Hugging Face's logo

Journalists on Hugging Face

community
Activity Feed Request to join this org

AI & ML interests

Democratizing access to useful AI tools and resources for journalists

Recent Activity

fdaudens  updated a Space 11 days ago
JournalistsonHF/first-llm-classifier
fdaudens  published a Space 20 days ago
JournalistsonHF/first-llm-classifier
evijit  authored a paper 21 days ago
Fully Autonomous AI Agents Should Not be Developed
View all activity

JournalistsonHF's activity

jsulz 
posted an update 6 days ago
view post
Post
700
As xet-team infrastructure begins backing hundreds of repositories on the Hugging Face Hub, we’re getting to put on our researcher hats and peer into the bytes. 👀 🤓

IMO, one of the most interesting ideas Xet storage introduces is a globally shared store of data.

When you upload a file through Xet, the contents are split into ~64KB chunks and deduplicated, but what if those same chunks already exist in another repo on the Hub?

If we can detect and reuse them, we skip them as well saving time and bandwidth for AI builders. More on how that works here:
🔗 https://huggingface.co/blog/from-chunks-to-blocks#scaling-deduplication-with-aggregation

Because of this, different repositories can share bytes we store. That opens up something cool - we can draw a graph of which repos actually share data at the chunk level, where:

- Nodes = repositories
- Edges = shared chunks
- Edge thickness = how much they overlap

xet-team/repo-graph

Come find the many BERT islands. Or see how datasets relate in practice, not just in theory. See how libraries or tasks can tie repositories together. You can play around with node size using storage/likes/downloads too.

The result is a super fun visualization from @saba9 and @znation that I’ve already lost way too much time to. I'm excited to see how the networks grow as we add more repositories!
jsulz 
posted an update 7 days ago
view post
Post
2843
What does it mean when models share the same bytes?

We've investigated some quants and have seen that a considerable portion of quantizations of the same model share the same bytes and can be deduplicated to save considerable upload time for quantizers on the Hub.

This space where we crack open a repo from @bartowski shows we can get significant dedupe xet-team/quantization-dedup

You can get a sense of why by reading this write-up: https://github.com/bartowski1182/llm-knowledge/blob/main/quantization/quantization.md

But what about finetuned models?

Since going into production the xet-team has migrated hundreds of repositories on the Hub to our storage layer, including classic "pre-Hub" open-source models like FacebookAI/xlm-roberta-large (XLM-R) from FacebookAI

XLM-R, introduced in 2019, set new benchmarks for multilingual NLP by learning shared representations across 100 languages. It was then fine-tuned on English, Spanish, Dutch, and German, generating language-specific derivations for each - check out the paper here Unsupervised Cross-lingual Representation Learning at Scale (1911.02116)

These finetunes share much of the same architecture and layout as XLM-R with similar training methods and goals. It makes sense that they would share bytes, but it's still fascinating to see.

We put together a similar space to explore these models to see where they overlap - check it out for yourself xet-team/finetune-dedupe

The darker each block in the heatmap, the more the bytes are shared. Clicking on a repos blocks shows all other repos that share blocks.
  • 1 reply
·
reach-vb 
authored a paper 7 days ago
jsulz 
posted an update 8 days ago
view post
Post
2013
The Llama 4 release - meta-llama/llama-4-67f0c30d9fe03840bc9d0164 - was a big one for the xet-team with every model backed by the storage infrastructure of the future for the Hub.

It's been a wild few days, and especially 🤯 to see every tensor file with a Xet logo next to it instead of LFS.

The attached graph shows requests per second to our content-addressed store (CAS) right as the release went live.

yellow = GETs; dashed line = launch time.

You can definitely tell when the community started downloading 👀

h/t to @rajatarya for the graph, the entire Xet crew to bring us to this point, and special shoutout to Rajat, @port8080 , @brianronan , @seanses , and @znation who made sure the bytes kept flying all weekend ⚡️
  • 1 reply
·
jsulz 
posted an update 10 days ago
view post
Post
3575
Huge week for xet-team as Llama 4 is the first major model on Hugging Face uploaded with Xet providing the backing! Every byte downloaded comes through our infrastructure.

Using Xet on Hugging Face is the fastest way to download and iterate on open source models and we've proved it with Llama 4 giving a boost of ~25% across all models.

We expect builders on the Hub to see even more improvements, helping power innovation across the community.

With the models on our infrastructure, we can peer in and see how well our dedupe performs across the Llama 4 family. On average, we're seeing ~25% dedupe, providing huge savings to the community who iterate on these state-of-the-art models. The attached image shows a few selected models and how they perform on Xet.

Thanks to the meta-llama team for launching on Xet!
fdaudens 
updated a Space 11 days ago
fdaudens 
published a Space 20 days ago
fdaudens 
updated a Space 22 days ago
louisbrulenaudet 
posted an update 23 days ago
view post
Post
900
I’ve just released logfire-callback on PyPI, designed to facilitate monitoring of Hugging Face Transformer training loops using Pydantic Logfire 🤗

The callback will automatically log training start with configuration parameters, periodic metrics and training completion ⏱️

Install the package using pip:
pip install logfire-callback

First, ensure you have a Logfire API token and set it as an environment variable:
export LOGFIRE_TOKEN=your_logfire_token

Then use the callback in your training code:
from transformers import Trainer, TrainingArguments
from logfire_callback import LogfireCallback

# Initialize your model, dataset, etc.

training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    # ... other training arguments
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    callbacks=[LogfireCallback()]  # Add the Logfire callback here
)

trainer.train()

If you have any feedback, please reach out at @louisbrulenaudet
jsulz 
posted an update 28 days ago
view post
Post
1937
If you've been following along with the Xet Team's ( xet-team ) work, you know we've been working to migrate the Hugging Face Hub from Git LFS and to Xet.

