emin temiz
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etemiz
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replied to
jsulz's
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about 1 hour ago
Something I love about working at Hugging Face is the opportunity to design and work in public. Right now, we’re redesigning the architecture that supports uploads and downloads on the Hub.
Datasets and models are growing fast, and so are the challenges of storing and transferring them efficiently. To keep up, we're introducing a new protocol for uploads and downloads, supported by a content-addressed store (CAS).
Here’s what’s coming:
📦 Smarter uploads: Chunk-level management enables advanced deduplication, compression, and reduces redundant transfers, speeding up uploads.
⚡ Efficient downloads: High throughput and low latency ensure fast access, even during high-demand model releases.
🔒 Enhanced security: Validate uploads before storage to block malicious or invalid data.
We analyzed 24 hours of global upload activity in October (88 countries, 130TB of data!) to design a system that scales with your needs.
The result? A proposed infrastructure with CAS nodes in us-east-1, eu-west-3, and ap-southeast-1.
🔗 Read the blog post for the full details: https://huggingface.co/blog/rearchitecting-uploads-and-downloads
🌟 Check out our interactive demo to explore the data yourself!
https://huggingface.co/spaces/xet-team/cas-analysis
We’d love to hear your feedback - let us know if you have questions or want to see more.
replied to
jsulz's
post
1 day ago
Something I love about working at Hugging Face is the opportunity to design and work in public. Right now, we’re redesigning the architecture that supports uploads and downloads on the Hub.
Datasets and models are growing fast, and so are the challenges of storing and transferring them efficiently. To keep up, we're introducing a new protocol for uploads and downloads, supported by a content-addressed store (CAS).
Here’s what’s coming:
📦 Smarter uploads: Chunk-level management enables advanced deduplication, compression, and reduces redundant transfers, speeding up uploads.
⚡ Efficient downloads: High throughput and low latency ensure fast access, even during high-demand model releases.
🔒 Enhanced security: Validate uploads before storage to block malicious or invalid data.
We analyzed 24 hours of global upload activity in October (88 countries, 130TB of data!) to design a system that scales with your needs.
The result? A proposed infrastructure with CAS nodes in us-east-1, eu-west-3, and ap-southeast-1.
🔗 Read the blog post for the full details: https://huggingface.co/blog/rearchitecting-uploads-and-downloads
🌟 Check out our interactive demo to explore the data yourself!
https://huggingface.co/spaces/xet-team/cas-analysis
We’d love to hear your feedback - let us know if you have questions or want to see more.
replied to
jsulz's
post
1 day ago
Something I love about working at Hugging Face is the opportunity to design and work in public. Right now, we’re redesigning the architecture that supports uploads and downloads on the Hub.
Datasets and models are growing fast, and so are the challenges of storing and transferring them efficiently. To keep up, we're introducing a new protocol for uploads and downloads, supported by a content-addressed store (CAS).
Here’s what’s coming:
📦 Smarter uploads: Chunk-level management enables advanced deduplication, compression, and reduces redundant transfers, speeding up uploads.
⚡ Efficient downloads: High throughput and low latency ensure fast access, even during high-demand model releases.
🔒 Enhanced security: Validate uploads before storage to block malicious or invalid data.
We analyzed 24 hours of global upload activity in October (88 countries, 130TB of data!) to design a system that scales with your needs.
The result? A proposed infrastructure with CAS nodes in us-east-1, eu-west-3, and ap-southeast-1.
🔗 Read the blog post for the full details: https://huggingface.co/blog/rearchitecting-uploads-and-downloads
🌟 Check out our interactive demo to explore the data yourself!
https://huggingface.co/spaces/xet-team/cas-analysis
We’d love to hear your feedback - let us know if you have questions or want to see more.
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Fine-tuning LLMs to 1.58bit: extreme quantization made easy
