Aurélien-Morgan CLAUDON

Aurelien-Morgan

AI & ML interests

None yet

Recent Activity

updated a Space about 20 hours ago
retrain-pipelines/README
View all activity

Organizations

Giskard's profile picture Gradio-Blocks-Party's profile picture Keras Dreambooth Event's profile picture Blog-explorers's profile picture huggingPartyParis's profile picture ZeroGPU Explorers's profile picture C4AI Community's profile picture Chinese LLMs on Hugging Face's profile picture Paris AI Running Club's profile picture cvmistralparis's profile picture Hugging Face Discord Community's profile picture Hugging Face Party @ PyTorch Conference's profile picture Nerdy Face's profile picture retrain-pipelines's profile picture

Aurelien-Morgan's activity

reacted to fdaudens's post with ❤️ about 21 hours ago
view post
Post
1491
⭐️ The AI Energy Score project just launched - this is a game-changer for making informed decisions about AI deployment.

You can now see exactly how much energy your chosen model will consume, with a simple 5-star rating system. Think appliance energy labels, but for AI.

Looking at transcription models on the leaderboard is fascinating: choosing between whisper-tiny or whisper-large-v3 can make a 7x difference. Real-time data on these tradeoffs changes everything.

166 models already evaluated across 10 different tasks, from text generation to image classification. The whole thing is public and you can submit your own models to test.

Why this matters:
- Teams can pick efficient models that still get the job done
- Developers can optimize for energy use from day one
- Organizations can finally predict their AI environmental impact

If you're building with AI at any scale, definitely worth checking out.

👉 leaderboard: https://lnkd.in/esrSxetj
👉 blog post: https://lnkd.in/eFJvzHi8

Huge work led by @sasha with @bgamazay @yjernite @sarahooker @regisss @meg
  • 1 reply
·
replied to AdinaY's post about 21 hours ago
reacted to AdinaY's post with 🔥😎 about 21 hours ago
view post
Post
1703
InspireMusic 🎵🔥 an open music generation framework by Alibaba FunAudio Lab
Model: FunAudioLLM/InspireMusic-1.5B-Long
Demo: FunAudioLLM/InspireMusic
✨ Music, songs, audio - ALL IN ONE
✨ High quality audio: 24kHz & 48kHz sampling rates
✨ Long-Form Generation: enables extended audio creation
✨ Efficient Fine-Tuning: precision (BF16, FP16, FP32) with user-friendly scripts
  • 1 reply
·
reacted to Kseniase's post with ❤️ 12 days ago
view post
Post
4828
8 Free Sources on Reinforcement Learning

With the phenomenon of DeepSeek-R1's top reasoning capabilities, we all saw the true power of RL. At its core, RL is a type of machine learning where a model/agent learns to make decisions by interacting with an environment to maximize a reward. RL learns through trial and error, receiving feedback in the form of rewards or penalties.

Here's a list of free sources that will help you dive into RL and how to use it:

1. "Reinforcement Learning: An Introduction" book by Richard S. Sutton and Andrew G. Barto -> https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf

2. Hugging Face Deep Reinforcement Learning Course -> https://huggingface.co/learn/deep-rl-course/unit0/introduction
You'll learn how to train agents in unique environments, using best libraries, share your results, compete in challenges, and earn a certificate.

3. OpenAI Spinning Up in Deep RL -> https://spinningup.openai.com/en/latest/index.html
A comprehensive overview of RL with many useful resources

4. "Reinforcement Learning and Optimal Control" books, video lectures and course material by Dimitri P. Bertsekas from ASU -> https://web.mit.edu/dimitrib/www/RLbook.html
Explores approximate Dynamic Programming (DP) and RL with key concepts and methods like rollout, tree search, and neural network training for RL and more.

5. RL Course by David Silver (Google DeepMind) -> https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPeb
Many recommend these video lectures as a good foundation

6. RL theory seminars -> https://sites.google.com/view/rltheoryseminars/home?authuser=0
Provides virtual seminars from different experts about RL advancements

7. "Reinforcement Learning Specialization" (a 4-course series on Coursera) -> https://www.coursera.org/learn/fundament

8. Concepts: RLHF, RLAIF, RLEF, RLCF -> https://www.turingpost.com/p/rl-f
Our flashcards easily explain what are these four RL approaches with different feedback
reacted to hexgrad's post with 👍 15 days ago
reacted to danielhanchen's post with ❤️🔥 about 1 month ago
view post
Post
4646
We fixed many bugs in Phi-4 & uploaded fixed GGUF + 4-bit versions! ✨

Our fixed versions are even higher on the Open LLM Leaderboard than Microsoft's!

