marksverdhei (Markus)

reacted to their post with 🤯👀 about 4 hours ago

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34

The hidden gem of open-source embedding models: LCO-Embedding
for text, image AND audio!

I found this model after reading the recent Massive Audio Embedding Benchmark (MAEB) paper, as it blew the other models out of the water on day zero. I've been using it personally for about a week, and searching my files by describing music, sound effects or images is both practical and entertaining. Really underrated model, would highly recommend checking it out: LCO-Embedding/LCO-Embedding-Omni-7B

PS: If you're looking you run this model on llama.cpp, i've gone ahead and quantized them for you here 👉 https://huggingface.co/collections/marksverdhei/lco-embedding-omni-gguf

posted an update about 4 hours ago

Post

34

The hidden gem of open-source embedding models: LCO-Embedding
for text, image AND audio!

I found this model after reading the recent Massive Audio Embedding Benchmark (MAEB) paper, as it blew the other models out of the water on day zero. I've been using it personally for about a week, and searching my files by describing music, sound effects or images is both practical and entertaining. Really underrated model, would highly recommend checking it out: LCO-Embedding/LCO-Embedding-Omni-7B

PS: If you're looking you run this model on llama.cpp, i've gone ahead and quantized them for you here 👉 https://huggingface.co/collections/marksverdhei/lco-embedding-omni-gguf

replied to their post 16 days ago

The best part:

reacted to their post with 🤯😔🤝👍🧠➕😎🤗❤️👀🚀🔥 16 days ago

Post

1312

🤔 Many cultures penalize or look down upon self-celebratory behavior. One such example is liking your own post. So why do i do it? Two reasons:
1. I disagree that self-celebratory behavior is inherently bad.
2. On the Huggingface hub, if your post has 0 reactions, it takes TWO whole clicks to react instead of one. So it is actually a UI hack that lowers the bar to engage.

So if you see me reacting to to my own post and thing 'Ugh, this guy is so full of himself' you are only half correct 😆

Now behold as I perform this magic trick called "Exhausting all reaction options for increased visual engagement" so you don't have to click twice to react. You're welcome!
Follow this aspiring 🤗 HF Hub influencer for more half-serious bloat in your feed 😜

1 reply

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posted an update 16 days ago

Post

1312

🤔 Many cultures penalize or look down upon self-celebratory behavior. One such example is liking your own post. So why do i do it? Two reasons:
1. I disagree that self-celebratory behavior is inherently bad.
2. On the Huggingface hub, if your post has 0 reactions, it takes TWO whole clicks to react instead of one. So it is actually a UI hack that lowers the bar to engage.

So if you see me reacting to to my own post and thing 'Ugh, this guy is so full of himself' you are only half correct 😆

Now behold as I perform this magic trick called "Exhausting all reaction options for increased visual engagement" so you don't have to click twice to react. You're welcome!
Follow this aspiring 🤗 HF Hub influencer for more half-serious bloat in your feed 😜

1 reply

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replied to their post 19 days ago

This image from qwen3 tts tech report visualizes how little data is actually inputted as the voice prompt when using the speaker embedding, yet it completely shapes how the audio comes out. Quite interesting indeed

posted an update 19 days ago

Post

1681

# The most underrated feature of Qwen3-TTS: Voice embeddings! 🧑‍🦰💬
https://huggingface.co/collections/marksverdhei/qwen3-voice-embedding

Did you know that Qwen3 TTS actually utilizes voice embedding?
Your voice is turned into a vector of 1024 (or 2048) dimensions,
and based on this vector alone you can get your custom voice.

But the coolest part is that this means that you can use math to modify voices, average voices. You can swap gender, pitch, mix and match vocies, and even create an emotion space! This also enables semantic voice search!

The voice embedding model is actually just a tiny encoder with just a few million parameters. I've ripped it out of the voice embeding model so you can use the embedding model standalone. Check out my collection! :D

1 reply

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reacted to their post with 👍 about 1 month ago

Post

4575

Poll: Will 2026 be the year of subquadratic attention?

The transformer architecture is cursed by its computational complexity.
It is why you run out of tokens and have to compact. But some would argue that this is a feature not a bug and that this is also why these models are so good. We've been doing a lot of research on trying to make equally good models that are computationally cheaper, But so far, none of the approaches have stood the test of time. Or so it seems.

Please vote, don't be shy. Remember that the Dunning-Kruger effect is very real, so the person who knows less about transformers than you is going to vote. We want everyone's opinion, no matter confidence.

👍 if you think at least one frontier model* will have no O(n^2) attention by the end of 2026
🔥 If you disagree

* Frontier models - models that match / outperform the flagship claude, gemini or chatgpt at the time on multiple popular benchmarks

4 replies

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