evadb VS llm-foundry

Hey friends!

We have built an app for getting insights about your favorite GitHub community using large language models.

The app uses LLMs to analyze the GitHub profiles of users who have starred the repository, capturing key details like the topics they are interested in. It takes screenshots of the stargazer's GitHub webpage, extracts text using an OCR model, and extracts insights embedded in the extracted text using LLMs.

This app is inspired by the “original” Stargazers app written by Spencer Kimball (CEO of CockroachDB). While the original app exclusively used the GitHub API, this LLM-powered app built using EvaDB additionally extracts insights from unstructured data obtained from the stargazers’ webpages.

Our analysis of the fast-growing GPT4All community showed that the majority of the stargazers are proficient in Python and JavaScript, and 43% of them are interested in Web Development. Web developers love open-source LLMs!

We found that directly using GPT-4 to generate the “golden” table is super expensive — costing $60 to process the information of 1000 stargazers. To maintain accuracy while also reducing cost, we set up an LLM model cascade in a SQL query, running GPT-3.5 before GPT-4, that lowers the cost to $5.5 for analyzing 1000 GitHub stargazers.

We’ve been working on this app for a month now and are excited to open source it today :)

Some useful links:

* Blog Post - https://medium.com/evadb-blog/stargazers-reloaded-llm-powere...

* GitHub Repository - https://github.com/pchunduri6/stargazers-reloaded/

* EvaDB - https://github.com/georgia-tech-db/evadb

Please let us know what you think!

The discord link seems to be not working. Just a heads up.

The YOLO example on your Github page is super interesting. We are finding it easier to get LLMs to write functions with a more constrained function interface in EvaDB. Here is an example of an YOLO function in EvaDB: https://github.com/georgia-tech-db/evadb/blob/staging/evadb/....

Once the function is loaded, it can be used in queries in this way:

  SELECT id, Yolo(data)

Neat idea, @shishirpatil! We are developing EvaDB [1] for shipping simpler, faster, and cost-effective AI apps. Can you share your thoughts on transforming the output of the Gorilla LLM to functions in EvaDB apps -- like this function that uses the HuggingFace API -- https://evadb.readthedocs.io/en/stable/source/tutorials/07-o...?

[1] https://github.com/georgia-tech-db/eva

Thanks for checking! Currently, we have a Docker image for deploying EVA [1]. We plan to release a Terraform config soon that will make it easier to deploy EVA DB on an AWS/Azure server with GPUs.

[1] https://github.com/georgia-tech-db/eva/tree/master/docker

EVA AI-Relational Database System; build simpler and faster AI-powered apps

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.