Mercury: Ultra-fast language models based on diffusion

by PaulHoule • 1 year ago

576 242 1 year ago

mynti• 1 year ago

is there a kind of nanogpt for diffusion language models? i would love to understand them better

nvtop• 1 year ago

This video has a live coding part which implements a masked diffusion generation process: https://www.youtube.com/watch?v=oot4O9wMohw

chc42• 1 year ago

Using the free playground link, and it is in fact extremely fast. The "diffusion mode" toggle is also pretty neat as a visualization, although I'm not sure how accurate it is - it renders as line noise and then refines, while in reality presumably those are tokens from an imprecise vector in some state space that then become more precise until it's only a definite word, right?

PaulHoule5• 1 year ago

It's insane how fast that thing is!

maelito• 1 year ago

Link : https://chat.inceptionlabs.ai/

sexy_seedbox• 1 year ago

Still cannot pass the stRawbeRRy or the Sally's 1 sister tests unfortunately...

icyfox• 1 year ago

Some text diffusion models use continuous latent space but they historically haven't done that well. Most the ones we're seeing now typically are trained to predict actual token output that's fed forward into the next time series. The diffusion property comes from their ability to modify previous timesteps to converge on the final output.

I have an explanation about one of these recent architectures that seems similar to what Mercury is doing under the hood here: https://pierce.dev/notes/how-text-diffusion-works/

chc42• 1 year ago

Oh neat, thanks! The OP is surprisingly light on details on how it actually works and is mostly benchmarks, so this is very appreciated :)

luckystarr• 1 year ago

I'm kind of impressed by the speed of it. I told it to write a MQTT topic pattern matcher based on a Trie and it spat out something reasonable on first try. It hat a few compilation issues though, but fair enough.

earthnail• 1 year ago

Tried it on some coding questions and it hallucinated a lot, but the appearance (i.e. if you’re not a domain expert) of the output is impressive.

TechDebtDevin2• 1 year ago

Oddly fast, almost instantaneous.

mike_hearn18• 1 year ago

A good chance to bring up something I've been flagging to colleagues for a while now: with LLM agents we are very quickly going to become even more CPU bottlenecked on testing performance than today, and every team I know of today was bottlenecked on CI speed even before LLMs. There's no point having an agent that can write code 100x faster than a human if every change takes an hour to test.

Maybe I've just got unlucky in the past, but in most projects I worked on a lot of developer time was wasted on waiting for PRs to go green. Many runs end up bottlenecked on I/O or availability of workers, and so changes can sit in queues for hours, or they flake out and everything has to start again.

As they get better coding agents are going to be assigned simple tickets that they turn into green PRs, with the model reacting to test failures and fixing them as they go. This will make the CI bottleneck even worse.

It feels like there's a lot of low hanging fruit in most project's testing setups, but for some reason I've seen nearly no progress here for years. It feels like we kinda collectively got used to the idea that CI services are slow and expensive, then stopped trying to improve things. If anything CI got a lot slower over time as people tried to make builds fully hermetic (so no inter-run caching), and move them from on-prem dedicated hardware to expensive cloud VMs with slow IO, which haven't got much faster over time.

Mercury is crazy fast and in a few quick tests I did, created good and correct code. How will we make test execution keep up with it?

TechDebtDevin2• 1 year ago

LLM making a quick edit, <100 lines... Sure. Asking an LLM to rubber-duck your code, sure. But integrating an LLM into your CI is going to end up costing you 100s of hours productivity on any large project. That or spend half the time you should be spending learning to write your own code, dialing down context sizing and prompt accuracy.

I really really don't understand the hubris around llm tooling, and don't see it catching on outside of personal projects and small web apps. These things don't handle complex systems well at all, you would have to put a gun in my mouth to let one of these things work on an important repo of mine without any supervision... And if I'm supervising the LLM I might as well do it myself, because I'm going to end up redoing 50% of its work anyways..

mike_hearn18• 1 year ago

I've used Claude with a large, mature codebase and it did fine. Not for every possible task, but for many.

Probably, Mercury isn't as good at coding as Claude is. But even if it's not, there's lots of small tasks that LLMs can do without needing senior engineer level skills. Adding test coverage, fixing low priority bugs, adding nice animations to the UI etc. Stuff that maybe isn't critical so if a PR turns up and it's DOA you just close it, but which otherwise works.

Note that many projects already use this approach with bots like Renovate. Such bots also consume a ton of CI time, but it's generally worth it.

DSingularity• 1 year ago

He is simply observing that if PR numbers and launch rates increase dramatically CI cost will become untenable.

kraftman2• 1 year ago

I keep seeing this argument over and over again, and I have to wonder, at what point do you accept that maybe LLM's are useful? Like how many people need to say that they find it makes them more productive before you'll shift your perspective?

blitzar2• 1 year ago

Do the opposite - integrate your CI into your LLM.

Make it run tests after it changes your code and either confirm it didnt break anything or go back and try again.

piva00• 1 year ago

I haven't worked in places using off-the-shelf/SaaS CI in more than a decade so I feel my experience has been quite the opposite from yours.

We always worked hard to make the CI/CD pipeline as fast as possible. I personally worked on those kind of projects at 2 different employers as a SRE: a smaller 300-people shop which I was responsible for all their infra needs (CI/CD, live deployments, migrated later to k8s when it became somewhat stable, at least enough for the workloads we ran, but still in its beta-days), then at a different employer some 5k+ strong working on improving the CI/CD setup which used Jenkins as a backend but we developed a completely different shim on top for developer experience while also working on a bespoke worker scheduler/runner.

I haven't experienced a CI/CD setup that takes longer than 10 minutes to run in many, many years, got quite surprised reading your comment and feeling spoiled I haven't felt this pain for more than a decade, didn't really expect it was still an issue.

mike_hearn18• 1 year ago

I think the prevalence of teams having a "CI guy" who often is developing custom glue, is a sign that CI is still not really working as well as it should given the age of the tech.

I've done a lot of work on systems software over the years so there's often tests that are very I/O or computation heavy, lots of cryptography, or compilation, things like that. But probably there are places doing just ordinary CRUD web app development where there's Playwright tests or similar that are quite slow.

A lot of the problems are cultural. CI times are a commons, so it can end in tragedy. If everyone is responsible for CI times then nobody is. Eventually management gets sick of pouring money into it and devs learn to juggle stacks of PRs on top of each other. Sometimes you get a lot of pushback on attempts to optimize CI because some devs will really scream about any optimization that might potentially go wrong (e.g. depending on your build system cache), even if caching nothing causes an explosion in CI costs. Not their money, after all.

kccqzy3• 1 year ago

Maybe I've just got unlucky in the past, but in most projects I worked on a lot of developer time was wasted on waiting for PRs to go green.

