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Check out OpenThoughts. It has a widely used dataset, a model that beats the deepseek's smaller reasoning models, and a paper that talks in detail about the data curation methodolo…

I wrote about this recently as well: https://madiator.substack.com/p/finetuning-is-so-back

Congrats! Great work, Dan! Indeed, Terminal bench is awesome :)

Right. The title conjures dreamy images in my mind that I was dying to see images!

That's the hope, but that's never how things go. Of course researchers and research will be tremendously affected, and that's a shame.

Yeah this was quite an interesting finding, given how Mistral has marketed this (OCR solved!)

The blogpost is linked here: https://news.ycombinator.com/item?id=42826392

Thanks! We created bespoke-stratos-32B - let me know if you have any questions.

Synthetic data got a bad reputation last year, but it is now an important component for all modern LLMs! In fact, we had also trained one model for -- ironically -- detecting hallu…

We are working on it!

Synthetic data generation is an essential step in training and evaluating LLMs/Agents/RAG pipelines, but tooling around this is still lacking. We're introducing Curator, an open-so…

Not sure why you are so upset about a small and neat study ("please, please stop"). If you ask it to summarize (without feeding the entire bible), it needs to know the bible. Knowl…

For the specific form of hallucination, which is called grounded factuality, we have trained a pretty good model that can detect if a claim is supported by a context. This is super…

Now, it will be nice to get the code snippets for these!

There are several types of hallucinations, and the most important one for RAG is grounded factuality. We built a model to detect this, and it does pretty well! Given a context and …

That's great, but it did not really write the program that the human asked it to do. :)

I am sure most people never asked these questions to a human doing this research.

I will be typing this comment on my Nokia's keyboard.

Yeah it wouldn't fit. GPT3 is 175B params, so even if you use 8 bit for each weight, you need 175×10^9÷2^30 = 163GiB of memory.