llm-foundry VS laion.ai

Related comment from gwern: https://news.ycombinator.com/item?id=38438859

Also - why qlora rather than a full finetune? Using LambdaLabs, It'd cost roughly the same as your quote. Cheaper I think if you're willing to gamble with fp8: https://github.com/mosaicml/llm-foundry/tree/main/scripts/tr.... And fewer hyperparameters to tune as well

Traditionally, training runs can "explode" and fail, but there are methods to incrementally back them up and resume when that happens, see https://www.mosaicml.com/blog/mpt-7b

MosaicML released the MPT-7B model, which has a context of 60k tokens, thanks to the ALiBi position encoding.

Most AI runners just ignore licensing and run LLaMA finetunes.

But if you want to avoid the non commercial LLaMA license, you have 3 good options for a base model.

- OpenLlama 13B

- MPT 30B

- Falcon 40B

Of these, Falcon 40B is very difficult to run (slow in 4 bit, basically requires a professional GPU, no good cpu offloading yet).

OpenLLaMA 13B only supports a context size of 2048 as of today... But that could change soon.

So you probably want MPT instruct 30B, specifically this one:

https://huggingface.co/TheBloke/mpt-30B-instruct-GGML

As the page says, you can try it out on a decent PC of your own with the OpenCL build of KoboldCPP. Change it to "instruct" mode, use the template on the page, offload as many layers as you can to your PC's dGPU, and run it in instruct mode. It may already work for your summarization needs.

If not, you can finetune it with MPT's code and summarization d

https://github.com/mosaicml/llm-foundry

Or train OpenLLaMA 13B with SuperHOT + summarization data using QLORA.

BTW. they finally merged a MPT patch to work with lora: https://github.com/mosaicml/llm-foundry/issues/304

Did you run into this error while using qlora on MPT30b?: https://github.com/mosaicml/llm-foundry/issues/413

Yes? Their github is under Apache, their base model is under apache, the training data is not theirs, and they provide scripts how to convert it for the pretrain step. They have scripts for pretraining and finetuning as well. Basically for everything.

mosaicml/llm-foundry: LLM training code for MosaicML foundation models (github.com)

MosaicML, a company that provides a platform for training and deploying large language models (LLMs), has recently released its second open-source foundation model called MPT-30B. The model is part of the MosaicML Foundation Series and comes after the smaller MPT-7B model that was launched in May 2023.

LAION (Large-scale Artificial Intelligence Open Network), a German non-profit established in 2020, is dedicated to advancing open-source models and datasets (primarily under Apache 2 and MIT licenses) to foster open research and the evolution of benevolent AI. Their datasets, encompassing both images and text, have been pivotal in the training of renowned text-to-image models like Stable Diffusion.

> there is going to be a "pre-GPT" internet training set from 2022

Well, yeah, there are several here, and I think all the major image generators are using some combination of them as their starting points: https://laion.ai/

> As AI increases as an overall % of all online posts and activity it will death spiral on model quality.

Nope, it will just mean that it will be more expensive to source additional training data on top of the massive trove of existing "clean" (from intentional poisoning) data (much of which isn't perfectly captioned and human work on improving captioning can improve its utility in model training, as can more advanced models with more advanced text encoders, etc.)

If poisoning was widespread, it wouldn't impact "big model" quality much -- they aren't grabbing new random data on the internet for continuous training. It might drive up the expense of community fine tuning, which often does depend on sourcing representative imagery for target styles or concepts from, among other places, the internet.

Also don't forget https://laion.ai/ from Germany. They focus more on datasets, but still.

I think the weird thing about this is that it's completely true right now but in X months it may be totally outdated advice.

For example, efforts like OpenMOE https://github.com/XueFuzhao/OpenMoE or similar will probably eventually lead to very competitive performance and cost-effectiveness for open source models. At least in terms of competing with GPT-3.5 for many applications.

Also see https://laion.ai/

I also believe that within say 1-3 years there will be a different type of training approach that does not require such large datasets or manual human feedback.

Billions. MJ's initial training dataset was from LAION: https://laion.ai/ . Not sure which version, and I am pretty sure additional data has been added since MJ v1, but MJ doesn't release anything more exact. However my guess is: more billions, lol.

Missing LAION and OpenAssistant: https://laion.ai/

As far as I am concerned weights of llama are not allowed for commercial use, but if you are willing to do full training and change it's all weights it would probably be fine. There was a discussion on this topic on forums and no one was sure, you can research it. Also you can take a look at laion.ai and dolly from databricks, they are open source and are allowed for commercial use, if they meet your needs.

that's OpenAssistant's / LAION AI model, HuggingFace provided the infrastructure.