I don't understand this. Developer time is so much more expensive than machine time. Do companies not just double their CI workers after hearing people complain? It's just a throw-more-resources problem. When I was at Google, it was somewhat common for me to debug non-deterministic bugs such as a missing synchronization or fence causing flakiness; and it was common to just launch 10000 copies of the same test on 10000 machines to find perhaps a single digit number of failures. My current employer has a clunkier implementation of the same thing (no UI), but there's also a single command to launch 1000 test workers to run all tests from your own checkout. The goal is to finish testing a 1M loc codebase in no more than five minutes so that you get quick feedback on your changes.

make builds fully hermetic (so no inter-run caching)

These are orthogonal. You want maximum deterministic CI steps so that you make builds fully hermetic and cache every single thing.

mark_undoio• 1 year ago

I don't understand this. Developer time is so much more expensive than machine time. Do companies not just double their CI workers after hearing people complain? It's just a throw-more-resources problem.

I'd personally agree. But this sounds like the kind of thing that, at many companies, could be a real challenge.

Ultimately, you can measure dollars spent on CI workers. It's much harder and less direct to quantify the cost of not having them (until, for instance, people start taking shortcuts with testing and a regression escapes to production).

That kind of asymmetry tends, unless somebody has a strong overriding vision of where the value really comes from, to result in penny pinching on the wrong things.

mike_hearn18• 1 year ago

I was also at Google for years. Places like that are not even close to representative. They can afford to just-throw-more-resources, they get bulk discounts on hardware and they pay top dollar for engineers.

In more common scenarios that represent 95% of the software industry CI budgets are fixed, clusters are sized to be busy most of the time, and you cannot simply launch 10,000 copies of the same test on 10,000 machines. And even despite that these CI clusters can easily burn through the equivalent of several SWE salaries.

These are orthogonal. You want maximum deterministic CI steps so that you make builds fully hermetic and cache every single thing.

Again, that's how companies like Google do it. In normal companies, build caching isn't always perfectly reliable, and if CI runs suffer flakes due to caching then eventually some engineer is gonna get mad and convince someone else to turn the caching off. Blaze goes to extreme lengths to ensure this doesn't happen, and Google spends extreme sums of money on helping it do that (e.g. porting third party libraries to use Blaze instead of their own build system).

In companies without money printing machines, they sacrifice caching to get determinism and everything ends up slow.

mystified5016• 1 year ago

IME it's less of a "throw more resources" problem and more of a "stop using resources in literally the worst way possible"

CI caching is, apparently, extremely difficult. Why spend a couple of hours learning about your CI caches when you can just download and build the same pinned static library a billion times? The server you're downloading from is (of course) someone else's problem and you don't care about wasting their resources either. The power you're burning by running CI for there hours instead of one is also someone else's problem. Compute time? Someone else's problem. Cloud costs? You bet it's someone else's problem.

Sure, some things you don't want to cache. I always do a 100% clean build when cutting a release or merging to master. But for intermediate commits on a feature branch? Literally no reason not to cache builds the exact same way you do on your local machine.

ronbenton• 1 year ago

Do companies not just double their CI workers after hearing people complain?

They do not.

I don't know if it's a matter of justifying management levels, but these discussions are often drawn out and belabored in my experience. By the time you get approval, or even worse, rejected, for asking for more compute (or whatever the ask is), you've spent way more money on the human resource time than you would ever spend on the requested resources.

IshKebab5• 1 year ago

Developer time is more expensive than machine time, but at most companies it isn't 10000x more expensive. Google is likely an exception because it pays extremely well and has access to very cheap machines.

Even then, there are other factors:

* You might need commercial licenses. It may be very cheap to run open source code 10000x, but guess how much 10000 Questa licenses cost.

* Moores law is dead Amdahl's law very much isn't. Not everything is embarrassingly parallel.

* Some people care about the environment. I worked at a company that spent 200 CPU hours on every single PR (even to fix typos; I failed to convince them they were insane for not using Bazel or similar). That's a not insignificant amount of CO2.

physicsguy• 1 year ago

Not really, in most small companies/departments, £100k a month is considered a painful cloud bill and adding more EC2 instances to provide cloud runners can add 10% to that easily.

wat10000• 1 year ago

Many companies are strangely reluctant to spend money on hardware for developers. They might refuse to spend $1,000 on a better laptop to be used for the next three years by an employee, whose time costs them that much money in a single afternoon.

wbl• 1 year ago

No it is not. Senior management often has a barely disguised contempt for engineering and spending money to do a better job. They listen much more to sales complain.

MangoToupe3• 1 year ago

Writing testing infrastructure so that you can just double workers and get a corresponding doubling in productivity is non-trivial. Certainly I've never seen anything like Google's testing infrastructure anywhere else I've worked.

socalgal2• 1 year ago

Even Google can not buy more old Intel Macs or Pixel 6s or Samsung S20s to increase their testing on those devices (as an example)

Maybe that affects less devs who don't need to test on actual hardware but plenty of apps do. Pretty much anything that touches a GPU driver for example like a game.

anp• 1 year ago

I’m currently at google (opinions not representative of my employer’s etc) and this is true for things that run in a data center but it’s a lot harder for things that need to be tested on physical hardware like parts of Android or CrOS.

wavemode3• 1 year ago

You're confusing throughput and latency. Lengthy CI runs increase the latency of developer output, but they don't significantly reduce overall throughput, given a developer will typically be working on multiple things at once, and can just switch tasks while CI is running. The productivity cost of CI is not zero, but it's way, way less than the raw wallclock time spent per run.

Then also factor in that most developer tasks are not even bottlenecked by CI. They are bottlenecked primarily by code review, and secondarily by deployment.

kevingadd• 1 year ago

My personal experience: We run over 1.1m test cases to verify every PR that I submit, and there are more test cases that don't get run on every commit and instead get run daily or on-demand.

At that scale getting quick turnaround is a difficult infrastructure problem, especially if you have individual tests that take multiple seconds or suites that take multiple minutes (we do, and it's hard to actually pull the execution time down on all of them).

I've never personally heard "we don't have the budget" or "we don't have enough machines" as answers for why our CI turnaround isn't 5 minutes, and it doesn't seem to me like the answer is just doubling the core count in every situation.

The scenario I work on daily (a custom multi-platform runtime with its own standard library) does by necessity mean that builds and testing are fairly complex though. I wouldn't be surprised if your assertion (just throw more resources at it) holds for more straightforward apps.

fy20• 1 year ago

My last company was unsure about paying $20/mo to get a Copilot license for all the engineers.

noelwelsh• 1 year ago

I believe the solution is to run CI locally, and upload some signed proof that CI completed successfully. Running the same tests locally and then on an ~10 times slower CI build always felts like a ridiculous waste of time to me.

joshstrange• 1 year ago

I don't understand this. Developer time is so much more expensive than machine time. Do companies not just double their CI workers after hearing people complain?

