big-chungus4

Well, it's better than "X had died" which pops up about once a day and usually contains someone unrelated to hacker news... While this one is inspiring and good to have on HN

Can this produce a useful model? So far 1 bit quants have been less useful than smaller models that use the same memory

My 1k parameter RNN is faster

> W

W what?

It goes the opposite way too. Small models need minimal harness like pi or they get overwhelmed by long system message and tool descriptions

How does he know that this attack is performed by OpenAI agents? I couldn't figure this out from the article

I think it's that when some code compiles to say assembler, the compiler doesn't prioritize readability and maintainability of the assembler code, since people are not expected to read and maintain it directly

What are people on about, there is tons to do in NMS, tons of missions, expeditions, build bases, build a corvet, hunt rate parts, ships, rifles, creatures, manage a settlement, automate crafting chains, find a lucrative trading route, and this new update apps even more things. I'd never consider NMS anything close to empty, there is so much variety in the content

That game is great to RL on to teach the model computer use, they might've actually done that for Astra

Well, do you have any ideas about how to make it scientific? And they clearly needed to do something based on their unnatural ranking

DeepSeek has been the most used model on open router by far, only temporarily overtaken by ox alpha when it was free, so it's the most representative experience

They are great base models for fine tuning on both text and visual tasks. Many OCR and object grounding models are based on small Qwen models, though they often replace vision encoder with a bigger one.

Qwen3 5-4B is the biggest model I can find tune in my laptop. And when I upgraded the model from Qwen3-4B to Qwen3.5-4B, both vanilla and fine tuned performances jumped significantly on a classification task.

Those models are great when you have very little data or very low diversity of examples, where it's not possible to train a neural net from scratch as it will just memorize the data. The best you can do is fine tune a generalist model that can already do the task for small number of steps until it starts over-fitting, or on some cases you can do even better though RL.

> We are releasing these architectural improvements ahead of time so that the community can prepare for the upcoming full family of Qwen4 models.

That gives me hope that "full family" means it will include smaller models like 4B.

I hope there is going to be a free endpoint... Unlike 35B-A3B, I am nowhere close to running it locally