monsoon-nlp (Nick Doiron)

reacted to seawolf2357's post with 👀 8 days ago

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Samsung Hacking Incident: Samsung Electronics' Official Hugging Face Account Compromised
Samsung Electronics' official Hugging Face account has been hacked. Approximately 17 hours ago, two new language models (LLMs) were registered under Samsung Electronics' official Hugging Face account. These models are:

https://huggingface.co/Samsung/MuTokenZero2-32B
https://huggingface.co/Samsung/MythoMax-L2-13B

The model descriptions contain absurd and false claims, such as being trained on "1 million W200 GPUs," hardware that doesn't even exist.
Moreover, community participants on Hugging Face who have noticed this issue are continuously posting that Samsung Electronics' account has been compromised.
There is concern about potential secondary and tertiary damage if users download these LLMs released under the Samsung Electronics account, trusting Samsung's reputation without knowing about the hack.
Samsung Electronics appears to be unaware of this situation, as they have not taken any visible measures yet, such as changing the account password.
Source: https://discord.gg/openfreeai

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reacted to merterbak's post with 🔥 about 2 months ago

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Meta has unveiled its Llama 4 🦙 family of models, featuring native multimodality and mixture-of-experts architecture. Two model families are available now:
Models🤗: meta-llama/llama-4-67f0c30d9fe03840bc9d0164
Blog Post: https://ai.meta.com/blog/llama-4-multimodal-intelligence/
HF's Blog Post: https://huggingface.co/blog/llama4-release

- 🧠 Native Multimodality - Process text and images in a unified architecture
- 🔍 Mixture-of-Experts - First Llama models using MoE for incredible efficiency
- 📏 Super Long Context - Up to 10M tokens
- 🌐 Multilingual Power - Trained on 200 languages with 10x more multilingual tokens than Llama 3 (including over 100 languages with over 1 billion tokens each)

🔹 Llama 4 Scout
- 17B active parameters (109B total)
- 16 experts architecture
- 10M context window
- Fits on a single H100 GPU
- Beats Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1

🔹 Llama 4 Maverick
- 17B active parameters (400B total)
- 128 experts architecture
- It can fit perfectly on DGX H100(8x H100)
- 1M context window
- Outperforms GPT-4o and Gemini 2.0 Flash
- ELO score of 1417 on LMArena currently second best model on arena

🔹 Llama 4 Behemoth (Coming Soon)
- 288B active parameters (2T total)
- 16 experts architecture
- Teacher model for Scout and Maverick
- Outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM benchmarks

posted an update about 2 months ago

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I was curious about the Block Diffusion hybrid model and tried retraining it on a DNA tokenizer + dataset 🧬. Too early to evaluate, but it generates sequences (AAATGG TTATTG CAAATC...) and was improving on the validation set during training
Model: monsoon-nlp/dna-blockdiff-papaya
Original paper: Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models (2503.09573)

reacted to daavoo's post with 👀 2 months ago

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🤖 🗺️Pushed an update to support processing entire areas (i.e. a city) in https://github.com/mozilla-ai/osm-ai-helper.

I have mapped and contributed to https://www.openstreetmap.org all(?) the swimming pools around my hometown, taking about 1h to process (+15 min verification) in a free Colab GPU🚀

Try it yourself: mozilla-ai/osm-ai-helper

And check the https://github.com/mozilla-ai/osm-ai-helper to find the demo notebooks.

reacted to clem's post with 🚀 2 months ago

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We just crossed 1,500,000 public models on Hugging Face (and 500k spaces, 330k datasets, 50k papers). One new repository is created every 15 seconds. Congratulations all!

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reacted to Yehor's post with 👍 2 months ago

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Published some datasets for researchers in Ukrainian NLP from my project https://ua-lawyer.com (Q&A platform in Ukraine):

Datasets:
- ua-l/topics
- ua-l/topics-train-test
- ua-l/topics-text-label

Model:
- https://huggingface.co/ua-l/topics-classifier

Space:
- ua-l/topics-classifier-demo

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replied to ashercn97's post 2 months ago

I would say, sort by "Mean (task)" and pick one of those. Or if you can, compare three of the best on your data. That holds unless you need a longer context, or you are in medical or similar field where there are domain-specific models

posted an update 3 months ago

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Genetic counselors help patients get 🧬 tests and understand their results. They need to study inheritance of several conditions, statistics, and patient care 🤓⚕️. I compiled 225 multiple-choice questions for the ABGC exam into a dataset: monsoon-nlp/genetic-counselor-multiple-choice
Llama 3.1 8B Instruct gets a 51% score.
I'm also creating a dataset of real-world open-ended questions (starting with Reddit) and am open to contributors

reacted to MohamedRashad's post with 🧠 3 months ago

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Today is a big day for the Arabic Language,

We have Navid-AI/The-Arabic-Rag-Leaderboard,
an Update for OALL/Open-Arabic-LLM-Leaderboard
and the release of atlasia/darija-chatbot-arena

All of this announcements was under 12 hours of time 🤯

reacted to davanstrien's post with ❤️ 5 months ago

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🇸🇰 Hovorte po slovensky? Help build better AI for Slovak!

We only need 90 more annotations to include Slovak in the next Hugging Face FineWeb2-C dataset ( data-is-better-together/fineweb-c) release!

Your contribution will help create better language models for 5+ million Slovak speakers.

Annotate here: data-is-better-together/fineweb-c.

Read more about why we're doing it: https://huggingface.co/blog/davanstrien/fineweb2-community

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reacted to MohamedRashad's post with 🚀 6 months ago

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A while back i shared this model MohamedRashad/arabic-small-nougat that was a finetune from facebook/nougat-small for the Arabic Language.