They don’t, and it’s annoying. Oh some do for sure but it’s the same with developer hardware. I’ve worked places where developers are waiting 4+ minutes for a build to complete when that could be halved or more by better developer hardware but companies are sometimes incredibly “penny wise and pound foolish”.

mathiaspoint2• 1 year ago

Good God I hate CI. Just let me run the build automation myself dammit! If you're worried about reproducibility make it reproducible and hash the artifacts, make people include the hash in the PR comment if you want to enforce it.

The amount of time people waste futzing around in eg Groovy is INSANE and I'm honestly inclined to reject job offers from companies that have any serious CI code at this point.

esafak• 1 year ago

It takes more work (serious CI code) to make CI run anywhere, such as your own computer. So you prefer companies that just use GHA? You can't get simpler than that.

droopyEyelids• 1 year ago

In most companies the CI/Dev Tools team is a career dead end. There is no possibility to show a business impact, it's just a money pit that leadership can't/won't understand (and if they do start to understand it, then it becomes _their_ money pit, which is a career dead end for them) So no one who has their head on straight wants to spend time improving it.

And you can't even really say it's a short sighted attitude. It definitely is from a developer's perspective, and maybe it is for the company if dev time is what decides the success of the business overall.

MangoToupe3• 1 year ago

it's just a money pit that leadership can't/won't understand

In my experience it's the opposite: they want more automated testing, but don't want to pay for the friction this causes on productivity.

yieldcrv• 1 year ago

then kill the CI/CD

these redundant processes are for human interoperability

blitzar2• 1 year ago

Yet, now I have added a LLM workflow to my coding the value of my old and mostly useless workflows is now 10x'd.

Git checkpoints, code linting and my naive suite of unit and integration tests are now crucial to my LLM not wasting too much time generating total garbage.

vjerancrnjak• 1 year ago

It’s because people don’t know how to write tests. All of the “don’t do N select queries in a for loop” comments made in PRs are completely ignored in tests.

Each test can output many db queries. And then you create multiple cases.

People don’t even know how to write code that just deals with N things at a time.

I am confident that tests run slowly because the code that is tested completely sucks and is not written for batch mode.

Ignoring batch mode, tests are most of the time written in a a way where test cases are run sequentially. Yet attempts to run them concurrently result in flaky tests, because the way you write them and the way you design interfaces does not allow concurrent execution at all.

Another comment, code done by the best AI model still sucks. Anything simple, like a music player with a library of 10000 songs is something it can’t do. First attempt will be horrible. No understanding of concurrent metadata parsing, lists showing 10000 songs at once in UI being slow etc.

So AI is just another excuse for people writing horrible code and horrible tests. If it’s so smart , try to speed up your CI with it.

rapind• 1 year ago

This will make the CI bottleneck even worse.

I agree. I think there are potentially multiple solutions to this since there are multiple bottlenecks. The most obvious is probably network overhead when talking to a database. Another might be storage overhead if storage is being used.

Frankly another one is language. I suspect type-safe, compiled, functional languages are going to see some big advantages here over dynamic interpreted languages. I think this is the sweet spot that grants you a ton of performance over dynamic languages, gives you more confidence in the models changes, and requires less testing.

Faster turn-around, even when you're leaning heavily on AI, is a competitive advantage IMO.

mike_hearn18• 1 year ago

It could go either way. Depends very much on what kind of errors LLMs make.

Type safe languages in theory should do well, because you get feedback on hallucinated APIs very fast. But if the LLM generally writes code that compiles, unless the compiler is very fast you might get out-run by an LLM just spitting out JavaScript at high speed, because it's faster to run the tests than wait for the compile.

The sweet spot is probably JIT compiled type safe languages. Java, Kotlin, TypeScript. The type systems can find enough bugs to be worth it, but you don't have to wait too long to get test results either.

rafaelmn2• 1 year ago

If anything CI got a lot slower over time as people tried to make builds fully hermetic (so no inter-run caching), and move them from on-prem dedicated hardware to expensive cloud VMs with slow IO, which haven't got much faster over time.

I am guesstimating (based on previous experience self-hosting the runner for MacOS builds) that the project I am working on could get like 2-5x pipeline performance at 1/2 cost just by using self-hosted runners on bare metal rented machines like Hetzner. Maybe I am naive, and I am not the person that would be responsible for it - but having a few bare metal machines you can use in the off hours to run regression tests, for less than you are paying the existing CI runner just for build, that speed up everything massively seems like a pure win for relatively low effort. Like sure everyone already has stuff on their plate and would rather pay external service to do it - but TBH once you have this kind of compute handy you will find uses anyway and just doing things efficiently. And knowing how to deal with bare metal/utilize this kind of compute sounds generally useful skill - but I rarely encounter people enthusiastic about making this kind of move. Its usually - hey lets move to this other service that has slightly cheaper instances and a proprietary caching layer so that we can get locked into their CI crap.

Its not like these services have 0 downtime/bug free/do not require integration effort - I just don't see why going bare metal is always such a taboo topic even for simple stuff like builds.

azeirah• 1 year ago

At the last place I worked at, which was just a small startup with 5 developers, I calculated that a server workstation in the office would be both cheaper and more performant than renting a similar machine in the cloud.

Bare metal makes such a big difference for test and CI scenarios. It even has an integrated a GPU to speed up webdev tests. Good luck finding an affordable machine in the cloud that has a proper GPU for this kind of a use-case

mike_hearn18• 1 year ago

Yep. For my own company I used a bare metal machine in Hetzner running Linux and a Windows VM along with a bunch of old MacBook Pros wired up in the home office for CI.

It works, and it's cheap. A full CI run still takes half an hour on the Linux machine (the product [1] is a kind of build system for shipping desktop apps cross platform, so there's lots of file IO and cryptography involved). The Macs are by far the fastest. The M1 Mac is embarrassingly fast. It can complete the same run in five minutes despite the Hetzner box having way more hardware. In fairness, it's running both a Linux and Windows build simultaneously.

I'm convinced the quickest way to improve CI times in most shops is to just build an in-office cluster of M4 Macs in an air conditioned room. They don't have to be HA. The hardware is more expensive but you don't rent per month, and CI is often bottlenecked on serial execution speed so the higher single threaded performance of Apple Silicon is worth it. Also, pay for a decent CI system like TeamCity. It helps reduce egregious waste from problems like not caching things or not re-using checkout directories. In several years of doing this I haven't had build caching related failures.

[1] https://hydraulic.dev/

adamcharnock• 1 year ago

2-5x pipeline performance at 1/2 cost just by using self-hosted runners on bare metal rented machines like Hetzner

This is absolutely the case. Its a combination of having dedicated CPU cores, dedicated memory bandwidth, and (perhaps most of all) dedicated local NVMe drives. We see a 2x speed up running _within VMs_ on bare metal.