Recently, we launched a waitlist to join the movement to Xet (join here! https://huggingface.co/join/xet ) but getting to this point was a journey.

From the initial proof of concept in August, to launching on the Hub internally, to migrating a set of repositories and routing a small chunk of download traffic on the Hub through our infrastructure. Every step of the way has been full of challenges, big and small, and well worth the effort.

Over the past few weeks, with real traffic flowing through our services we’ve tackled some truly gnarly issues (unusual upload/download patterns, memory leaks, load imbalances, and more) and resolved each without major disruptions.

If you're curious about how this sliver of Hub infrastructure looks as we routed traffic through it for the first time (and want a deep dive full of Grafana and Kibana charts 🤓) I have a post for you.

Here's an inside look into the day of our first migrations and the weeks following, where we pieced together solutions in real time.

https://huggingface.co/blog/xet-on-the-hub
fdaudens 
updated 2 collections 28 days ago
jsulz 
posted an update about 1 month ago
view post
Post
1434
It's finally here ❤️

Build faster than ever with lightning fast upload and download speeds starting today on the Hub ⚡

Xet storage is rolling out access across the Hub - join the waitlist here https://huggingface.co/join/xet

You can apply for yourself, or your entire organization. Head over to your account settings for more information or join anywhere you see the Xet logo on a repository you know.

Have questions? Join the conversation below 👇 or open a discussion on the Xet team page xet-team/README
·
jsulz 
posted an update about 1 month ago
view post
Post
2107
If you haven't already, I strong recommend reading @chiphuyen 's AI Engineering https://www.goodreads.com/en/book/show/216848047-ai-engineering

It comes complete with a section on open source AI (of obvious interest to the crowd here) and more than one mention of the Hugging Face community 🤗

In my opinion, one of the best parts is that it is a compendium for seminal and cutting-edge AI resources, with nearly 250 arXiv papers cited. I've done my best to collect them all in a single place, organized by chapter and by order in which they appear in the book:
jsulz/ai-engineering-67c5abe02c8596b5c089934c

Happy reading 🤓
fdaudens 
updated a Space about 2 months ago
fdaudens 
published a Space about 2 months ago
jsulz 
posted an update about 2 months ago
view post
Post
3570
Time flies!

Six months after joining Hugging Face the Xet team is kicking off the first migrations from LFS to our storage for a number of repositories on the Hub.

More on the nitty gritty details behind the migration soon, but here are the big takeaways:

🤖 We've successfully completed the first migrations from LFS -> Xet to test the infrastructure and prepare for a wider release

✅ No action on your part needed - you can work with a Xet-backed repo like any other repo on the Hub (for now - major improvements on their way!)

👀 Keep an eye out for the Xet logo to see if a repo you know is on our infra! See the screenshots below to spot the difference 👇

⏩ ⏩ ⏩ Blazing uploads and downloads coming soon. W’re gearing up for a full integration with the Hub's Python library that will make building on the Hub faster than ever - special thanks to @celinah and @Wauplin for their assistance.

🎉 Want Early Access? If you’re curious and want to test it out the bleeding edge that will power the development experience on the Hub, we’d love to partner with you. Let me know!

This is the culmination of a lot of effort from the entire team. Big round of applause to @sirahd @brianronan @jgodlewski @hoytak @seanses @assafvayner @znation @saba9 @rajatarya @port8080 @yuchenglow
  • 1 reply
·
louisbrulenaudet 
posted an update about 2 months ago
view post
Post
3272
I am pleased to introduce my first project built upon Hugging Face’s smolagents framework, integrated with Alpaca for financial market analysis automation 🦙🤗

The project implements technical indicators such as the Relative Strength Index (RSI) and Bollinger Bands to provide momentum and volatility analysis. Market data is retrieved through the Alpaca API, enabling access to historical price information across various timeframes.

AI-powered insights are generated using Hugging Face’s inference API, facilitating the analysis of market trends through natural language processing with DuckDuckGo search integration for real-time sentiment analysis based on financial news 🦆

Link to the GitHub project: https://github.com/louisbrulenaudet/agentic-market-tool
fdaudens 
updated a collection about 2 months ago
jsulz 
posted an update 2 months ago
view post
Post
3100
Toward the end of last year, the Xet team provided an inside look into the foundations of how we plan to enable rapid experimentation and iteration for the AI builders on the Hub: https://huggingface.co/blog/from-files-to-chunks

But it turns out chunks aren't all you need!

Our goal is to bring:
🚀 Faster uploads
⏬ Speedy downloads
💪 All without sacrificing your workflow

To do that, we need the infrastructure and system and design to back it up. As we prepare to roll out the first Xet-backed repositories on the Hub, we wrote up a post explaining the nitty gritty details of the decisions that bring this to life https://huggingface.co/blog/from-chunks-to-blocks

Complete with an interactive visualization that shows the power of deduplication in action - taking a 191GB repo to ~97GB and shaving a few hours off upload speeds.

The darker each block in the heatmap, the more we dedupe, the less we have to transfer. Clicking on a file's blocks shows all other files that share blocks.

Check it out and explore for yourself! xet-team/quantization-dedup