Jonathan Frankle

This episode is sponsored by Oracle. OCI is the next-generation cloud designed for every workload – where you can run any application, including any AI projects, faster and more securely for less. On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking. Join Modal, Skydance Animation, and today's innovative AI tech companies who upgraded to OCI…and saved. Try OCI for free at http://oracle.com/eyeonai What if you could fine-tune an AI model without any labeled data—and still outperform traditional training methods? In this episode of Eye on AI, we sit down with Jonathan Frankle, Chief Scientist at Databricks and co-founder of MosaicML, to explore TAO (Test-time Adaptive Optimization)—Databricks' breakthrough tuning method that's transforming how enterprises build and scale large language models (LLMs). Jonathan explains how TAO uses reinforcement learning and synthetic data to train models without the need for expensive, time-consuming annotation. We dive into how TAO compares to supervised fine-tuning, why Databricks built their own reward model (DBRM), and how this system allows for continual improvement, lower inference costs, and faster enterprise AI deployment. Whether you're an AI researcher, enterprise leader, or someone curious about the future of model customization, this episode will change how you think about training and deploying AI. Explore the latest breakthroughs in data and AI from Databricks: https://www.databricks.com/events/dataaisummit-2025-announcements Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI

Jonathan Frankle

Chief AI Scientist, Databricks

DatabricksMosaicMLMassachusetts Institute of TechnologyMIT

It’s return guest season here at Latent Space! We last talked to Kanjun in October and Jonathan in May (and December post Databricks acquisition): Imbue and Databricks are back for a rare treat: a double-header interview talking about DBRX from Databricks and Imbue 70B, a new internal LLM that “outperforms GPT-4o” zero-shot on a range of reasoning and coding-related benchmarks and datasets, while using 7x less data than Llama 3 70B. While Imbue, being an agents company rather than a model provider, are not releasing their models today, they are releasing almost everything else: * Cleaned-up and extended versions of 11 of the most popular NLP reasoning benchmarks * An entirely new code-focused reasoning benchmark * A fine-tuned 70B model, built with Meta Llama 3, to identify ambiguity * A new dataset of 450,000 human judgments about ambiguity * Infrastructure scripts for bringing a cluster from bare metal to robust, high performance training * Our cost-aware hyperparameter optimizer, CARBS, which automatically and systematically fine-tunes all hyperparameters to derive optimum performance for models of any size As well as EXTREMELY detailed posts on the infrastructure needs, hyperparameter search, and clean versions of the sorry state of industry standard benchmarks. This means for the FIRST TIME (perhaps since Meta’s OPT-175B in 2022?) you have this level of educational detail into the hardware and ML nitty gritty of training extremely large LLMs, and if you are in fact training LLMs of this scale you now have evals, optimizers, scripts, and human data/benchmarks you can use to move the industry forward together with Imbue. We are busy running the sold-out AI Engineer World’s Fair today, and so are unable to do our usual quality writeup, however, please enjoy our show notes and the excellent conversation! Thanks also to Kanjun, Ashley, Tom and the rest of team Imbue for setting up this interview behind the scenes. Video pod Timestamps * [00:00:00] Introduction and catch up with guests * [00:01:55] Databricks' text to image model release * [00:03:46] Details about the DBRX model * [00:05:26] Imbue's infrastructure, evaluation, and hyperparameter optimizer releases * [00:09:18] Challenges of training foundation models and getting infrastructure to work * [00:12:03] Details of Imbue's cluster setup * [00:18:53] Process of bringing machines online and common failures * [00:22:52] Health checks and monitoring for the cluster * [00:25:06] Typical timelines and team composition for setting up a cluster * [00:27:24] Monitoring GPU utilization and performance * [00:29:39] Open source tools and libraries used * [00:32:33] Reproducibility and portability of cluster setup * [00:35:57] Infrastructure changes needed for different model architectures * [00:40:49] Imbue's focus on text-only models for coding and reasoning * [00:42:26] CARBS hyperparameter tuner and cost-aware optimization * [00:51:01] Emergence and CARBS * [00:53:18] Evaluation datasets and reproducing them with high quality * [00:58:40] Challenges of evaluating on more realistic tasks * [01:06:01] Abstract reasoning benchmarks like ARC * [01:10:13] Long context evaluation and needle-in-a-haystack tasks * [01:13:50] Function calling and tool use evaluation * [01:19:19] Imbue's future plans for coding and reasoning applications * [01:20:14] Databricks' future plans for useful applications and upcoming blog posts Transcript SWYX [00:00:00]: Welcome to the Latent Space Podcast, another super special edition. Today, we have sort of like a two-header. John Frankel from Mosaic Databricks, or Databricks Mosaic, and Josh Albrecht from MBU. Welcome. JOSH [00:00:12]: Hey, glad to be here. SWYX [00:00:14]: Thank you for having us. Hey, so both of you are kind of past guests. Jonathan, you were actually one of the most popular episodes from last year talking about MPT7B. Remember the days when we trained large models and there was 7B? JONATHAN [00:00:30]: Yeah, back when reproducing LLAMA1-7B was considered a huge accomplishment for the field. Those are the good old days. I miss that. SWYX [00:00:38]: As the things have accelerated a lot. Actually, let's do a quick catch up and Josh, you can chime on in as well. So Databricks got acquired. I talked to you at New York. JONATHAN [00:00:45]: Mosaic got acquired, although sometimes it feels like Mosaic acquired Databricks because, you know, we're having a lot of fun being here. But, you know, yeah. SWYX [00:00:52]: Yeah. I mean, you are chief scientist now of Databricks. JONATHAN [00:00:55]: Chief AI scientist. Careful with the title. As much as I would love to understand how Spark works, I'm going to have to defer that to much smarter people than me. SWYX [00:01:03]: Got it. And I don't know about like what you would highlight so far as a post-acquisition, but the most recent news is that you guys released DBRX. Is that the thing that most people should be aware of? JONATHAN [00:01:13]: Actually, that's no