And knowing how to deal with bare metal/utilize this kind of compute sounds generally useful skill - but I rarely encounter people enthusiastic about making this kind of move

We started our current company for this reason [0]. A lot of people know this makes sense on some level, but not many people want to do it. So we say we'll do it for you, give you the engineering time needed to support it, and you'll still save money.

I just don't see why going bare metal is always such a taboo topic even for simple stuff like builds.

It is decreasingly so from what I see. Enough people have been variously burned by public cloud providers to know they are not a panacea. But they just need a little assistance in making the jump.

[0] https://lithus.eu

TheDudeMan• 1 year ago

This is because coders didn't spend enough time making their tests efficient. Maybe LLM coding agents can help with that.

grogenaut• 1 year ago

Before cars people spent little on petroleum products or motor oil or gasoline or mechanics. Now they do. That's how systems work. You wanna go faster well you need better roads, traffic lights, on ramps, etc. you're still going faster.

Use AI to solve the IP bottlenecks or build more features that ear more revenue that buy more ci boxes. Same as if you added 10 devs which you are with AI so why wouldn't some of the dev support costs go up.

Are you not in a place where you can make an efficiency argument to get more ci or optimize? What's a ci box cost?

daxfohl• 1 year ago

There are a couple mitigating considerations

1. As implementation phase gets faster, the bottleneck could actually switch to PM. In which case, changes will be more serial, so a lot fewer conflicts to worry about.

2. I think we could see a resurrection of specs like TLA+. Most engineers don't bother with them, but I imagine code agents could quickly create them, verify the code is consistent with them, and then require fewer full integration tests.

3. When background agents are cleaning up redundant code, they can also clean up redundant tests.

4. Unlike human engineering teams, I expect AIs to work more efficiently on monoliths than with distributed microservices. This could lead to better coverage on locally runnable tests, reducing flakes and CI load.

5. It's interesting that even as AI increases efficiency, that increased velocity and sheer amount of code it'll write and execute for new use cases will create its own problems that we'll have to solve. I think we'll continue to have new problems for human engineers to solve for quite some time.

valenterry• 1 year ago

2. I think we could see a resurrection of specs like TLA+.

I think so too. But it's not gonna be TLA+. It's just gonne be programming languages that allow to catch problems with their typesystem much more comprehensively, allowing AI to iterate quickly without even having to run unit-tests.

While developers don't want to spend the time to learn it and prefer easy-to-learn languages such as golang, LLMs only have to be trained once and then you can reap the benefits permanently.

SoftTalker• 1 year ago

Wow, your story gives me flashbacks to the 1990s when I worked in a mainframe environment. Compile jobs submitted by developers were among the lowest priorities. I could make a change to a program, submit a compile job, and wait literally half a day for it to complete. Then I could run my testing, which again might have to wait for hours. I generally had other stuff I could work on during those delays but not always.

trhway• 1 year ago

There's no point having an agent that can write code 100x faster than a human if every change takes an hour to test.

Testing every change incrementally is a vestige of the code being done by humans (and thus of the current approach where AI helps and/or replaces one given human), in small increments at that, and of the failures being analyzed by individual humans who can keep in their head only limited number of things/dependencies at once.

ASinclair• 1 year ago

Call me a skeptic but I do not believe LLMs are significantly altering the time between commits so much that CI is the problem.

However, improving CI performance is valuable regardless.

gdiamos4• 1 year ago

This sounds like a strawman.

GPUs can do 1 million trillion instructions per second.

Are you saying it’s impossible to write a test that finishes in less than one second on that machine?

Is that a fundamental limitation or an incredibly inefficient test?

nradclif• 1 year ago

A million trillion operations per second is literally an exaflop. That's one hell of a GPU you have.

mike_hearn18• 1 year ago

It's amazing how easy it is to write tests that are slow. Taking >1 second per test is absolutely normal.

Is that a fundamental limitation or an incredibly inefficient test?

That's the million dollar/month question. If an LLM can diffuse a patch in 3 seconds but it takes 3 hours to test then we have a problem, especially if the LLM needs more test feedback than a human would. But is it a fundamental problem or is it "just" a matter of effort?

I mostly work with JVM based apps in recent years and there's lots of low hanging fruit in tests there. JIT compilation is both a blessing and a curse. You don't waste any time compiling the tests themselves (to machine code), but also, the code that does get compiled is forgotten between runs and build systems like to test different modules in different processes. So every test run of every module starts with slow warmup. There is a lot of work being done at the moment on improving that situation, but a lot of it boils down to poor build systems and that's harder to fix (nobody agrees what a good build system looks like...)

In one of my current projects, I've made the entire test suite run in parallel at the level of individual test classes. This took a bit of work to stop different tests messing with each other's state inside the database, and it revealed some genuine race conditions when apparently unrelated features interacted in buggy ways. But it was definitely worth it for local testing. Unfortunately the CI configuration was then written in such a way that it starts by compiling one of its dependencies, which blows up test time to the point where improvements to the actual tests are nearly irrelevant. This particular CI system is non-standard/in house, and I haven't figured out how to fix it yet.

This kind of story is typical. Many such cases.

mrkeen2• 1 year ago

Maybe I've just got unlucky in the past, but in most projects I worked on a lot of developer time was wasted on waiting for PRs to go green. Many runs end up bottlenecked on I/O or availability of workers

No, this is common. The devs just haven't grokked dependency inversion. And I think the rate of new devs entering the workforce will keep it that way forever.

Here's how to make it slow:

* Always refer to "the database". You're not just storing and retrieving objects from anywhere - you're always using the database.

* Work with statements, not expressions. Instead of "the balance is the sum of the transactions", execute several transaction writes (to the database) and read back the resulting balance. This will force you to sequentialise the tests (simultaneous tests would otherwise race and cause flakiness) plus you get to write a bunch of setup and teardown and wipe state between tests.

* If you've done the above, you'll probably need to wait for state changes before running an assertion. Use a thread sleep, and if the test is ever flaky, bump up the sleep time and commit it if the test goes green again.

zbentley• 1 year ago

Instead of "the balance is the sum of the transactions", execute several transaction writes (to the database) and read back the resulting balance

Er, doesn’t this boil down to saying “not testing database end state (trusting in transactionality) is faster than testing it”?

I mean sure, trivially true, but not a good idea. I’ve seen lots of bugs caused by code that unexpectedly forced a commit, or even opened/used/committed a whole new DB connection, somewhere buried down inside a theoretically externally-transactional request handler. Bad code, to be sure, but common in many contexts in my experience.

bvrmn• 1 year ago

Nah. Tests could be run in N processes each with own database configured to skip full fsync. It resolves most of the issues and makes testing much much simpler.

pamelafox2• 1 year ago

For Python apps, I've gotten good CI speedups by moving over to the astral.sh toolchain, using uv for the package installation with caching. Once I move to their type-checker instead of mypy, that'll speed the CI up even more. The playwright test running will then probably be the slowest part, and that's only in apps with frontends.