Today this humble project has been scaled with new models, new datasets, new space, and a new paper

Check everything throught this collection here:
MohamedRashad/arabic-nougat-673a3f540bd92904c9b92a8e

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reacted to fdaudens's post with 🤗 6 months ago

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🦋 Hug the butterfly! You can now add your Bluesky handle to your Hugging Face profile! ✨

reacted to m-ric's post with 😎 7 months ago

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I'm very proud to have supported @CGIAR and @Digigreen in making http://Farmer.chat, an app that supports 20k smallholder farmers on a daily basis 🌾

There are ~500 million smallholder farmers globally, playing a critical role in global food security. Having access to accurate information is essential for them.

💬 An “agricultural extension service” offers technical advice on agriculture, and also supplies farmers with the necessary inputs and services to support their agricultural production.

But agriculture extension agents are not in large enough numbers to cope with all the requests, especially in countries like Kenya, India, Ethiopia, and Nigeria.

🚀 So the team set out to build an app called http://Farmer.Chat, to provide an agricultural extension service, by building on the immense knowledge accumulated by CGIAR.

✨ The app is technically impressive: behind the Whatsapp-type UX, an agent interprets the user's intent, and identifies which tool to call to best answer their request: weather API, RAG on a CGIAR-provided knowledge base, market data, etc. The RAG on the knowledge base is in itself a work of art.

🎯 A key part of building such a complex system is to be able to evaluate it properly. During our bi-weekly sessions with the team, I could support them in implementing the method called "LLM-as-a-judge" to tackle this problem.

It worked really well : thanks to the amazing work of the team, the app now successfully answered over 300 thousand requests, in 6 different languages, and it keeps growing!

➡️ @Vinsingh , @rajgreen and I just wrote a blog post to describe how the app works, especially the LLM-as-a-judge system!

Read it here 👉 https://huggingface.co/blog/digital-green-llm-judge

reacted to Tonic's post with 👀 7 months ago

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🙋🏻‍♂️ hey there folks ,

really enjoying sharing cool genomics and protein datasets on the hub these days , check out our cool new org :

seq-to-pheno

scroll down for the datasets, still figuring out how to optimize for discoverability , i do think on that part it will be better than zenodo[dot}org , it would be nice to write a tutorial about that and compare : we already have more downloads than most zenodo datasets from famous researchers !

reacted to nyuuzyou's post with 👀 7 months ago

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🎙 Introducing LiveATC Recordings (Partial 2024-08-26) Dataset - nyuuzyou/liveatc

Dataset highlights:

- 21,172 air traffic control audio recordings from LiveATC.net for August 26, 2024
- Multilingual content, primarily in English with potential for other languages
- Each entry includes: audio file, ICAO airport code, facility type, date, and time
- Contains original MP3 files stored in .tar.zst archives, organized by ICAO airport code
- Data covers various airports and ATC facilities worldwide
- Subject to LiveATC.net's Terms of Use for personal, non-commercial use only

The dataset can be used for audio classification, automatic speech recognition, and analysis of air traffic control communications. The inclusion of recordings from multiple airports allows for comparative analysis across different locations and facility types.

reacted to Tonic's post with 👀 8 months ago

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🙋🏻‍♂️ Hey there folks ,

🦎Salamandra release by @mvillegas and team
@BSC_CNS

BSC-LT is absolutely impressive so far !

perhaps the largest single training dataset of high quality text to date of 7.8 trillion tokens in 35 European languages and code.

the best part : the data was correctly licenced so it's actually future-proof!

the completions model is really creative and instruct fine tuned version is very good also.

now you can use such models for multi-lingual enterprise applications with further finetunes , long response generation, structured outputs (coding) also works.

check out 👇🏻
the collection : BSC-LT/salamandra-66fc171485944df79469043a
the repo : https://github.com/langtech-bsc/salamandra
7B-Instruct demo : Tonic/Salamandra-7B

reacted to clem's post with 🚀 8 months ago

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Very few people realize that most of the successful AI startups got successful because they were focused on open science and open-source for at least their first few years. To name but a few, OpenAI (GPT, GPT2 was open-source), Runway & Stability (stable diffusion), Cohere, Mistral and of course Hugging Face!

The reasons are not just altruistic, it's also because sharing your science and your models pushes you to build AI faster (which is key in a fast-moving domain like AI), attracts the best scientists & engineers and generates much more visibility, usage and community contributions than if you were 100% closed-source. The same applies to big tech companies as we're seeing with Meta and Google!

More startups and companies should release research & open-source AI, it's not just good for the world but also increases their probability of success!

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reacted to pain's post with ❤️ 8 months ago

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We have published an excellent paper for Arabic CLIP model.

Paper link:
https://aclanthology.org/2024.arabicnlp-1.9/

More information in this website:
https://arabic-clip.github.io/Arabic-CLIP/

All datasets, models, and demo are published to Huggingface:

Arabic-Clip

The codes are published to github:
https://github.com/Arabic-Clip/Arabic-CLIP

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reacted to ezgikorkmaz's post with 👀 8 months ago

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If you are interested in adversarial machine learning and AI safety, find my curated reading list below:

GitHub: https://github.com/EzgiKorkmaz/adversarial-reinforcement-learning

reacted to Tonic's post with 🚀 9 months ago

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🙋🏻‍♂️hey there folks ,

✒️InkubaLM has been trained from scratch using 1.9 billion tokens of data for five African languages, along with English and French data, totaling 2.4 billion tokens of data. It is capable of understanding and generating content in five African languages: Swahili, Yoruba, Hausa, isiZulu, and isiXhosa, as well as English and French.

model lelapa/InkubaLM-0.4B
demo Tonic/Inkuba-0.4B

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