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1024m  authored a paper 25 days ago
Robust and Fine-Grained Detection of AI Generated Texts
1024m  authored a paper 27 days ago
Improving Multilingual Capabilities with Cultural and Local Knowledge in Large Language Models While Enhancing Native Performance
1024m  authored a paper about 1 month ago
Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation
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m-ric 
posted an update 3 days ago
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I've made an open version of Google's NotebookLM, and it shows the superiority of the open source tech task! 💪

The app's workflow is simple. Given a source PDF or URL, it extracts the content from it, then tasks Meta's Llama 3.3-70B with writing the podcast script, with a good prompt crafted by @gabrielchua ("two hosts, with lively discussion, fun notes, insightful question etc.")
Then it hands off the text-to-speech conversion to Kokoro-82M, and there you go, you have two hosts discussion any article.

The generation is nearly instant, because:
> Llama 3.3 70B is running at 1,000 tokens/seconds with Cerebras inference
> The audio is generated in streaming mode by the tiny (yet powerful) Kokoro, generating voices faster than real-time.

And the audio generation runs for free on Zero GPUs, hosted by HF on H200s.

Overall, open source solutions rival the quality of closed-source solutions at close to no cost!

Try it here 👉👉 m-ric/open-notebooklm
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merve 
posted an update 5 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 6 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 9 days ago
fdaudens 
posted an update 9 days ago
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Forget everything you know about transcription models - NVIDIA's parakeet-tdt-0.6b-v2 changed the game for me!

Just tested it with Steve Jobs' Stanford speech and was speechless (pun intended). The video isn’t sped up.

3 things that floored me:
- Transcription took just 10 seconds for a 15-min file
- Got a CSV with perfect timestamps, punctuation & capitalization
- Stunning accuracy (correctly captured "Reed College" and other specifics)

NVIDIA also released a demo where you can click any transcribed segment to play it instantly.

The improvement is significant: number 1 on the ASR Leaderboard, 6% error rate (best in class) with complete commercial freedom (cc-by-4.0 license).

Time to update those Whisper pipelines! H/t @Steveeeeeeen for the finding!

Model: nvidia/parakeet-tdt-0.6b-v2
Demo: nvidia/parakeet-tdt-0.6b-v2
ASR Leaderboard: hf-audio/open_asr_leaderboard
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fdaudens 
posted an update 11 days ago
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I just gave my chatbots a massive upgrade: they can now generate audio from text, modify images — you name it. Here’s how:

The Gradio team shipped MCP support. That means you can plug any AI app built with it into Claude or Cursor using the Model Context Protocol (MCP) — think of it like a USB port for LLMs.

I put it to the test:
- Whipped up a quick text-to-speech app with Kokoro on HF (with an LLM riding shotgun, naturally)
- Added "mcp_server=True" in the code
- Connected it to Claude

Now I can generate audio from any text. The possibilities are next-level: you can potentially plug any of the 500K+ AI apps on Hugging Face to your favorite LLM.

Is this the new UI for AI?

- My tts app (feel free to use/duplicate it): fdaudens/kokoro-mcp
- Blog post: https://huggingface.co/blog/gradio-mcp
alielfilali01 
posted an update 11 days ago
fdaudens 
posted an update 12 days ago
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Want to know which AI models are least likely to hallucinate — and how to keep yours from spiking hallucinations by 20%?

A new benchmark called Phare, by Giskard, tested leading models across multiple languages, revealing three key findings:

1️⃣ Popular models aren't necessarily factual. Some models ranking highest in user satisfaction benchmarks like LMArena are actually more prone to hallucination.

2️⃣ The way you ask matters - a lot. When users present claims confidently ("My teacher said..."), models are 15% less likely to correct misinformation vs. neutral framing ("I heard...").

3️⃣ Telling models to "be concise" can increase hallucination by up to 20%.

What's also cool is that the full dataset is public - use them to test your own models or dive deeper into the results! H/t @davidberenstein1957 for the link.

- Study: https://www.giskard.ai/knowledge/good-answers-are-not-necessarily-factual-answers-an-analysis-of-hallucination-in-leading-llms
- Leaderboard: https://phare.giskard.ai/
- Dataset: giskardai/phare
merve 
posted an update 12 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 17 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
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fdaudens 
posted an update 18 days ago
victor 
posted an update 18 days ago
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DIA TTS is just amazing - please share your funniest gens (here is mine) 😂
nari-labs/Dia-1.6B
merve 
posted an update 19 days ago
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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 👏
m-ric 
posted an update 24 days ago
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New king of open VLMs: InternVL3 takes Qwen 2.5's crown! 👑

InternVL have been a wildly successful series of model : and the latest iteration has just taken back their crown thanks to their superior, natively multimodal vision training pipeline.

➡️ Most of the vision language models (VLMs) these days are built like Frankenstein : take a good text-only Large Language Model (LLM) backbone, stitch a specific vision transformer (ViT) on top of it. Then the training is sequential 🔢 : 1. Freeze the LLM weights while you train the ViT only to work with the LLM part, then 2. Unfreeze all weights to train all weights in order to work together.

