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ben burtenshaw PRO

burtenshaw

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updated a dataset 13 minutes ago

agents-course/certificates

updated a dataset about 1 hour ago

mcp-course/certificates

reacted to sergiopaniego's post with 🔥 about 3 hours ago

fine-tuning a 14B model with TRL + SFT on a free Colab (T4 GPU)? thanks to the latest TRL optimizations, you actually can! sharing a new notebook showing how to do it 😎 colab: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_trl_lora_qlora.ipynb notebooks in TRL: https://github.com/huggingface/trl/tree/main/examples/notebooks

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reacted to sergiopaniego's post with 🔥 about 3 hours ago

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fine-tuning a 14B model with TRL + SFT on a free Colab (T4 GPU)?
thanks to the latest TRL optimizations, you actually can!
sharing a new notebook showing how to do it 😎

colab: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_trl_lora_qlora.ipynb

notebooks in TRL: https://github.com/huggingface/trl/tree/main/examples/notebooks

reacted to evijit's post with 🔥 29 days ago

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AI for Scientific Discovery Won't Work Without Fixing How We Collaborate.

My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.

Key findings:

🚨 The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
📊 Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
⚠️ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
🔍 Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.

Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.

Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!

Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join:

hugging-science
Discord: https://discord.com/invite/VYkdEVjJ5J

reacted to sergiopaniego's post with 🤗🔥 about 1 month ago

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💥 Tons of new material just landed in the smol-course! 🧑‍💻

> evaluation
> alignment
> VLMs
> quizzes
> assignments!
> certificates!👩‍🎓

go learn! 👉 https://huggingface.co/learn/smol-course/unit0/1

1 reply

reacted to lysandre's post with 👍🔥🚀 about 2 months ago

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We're kick-starting the process of Transformers v5, with @ArthurZ and @cyrilvallez !

v5 should be significant: we're using it as a milestone for performance optimizations, saner defaults, and a much cleaner code base worthy of 2025.

Fun fact: v4.0.0-rc-1 came out on Nov 19, 2020, nearly five years ago!

6 replies

reacted to sergiopaniego's post with 🔥🧠 about 2 months ago

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gpt-oss was possible thanks to new engineering efforts in 🤗 transformers. We just dropped a blog covering them:

- Kernels from the Hub
- MXFP4 Quantization
- Tensor & Expert Parallelism
- Dynamic Sliding Window & Cache
- Continuous Batching & Paged Attention

Grab a coffee & dive in! ☕️

https://huggingface.co/blog/faster-transformers

posted an update about 2 months ago

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Smol course has a distinctive approach to teaching post-training, so I'm posting about how it’s different to other post-training courses, including the llm course that’s already available.

In short, the smol course is just more direct that any of the other course, and intended for semi-pro post trainers.

- It’s a minimal set of instructions on the core parts.
- It’s intended to bootstrap real projects you're working on.
- The material handsover to existing documentation for details
- Likewise, it handsover to the LLM course for basics.
- Assessment is based on a leaderboard, without reading all the material.

To start the smol course, follow here:

smol-course

reacted to tomaarsen's post with ❤️ about 2 months ago

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ModernBERT goes MULTILINGUAL! One of the most requested models I've seen, The Johns Hopkins University's CLSP has trained state-of-the-art massively multilingual encoders using the ModernBERT architecture: mmBERT.

Model details:
- 2 model sizes:
- jhu-clsp/mmBERT-small
- jhu-clsp/mmBERT-base
- Uses the ModernBERT architecture, but with the Gemma2 multilingual tokenizer (so: flash attention, alternating global/local attention, unpadding/sequence packing, etc.)
- Maximum sequence length of 8192 tokens, on the high end for encoders
- Trained on 1833 languages using DCLM, FineWeb2, and many more sources
- 3 training phases: 2.3T tokens pretraining on 60 languages, 600B tokens mid-training on 110 languages, and 100B tokens decay training on all 1833 languages.
- Both models are MIT Licensed, and the full datasets and intermediary checkpoints are also publicly released

Evaluation details:
- Very competitive with ModernBERT at equivalent sizes on English (GLUE, MTEB v2 English after finetuning)
- Consistently outperforms equivalently sized models on all Multilingual tasks (XTREME, classification, MTEB v2 Multilingual after finetuning)
- In short: beats commonly used multilingual base models like mDistilBERT, XLM-R (multilingual RoBERTa), multilingual MiniLM, etc.
- Additionally: the ModernBERT-based mmBERT is much faster than the alternatives due to its architectural benefits. Easily up to 2x throughput in common scenarios.

