metadata

inference: false
license: other

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Jon Durbin's Airoboros MPT 30B GPT4 1.4 GGML

These files are GGML format model files for Jon Durbin's Airoboros MPT 30B GPT4 1.4.

Please note that these GGMLs are not compatible with llama.cpp, or currently with text-generation-webui. Please see below for a list of tools known to work with these model files.

KoboldCpp just added GPU accelerated (OpenCL) support for MPT models, so that is the client I recommend using for these models.

Note: Please make sure you're using KoboldCpp version 1.32.3 or later, as a number of MPT-related bugs are fixed.

Repositories available

Prompt template

USER: prompt
ASSISTANT:

A note regarding context length: 8K

The base model has an 8K context length. KoboldCpp supports 8K context if you manually set it to 8K by adjusting the text box above the slider:

It is currently unknown as to increased context is compatible with other MPT GGML clients.

If you have feedback on this, please let me know.

Compatibilty

These files are not compatible with text-generation-webui, llama.cpp, or llama-cpp-python.

Currently they can be used with:

KoboldCpp, a powerful inference engine based on llama.cpp, with good UI and GPU accelerated support for MPT models: KoboldCpp
The ctransformers Python library, which includes LangChain support: ctransformers
The LoLLMS Web UI which uses ctransformers: LoLLMS Web UI
rustformers' llm
The example mpt binary provided with ggml

As other options become available I will endeavour to update them here (do let me know in the Community tab if I've missed something!)

Tutorial for using LoLLMS Web UI

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
airoboros-mpt-30b-gpt4.ggmlv0.q4_0.bin	q4_0	4	16.85 GB	19.35 GB	Original llama.cpp quant method, 4-bit.
airoboros-mpt-30b-gpt4.ggmlv0.q4_1.bin	q4_1	4	18.73 GB	21.23 GB	Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
airoboros-mpt-30b-gpt4.ggmlv0.q5_0.bin	q5_0	5	20.60 GB	23.10 GB	Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
airoboros-mpt-30b-gpt4.ggmlv0.q5_1.bin	q5_1	5	22.47 GB	24.97 GB	Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
airoboros-mpt-30b-gpt4.ggmlv0.q8_0.bin	q8_0	8	31.83 GB	34.33 GB	Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

Discord

For further support, and discussions on these models and AI in general, join us at:

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Thanks, and how to contribute.

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Original model card: Jon Durbin's Airoboros MPT 30B GPT4 1.4

Overview

This is a test of qlora fine-tuning of the mpt-30b model, with 5 epochs.

qlora compatible model: https://huggingface.co/jondurbin/mpt-30b-qlora-compatible

My fork of qlora with mpt-30b support: https://github.com/jondurbin/qlora

Differences in the qlora scripts:

requires adding --mpt True for mpt-based models
uses --num_train_epochs instead of --max_steps
uses airoboros prompt format (mostly 1:1 with vicuna) rather than alpaca, and expects an input file in JSONL format with "instruction" and "response"

I think there's a bug in gradient accumulation, so if you try this, maybe set gradient accumulation steps to 1

See the mpt-30b-qlora-compatible model card for training details.

This is not as high quality as the llama-33b versions unfortunately, but I don't have a great answer as to why. Perhaps there are fewer forward layers that can be tuned?

License and usage

This is a real gray area, here's why:

the dataset was generated with gpt-4, via https://github.com/jondurbin/airoboros
the ToS for openai API usage has a clause preventing the output from being used to train a model that competes with OpenAI
- what does compete actually mean here?
- a 30b parameter model isn't anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
the training data used in essentially all large language models includes a significant of copyrighted or otherwise unallowable licensing in the first place
other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2

I am purposingly not placing a license on here because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly. Your best bet is probably to avoid using this commercially, especially since it didn't perform quite as well as expected using qlora.