higgsfield

slurm

Hey there! Higgsfield AI.

We have a massive GPU cluster and developed our own infrastructure to manage the cluster and train massive models.

There's how it works:

- You upload the dataset with preconfigured format into HuggingFaсe [1]. Choose your LLM (e.g. LLaMa 70B, Mistral 7B)

- Place your submission into the queue

- Wait for it to get trained.

- Then you get your trained model there on HuggingFace.

Essentially, why would we want to do it?

We already have an experience with training big LLMs.

We could achieve near-perfect infrastructure performance for training.

Sometimes GPUs have just nothing to train.

Thus we thought it would be cool if we could utilize our GPU cluster 100%. And give back to Open Source community (already built an e2e distributed training framework [2]).

This is in an early stage, so you can expect some bugs.

Any thoughts, opinions, or ideas are quite welcome!

[1]: https://github.com/higgsfield-ai/higgsfield/blob/main/tutori...

[2]: https://github.com/higgsfield-ai/higgsfield

Node.js/AWS lambda/DynamoDB/Kinesis for backend

Pytorch/k8s/AWS fargate for Deep learning api

Clingo/Prolog for symbolic AI

React.js/flexbox/sass for frontend

SwiftUI for iOS

I'm solo developer

What:

Node.js/AWS lambda/DynamoDB/Kinesis for backend

Pytorch/k8s/AWS fargate for Deep learning api

Clingo/Prolog for symbolic AI

React.js/flexbox/sass for frontend

SwiftUI for iOS

Why: each tool is optimal for its use case

Impact: move fast and break things)

AGI -> basic income

Location: Kazakhstan Remote: Yes Willing to relocate: Yes (need visa sponsorship outside Kazakhstan) Technologies: Objective-C, Swift Résumé/CV: http://www.anai.com/author/aida-tolegen/

Email: [email protected]

GitHub: https://github.com/aidats

Software developer, whose passion is the creation of elegant interfaces with unmatched attention to details. I also understand the importance of creating highly readable and easily maintainable source code. I am constantly striving to learn new technologies and look to ways to better myself.

this is old too

P(A|B) = P(B|A) * P(A) / P(B)

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