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Jared Sulzdorf PRO

jsulz
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https://www.jsulz.com/

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jsulz's activity

reacted to merve's post with ❤️🤗👍🚀🔥 4 days ago
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Meta released Llama Guard 4 and new Prompt Guard 2 models 🔥

Llama Guard 4 is a new model to filter model inputs/outputs both text-only and image 🛡️ use it before and after LLMs/VLMs! meta-llama/Llama-Guard-4-12B

Prompt Guard 2 22M & 86M are smol models to prevent model jailbreaks and prompt injections ⚔ meta-llama/Llama-Prompt-Guard-2-22M meta-llama/Llama-Guard-4-12B
Both come with new release of transformers 🤗

Try the model right away 👉🏻https://github.com/huggingface/huggingface-llama-recipes/blob/main/llama_guard_4.ipynb

Read our blog to learn more and easily get started 👉🏻 https://huggingface.co/blog/llama-guard-4 🦙
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reacted to AdinaY's post with 🚀 4 days ago
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DeepSeek, Alibaba, Skywork, Xiaomi, Bytedance.....
And that’s just part of the companies from the Chinese community that released open models in April 🤯

zh-ai-community/april-2025-open-releases-from-the-chinese-community-67ea699965f6e4c135cab10f

🎬 Video
> MAGI-1 by SandAI
> SkyReels-A2 & SkyReels-V2 by Skywork
> Wan2.1-FLF2V by Alibaba-Wan

🎨 Image
> HiDream-I1 by Vivago AI
> Kimi-VL by Moonshot AI
> InstantCharacter by InstantX & Tencent-Hunyuan
> Step1X-Edit by StepFun
> EasyControl by Shanghai Jiaotong University

🧠 Reasoning
> MiMo by Xiaomi
> Skywork-R1V 2.0 by Skywork
> ChatTS by ByteDance
> Kimina by Moonshot AI & Numina
> GLM-Z1 by Zhipu AI
> Skywork OR1 by Skywork
> Kimi-VL-Thinking by Moonshot AI

🔊 Audio
> Kimi-Audio by Moonshot AI
> IndexTTS by BiliBili
> MegaTTS3 by ByteDance
> Dolphin by DataOceanAI

🔢 Math
> DeepSeek Prover V2 by Deepseek

🌍 LLM
> Qwen by Alibaba-Qwen
> InternVL3 by Shanghai AI lab
> Ernie4.5 (demo) by Baidu

📊 Dataset
> PHYBench by Eureka-Lab
> ChildMandarin & Seniortalk by BAAI

Please feel free to add if I missed anything!
posted an update 5 days ago
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At xet-team we've been hard at work bringing a new generation of storage to the Hugging Face community, and we’ve crossed some major milestones:

👷 Over 2,000 builders and nearing 100 organizations with access to Xet
🚀 Over 70,000 model and dataset repositories are Xet-backed
🤯 1.4 petabytes managed by Xet

As we move repos from LFS to Xet for everyone we onboard, we’re pushing our content-addressed store (CAS). Check out the chart below 👇 of CAS hitting up to 150 Gb/s throughput this past week.

All of this growth is helping us build richer insights. We expanded our repo graph, which maps how Xet-backed repositories on the Hub share bytes with each other.

Check out the current network in the image below (nodes are repositories, edges are where repos share bytes) and visit the space to see how different versions of Qwen, Llama, and Phi models are grouped together xet-team/repo-graph

Join the waitlist to get access! https://huggingface.co/join/xet
reacted to danielhanchen's post with 🚀❤️🤗🔥 10 days ago
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5681
🦥 Introducing Unsloth Dynamic v2.0 GGUFs!
Our v2.0 quants set new benchmarks on 5-shot MMLU and KL Divergence, meaning you can now run & fine-tune quantized LLMs while preserving as much accuracy as possible.

Llama 4: unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
DeepSeek-R1: unsloth/DeepSeek-R1-GGUF-UD
Gemma 3: unsloth/gemma-3-27b-it-GGUF

We made selective layer quantization much smarter. Instead of modifying only a subset of layers, we now dynamically quantize all layers so every layer has a different bit. Now, our dynamic method can be applied to all LLM architectures, not just MoE's.

Blog with Details: https://docs.unsloth.ai/basics/dynamic-v2.0

All our future GGUF uploads will leverage Dynamic 2.0 and our hand curated 300K–1.5M token calibration dataset to improve conversational chat performance.

For accurate benchmarking, we built an evaluation framework to match the reported 5-shot MMLU scores of Llama 4 and Gemma 3. This allowed apples-to-apples comparisons between full-precision vs. Dynamic v2.0, QAT and standard iMatrix quants.

Dynamic v2.0 aims to minimize the performance gap between full-precision models and their quantized counterparts.
reacted to fdaudens's post with 🔥 10 days ago
reacted to clem's post with ❤️ 12 days ago
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Energy is a massive constraint for AI but do you even know what energy your chatGPT convos are using?

We're trying to change this by releasing ChatUI-energy, the first interface where you see in real-time what energy your AI conversations consume. Great work from @jdelavande powered by spaces & TGI, available for a dozen of open-source models like Llama, Mistral, Qwen, Gemma and more.

jdelavande/chat-ui-energy

Should all chat interfaces have this? Just like ingredients have to be shown on products you buy, we need more transparency in AI for users!
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reacted to yjernite's post with 🔥 18 days ago
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Today in Privacy & AI Tooling - introducing a nifty new tool to examine where data goes in open-source apps on 🤗

HF Spaces have tons (100Ks!) of cool demos leveraging or examining AI systems - and because most of them are OSS we can see exactly how they handle user data 📚🔍

That requires actually reading the code though, which isn't always easy or quick! Good news: code LMs have gotten pretty good at automatic review, so we can offload some of the work - here I'm using Qwen/Qwen2.5-Coder-32B-Instruct to generate reports and it works pretty OK 🙌

The app works in three stages:
1. Download all code files
2. Use the Code LM to generate a detailed report pointing to code where data is transferred/(AI-)processed (screen 1)
3. Summarize the app's main functionality and data journeys (screen 2)
4. Build a Privacy TLDR with those inputs

It comes with a bunch of pre-reviewed apps/Spaces, great to see how many process data locally or through (private) HF endpoints 🤗

Note that this is a POC, lots of exciting work to do to make it more robust, so:
- try it: yjernite/space-privacy
- reach out to collab: yjernite/space-privacy
reacted to thomwolf's post with 🤗❤️🚀 19 days ago
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If you've followed the progress of robotics in the past 18 months, you've likely noticed how robotics is increasingly becoming the next frontier that AI will unlock.

At Hugging Face—in robotics and across all AI fields—we believe in a future where AI and robots are open-source, transparent, and affordable; community-built and safe; hackable and fun. We've had so much mutual understanding and passion working with the Pollen Robotics team over the past year that we decided to join forces!

You can already find our open-source humanoid robot platform Reachy 2 on the Pollen website and the Pollen community and people here on the hub at pollen-robotics

We're so excited to build and share more open-source robots with the world in the coming months!
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posted an update 26 days ago
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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!
replied to their post 27 days ago
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What else would folks find interesting to explore?

Certain model trees? Overlap between a set of datasets?

Anything else?
posted an update 27 days ago
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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.
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