Check out the full blogpost with more details. It's super dense & gets straight to the point: https://huggingface.co/blog/mmbert

Based on these results, mmBERT should be the new go-to multilingual encoder base models at 300M and below. Do note that the mmBERT models are "base" models, i.e. they're currently only trained to perform Mask Filling. They'll need to be finetuned for downstream tasks like semantic search, classification, clustering, etc.

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

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upgrade your transformers 🔥
it comes with insanely capable models like merve/sam2-66ac9deac6fca3bc5482fe30, microsoft/kosmos-2.5, and more 🫡
I built a notebook you can run with free Colab T4 to walk through the API for new models 🙋🏻‍♀️ merve/smol-vision

fine-tuning will follow-up soon!

posted an update about 2 months ago

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new smol course

If you’re building with or learning about post training AI models right now, we have a new FREE and CERTIFIED course.

🔗 Follow the org to join in

smol-course

The course builds on smol course v1 which was the fastest way to learn to train your custom AI models. It now has:

- A leaderboard for students to submit models to
- Certification based on exams and leaderboards
- Prizes based on Leaderboards
- Up to date content on TRL and SmolLM3
- Deep integration with the Hub’s compute for model training and evaluation

We will release chapters every few weeks, so you can follow the org to stay updated.

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posted an update 2 months ago

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The open source AI community is just made of people who are passionate and care about their work. So we thought it would be cool to share our favourite icons of the community with a fun award.

Winners get free Hugging Face Pro Subscriptions, Merchandise, or compute credits for the hub.

🔗 Follow and nominate here:

community-spotlight

This is a new initiative to recognise and celebrate the incredible work being done by community members. It's all about inspiring more collaboration and innovation in the world of machine learning and AI.

They're highlighting contributors in four key areas:
- model creators: building and sharing innovative and state-of-the-art models.
- educators: sharing knowledge through posts, articles, demos, and events.
- tool builders: creating the libraries, frameworks, and applications that we all use.
- community champions: supporting and mentoring others in forums.

Know someone who deserves recognition? Nominate them by opening a post in the Hugging Face community forum.

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reacted to salma-remyx's post with 🤯🔥 2 months ago

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GitRank

We built an agent to surface and implement high-potential ideas for your repo, asynchronously generating containers, tests, and PRs so you can evaluate what works and double down on it.

Check out the demo: https://youtu.be/frgPsTclc1k

Come replicate and specialize a test for your repo! GitRank is live on Remyx.
Docs: https://docs.remyx.ai
App: https://engine.remyx.ai
Example PR here: https://github.com/smellslikeml/experimental-vqasynth/pull/727

reacted to frimelle's post with ❤️🔥 3 months ago

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🗺️ New blog post 🗺️
Old Maps, New Terrain: Updating Labour Taxonomies for the AI Era

For decades, we’ve relied on labour taxonomies like O*NET to understand how technology changes work. These taxonomies break down jobs into tasks and skills, but they were built in a world before most work became digital-first, and long before generative AI could create marketing campaigns, voiceovers, or even whole professions in one step. That leaves us with a mismatch: we’re trying to measure the future of work with tools from the past.

With @yjernite we describe why these frameworks are falling increasingly short in the age of generative AI. We argue that instead of discarding taxonomies, we need to adapt them. Imagine taxonomies that:
✨ Capture new AI-native tasks and hybrid human-AI workflows
✨ Evolve dynamically as technology shifts
✨ Give workers a voice in deciding what gets automated and what stays human

If we don’t act, we’ll keep measuring the wrong things. If we do, we can design transparent, flexible frameworks that help AI strengthen, not erode, the future of work.

Read the full article here: https://huggingface.co/blog/frimelle/ai-labour-taxonomies

reacted to AdinaY's post with 🔥 3 months ago

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✨ DeepSeek V3.1 just dropped on the hub.
deepseek-ai/DeepSeek-V3.1-Base

replied to their post 4 months ago

I also made a collection of handy things relating to Kimi k2 https://huggingface.co/collections/burtenshaw/kimi-k2-68764828ae1aa753c7061620

ben burtenshaw PRO

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