or /v1/chat/completions, which is particularly useful in that it supports gpt-4 and gpt-3.5-turbo (which is 1/10 the cost of text-davinci-003).

Key differences

• support for either /v1/completions or /v1/chat/completions APIs (which allows gpt-3.5-turbo instead of text-davinci-003, as well as gpt-4 if you have access)
• support for custom topics list, custom topic generation prompt, or completely random topics
• in-memory vector db (Chroma) for similarity comparison, which is much faster than calculating rouge score for each generated instruction
• (seemingly) better prompts, which includes injection of random topics to relate the instructions to, which creates much more diverse synthetic instructions
• asyncio producers with configurable batch size
• several "instructors", each targetting specific use-cases, such as Orca style reasoning/math, role playing, etc.
• tries to ensure the context, if provided, is relevant to the topic and contains all the information that would be necessary to respond to the instruction, and nost just a link to article/etc.
• generally speaking, this implementation tries to reduce some of the noise

Generating instructions

NEW - 2023-07-18

To better accomodate the plethora of options, the configuration has been moved to a YAML config file.

Please create a copy of example-config.yaml and configure as desired.

Once you have the desired configuration, run:

airoboros generate-instructions --config-path /path/to/config.yaml

Generating topics

NEW - 2023-07-18

Again, this is now all YAML configuration based! Please create a customized version of the YAML config file, then run:

airoboros generate-topics --config-path /path/to/config.yaml

You can override the topic_prompt string in the configuration to use a different topic generation prompt.

Support the work

https://bmc.link/jondurbin

ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11

BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf

Models (research use only):

gpt-4 versions

llama-2 base model

Latest version (2.0 / m2.0 datasets)

• airoboros-l2-7b-gpt4-2.0
• airoboros-l2-7b-gpt4-m2.0
• airoboros-l2-13b-gpt4-2.0
• airoboros-l2-13b-gpt4-m2.0

Previous generation (1.4.1 dataset)

• airoboros-l2-70b-gpt4-1.4.1
• airoboros-l2-13b-gpt4-1.4.1
• airoboros-l2-7b-gpt4-1.4.1

original llama base model

Latest version (2.0 / m2.0 datasets)

• airoboros-33b-gpt4-2.0
• airoboros-33b-gpt4-m2.0

Previous generation (1.4.1 dataset)

• airoboros-65b-gpt4-1.4
• airoboros-33b-gpt4-1.4
• airoboros-13b-gpt4-1.4
• airoboros-7b-gpt4-1.4
• older versions on HF as well

mpt-30b base model

• airoboros-mpt-30bb-gpt4-1.4

gpt-3.5-turbo versions

• airoboros-gpt-3.5-turbo-100k-7b
• airoboros-13b
• airoboros-7b

Datasets (subject to OpenAI license):

• airoboros-gpt-3.5-turbo
• airoboros-gpt4
• airoboros-gpt4-1.1
• airoboros-gpt4-1.2
• airoboros-gpt4-1.3
• airoboros-gpt4-1.4
• airoboros-gpt4-2.0 (June only GPT4)
• airoboros-gpt4-m2.0 (recommended)

Coming soon

Scripts for fine-tuning various models using the self-instruct (and human-generated) prompts.