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pcuenq  new activity about 1 month ago
meta-llama/Llama-Guard-4-12B:Fix rope and use static cache
pcuenq  updated a model about 1 month ago
meta-llama/Llama-Guard-4-12B
pcuenq  authored a paper about 2 months ago
SmolVLM: Redefining small and efficient multimodal models
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merve 
posted an update 2 days ago
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2068
Google released MedGemma on I/O'25 👏 google/medgemma-release-680aade845f90bec6a3f60c4

> 4B and 27B instruction fine-tuned vision LMs and a 4B pre-trained vision LM for medicine
> available with transformers from the get-go 🤗

they also released a cool demo for scan reading ➡️ google/rad_explain

use with transformers ⤵️
  • 1 reply
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burtenshaw 
posted an update 2 days ago
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1343
MCP course is now LIVE! We just dropped quizzes, videos, and live streams to make it a fully interactive course:

🔗 join in now: mcp-course

- It’s still free!
- Video 1 walks you through onboarding to the course
- The first live session is next week!
- You can now get a certificate via exam app
- We improved and written material with interactive quizzes

If you’re studying MCP and want a live, interactive, visual, certified course, then join us on the hub!
merve 
posted an update 2 days ago
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2646
Bu post'u çevirebilirsiniz 🤗💗
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merve 
posted an update 2 days ago
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2170
tis the year of any-to-any/omni models 🤠
ByteDance-Seed/BAGEL-7B-MoT 7B native multimodal model that understands and generates both image + text

it outperforms leading VLMs like Qwen 2.5-VL 👏 and has Apache 2.0 license 😱
merve 
posted an update 4 days ago
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1650
NVIDIA released new vision reasoning model for robotics: Cosmos-Reason1-7B 🤖 nvidia/cosmos-reason1-67c9e926206426008f1da1b7

> first reasoning model for robotics
> based on Qwen 2.5-VL-7B, use with Hugging Face transformers or vLLM 🤗
> comes with SFT & alignment datasets and a new benchmark 👏
reach-vb 
posted an update 5 days ago
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2981
hey hey @mradermacher - VB from Hugging Face here, we'd love to onboard you over to our optimised xet backend! 💥

as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!

in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.

p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage

p.p.s. this is fully backwards compatible so everything will work as it should! 🤗
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merve 
posted an update 5 days ago
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2515
It was the week of video generation at @huggingface , on top of many new LLMs, VLMs and more!
Let’s have a wrap 🌯 merve/may-16-releases-682aeed23b97eb0fe965345c

LLMs 💬
> Alibaba Qwen released WorldPM-72B, new World Preference Model trained with 15M preference samples (OS)
> II-Medical-8B, new LLM for medical reasoning that comes in 8B by Intelligent-Internet
> TRAIL is a new dataset by Patronus for trace error reasoning for agents (OS)

Multimodal 🖼️💬
> Salesforce Research released BLIP3o, a new any-to-any model with image-text input and image-text output 💬it’s based on an image encoder, a text decoder and a DiT, and comes in 8B
> They also released pre-training and fine-tuning datasets
> MMMG is a multimodal generation benchmark for image, audio, text (interleaved)

Image Generation ⏯️
> Alibaba Wan-AI released Wan2.1-VACE, video foundation model for image and text to video, video-to-audio and more tasks, comes in 1.3B and 14B (OS)
> ZuluVision released MoviiGen1.1, new cinematic video generation model based on Wan 2.1 14B (OS)
> multimodalart released isometric-skeumorphic-3d-bnb, an isometric 3D asset generator (like AirBnB assets) based on Flux
> LTX-Video-0.9.7-distilled is a new real-time video generation (text and image to video) model by Lightricks
> Hidream_t2i_human_preference is a new text-to-image preference dataset by Rapidata with 195k human responses from 38k annotators

Audio 🗣️
> stabilityai released stable-audio-open-small new text-to-audio model
> TEN-framework released ten-vad, voice activity detection model (OS)
merve 
posted an update 8 days ago
burtenshaw 
posted an update 8 days ago
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2942
We're thrilled to announce the launch of our comprehensive Model Context Protocol (MCP) Course! This free program is designed to take learners from foundational understanding to practical application of MCP in AI.

Follow the course on the hub: mcp-course

In this course, you will:
📖 Study Model Context Protocol in theory, design, and practice.
🧑‍💻 Learn to use established MCP SDKs and frameworks.
💾 Share your projects and explore applications created by the community.
🏆 Participate in challenges and evaluate your MCP implementations.
🎓 Earn a certificate of completion.

