Overview

This is a fine-tuned 13b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros

The dataset used to fine-tune this model is available here, with a specific focus on:

• trivia
• math/reasoning (although it still sucks)
• coding
• multiple choice and fill-in-the-blank
• context-obedient question answering
• theory of mind
• misc/general

This model was fine-tuned with a fork of FastChat, and therefore uses the standard vicuna template:

A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. USER: [prompt] ASSISTANT:

So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).

NOTE: an earlier version claimed context length of 4096 - this did not work! I modified the code to train with with 4096, and several instructions are beyond 2048. I tested a few prompts beyond 2048, and they seem to produce fairly coherent responses with increased context length for a couple hundred tokens beyond 2048, but I did not properly test up to 4096. As it turns out, it would appear without a massive fine-tune of the base model on a larger context window, this won't work. Sorry!

The most important bit, to me, is the context obedient question answering support, without extensive prompt engineering.

Usage

The easiest way to get started is to use my fork of FastChat, which is mostly the same but allows for the increased context length and adds support for multi-line inputs:

pip install git+https://github.com/jondurbin/FastChat

Then, you can invoke it like so (after downloading the model):

python -m fastchat.serve.cli
 --model-path airoboros-13b-gpt4 \
 --temperature 0.5 \
 --no-history

Context obedient question answering

By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.

The format for a closed-context prompt is as follows:

BEGININPUT
BEGINCONTEXT
url: https://some.web.site/123
date: 2023-06-01
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION

It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.

The only prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!

I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.

• BEGININPUT - denotes a new input block
• BEGINCONTEXT - denotes the block of context (metadata key/value pairs) to associate with the current input block
• ENDCONTEXT - denotes the end of the metadata block for the current input
• [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
• ENDINPUT - denotes the end of the current input block
• [repeat as many input blocks in this format as you want]
• BEGININSTRUCTION - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
• [instruction(s)]
• ENDINSTRUCTION - denotes the end of instruction set

It sometimes works without ENDINSTRUCTION, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.

Here's a trivial, but important example to prove the point:

BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION

And the response:

Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123

The prompt itself should be wrapped in the vicuna1.1 template if you aren't using fastchat with the conv-template vicuna_v1.1 as described:

USER: BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
 ASSISTANT:

NOTE: Thanks /u/tareq_al_muntasir for testing and finding an issue with many questions and answer pairs in the context. If you ask a question of a document with question answer pairs, it may continue generating beyond your actual question. You can "fix" it by replacing question marks with periods in the input texts. Or, you might be able to add a preamble to the prompt, like "Be sure to only respond to the instructions in the BEGININSTRUCTION block.

Other stuff

Better coding

Example:

Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate

Word games / trivia

USER: This anagram of "silent" is what you do when paying attention and hearing someone.
ASSISTANT: listen

USER: Give me a list of 7 words that start with EN
ASSISTANT: 1. Endlessly
2. Enthusiastically
3. Enormously
4. Enveloping
5. Enduring
6. Enlightening
7. Encouraging

Multiple choice

USER: Which of the following astronomical objects is composed primarily of ice and rock, and is located in the Kuiper Belt?

A) Mercury
B) Venus
C) Pluto
D) Jupiter
ASSISTANT: [C, Pluto]

Writing

Usage and License Notices

All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:

• the base model is LLaMa, which has it's own special research license
• the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai

So, to reiterate: this model (and datasets) cannot be used commercially.