The one thing I love is how Opus drafts its plan. Data flow is a masterpiece - and yes, I started to carefully read the details of what it's going to proceed, because A) it makes mistakes or misunderstands, B) it's highly educational.

Would you like to build your own SYNTH-like datasets or contribute to the development of SYNTHLabs?

Now you can. SYNTHLabs is fully open and waiting for your contribution!

What's inside?

I'm back here after a huge break to check out how bsky works now 😀

Here is my experimental Llama 3.2 3B with o1-like thinking. It utilizes Thoughts when needed, so don't be surprised when it's not.

Enjoy!

Give some likes to make me feel better 😂

huggingface.co/mkurman/llam...

storm.genie.stanford.edu - A great tool from Stanford for creating articles. For me, a stronger Gemini with Deep Thinking. Definitely worth trying!

Deepseek MTP is something you should definitely look at

Predicting the next token as a learning objective is insufficient for optimal LLM training.

HDIC - How Do I Contribute?

A new technique we are working on seems to have a huge impact on language models' generative capabilities, allowing the layers to self-esteem their contribution to the final prediction.

RIP JetBrains subscription ☠️ after six years, it became too heavy to use as a daily IDE. I‘m now on the VS Code team.

What research tools would you recommend for searching and analyzing scientific papers?

We built a new small language model SmolLM2-MedIT-Upscale-2B, based on SmolLM2-1.7B-Instruct from Hugging Face. The premise was simple - increasing the vector in attention layers would positively impact the model's capabilities.

What did we prove? 1/4

This video clip excellently demonstrates the exceptional uses of AI.

youtu.be/MMryYio0v6k?...

It looks like we can scale up any model not only in-depth but also in width. Insane.
#llm