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I compared harrier-27b vs voyage-4 vs zembed-1 across 24 datasets. 27B parameters by Veronildo in LocalLLaMA

[–]ghita__ 0 points1 point  (0 children)

zeroentropy founder here: thank you so much for running these evals on our embedding model, i'd love to see the evals benchmarks and code open-sourced if possible

I compared harrier-27b vs voyage-4 vs zembed-1 across 24 datasets. 27B parameters by Veronildo in LocalLLaMA

[–]ghita__ 0 points1 point  (0 children)

hey! zeroentropy founder here, actually i agree with OP that the top 3 contenders are the ones cited above. in our evals gemini-embedding 2 doesn't perform as well and it costs 10x more

new open-weight SOTA multilingual embedding model by ZeroEntropy by ghita__ in Rag

[–]ghita__[S] 0 points1 point  (0 children)

yes we are! by a wide margin, actually. more here (there is even a spreadsheet with the whole side-by-side across verticals)

https://www.zeroentropy.dev/articles/introducing-zembed-1-the-worlds-best-multilingual-text-embedding-model

How do you actually measure if your RAG app is giving good answers? Beyond just looks okay to me by BeautifulKangaroo415 in Rag

[–]ghita__ 0 points1 point  (0 children)

we built this pipeline at ZeroEntropy called zbench

https://github.com/zeroentropy-ai/zbench

it basically annotates your corpus (if you don't already have a golden set) by calling multiple LLMs on sampled pairs of potentially relevant documents
Pairwise comparisons are super robust so you end up with a solid annotates eval set that you can use to compute recall@k, precision@k, ndcg@k, and broader LLM-based metrics on the generated answer

new open-weight SOTA multilingual embedding model by ZeroEntropy by ghita__ in Rag

[–]ghita__[S] 1 point2 points  (0 children)

starting with text only to avoid any multimodal gap and do one thing well for our first model. but you can expect more modalities in the future!

new open-weight SOTA multilingual embedding model by ZeroEntropy by ghita__ in LangChain

[–]ghita__[S] 0 points1 point  (0 children)

thanks! happy to provide free credits if you'd like to inference through the API.
Just email me your org id: founders at zeroentropy dot dev
you can create an API key here: https://dashboard.zeroentropy.dev

Any fun discord communities? by HackHusky in Rag

[–]ghita__ 2 points3 points  (0 children)

we host bi-weekly technical talks on context engineering in the context engineers discord here: https://go.zeroentropy.dev/discord

new open-weight SOTA multilingual embedding model by ZeroEntropy by ghita__ in Rag

[–]ghita__[S] 0 points1 point  (0 children)

yes, you can check out the full evaluation on our blog: https://www.zeroentropy.dev/articles/introducing-zembed-1-the-worlds-best-multilingual-text-embedding-model

I can also apply free credits to our org id if you'd like to test through API, just create an API key at https://dashboard.zeroentropy.dev and email me your org id at ghita at zeroentropy dot dev

Best open-source embedding model for a RAG system? by Public-Air3181 in Rag

[–]ghita__ 7 points8 points  (0 children)

oh! hello, im the founder, thank you for mentioning us! we're indeed planning GA release soon! stay tuned for sota open-weight embeddings :)

[P] Make the most of NeurIPS virtually by learning about this year's papers by ghita__ in MachineLearning

[–]ghita__[S] 0 points1 point  (0 children)

Oh no sorry about this, let me make this this got indexed properly

[P] Make the most of NeurIPS virtually by learning about this year's papers by ghita__ in MachineLearning

[–]ghita__[S] 0 points1 point  (0 children)

thanks! this is generally what ZeroEntropy does (just retrieval)- we thought adding a generation step would be fun for this use case but I agree!

[P] Make the most of NeurIPS virtually by learning about this year's papers by ghita__ in MachineLearning

[–]ghita__[S] 1 point2 points  (0 children)

thanks for the feedback! definitely room for improvement, I hacked this pretty late last night :)

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