🚀 Exciting news! Apple has released its own open-source LLM, DCLM-7B. Everything is open-source, including the model weights and datasets. 💡Why should you be excited? 1. The datasets and tools released as part of this research lay the groundwork for future advancements in

Kudos to Apple. They publish their new 7B model not only open weight, but also open data-set! And in this ranking Apple even takes 1st place! An outstanding achievement that others should take as an example and be just as transparent. huggingface.co/datasets/mlfou…

Apple shows off open AI prowess: new models outperform Mistral and Hugging Face offerings venturebeat.com/ai/apple-shows…

DCLM models keep on coming! This time we release (by far) the best open-data 1B model!

Datacomp-LM (DCLM) was presented today in ICLM FOMO workshop. DCLM is a data-centric benchmark for LLMs. It is also the state of the art open-source LLM and the state of the art open training dataset. Probably the most important finding is that data curation algorithms that

As Apple Intelligence is rolling out to our beta users today, we are proud to present a technical report on our Foundation Language Models that power these features on devices and cloud: machinelearning.apple.com/research/apple…. 🧵

github.com/mlfoundations/… I’m excited to introduce Evalchemy 🧪, a unified platform for evaluating LLMs. If you want to evaluate an LLM, you may want to run popular benchmarks on your model, like MTBench, WildBench, RepoBench, IFEval, AlpacaEval etc as well as standard pre-training

Introducing the next generation: Claude Opus 4 and Claude Sonnet 4. Claude Opus 4 is our most powerful model yet, and the world’s best coding model. Claude Sonnet 4 is a significant upgrade from its predecessor, delivering superior coding and reasoning.

Thrilled to see Terminal-Bench on the Claude 4 model card. We're just getting started! Come join our community to help us build the best framework for evaluating agents on valuable tasks

Excited to see how people will use the model and what engineers will build with it! Feeling privileged to have gotten the opportunity to work on it with an amazing team. If you’re interested in working on the next one, apply online - my team and many others are hiring!

This is one of the main reasons we built Terminal-Bench (and why Anthropic cites it in their Claude 4 headline!). The terminal is an underrated tool and improving the ability of agents to use it effectively translates to agents becoming really good at using a computer.

Lucas Beyer (bl16) Thanks for the kind words, Lucas! I hope we get a chance to work together some day, I'm a big fan of your work. BTW my lab is always looking for good postdocs. Comp is probably worse than OpenAI, but long-time lab members get to go on runs with Vaishaal Shankar's dog Kaya. He's great!

had a lot of fun working on this. try it out!

So after all these hours talking about AI, in these last five minutes I am going to talk about: Horses. Engines, steam engines, were invented in 1700. And what followed was 200 years of steady improvement, with engines getting 20% better a decade. For the first 120 years of

A statement on the comments from Secretary of War Pete Hegseth. anthropic.com/news/statement…