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nielsr
updated
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dataset
19 minutes ago
Add ParaAttention Images and Videos
2
#416 opened about 22 hours ago
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chengzeyi
Add images to optimum-tpu documentation
3
#422 opened about 17 hours ago
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baptistecolle
baptistecolle
updated
a
dataset
about 17 hours ago
Add images for PaliGemma 2 on Vertex AI
#421 opened about 19 hours ago
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alvarobartt
alvarobartt
updated
a
dataset
about 19 hours ago
multimodalart
updated
a
dataset
about 19 hours ago
hlarcher
updated
a
dataset
about 20 hours ago
Add more optimized images
#420 opened about 20 hours ago
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tomaarsen
tomaarsen
updated
a
dataset
about 20 hours ago
Move recent image under blog instead
#419 opened about 20 hours ago
by
tomaarsen
Add required svg image
#418 opened about 20 hours ago
by
tomaarsen
Add images for PaliGemma 2 on GKE
#417 opened about 21 hours ago
by
alvarobartt
Add images for PaliGemma 2 on GKE
#417 opened about 21 hours ago
by
alvarobartt
Add ParaAttention Images and Videos
2
#416 opened about 22 hours ago
by
chengzeyi
sayakpaul
updated
a
dataset
1 day ago
Post
1487
🤗👤 💻 Speaking of AI agents ...
...Is easier with the right words ;)
My colleagues @meg @evijit @sasha and @giadap just published a wonderful blog post outlining some of the main relevant notions with their signature blend of value-informed and risk-benefits contrasting approach. Go have a read!
https://huggingface.co/blog/ethics-soc-7
...Is easier with the right words ;)
My colleagues @meg @evijit @sasha and @giadap just published a wonderful blog post outlining some of the main relevant notions with their signature blend of value-informed and risk-benefits contrasting approach. Go have a read!
https://huggingface.co/blog/ethics-soc-7
Post
290
Learn how to deploy multiple LoRA adapters on Vertex AI with this blogpost, using Hugging Face Deep Learning Containers on GCP.
https://medium.com/google-cloud/open-models-on-vertex-ai-with-hugging-face-serving-multiple-lora-adapters-on-vertex-ai-e3ceae7b717c
https://medium.com/google-cloud/open-models-on-vertex-ai-with-hugging-face-serving-multiple-lora-adapters-on-vertex-ai-e3ceae7b717c
lysandre
updated
a
dataset
1 day ago
davanstrien
posted
an
update
1 day ago
Post
2122
Introducing scandi-fine-web-cleaner
davanstrien/scandi-fine-web-cleaner, the first model trained on FineWeb-C community annotations!
FineWeb2 is a massive multilingual dataset for pre-training language models. Like any web-scale dataset, it contains low-quality content. How can we improve it?
Over the past months, an amazing community of 400+ annotators has been labelling content quality (using Argilla) across 23 languages through the FineWeb-C initiative.
Today, I'm happy to share the first classifier trained on this data.
🔍 What we've built:
- A lightweight classifier that efficiently removes low-quality content
- 90%+ precision demonstrated on Danish & Swedish
- Can process the 43M+ documents in Danish FineWeb2 with minimal compute
🌍 Why this matters: The approach can be reproduced for any of the 23 languages in FineWeb-C ( data-is-better-together/fineweb-c). We can improve training data quality at scale without massive compute resources by starting with community annotations and training small, efficient classifiers.
Want to build a classifier for your language? Check out the full blog post with code examples and implementation details: https://danielvanstrien.xyz/posts/2025/FineWeb-c/scandinavian-content-filtering-fineweb.html
FineWeb2 is a massive multilingual dataset for pre-training language models. Like any web-scale dataset, it contains low-quality content. How can we improve it?
Over the past months, an amazing community of 400+ annotators has been labelling content quality (using Argilla) across 23 languages through the FineWeb-C initiative.
Today, I'm happy to share the first classifier trained on this data.
🔍 What we've built:
- A lightweight classifier that efficiently removes low-quality content
- 90%+ precision demonstrated on Danish & Swedish
- Can process the 43M+ documents in Danish FineWeb2 with minimal compute
🌍 Why this matters: The approach can be reproduced for any of the 23 languages in FineWeb-C ( data-is-better-together/fineweb-c). We can improve training data quality at scale without massive compute resources by starting with community annotations and training small, efficient classifiers.
Want to build a classifier for your language? Check out the full blog post with code examples and implementation details: https://danielvanstrien.xyz/posts/2025/FineWeb-c/scandinavian-content-filtering-fineweb.html
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