Open-Assistant VS hivemind

For open assistant, the code: https://github.com/LAION-AI/Open-Assistant/tree/main/inference

Is this being used to collect chat results for synthetic data and/or training like https://github.com/LAION-AI/Open-Assistant did? I believe they gave away GPT-4 api calls via a text interface and absorbed the cost to later build a dataset of chats.

https://open-assistant.io seems to have the same guardrails, as ChatGPT. Tried it on several prompts and it wouldn't comply.

Open-Assistant [https://open-assistant.io/]

Open Assistant by LAION AI on GitHub

To add to this if you want something for free or at least close to free, contribute to OpenSource projects like https://open-assistant.io/

Also, there are fairly useful alternatives like GPT4ALL and Open Assistant that you can run locally.

https://open-assistant.io - oasst-sft-6-llama-30b

https://github.com/learning-at-home/hivemind is also relevant

I really hope you'll join me, for the Petals support, at least! A single docker-compose.yml file is all we need, for now. If we are able to find enough people willing to host some smaller models, perhaps we could expand into the Hivemind, and create our own, custom foundation model one day?

Decentralized deep learning: https://github.com/learning-at-home/hivemind

https://github.com/learning-at-home/hivemind - same people behind it, was made before petals I think.

not if we rise to meet that challenge. here's a few tools that facilitate AI research in the face of an advanced persistent threat: Hivemind- a distributed Pytorch framework

yeah, there's Hivemind. and there's research wrt how to chunk out training workload so it can be scaled up. not sure why there's commentary that latency issues would limit this sort of enterprise, the architecture typically isn't designed for liveness. other subfields of distributed training/inference include zero-knowledge machine learning. besides all of that, there's also adversarial computation like SafetyNets and refereed delegation of computation.

We already have the software for it. There are some projects, but the one I'm most familiar with is https://github.com/learning-at-home/hivemind for training and it's sister project https://petals.ml/ for running large models distributed.

I'm not entirely how the approach they're using works [0], but I study federated learning and one of the highly-cited survey papers has several chapters (5 and 6 in particular) addressing potential attacks, failure modes, and bias [1].

0: https://github.com/learning-at-home/hivemind

1: https://arxiv.org/abs/1912.04977

The Hivemind project is just that

https://github.com/learning-at-home/hivemind