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merve 
posted an update 2 days ago
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A ton of impactful models and datasets in open AI past week, let's summarize the best 🤩 merve/releases-apr-21-and-may-2-6819dcc84da4190620f448a3

💬 Qwen made it rain! They released Qwen3: new dense and MoE models ranging from 0.6B to 235B 🤯 as well as Qwen2.5-Omni, any-to-any model in 3B and 7B!
> Microsoft AI released Phi4 reasoning models (that also come in mini and plus sizes)
> NVIDIA released new CoT reasoning datasets
🖼️ > ByteDance released UI-TARS-1.5, native multimodal UI parsing agentic model
> Meta released EdgeTAM, an on-device object tracking model (SAM2 variant)
🗣️ NVIDIA released parakeet-tdt-0.6b-v2, a smol 600M automatic speech recognition model
> Nari released Dia, a 1.6B text-to-speech model
> Moonshot AI released Kimi Audio, a new audio understanding, generation, conversation model
👩🏻‍💻 JetBrains released Melium models in base and SFT for coding
> Tesslate released UIGEN-T2-7B, a new text-to-frontend-code model 🤩
merve 
posted an update 3 days ago
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A real-time object detector much faster and accurate than YOLO with Apache 2.0 license just landed to Hugging Face transformers 🔥

D-FINE is the sota real-time object detector that runs on T4 (free Colab) 🤩

> Collection with all checkpoints and demo ustc-community/d-fine-68109b427cbe6ee36b4e7352

Notebooks:
> Tracking https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_tracking.ipynb
> Inference https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_inference.ipynb
> Fine-tuning https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_finetune_on_a_custom_dataset.ipynb
h/t @vladislavbro @qubvel-hf @ariG23498 and the authors of the paper 🎩

Regular object detectors attempt to predict bounding boxes in (x, y, w, h) pixel perfect coordinates, which is very rigid and hard to solve 🥲☹️



D-FINE formulates object detection as a distribution for bounding box coordinates, refines them iteratively, and it's more accurate 🤩

Another core idea behind this model is Global Optimal Localization Self-Distillation ⤵️

this model uses final layer's distribution output (sort of like a teacher) to distill to earlier layers to make early layers more performant.

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merve 
posted an update 6 days ago
jsulz 
posted an update 8 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
merve 
posted an update 9 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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