(Also, Hi Mike, pretty sure I worked with you at Google Maps back in early 2000s, you were my favorite SRE so I trust your opinion on this!)

mike_hearn18• 1 year ago

Hi! :)

Astral's work is great but I wonder how they plan to become sustainable. Maybe it's one of those VC plays where they don't intend to ever really make money and it's essentially a productivity subsidy for the other startups.

My experience has been that most apps are bottlenecked on CPU outside of themselves during CI. Either in JIT runtimes, databases, browsers, or libraries they invoke. I guess now maybe models too. So implementation language won't necessarily make a huge difference to this - we need fresh ideas for how to make order of magnitude improvements here. They will probably vary between ecosystems.

drzaiusx112• 1 year ago

The nice part about most CI workloads is that they can almost always be split up and executed in parallel. Make sure you're utilizing every core on every CI worker and your worker pools are appropriately sized for the workload. Use spot instances and add auto scaling where it makes sense. No one should be waiting more than a few minutes for a PR build. Exception being compile time which can vary significantly between languages. I have a couple projects that are stuck on ancient compilers because of CPU architecture and C variant, so those will always be a dog without effort to move to something better. Ymmv

drzaiusx112• 1 year ago

As an example we recently had a Ruby application that had a test suite that was taking literally an hour per build, but turned out it was running entirely sequential by default, using only 1 core. I spent an afternoon migrating our CI runners to split the workload across all available cores and now it's 5 minutes per build. And that was just the low hanging fruit, it can be significantly improved further but there's obviously diminishing returns

theptip• 1 year ago

This might end up being less of an issue.

If I am coding, I want to stay in the flow and get my PR green asap, so I can continue on the project.

If I am orchestrating agents, I might have 10 or 100 PRs in the oven. In that case I just look at the ones that finish CI.

It’s gonna be less, or at least different, kind of flow IMO. (Until you can just crank out design docs and whiteboard sessions and have the agents fully autonomously get their work green.)

Art9681• 1 year ago

Any modern MacBook can run those tests 100x faster than the crappy cloud runners most companies use. You can also configure runners that run locally and get the benefit of those speed gains. So all of this is really a business and technical problem that is solved for those who want to solve it. It can be solved very cheap, or it can be solved very expensive. Regardless, it's precisely those types of efficiency gains that motivate companies to finally do something about it.

And if not, then enjoy being paid waiting for CI to go green. Maybe it's a reminder to go take a break.

It will be worse when the process is super optimized and the expectation changes. So now instead of those 2 PRs that went to prod today because everyone knows CI takes forever, you'll be expected to push 8 because in our super optimized pipeline it only takes seconds. No excuses. Now the bottleneck is you.

hansvm• 1 year ago

- Just spin up more test instances. If the AI is as good as people claim then it's still way cheaper than extra programmers.

- Write fast code. At $WORK we can test roughly a trillion things per CPU physical core year for our primary workload, and that's in a domain where 20 microsecond processing time is unheard of. Orders of magnitude speed improvements pay dividends quickly.

- LLMs don't care hugely about the language. Avoid things like rust where compile times are always a drag.

- That's something of a strange human problem you're describing. Once the PR is reviewed, can't you just hit "auto-merge" and go to the next task, only circling back if the code was broken? Why is that a significant amount of developer time?

- The thing you're observing is something every growing team witnesses. You can get 90% of the way to what you want by giving the build system a greenfield re-write. If you really have to run 100x more tests, it's worth a day or ten sanity checking docker caching or whatever it is your CI/CD is using. Even hermetic builds have inter-run caching in some form; it's just more work to specify how the caches should work. Put your best engineer on the problem. It's important.

- Be as specific as possible in describing test dependencies. The fastest tests are the ones which don't run.

- Separate out unit tests from other forms of tests. It's hard to write software operating with many orders of magnitude of discrepancies, and tests are no exception. Your life is easier if conceptually they have a separate budget (e.g., continuous fuzz testing or load testing or whatever). Unit tests can then easily be fast enough for a developer to run all the changed ones on precommit. Slower tests are run locally when you think they might apply. The net effect is that you don't have the sort of back-and-forth with your CI that actually causes lost developer productivity because the PR shouldn't have a bunch of bullshit that's green locally and failing remotely.

mike_hearn18• 1 year ago

These are all good suggestions, albeit many are hard to implement in practice.

That's something of a strange human problem you're describing.

Are we talking about agent-written changes now, or human? Normally reviewers expect tests to pass before they review something, otherwise the work might change significantly after they did the review in order to fix broken tests. Auto merges can fail due to changes that happened in the meantime, they're aren't auto in many cases.

Once latency goes beyond a minute or two people get distracted and start switching tasks to something else, which slows everything down. And yes code review latency is a problem as well, but there are easier fixes for that.

elbear• 1 year ago

CI should just run on each developer's machine. As in, each developer should have a local instance of the CI setup in a VM or a docker container. If tests pass, the result is reported to a central server.

pplonski86• 1 year ago

We write and run tests to build trust in our code changes. But maybe tests aren’t the only way to achieve that trust.

When I was younger, I had a friend who was a senior software engineer. I remember he would make changes to production systems without even running the application locally or executing any tests, and yet his changes never failed. The team had a high level of trust in all his code changes.

mdnahas• 1 year ago

We don’t. We switch to proven-correct code. Languages like Lean, Coq, and Idris allow proofs of correctness for code. The LLM can generate proofs for most of the correctness conditions.

CI is still needed for performance, UI testing, etc. but it can have a much smaller role than it does now.

dmitrycube• 1 year ago

100% agree.

One of the core premises of what we've been trying to do with our product (Testkube) is to decouple Testing from CI/CD's. Those were never built with testing in mind, let alone scaling to 100's or 1000's of efficient executions. We have a light weight open-source agent, which lives inside a K8s cluster, tests are stored as CRD's cloned from your GIT, executed as K8's jobs. Create whatever heuristics or parallelization necessary, leverage the power of K8s to dynamically scale compute resources as needed, trigger executions by whatever means (GitHub Actions, K8s' events, schedule, etc.), do it on your existing infra.

Admittedly, we don't solve the test creation problem. If there are new tools out there which could automagically generate tests along with code, please share.

fastball• 1 year ago

ICYMI, DeepMind also has a Gemini model that is diffusion-based[1]. I've tested it a bit and while (like with this model) the speed is indeed impressive, the quality of responses was much worse than other Gemini models in my testing.

[1] https://deepmind.google/models/gemini-diffusion/

tripplyons2• 1 year ago

Is the Gemini Diffusion demo free? I've been on the waitlist for it for a few weeks now.

rany_• 1 year ago

Yes it is.

