maelstrom VS unilm

Maelstrom [1], a workbench for learning distributed systems from the creator of Jepsen, includes a simple (model-checked) implementation of Raft and an excellent tutorial on implementing it.

Raft is a simple algorithm, but as others have noted, the original paper includes many correctness details often brushed over in toy implementations. Furthermore, the fallibility of real-world hardware (handling memory/disk corruption and grey failures), the requirements of real-world systems with tight latency SLAs, and a need for things like flexible quorum/dynamic cluster membership make implementing it for production a long and daunting task. The commit history of etcd and hashicorp/raft, likely the two most battle-tested open source implementations of raft that still surface correctness bugs on the regular tell you all you need to know.

The tigerbeetle team talks in detail about the real-world aspects of distributed systems on imperfect hardware/non-abstracted system models, and why they chose viewstamp replication, which predates Paxos but looks more like Raft.

[1]: https://github.com/jepsen-io/maelstrom/

[2]: https://github.com/tigerbeetle/tigerbeetle/blob/main/docs/DE...

I've tried to run the echo example and I'me getting some problems. I assume it works as the same example in the maelstrom's getting started ready (https://github.com/jepsen-io/maelstrom/blob/main/doc/01-getting-ready/index.md).

Love it. Thanks for putting this together! The actual challenges here [0].

Though I'm curious: are these different from the chapters in the Maelstrom documentation [1]? There seems to be a bit of overlap anyway.

[0] https://fly.io/dist-sys/

[1] https://github.com/jepsen-io/maelstrom#documentation

+1 On this, the real proof would have been testing both models side-by-side.

It seems that it may be published on GitHub [1] according to HuggingFace [2].

[1] https://github.com/microsoft/unilm/tree/master/bitnet

[2] https://huggingface.co/papers/2402.17764

e5-mistral is essentially a distillation from gpt-4 to a smaller model. You can see here https://github.com/microsoft/unilm/blob/16da2f193b9c1dab0a69...

they actually have custom prompts for each dataset being tested.

Question would be, if you haven't seen the task before, what is a good prompt to prepend for your task?

IMO e5-mistral is overfit to MTEB

Layout LM v1, v2 and v3 models [ Github ] DocBERT [ Github ]

The repository is available here.

It is missing kosmos-2. I remember its image captioning was(demo currently down) really good and it's almost as fast as llava and lavin.

Should be this: https://github.com/microsoft/unilm/

This is from Microsoft Research (Asia). https://aka.ms/GeneralAI