open_llama VS llm-foundry

The RedPajama dataset was adapted by the OpenLLaMA project at UC Berkeley, creating an open-source LLaMA equivalent without Meta’s restrictions. The model's later version also included data from Falcon and StarCoder. This highlights the importance of open-source models and datasets, enabling free repurposing and innovation.

OpenLLaMA is though. https://github.com/openlm-research/open_llama

All of these are surmountable problems.

We can beat OpenAI.

We can drain their moat.

#1: 🌞 Open-source Reproduction of Meta AI’s LLaMA OpenLLaMA-13B released. (trained for 1T tokens) | 0 comments #2: 🎉 #1 on HuggingFace.co's Leaderboard Model Falcon 40B is now Free (Apache 2.0 License) | 0 comments #3: 😍 Have you seen this repo? "running LLMs on consumer-grade hardware. compatible models: llama.cpp, alpaca.cpp, gpt4all.cpp, rwkv.cpp, whisper.cpp, vicuna, koala, gpt4all-j, cerebras and many others!" | 0 comments

Trained OpenLLaMA models are from the OpenLM Research team in collaboration with Stability AI: https://github.com/openlm-research/open_llama

I can't use Llama or any model from the Llama family, due to license restrictions. Although now there's also the OpenLlama family of models, which have the same architecture but were trained on an open dataset (RedPajama, the same dataset the base model in my app was trained on). I'd love to pursue the direction of extended context lengths for on-device LLMs. Likely in a month or so, when I've implemented all the product feature that I currently have on my backlog.

https://github.com/openlm-research/open_llama#update-0615202...).

XGen-7B is probably the superior 7B model, it's trained on more tokens and a longer default sequence length (although both presumably can adopt SuperHOT (Position Interpolation) to extend context), but larger models still probably perform better on an absolute basis.

Compare it to openllama. It github doesn't have a single script on how to do anything.

OpenLLaMA models up to 13B parameters have now been trained on 1T tokens:

https://github.com/openlm-research/open_llama

The deployed LLM binary, orca mini, has 3 billion parameters. Orca mini is based on the OpenLLaMA project.

OpenLM Research released its 1T token version of OpenLLaMA 13B - the permissively licensed open source reproduction of Meta AI's LLaMA large language model. [Details].

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.