MattSayar• 1 year ago

It took me a few weeks to get access. In the meantime Simon has a decent demo here https://simonwillison.net/2025/May/21/gemini-diffusion/

Powdering7082• 1 year ago

From my minor testing I agree that it's crazy fast and not that good at being correct

thelastbender12• 1 year ago

The speed here is super impressive! I am curious - are there any qualitative ways in which modeling text using diffusion differs from that using autoregressive models? The kind of problems it works better on, creativity, and similar.

orbital-decay• 1 year ago

One works in the coarse-to-fine direction, another works start-to-end. Which means different directionality biases, at least. Difference in speed, generalization, etc. is less clear and needs to be proven in practice, as fundamentally they are closer than it seems. Diffusion models have some well-studied shortcuts to trade speed for quality, but nothing stops you from implementing the same for the other type.

ekunazanu• 1 year ago

I once read that diffusion is essentially just autoregression in the frequency domain. Honestly, that comparison didn’t seem too far off.

JimDabell2• 1 year ago

Pricing:

US$0.000001 per output token ($1/M tokens)

US$0.00000025 per input token ($0.25/M tokens)

https://platform.inceptionlabs.ai/docs#models

asaddhamani2• 1 year ago

The pricing is a little on the higher side. Working on a performance-sensitive application, I tried Mercury and Groq (Llama 3.1 8b, Llama 4 Scout) and the performance was neck-and-neck but the pricing was way better for Groq.

But I'll be following diffusion models closely, and I hope we get some good open source ones soon. Excited about their potential.

tripplyons2• 1 year ago

Good to know. I didn't realize how good the pricing is on Groq!

empiko• 1 year ago

I strongly believe that this will be a really important technique in the near future. The cost saving this might create is mouth watering.

NitpickLawyer• 1 year ago

I strongly believe that this will be a really important technique in the near future.

I share the same belief, but regardless of cost. What excites me is the ability to "go both ways", edit previous tokens after others have been generated, using other signals as "guided generation", and so on. Next token prediction works for "stories", but diffusion matches better with "coding flows" (i.e. going back and forth, add something, come back, import something, edit something, and so on).

It would also be very interesting to see how applying this at different "abstraction layers" would work. Say you have one layer working on ctags, one working on files, and one working on "functions". And they all "talk" to each other, passing context and "re-diffusing" their respective layers after each change. No idea where the data for this would come, maybe from IDEs?

sansseriff• 1 year ago

I wonder if there's a way to do diffusion within some sort of schema-defined or type constrained space.

A lot of people these days are asking for structured output from LLMs so that a schema is followed. Even if you train on schema-following with a transformer, you're still just 'hoping' in the end that the generated json matches the schema.

I'm not a diffusion excerpt, but maybe there's a way to diffuse one value in the 'space' of numbers, and another value in the 'space' of all strings, as required by a schema:

{ "type": "object", "properties": { "amount": { "type": "number" }, "description": { "type": "string" } }, "required": ["amount", "description"] }

I'm not sure how far this could lead. Could you diffuse more complex schemas that generalize to a arbitrary syntax tree? E.g. diffuse some code in a programming language that is guaranteed to be type-safe?

baalimago• 1 year ago

I, for one, am willing to trade accuracy for speed. I'd rather have 10 iterations of poor replies which forces me to ask the right question than 1 reply which takes 10 times as long and _maybe_ is good, since it tries to reason about my poor question.

PaulHoule5• 1 year ago

Personally I like asking coding agents a question and getting an answer back immediately. Systems like Junie that go off and research a bunch of irrelevant things than ask permission than do a lot more irrelevant research, ask more permission and such and then 15 minutes later give you a mountain of broken code are a waste of time if you ask me. (Even if you give permission in advance)

beef_rendang• 1 year ago

Use case for diffusion based text generation models (as opposed to image models)?

pmxi2• 1 year ago

This is cool. I think faster models can unlock entirely new usage paradigms, like how faster search enables incremental search.

physix• 1 year ago

I was curious to know the statistics on the mentions of various programming languages on HN over the years, so I got me a copy of all HN comments from a BigTable public source. But now I need to interpret each comment and so what I need is a semantic grep. The easiest would be to prompt an LLM.

Comments are pretty short, but there are many millions of them. So getting high throughput at minimum cost is key.

I'm hoping that Inception might be able to churn through this quickly.

If you folks have other ideas or suggestions, what might also work, I'd love to hear them!

The idea is having a semgrep command line tool. If latencies are dropping dramatically, it might be feasible.

amelius2• 1 year ago

Damn, that is fast. But it is faster than I can read, so hopefully they can use that speed and turn it into better quality of the output. Because otherwise, I honestly don't see the advantage, in practical terms, over existing LLMs. It's like having a TV with a 200Hz refresh rate, where 100Hz is just fine.

pmxi2• 1 year ago

There are plenty of LLM use cases where the output isn’t meant to be read by a human at all. e.g:

parsing unstructured text into structured formats like JSON

translating between natural or programming languages

serving as a reasoning step in agentic systems

So even if it’s “too fast to read,” that speed can still be useful

amelius2• 1 year ago

Sure, but I was talking about the chat interface, sorry if that was not clear.

martinald3• 1 year ago

You're missing another big advantage is cost. If you can do 1000tok/s on a $2/hr H100 vs 60tok/s on the same hardware, you can price it at 1/40th of the price for the same margin.

Legend2440• 1 year ago

This lets you do more (potentially a lot more) reasoning steps and tool calls before answering.

irthomasthomas• 1 year ago

I've used mercury quite a bit in my commit message generator. I noticed it would always produce the exact same response if you ran it multiple times, and increasing temperature didn't affect it. To get some variability I added a $(uuidgen) to the prompt. Then I could run it again for a new response if I didn't like the first.

everlier• 1 year ago

Something like https://github.com/av/klmbr could also work

seydor• 1 year ago

I wonder if diffusion llms solve the hallucination problem more effectively. In the same way that image models learned to create less absurd images, dllms can perhaps learn to create sensical responses more predictably

awaymazdacx5• 1 year ago

Having token embeddings with diffusion models, for 16x16 transformer encoding. Image is tokenized before transformers compile it. If decomposed virtualization modulates according to a diffusion model.

storus• 1 year ago

Can Mercury use tools? I haven't seen it described anywhere. How about streaming with tools?

shibeprime• 1 year ago

For tools they say coming soon in their api docs here https://platform.inceptionlabs.ai/docs#models

nashashmi• 1 year ago

I guess this makes specific language patterns cheaper and more artistic language patterns more expensive. This could be a good way to limit pirated and masqueraded materials submitted by students.

true_blue• 1 year ago

I tried the playground and got a strange response. I asked for a regex pattern, and the model gave itself a little game-plan, then it wrote the pattern and started to write tests for it. But it never stopped writing tests. It continued to write tests of increasing size until I guess it reached a context limit and the answer was canceled. Also, for each test it wrote, it added a comment about if the test should pass or fail, but after about the 30th test, it started giving the wrong answer for those too, saying that a test should fail when actually it should pass if the pattern is correct. And after about the 120th test, the tests started to not even make sense anymore. They were just nonsense characters until the answer got cut off.