GGUFs: unsloth/phi-4-GGUF
Dynamic 4-bit: unsloth/phi-4-unsloth-bnb-4bit

You can also now finetune Phi-4 for free on Colab: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb

Read our blogpost for more details on bug fixes etc: https://unsloth.ai/blog/phi4
reacted to BrigitteTousi's post with 👀 about 1 month ago
view post
Post
1199
Community fine-tuned models are more carbon efficient than the models they are derived from! 🥳🌿

@alozowski @clefourrier @SaylorTwift @albertvillanova evaluated CO₂ emissions associated with model inference for over 3000 models on the Open LLM Leaderboard. Interesting trends and new insights emerged...👀

Blog Post: https://huggingface.co/blog/leaderboard-emissions-analysis

Leaderboard: open-llm-leaderboard/open_llm_leaderboard
reacted to andrewrreed's post with 👍 about 1 month ago
view post
Post
2717
🚀 Supercharge your LLM apps with Langfuse on Hugging Face Spaces!

Langfuse brings end-to-end observability and tooling to accelerate your dev workflow from experiments through production

Now available as a Docker Space directly on the HF Hub! 🤗

🔍 Trace everything: monitor LLM calls, retrieval, and agent actions with popular frameworks
1⃣ One-click deployment: on Spaces with persistent storage and integrated OAuth
🛠 Simple Prompt Management: Version, edit, and update without redeployment
✅ Intuitive Evals: Collect user feedback, run model/prompt evaluations, and improve quality
📊 Dataset Creation: Build datasets directly from production data to enhance future performance

Kudos to the Langfuse team for this collab and the awesome, open-first product they’re building! 👏 @marcklingen @Clemo @MJannik

🔗 Space: langfuse/langfuse-template-space
🔗 Docs: https://huggingface.co/docs/hub/spaces-sdks-docker-langfuse
  • 1 reply
·
reacted to tomaarsen's post with ❤️ about 1 month ago
view post
Post
3004
That didn't take long! Nomic AI has finetuned the new ModernBERT-base encoder model into a strong embedding model for search, classification, clustering and more!

Details:
🤖 Based on ModernBERT-base with 149M parameters.
📊 Outperforms both nomic-embed-text-v1 and nomic-embed-text-v1.5 on MTEB!
🏎️ Immediate FA2 and unpacking support for super efficient inference.
🪆 Trained with Matryoshka support, i.e. 2 valid output dimensionalities: 768 and 256.
➡️ Maximum sequence length of 8192 tokens!
2️⃣ Trained in 2 stages: unsupervised contrastive data -> high quality labeled datasets.
➕ Integrated in Sentence Transformers, Transformers, LangChain, LlamaIndex, Haystack, etc.
🏛️ Apache 2.0 licensed: fully commercially permissible

Try it out here: nomic-ai/modernbert-embed-base

Very nice work by Zach Nussbaum and colleagues at Nomic AI.
reacted to AdinaY's post with 🔥 about 2 months ago
reacted to AdinaY's post with 👀 about 2 months ago
view post
Post
3033
QvQ-72B-Preview🎄 an open weight model for visual reasoning just released by Alibaba_Qwen team
Qwen/qvq-676448c820912236342b9888
✨ Combines visual understanding & language reasoning.
✨ Scores 70.3 on MMMU
✨ Outperforms Qwen2-VL-72B-Instruct in complex problem-solving
replied to FranckAbgrall's post about 2 months ago
view reply

That's cool. A little subtle, though. Would you consider a different color for the "dialog bubble" icon too? Making it for instance (dark) golden yellow, plus the mouseover text ?

replied to clem's post about 2 months ago
view reply

Everyone got off the waitlist. So cool. So, you managed to privatize the street for many robots to greet us ?

reacted to m-ric's post with 👍 about 2 months ago
view post
Post
2536
𝐇𝐮𝐠𝐠𝐢𝐧𝐠 𝐅𝐚𝐜𝐞 𝐫𝐞𝐥𝐞𝐚𝐬𝐞𝐬 𝐏𝐢𝐜𝐨𝐭𝐫𝐨𝐧, 𝐚 𝐦𝐢𝐜𝐫𝐨𝐬𝐜𝐨𝐩𝐢𝐜 𝐥𝐢𝐛 𝐭𝐡𝐚𝐭 𝐬𝐨𝐥𝐯𝐞𝐬 𝐋𝐋𝐌 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝟒𝐃 𝐩𝐚𝐫𝐚𝐥𝐥𝐞𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 🥳

🕰️ Llama-3.1-405B took 39 million GPU-hours to train, i.e. about 4.5 thousand years.

👴🏻 If they had needed all this time, we would have GPU stories from the time of Pharaoh 𓂀: "Alas, Lord of Two Lands, the shipment of counting-stones arriving from Cathay was lost to pirates, this shall delay the building of your computing temple by many moons "

🛠️ But instead, they just parallelized the training on 24k H100s, which made it take just a few months.
This required parallelizing across 4 dimensions: data, tensor, context, pipeline.
And it is infamously hard to do, making for bloated code repos that hold together only by magic.

