---
license: cc-by-nc-4.0
datasets:
- jondurbin/airoboros-gpt4-1.2
---

### Overview

This is a qlora fine-tuned 33b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros

This is mostly an extension of [1.1](https://huggingface.co/jondurbin/airoboros-33b-gpt4-1.1) with thousands of new training data and an update to allow "PLAINFORMAT" at the end of coding prompts to just print the code without backticks or explanations/usage/etc.

The dataset used to fine-tune this model is available [here](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.2), with a specific focus on:
- coding
- math/reasoning (using orca style ELI5 instruction/response pairs)
- trivia
- role playing
- 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 [qlora](https://github.com/jondurbin/qlora), which among other things was updated to use a slightly modified vicuna template to be compatible with the 7b/13b versions:

```
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).

### Usage

To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a `--no-history` option to prevent input tokenization errors.
```
pip install git+https://github.com/jondurbin/FastChat
```

Be sure you are pulling the latest branch!

Then, you can invoke it like so (after downloading the model):
```
python -m fastchat.serve.cli \
  --model-path airoboros-33b-gpt4-1.2 \
  --temperature 0.5 \
  --max-new-tokens 2048 \
  --no-history
```

Alternatively, please check out TheBloke's quantized versions:

- https://huggingface.co/TheBloke/airoboros-33B-gpt4-1.2-GPTQ
- https://huggingface.co/TheBloke/airoboros-33B-gpt4-1.2-GGML

### Coding updates from gpt4/1.1:

I added a few hundred instruction/response pairs to the training data with "PLAINFORMAT" as a single, all caps term at the end of the normal instructions, which produce plain text output instead of markdown/backtick code formatting.

It's not guaranteed to work all the time, but mostly it does seem to work as expected.

So for example, instead of:
```
Implement the Snake game in python.
```

You would use:
```
Implement the Snake game in python.  PLAINFORMAT
```

### Other updates from gpt4/1.1:

- Several hundred role-playing data.
- A few thousand ORCA style reasoning/math questions with ELI5 prompts to generate the responses (should not be needed in your prompts to this model however, just ask the question).
- Many more coding examples in various languages, including some that use specific libraries (pandas, numpy, tensorflow, etc.)

### 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.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-33b-gpt4-1.2)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 52.2   |
| ARC (25-shot)         | 64.42          |
| HellaSwag (10-shot)   | 84.93    |
| MMLU (5-shot)         | 60.35         |
| TruthfulQA (0-shot)   | 49.18   |
| Winogrande (5-shot)   | 77.51   |
| GSM8K (5-shot)        | 9.78        |
| DROP (3-shot)         | 19.21         |