The pattern it made was also wrong, but I think the first issue is more interesting.

fiatjaf• 1 year ago

This is too funny to be true.

beders• 1 year ago

I think that's a prime example showing that token prediction simply isn't good enough for correctness. It never will be. LLMs are not designed to reason about code.

ianbicking2• 1 year ago

FWIW, I remember regular models doing this not that long ago, sometimes getting stuck in something like an infinite loop where they keep producing output that is only a slight variation on previous output.

data-ottawa• 1 year ago

if you shrink the context window on most models you'll get this type of behaviour. If you go too small you end up with basically gibberish even on modern models like Gemini 2.5.

Mercury has a 32k context window according to the paper, which could be why it does that.

_kidlike• 1 year ago

I had this happen to me on Claude Sonnet once. It started spitting out huge blocks of source code completely unrelated to my prompt, seemingly from its training data, and switching codebases once in a while... like, a few thousand lines of some C program, then switching to another JavaScript one, etc. it was insane!

CSSer• 1 year ago

Sounds like solidgoldmagikarp[0]. There must've been something in your prompt that is over-represented throughout the training data.

[0] https://www.lesswrong.com/posts/jbi9kxhb4iCQyWG9Y/explaining...

throwaway314155• 1 year ago

This is common amongst _all_ of the smaller LLM's.

skybrian• 1 year ago

Company blog post: https://www.inceptionlabs.ai/introducing-mercury-our-general...

News coverage from February: https://techcrunch.com/2025/02/26/inception-emerges-from-ste...

mtillman• 1 year ago

Ton of performance upside in most GPU adjacent code right now.

However, is this what arXiv is for? It seems more like marketing their links than research. Please correct me if I'm wrong/naive on this topic.

ricopags• 1 year ago

not wrong, per se, but it's far from the first time

eden-u4• 1 year ago

No open model/weights?

krasin• 1 year ago

Not only they do not release models/weights. They don't even tell the size of the models!

The linked whitepaper is pretty useless, and I am saying as a big fan of diffusion-transformers-for-not-just-images-or-videos approach.

Also, Gemini Diffusion ([1]) is way better at coding than Mercury offering.

1. https://deepmind.google/models/gemini-diffusion/

gdiamos4• 1 year ago

I think the LLM dev community is underestimating these models. E.g. there is no LLM inference framework that supports them today.

Yes the diffusion foundation models have higher cross entropy. But diffusion LLMs can also be post trained and aligned, which cuts the gap.

IMO, investing in post training and data is easier than forcing GPU vendors to invest in DRAM to handle large batch sizes and forcing users to figure out how to batch their requests by 100-1000x. It is also purely in the hands of LLM providers.

mathiaspoint2• 1 year ago

You can absolutely tune causal LLMs. In fact the original idea with GPTs was that you had to tune them before they'd be useful for anything.

gdiamos4• 1 year ago

Yes I agree you can tune autoregressive LLMs

You can also tune diffusion LLMs

After doing so, the diffusion LLM will be able to generate more tokens/sec during inference

KaranSohi• 1 year ago

We have used their LLM in our company and it's great! From Accuracy to speed of response generation, this model seems very promising!

ceroxylon• 1 year ago

The output is very fast but many steps backwards in all of my personal benchmarks. Great tech but not usable in production when it is over 60% hallucinations.

mike_hearn18• 1 year ago

That might just depend on how big it is/how much money was spent on training. The neural architecture can clearly work. Beyond that catching up may be just a matter of effort.

mmaunder2• 1 year ago

Holy shit that is fast. Try the playground. You need to get that visceral experience to truly appreciate what the future looks like.

mmaunder2• 1 year ago

Code output is verifiable in multiple ways. Combine that with this kind of speed (and far faster in future) and you can brute force your way to a killer app in a few minutes.

OneOffAsk• 1 year ago

Yes, exactly. The demo of Gemini's Diffusion model [0] was really eye-opening to me in this regard. Since then, I've been convinced the future of lots of software engineering is basically UX and SQA: describe the desired states, have an LLM fill in the gaps based on its understanding of human intent, and unit test it to verify. Like most engineering fields, we'll have an empirical understanding of systems as opposed to the analytical understanding of code we have today. I'd argue most complex software is already only approximately understood even before LLMs. I doubt the quality of software will go up (in fact the opposite), but I think this work will scale much better and be much, much more boring.

[0] https://simonwillison.net/2025/May/21/gemini-diffusion/

jonplackett• 1 year ago

Wow, this thing is really quite smart.

I was expecting really crappy performance but just chatting to it, giving it some puzzles, it feels very smart and gets a lot of things right that a lot of other models don't.

ahmedhawas123• 1 year ago

Reinforcement learning really helped Transformer based LLMs evolve in terms of quality and reasoning which we saw as DeepSeek was launched. I am curious if what this is is equivalent to an early GPT 4o that has not yet reaped the benefits of add-on technologies that helped improve the quality?

M4v3R• 1 year ago

I am personally very excited for this development. Recently I AI-coded a simple game for a game jam and half the time was spent waiting for the AI agent to finish its work so I can test it. If instead of waiting 1-2 minutes for every prompt to be executed and implemented I could wait 10 seconds instead that would be literally game changing. I could test 5-10 different versions of the same idea in the time it took me to test one with the current tech.

Of course this model is not as advanced yet for this to be feasible, but so was Claude 3.0 just over a year ago. This will only get better over time I’m sure. Exciting times ahead of us.

ianbicking2• 1 year ago

For something a little different than a coding task, I tried using it in my game: https://www.playintra.win/ (in settings you can select Mercury, the game uses OpenRouter)

At first it seemed pretty competent and of course very fast, but it seemed to really fall apart as the context got longer. The context in this case is a sequence of events and locations, and it needs to understand how those events are ordered and therefore what the current situation and environment are (though there's also lots of hints in the prompts to keep it focused on the present moment). It's challenging, but lots of smaller models can pull it off.

But also a first release and a new architecture. Maybe it just needs more time to bake (GPT 3.5 couldn't do these things either). Though I also imagine it might just perform _differently_ from other LLMs, not really on the same spectrum of performance, and requiring different prompting.

armcat3• 1 year ago

I've been looking at the code on their chat playground, https://chat.inceptionlabs.ai/, and they have a helper function `const convertOpenAIMessages = (convo) => { ... }`, which also contains `models: ['gpt-3.5-turbo']`. I also see in API response: `"openai": true`. Is it actually using OpenAI, or is it actually calling its dLLM? Does anyone know?

