I really appreciate that EmbeddingGemma 2 is under the Apache 2.0 license.
For embedding models in particular, I don't think it makes sense to use a closed, proprietary, hosted-only model.
Most applications of embedding models involve calculating thousands or even millions of embedding vectors and storing them for later comparison.
If your model is proprietary, the vendor is likely someday going to decide to stop offering that model. They'll have a better model to replace it, but you still need to pay to re-calculate those millions of stored existing vectors.
(In April 2024 OpenAI offered to "cover the financial cost of users re-embedding content with these new models" - https://openai.com/index/gpt-4-api-general-availability/ - but I don't think that's something we can rely on from every provider.)
Notably, I don't want to host the model myself. I'd much rather pay a provider for a hosted model while knowing that if they ever stop hosting it I can run the open weights version myself - or find another vendor who can do that for me.
Hats off to google for offering OSS (or at least open weights + license) a model that would be probably pretty closed to what they would ship in their Android phones.
Finally. I was getting annoyed that there's been an inflection point in how LLMs/agents work but there hasn't been a good moderate-size embeddings model, and this one is multimodal too! 270M for text only is great compared to older embedding models, and a total 440M for text + vision is also fair.
I also may or may not have a tool for much faster local embedding creation that I calibrated for EmbeddingGemma but didn't want to release until a better embedding model came along.
Not just vision with video, but also audio, it really seems amazing.
I’m not sure how it can handle vicinity of pairs of embeddings with for example some words and the audio where they’re spoken or an image where the text is handled. Building local multimodal search with this would be amazing.
I’ve explored this stuff with CLIP and it’s interesting how image (but also audio) embedding carries both the clean “text” content information but also the stylistic and visual/audio tone information, the two can even kind of be linearly separated.
I really appreciate that EmbeddingGemma 2 is under the Apache 2.0 license.
For embedding models in particular, I don't think it makes sense to use a closed, proprietary, hosted-only model.
Most applications of embedding models involve calculating thousands or even millions of embedding vectors and storing them for later comparison.
If your model is proprietary, the vendor is likely someday going to decide to stop offering that model. They'll have a better model to replace it, but you still need to pay to re-calculate those millions of stored existing vectors.
(In April 2024 OpenAI offered to "cover the financial cost of users re-embedding content with these new models" - https://openai.com/index/gpt-4-api-general-availability/ - but I don't think that's something we can rely on from every provider.)
Notably, I don't want to host the model myself. I'd much rather pay a provider for a hosted model while knowing that if they ever stop hosting it I can run the open weights version myself - or find another vendor who can do that for me.
Parameter count split is interesting:
740M total (270M text, 170M vision, 300M audio)
Hats off to google for offering OSS (or at least open weights + license) a model that would be probably pretty closed to what they would ship in their Android phones.
I'm considering adding this in my harness after some testing, this seems like a really nice embedding model!
Finally. I was getting annoyed that there's been an inflection point in how LLMs/agents work but there hasn't been a good moderate-size embeddings model, and this one is multimodal too! 270M for text only is great compared to older embedding models, and a total 440M for text + vision is also fair.
I also may or may not have a tool for much faster local embedding creation that I calibrated for EmbeddingGemma but didn't want to release until a better embedding model came along.
Not just vision with video, but also audio, it really seems amazing.
I’m not sure how it can handle vicinity of pairs of embeddings with for example some words and the audio where they’re spoken or an image where the text is handled. Building local multimodal search with this would be amazing. I’ve explored this stuff with CLIP and it’s interesting how image (but also audio) embedding carries both the clean “text” content information but also the stylistic and visual/audio tone information, the two can even kind of be linearly separated.
why isn't this being compared to siglip2 (also from google)? because that one isn't fully multimodal? or because it's a different org/team?