counterpoint: GPT-5 does this, it says it doesn’t know rather than hallucinate, the world hasn’t fallen apart

ModernBERT goes MULTILINGUAL! One of the most requested models I've seen, @jhuclsp.bsky.social has trained state-of-the-art massively multilingual encoders using the ModernBERT architecture: mmBERT. Stronger than an existing models at their sizes, while also much faster! Details in 🧵

ChatGPT has been the best technical search engine since o4-mini. Thinking Mini still makes for a good faster search if you don’t need the extra reasoning ability.

Good LLMs do know and/or can reason about these things. Small, cheap, and often free LLMs are the models which cannot. Remember the glue on pizza Reddit post that the subpar Google AI cited uncritically? Bing’s then integration of GPT 3.5 recognized the Reddit post as sarcasm.

Writing Speed-of-Light Flash Attention for 5090 in CUDA C++ by Thien Tran He walkthrough how he learned to implement Flash Attention for 5090 in CUDA C++. The main objective is to learn writing attention in CUDA C++,

Microsoft made a useful LLM copilot tool that could summarize text in spreadsheets. They provided clear instructions about how to use it and not to use it. In response, journalists are now mocking them for doing exactly the right thing and showing how to use and not use the tools.

Reports of AI eating entry level jobs are greatly exaggerated. My guess is current and near-future LLMs are more likely to increase the demand for programmers, not decrease demand (Jevons Paradox).

One of the questions we debated while training ModernBERT was whether a modern trained encoder would unlock zero-shot reasoning using only it's generative head? Spoilers: the answer is yes.

o3-mini is really good at writing internal documentation - feed it a codebase, get back a detailed explanation of how specific aspects of it work simonwillison.net/2025/Feb/5/o...

If you want to quickly catch up on all the open modeling things (DeepSeek, ModernBERT, etc.), this was a great overview, by @natolambert.bsky.social. I somehow got into an argument last week with someone who was insisting that all models are industrial blackboxes... and I wish I'd had this on hand.

In addition to being the best retrieval model under 300M params on METB (without extra work), and top 10 for under 1B, here's a fun tidbit from Alibaba's GTE ModernBERT model card: gte-modernbert-base beats gte-qwen1.5-7b on LoCo long context retrieval with 7B less parameters.

The newest extremely strong embedding model based on ModernBERT-base is out: `cde-small-v2`. Both faster and stronger than its predecessor, this one tops the MTEB leaderboard for its tiny size! Details in 🧵

ModernBERT-embed-base is awesome because it allows to use ModernBERT-base for various tasks out-of-the-box But the large variant of ModernBERT is also awesome... So today, @lightonai.bsky.social is releasing ModernBERT-embed-large, the larger and more capable iteration of ModernBERT-embed!

ModernBERT is officially released on Transformers v4.48.0. You no longer need to install from git to use. If you are plugging ModernBERT into an existing encoder finetuning pipeline, try increasing the learning rate. We've found that ModernBERT tends to prefer a higher LR than older models.

The good: 32GB The bad: $2,000 The Ugly*: PCIe 5 without NVLink

Via @simonwillison.net's excellent blog, I found this great quote about AI models, from @benjaminwarner.dev et al. www.answer.ai/posts/2024-1... It seems to me that AI will be most relevant in people's lives because the Honda Civic is ubiquitous, not so much because everyone is driving a Ferrari.

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 in 🧵

This week we released ModernBERT, the first encoder to reach SOTA on most common benchmarks across language understanding, retrieval, and code, while running twice as fast as DeBERTaV3 on short context and three times faster than NomicBERT & GTE on long context.

Great blog post (by a 15-author team!) on their release of ModernBERT, the continuing relevance of encoder-only models, and how they relate to, say, GPT-4/llama. Accessible enough that I might use this as an undergrad reading.

I feel the need for speed.