🤏 𝗕𝘂𝘁 𝗻𝗼𝘄 𝘄𝗲 𝗱𝗼𝗻'𝘁 𝗻𝗲𝗲𝗱 𝗵𝘂𝗴𝗲 𝗿𝗲𝗽𝗼𝘀 𝗮𝗻𝘆𝗺𝗼𝗿𝗲! Instead of building mega-training codes, Hugging Face colleagues cooked in the other direction, towards tiny 4D parallelism libs. A team has built Nanotron, already widely used in industry.
And now a team releases Picotron, a radical approach to code 4D Parallelism in just a few hundred lines of code, a real engineering prowess, making it much easier to understand what's actually happening!

⚡ 𝗜𝘁'𝘀 𝘁𝗶𝗻𝘆, 𝘆𝗲𝘁 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹:
Counting in MFU (Model FLOPs Utilization, how much the model actually uses all the compute potential), this lib reaches ~50% on SmolLM-1.7B model with 8 H100 GPUs, which is really close to what huge libs would reach. (Caution: the team is leading further benchmarks to verify this)

Go take a look 👉 https://github.com/huggingface/picotron/tree/main/picotron
  • 1 reply
·
reacted to yjernite's post with 👀 2 months ago
view post
Post
2230
🇪🇺 Policy Thoughts in the EU AI Act Implementation 🇪🇺

There is a lot to like in the first draft of the EU GPAI Code of Practice, especially as regards transparency requirements. The Systemic Risks part, on the other hand, is concerning for both smaller developers and for external stakeholders.

I wrote more on this topic ahead of the next draft. TLDR: more attention to immediate large-scale risks and to collaborative solutions supported by evidence can help everyone - as long as developers disclose sufficient information about their design choices and deployment contexts.

Full blog here, based on our submitted response with @frimelle and @brunatrevelin :

https://huggingface.co/blog/yjernite/eu-draft-cop-risks#on-the-proposed-taxonomy-of-systemic-risks
  • 2 replies
·
reacted to julien-c's post with 👍 2 months ago
view post
Post
9584
After some heated discussion 🔥, we clarify our intent re. storage limits on the Hub

TL;DR:
- public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible
- private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise)

docs: https://huggingface.co/docs/hub/storage-limits

We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community 🔥

cc: @reach-vb @pierric @victor and the HF team
·
reacted to dvilasuero's post with ❤️ 2 months ago
view post
Post
2367
🌐 Announcing Global-MMLU: an improved MMLU Open dataset with evaluation coverage across 42 languages, built with Argilla and the Hugging Face community.

Global-MMLU is the result of months of work with the goal of advancing Multilingual LLM evaluation. It's been an amazing open science effort with collaborators from Cohere For AI, Mila - Quebec Artificial Intelligence Institute, EPFL, Massachusetts Institute of Technology, AI Singapore, National University of Singapore, KAIST, Instituto Superior Técnico, Carnegie Mellon University, CONICET, and University of Buenos Aires.

🏷️ +200 contributors used Argilla MMLU questions where regional, dialect, or cultural knowledge was required to answer correctly. 85% of the questions required Western-centric knowledge!

Thanks to this annotation process, the open dataset contains two subsets:

1. 🗽 Culturally Agnostic: no specific regional, cultural knowledge is required.
2. ⚖️ Culturally Sensitive: requires dialect, cultural knowledge or geographic knowledge to answer correctly.

Moreover, we provide high quality translations of 25 out of 42 languages, thanks again to the community and professional annotators leveraging Argilla on the Hub.

I hope this will ensure a better understanding of the limitations and challenges for making open AI useful for many languages.

Dataset: CohereForAI/Global-MMLU
reacted to jsulz's post with 👍 2 months ago
view post
Post
1411
Doing a lot of benchmarking and visualization work, which means I'm always searching for interesting repos in terms of file types, size, branches, and overall structure.

To help, I built a Space jsulz/repo-info that lets you search for any repo and get back:

- Treemap of the repository, color coded by file/directory size
- Repo branches and their size
- Cumulative size of different file types (e.g., the total size of all the safetensors in the repo)

And because I'm interested in how this will fit in our work to leverage content-defined chunking for versioning repos on the Hub
- https://huggingface.co/blog/from-files-to-chunks - everything has the number of chunks (1 chunk = 64KB) as well as the total size in bytes.

Some of the treemaps are pretty cool. Attached are black-forest-labs/FLUX.1-dev and for fun laion/laion-audio-preview (which has nearly 10k .tar files 🤯)

  • 2 replies
·