At the end of this course, you'll understand how MCP works and how to build your own AI applications that leverage external data and tools using the latest MCP standards.
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merve 
posted an update 12 days ago
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4998
VLMS 2025 UPDATE 🔥

We just shipped a blog on everything latest on vision language models, including
🤖 GUI agents, agentic VLMs, omni models
📑 multimodal RAG
⏯️ video LMs
🤏🏻 smol models
..and more! https://huggingface.co/blog/vlms-2025
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merve 
posted an update 18 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 19 days ago
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6545
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 22 days ago
burtenshaw 
posted an update 23 days ago
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2096
Qwen 3 Fine tuning >> MoE. Update the experiment thread to include config and script for fine-tuning the Qwen3-30B-A3B model.

The goal is to make a low latency non-thinking model for a daily driver coding, so 3 billion parameters active should be perfect.

✔️ training running
✔️ evals running
⏭️ improve dataset

The moe isn't going to fit into colab's A100 even with quantization (🙏 @UnslothAI ). So I've been working on HF spaces' H100s for this. Everything is available in the tread and I'll share more tomorrow.

burtenshaw/Qwen3-Code-Lite#1
merve 
posted an update 24 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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merve 
posted an update 29 days ago
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Don't sleep on new AI at Meta Vision-Language release! 🔥

facebook/perception-encoder-67f977c9a65ca5895a7f6ba1
facebook/perception-lm-67f9783f171948c383ee7498

Meta dropped swiss army knives for vision with A2.0 license 👏
> image/video encoders for vision language modelling and spatial understanding (object detection etc) 👏
> The vision LM outperforms InternVL3 and Qwen2.5VL 👏
> They also release gigantic video and image datasets

The authors attempt to come up with single versatile vision encoder to align on diverse set of tasks.

They trained Perception Encoder (PE) Core: a new state-of-the-art family of vision encoders that can be aligned for both vision-language and spatial tasks. For zero-shot image tasks, it outperforms latest sota SigLIP2 👏



> Among fine-tuned ones, first one is PE-Spatial. It's a model to detect bounding boxes, segmentation, depth estimation and it outperforms all other models 😮



> Second one is PLM, Perception Language Model, where they combine PE-Core with Qwen2.5 LM 7B. it outperforms all other models (including InternVL3 which was trained with Qwen2.5LM too!)

The authors release the following checkpoints in sizes base, large and giant:

> 3 PE-Core checkpoints (224, 336, 448)
> 2 PE-Lang checkpoints (L, G)
> One PE-Spatial (G, 448)
> 3 PLM (1B, 3B, 8B)
> Datasets



Authors release following datasets 📑
> PE Video: Gigantic video datasete of 1M videos with 120k expert annotations ⏯️
> PLM-Video and PLM-Image: Human and auto-annotated image and video datasets on region-based tasks
> PLM-VideoBench: New video benchmark on MCQA
  • 2 replies
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pcuenq 
in meta-llama/Llama-Guard-4-12B about 1 month ago

Fix rope and use static cache
#1 opened about 1 month ago by
pcuenq
burtenshaw 
posted an update about 1 month ago
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2514
The rebooted LLM course starts today with an overhauled chapter 1 on Transformers:

👉 Follow the org to join the course: huggingface-course

We’re starting from the foundations of modern generative AI by looking at transformers. This chapter is expanded in depth and features so contains new material like:

FREE and CERTIFIED exam on fundamentals of transformers
deeper exploration of transformer architectures and attention mechanisms
end -to-end exploration of inference strategies for prefill and decode steps

The course has leveled up in complexity and depth, so this a great time to join in if you want to build you own AI models.
pcuenq 
updated a model about 1 month ago
merve 
posted an update about 1 month ago
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3420
New foundation model on image and video captioning just dropped by NVIDIA AI 🔥

Describe Anything Model (DAM) is a 3B vision language model to generate detailed captions with localized references 😮

The team released the models, the dataset, a new benchmark and a demo 🤩 nvidia/describe-anything-680825bb8f5e41ff0785834c

Most of the vision LMs focus on image as a whole, lacking localized references in captions, and not taking in visual prompts (points, boxes, drawings around objects)

DAM addresses this on two levels: new vision backbone that takes in focal crops and the image itself, and a large scale dataset 👀

They generate a dataset by extending existing segmentation and referring expression generation datasets like REFCOCO, by passing in the images and classes to VLMs and generating captions.

Lastly, they also release a new benchmark again with self-supervision, they use an LLM to evaluate the detailed captions focusing on localization 👏