💫 The Shanghai Lab decided to challenge this paradigm and chose this approach that they call "native". For each of their model sizes, they still start from a good LLM (mostly Qwen-2.5 series, did I tell you I'm a huge fan of Qwen? ❤️), and stitch the ViT, but they don't freeze anything : they train all weights together with interleaved text and image understanding data in a single pre-training phase 🎨.

They claim it results in more seamless interactions between modalities. And the results prove them right: they took the crown of top VLMs, at nearly all sizes, from their Qwen-2.5 parents. 👑
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fdaudens 
posted an update 25 days ago
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Just tested something this morning that feels kind of game-changing for how we publish, discover, and consume news with AI: connecting Claude directly to the New York Times through MCP.

Picture this: You ask Claude about a topic, and it instantly pulls verified and trusted NYT content — no more guessing if the info is accurate.

The cool part? Publishers stay in control of what they share via API, and users get fast, reliable access through the AI tools they already use. Instead of scraping random stuff off the web, we get a future where publishers actively shape how their journalism shows up in AI.

It’s still a bit technical to set up right now, but this could get super simple soon — like installing apps on your phone, but for your chatbot. And you keep the brand connection, too.

Not saying it solves everything, but it’s definitely a new way to distribute content — and maybe even find some fresh value in the middle of this whole news + AI shakeup. Early movers will have a head start.

Curious what folks think — could MCPs be a real opportunity for journalism?
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merve 
posted an update 28 days ago
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sooo many open AI releases past week, let's summarize! 🤗
merve/april-11-releases-67fcd78be33d241c0977b9d2

multimodal
> Moonshot AI released Kimi VL Thinking, first working open-source multimodal reasoning model and Kimi VL Instruct, both 16B MoEs with 3B active params (OS)
> InternVL3 released based on Qwen2.5VL, 7 ckpts with various sizes (1B to 78B)

LLMs
> NVIDIA released Llama-3_1-Nemotron-Ultra-253B-v1 an LLM built on Llama 405B for reasoning, chat and tool use
> Agentica released DeepCoder-14B-Preview, fine-tuned version of DeepSeek-R1-Distilled-Qwen-14B on problem-test pairs, along with the compiled dataset
> Zyphra/ZR1-1.5B is a new small reasoning LLM built on R1-Distill-1.5B (OS)
> Skywork-OR1-32B-Preview is a new reasoning model by Skywork

Image Generation
> HiDream releases three new models, HiDream I1 Dev, I1 Full, and I1 fast for image generation (OS)

*OS ones have Apache 2.0 or MIT licenses
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fdaudens 
posted an update about 1 month ago
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Want AI that truly understands your country's culture? Public institutions are sitting on the next AI revolution - and here's the practical guide to unlock it.

I've had fascinating conversations recently about sovereign AI, with people trying to solve this recurring question: "How do we build AI that truly understands our culture?"

This guide by @evijit and @yjernite brings lots of insights about this question. It's not just about throwing data at models. It's about partnering cultural expertise with tech infrastructure in ways we're just starting to figure out.

An example? The National Library of Norway already has 150+ AI models on Hugging Face. They're not just digitizing books - they're building AI that thinks in Norwegian, understands Norwegian values, and serves Norwegian citizens.

This is sovereign AI in practice: technology that understands your culture, values, and languages.

Especially loved the practical examples on how to do this:
- Real examples from museums, libraries, and government agencies
- How to convert complex documents (PDFs, PowerPoints) into ML-ready formats
- Code templates for processing public data
- Technical recipes for sharing datasets on open platforms

The stakes? Citizens' ability to leverage their collective digital intelligence.

The technology is ready. The infrastructure exists. The guide shows exactly how to use it. What's needed is your cultural expertise to shape these tools.

Check it out: https://huggingface.co/blog/evijit/public-org-data-ai

P.s.: Building cool projects in a public institution? Share them in the comments for others to learn from!
fdaudens 
posted an update about 1 month ago
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Do chatbots lie about Céline Dion? We now have answers, not speculation.

Ai2 just released OLMoTrace and it's a game-changer for transparency. You can literally see where an AI's responses come from in its training data - in real time.

The demo shows results about Céline. So I tried it out myself! Watch what happens in the video.

For journalists, researchers studying hallucinations and anyone who needs to trust their AI, this is like getting X-ray vision into AI systems. When the model made claims, I could instantly verify them against original sources. When it hallucinated, I could see why.

You can finally 1) understand how LLMs actually work and 2) verify if what they're saying is true. No more blind trust.

This pushes the open data movement to the next level.

👉 Blog post: https://allenai.org/blog/olmotrace
👉 Paper: https://www.datocms-assets.com/64837/1743890415-olmotrace.pdf

P.S.: A word of caution: never use a chatbot as a knowledge base. It's not Google. Better use it with a connection to the internet.
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fdaudens 
posted an update about 1 month ago
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🎨 Designers, meet OmniSVG! This new model helps you create professional vector graphics from text/images, generate editable SVGs from icons to detailed characters, convert rasters to vectors, maintain style consistency with references, and integrate into your workflow.

@OmniSVG
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loubnabnl 
authored a paper about 1 month ago