Also: you can turn on "Diffusion Effect" in the top-right corner, but this just seems to be an "animation gimmick" right?

Alifatisk8• 1 year ago

The speed of the response is waaay to quick for using OpenAi as backend, it's almost instant!

armcat3• 1 year ago

I've been asking bespoke questions and the timing is >2 seconds, and slower than what I get for the same questions to ChatGPT (using gpt-4.1-mini). I am looking at their call stack and what I see: "verifyOpenAIConnection()", "generateOpenAIChatCompletion()", "getOpenAIModels()", etc. Maybe it's just so it's compatible with OpenAI API?

Alifatisk8• 1 year ago

Love the ui in the playground, it reminds me of Qwen chat.

We have reached a point where the bottlenecks in genAI is not the knowledge or accuracy, it is the context window and speed.

Luckily, Google (and Meta?) has pushed the limits of the context window to about 1 million tokens which is incredible. But I feel like todays options are still stuck about ~128k token window per chat, and after that it starts to forget.

Another issue is the time time it takes for inference AND reasoning. dLLMs is an interesting approach at this. I know we have Groqs hardware aswell.

I do wonder, can this be combined with Groqs hardware? Would the response be instant then?

How many tokens can each chat handle in the playground? I couldn't find so much info about it.

Which model is it using for inference?

Also, is the training the same on dLLMs as on the standardised autoregressive LLMs? Or is the weights and models completely different?

martinald3• 1 year ago

I agree entirely with you. While Claude Code is amazing, it is also slow as hell and the context issue keeps coming up (usually at what feels like the worst possible time for me).

It honestly feels like dialup most LLMs (apart from this!).

AFIAK with traditional models context size is very memory intensive (though I know there are a lot of things that are trying to 'optimize' this). I believe memory usage grows at the square of context length, so even 10xing context length requires 100x the memory.

(Image) diffusion does not grow like that, it is much more linear. But I have no idea (yet!) about text diffusion models if someone wants to chip in :).

kadushka6• 1 year ago

We have reached a point where the bottlenecks in genAI is not the knowledge or accuracy, it is the context window and speed.

You’re joking, right? I’m using o3 and it couldn’t do half of the coding tasks I tried.

Alifatisk8• 1 year ago

I've been in similar situations, I've realized, you make these llms accomplish lots of difficult tasks if you prompt it correctly, and that is a form of art! If a colleague of mine, who's incredible at prompting, did impressive things with gpt-3, so I am sure o3 can do even more wilder stuff.

totetsu• 1 year ago

Its a fork/implementation of openwebui isn't it?

Alifatisk8• 1 year ago

I don't now, I couldn't find any reference to any project in either QwenChat or Inceptionlabs playground. But looking at openwebui, it could be it? But with a slight redesign on the chat section.

runeblaze• 1 year ago

I mean we don't really talk about the accuracy of generative models. It is more of a discriminative model thing.

But besides this, the current gen of models still, like, hallucinates more than many would like

mxs_• 1 year ago

In their tech report, they say this is based on:

"Our methods extend [28] through careful modifications to the data and computation to scale up learning."

[28] is Lou et al. (2023), the "Score Entropy Discrete Diffusion" (SEDD) model (https://arxiv.org/abs/2310.16834).

I wrote the first (as far as I can tell) independent from-scratch reimplementation of SEDD:

https://github.com/mstarodub/dllm

My goal was making it as clean and readable as possible. I also implemented the more complex denoising strategy they described (but didn't implement).

It runs on a single GPU in a few hours on a toy dataset.

mseri• 1 year ago

Sounds all cool and interesting, however:

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numpad0• 1 year ago

Is parameter count published? I'm by no means expert, but failure modes remind me of Chinese 1B class models.

flockonus• 1 year ago

If anyone else is curious about the claim "Copilot Arena, where the model currently ranks second on quality"

This seems to be the link, mind blowing results if indeed is the case: https://lmarena.ai/leaderboard/copilot

cavisne• 1 year ago

Are there any rules for what can be uploaded to arxiv?

This is a marketing page turned into a PDF, I guess who cares but could someone upload like a facebook marketplace listing screenshotted into a PDF?

wavemode3• 1 year ago

Yes, arxiv requires that submissions must be scientific research. And not just anyone can publish on arxiv, you need endorsement by existing users.

That that scientific research is in pursuit of a commercial product, or that the paper submitted is of low quality, is not something they would filter however.

EigenLord• 1 year ago

Diffusion is just the logically most optimally behavior for searching massively parallel spaces without informed priors. We need to think beyond language modeling however and start to view this in terms of drug discovery etc. A good diffusion model + the laws of chemistry could be god-tier. I think language modeling has the AI community's in its grips right now and they aren't seeing the applications of the same techniques to real world problems elsewhere.

dawnofdusk• 1 year ago

Actually in most deep learning schemes for science adding in the "laws of nature" as constraints makes things much worse. For example, all the best weather prediction models utilize basically zero fluid dynamics. Even though a) global weather can be in principle predicted by using the Navier-Stokes equations and b) deep learning models can be used to approximately evaluate the Navier-Stokes equations, we now know that incorporating physics into these models is mostly a mistake.

The intuitive reason might be that unconstrained optimization is easier than constrained optimization, particularly in high dimensions, but no one really knows the real reason. It may be that we are not yet at the end of the "bigger is better" regime, and at the true frontier we must add the laws of natures to eke out the last remaining bits of performance possible.

anotherpaul• 1 year ago

Well diffusion models have long already made the jump to biology at least. Esm3 and alphafold 3 both are diffusion based.

kazinator• 1 year ago

Google has Gemini Diffusion in the works. I joined the beta. Roughly speeking it "feels" a lot like 2.5 Flash in the style of its interaction and accuracy. But the walls of text appear almost instantaneously; you don't notice any scrolling.

loaderchips• 1 year ago

i wonder how fast this would be when run on something like groq

convery• 1 year ago

It certainly is fast, but I'm curious if LLMs ever will figure out how bitshifts work..

e.g. from the playground: `static const uint64_t MERSENNE_PRIME = (1ULL << 127) - 1;` which it insists is the correct way to store a 128-bit integer in followup questions.

ethan_smith• 1 year ago

For 128-bit values in C++, you'd need uint128_t (compiler extension) or a library like Boost.Multiprecision since 1ULL << 127 overflows the 64-bit type before subtraction occurs.

londons_explore• 1 year ago

It's a little sad that the "diffusion effect" checkbox is just that - an effect.

It would be neat to show to the user all the real intermediate steps.

202282020008• 1 year ago

hello

allen-munsch• 1 year ago

Tried my hand at implementing the theory, from the text, without looking at any code examples: https://github.com/allen-munsch/quicksilver

Roughly correlated to what I was seeing, though i don't know any of the secret sauce.

Mercury: Ultra-fast language models based on diffusion

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