SubQ 1.1 Small
subq.ai
SubQ 1.1 Small
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Re: SubQ 1.1 Small
#2Re: SubQ 1.1 Small
#3This one's interesting, and I think the next frontier for LLMs should really just be, how can we get something like Opus 4.6 to cost drastically less, for the same output? I say 4.6 because from 4.6 onwards it's been pretty darn good, at least for me, always feels like every model upgrade someone hates it, heck even 4.5 was fine.
Re: SubQ 1.1 Small
#4This one's interesting, and I think the next frontier for LLMs should really just be, how can we get something like Opus 4.6 to cost drastically less, for the same output? I say 4.6 because from 4.6 onwards it's been pretty darn good, at least for me, always feels like every model upgrade someone hates it, heck even 4.5 was fine.
Yes - I want that and dramatically faster. Newer models don't seem to need any more or less guidance and iteration, so let's make the time-to-wrong-answer as short as possible.
Re: SubQ 1.1 Small
#5Disappointing they don't actually say how their sparse attention mechanism works.
Re: SubQ 1.1 Small
#6I don’t understand why this lab is allergic to providing details on what they actually made, especially when Chinese labs are more than willing to share architectural specs/code/kernels (eg NSA/FSA, RAMBa, HISA, DSA LightningIndexer, etc). I don’t doubt that they’ve done something here, but the lack of details makes me default not trust this, particularly when this is the second time that they’ve released a “technical report” that just waxes poetic about the concept.
Re: SubQ 1.1 Small
#7> SubQ 1.1 Small scores near-perfect at 1M, 2M, 6M, and 12M tokens. The model was trained predominantly at 1M tokens yet the retrieval held near perfectly at 12x that length, despite compressing attention to just 0.13% of relationships. This generalization is a direct consequence of SSA routing attention based on content relevance rather than fixed positional patterns.
If the results persists from 1M to 12M, why not 24M or 48M? Sounds almost too good to be true.
With back of the napkin math from inside my head, that'd be like 0.5/1 million LOC, depending on language/code density, could just fold the entire codebase into one prompt if it's a small one, that'd be neat :)
Re: SubQ 1.1 Small
#8There was, let's say, significant skepticism the last time they announced something. What's changed?
Re: SubQ 1.1 Small
#9This one's interesting, and I think the next frontier for LLMs should really just be, how can we get something like Opus 4.6 to cost drastically less, for the same output? I say 4.6 because from 4.6 onwards it's been pretty darn good, at least for me, always feels like every model upgrade someone hates it, heck even 4.5 was fine.
Yes - I want that and dramatically faster. Newer models don't seem to need any more or less guidance and iteration, so let's make the time-to-wrong-answer as short as possible.
I'm not as crazy about speed as long as it's reasonably as "quick" as Opus. Which is faster than most developers can spit out code. I do get annoyed with Claude Code because it looks like it chooses to be as slow as possible, but maybe that's by design so its not pounding their backend every milisecond? Would probably be bad.
Local inference is insanely fast on my M4 Pro MBP though, so I can understand where you're coming from, but I don't need it too much faster. I still need time to review, test, review and provide feedback to the model. Fast is okay I guess for true vibe coding.
Re: SubQ 1.1 Small
#10I don’t understand why this lab is allergic to providing details on what they actually made, especially when Chinese labs are more than willing to share architectural specs/code/kernels (eg NSA/FSA, RAMBa, HISA, DSA LightningIndexer, etc). I don’t doubt that they’ve done something here, but the lack of details makes me default not trust this, particularly when this is the second time that they’ve released a “technica…
Business wise, it would make sense to hold off on details till they're at least ready to serve. Look at what happened with Open AI and reasoning models. Everyone struggled with getting RL to work with LLMs for a good while. Open AI figured it out, and a few months later everyone had their prototypes out in short order. Don't forget who these labs employ. They're some of the brightest people around. Sub-q aren't really in a position for that lol. If they'd shared details at the first announcement for instance, the big labs might have had something out by now while they're still pulling resources to scale and then what ?