--- 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.