longer the most recent news. Honestly, the most recent news, we announced this, but it was at our Data and AI Summit last week. So it was announced among like 100,000 other things, is that we finally released our text to image model, which has been a year in the making through a collaboration directly with Shutterstock. There was a lot of work put into finding a dataset that we were comfortable with working on and trying to build a model that honestly, I felt like I could trust and that others might be able to trust to put out in the world. So that model was released last week. It's unfortunately just available via API due to the fact that the data is quite sensitive and quite valuable. It's Shutterstock's entire business in a lot of ways, but I'm still really excited that there's now a model that is trained on a dataset where the provenance of every single image is known, and it's a damn good model. So I'm really proud of the team on that. SWYX [00:01:55]: Yeah, amazing. Josh, do you have any thoughts on image model questions? JOSH [00:01:59]: That is not my area of expertise, but I was excited to see the release of it last week as well, and very happy that you guys did a nice job on the data side of everything there. So that was cool to see. SWYX [00:02:09]: I think what's unusual is like, I think Shutterstock's doing multiple deals in multiple labs. So what is the Shutterstock model? Like, I guess, is this the house model for Shutterstock? Is this Databricks' version of the Shutterstock model? Like, what is this? JONATHAN [00:02:22]: The way that I would think about it is that Shutterstock is doing an amazing business in AI across the board. Their dataset is kind of widely known to be the best stock photos dataset in the world, the most comprehensive, the biggest. When you think about like, what dataset am I going to train a multimodal model on? You call Shutterstock. And I, at least I've heard in the news, like OpenAI, Google, Meta, Apple have all called Shutterstock and made those deals. So a lot of models have had Shutterstock data incorporated into them. But this is the only model I know of so far where it was, you know, exclusively and specifically trained just on the vanilla Shutterstock data. There was nothing else mixed in. We didn't go and scrape the web and find other data or combined datasets or anything like that. And so this is, in some sense, the house blend. But the other piece is that it's just a dataset where the provenance of every image is known in public. Where did the data come from? It is the Shutterstock collection. That's it. You know, nothing less, nothing more. And certainly being at Databricks, if I've learned one thing, I've learned about enterprise customers and what they want out of AI. And one of the things they ask for most is just, what can you tell me about the data the model was trained on? And here, especially for text to image models, where images are just tricky subject matter, there's been a lot of kind of legal conversation about images, especially. It's nice to just have something where I can point to it and say, you know, if you want to know where the images came from, these are what they are and this is how they got there. SWYX [00:03:36]: I will talk a little bit about Databricks because it's relevant to the rest of today's episode. So Databricks, sorry, I keep misspeaking. It's DBRX. JONATHAN [00:03:46]: DBRX, actually, there's been a pronunciation update. It is now D-B-Rex. So we have decided to add a dinosaur mascot because what model doesn't like a mascot? So literally, I wish I could pull it up. There is a little plush dinosaur that we had made. It's like the world's cutest dinosaur, but it is the official mascot of D-B-Rex. And there's a little dinosaur logo that, you know, you'll probably see around a little bit more because DBRX is a mouthful, but D-B-Rex, like, you know, it's just kind of... SWYX [00:04:13]: Rolls off the tongue. I love mascots. Like every company should have a mascot. And I think Hugging Face got it right. You need an emoji mascot because that's the minimal viable image. JONATHAN [00:04:21]: I probably shouldn't talk at all about, you know, Velociraptor, but, you know, that's a, maybe that's something we can talk about later in the summer. I'll just leave it at that. SWYX [00:04:28]: Okay. That's a hint to names. I feel like your names leak a lot of alpha. So just to quickly cover the headline details, DBRX, as Make Sure Experts model, that's fairly big, 132 billion total parameters, so 36 billion active on any input, pre-trained on 12 trillion tokens of text and code, and did really well on evals to the point where you had to dye your hair blue. That's my high level conclusion. JONATHAN [00:04:53]: Never make a bet with your team two weeks out from model launch, even when, you know, human eval is looking quite bad. Because if you set some bar, even if it's arbitrary and you think there's no way in hell they're going to hit it, apparently money doesn't motivate people anymore. Humiliating their boss motivates people. So Josh, you should really take a hint from this. You know, you cannot pay someone enough money to make up for you dyeing your hair blue. JOSH [00:05:15]: I'll keep that in mind for our next model. SWYX [00:05:17]: It works. So speaking of Imbue's next model, perhaps Josh, you want to actually just say hi to the general sort of latent space audience and talk about what we're releasing today. Yeah. JOSH [00:05:26]: I'm Josh, CTO of Imbue, and we're not releasing the model. We're not releasing the weights, but we are releasing a bunch of different things that should make it easier for other people to make their own models. So I think right now, training foundation models from scratch is like a very difficult, time-consuming, expensive, kind of risky endeavor, especially for smaller companies. And the things that we're releasing hopefully make that at least a little bit easier. So the things that we're releasing fall into kind of three different buckets. One is infrastructure and scripts for dealing with the kind of hardware and hardware failures and understanding how well is the actually lowest level of thing actually working so that you can actually do your training at all and at a reasonable speed without having to constantly restart, etc. So infrastructure and training scripts. A second set of things is around the evaluation. So after you've trained it, like how well is this actually working and how do you know how well it's working? We're releasing a whole bunch of different data there, a new benchmark about code, reasoning, understanding, as well as our own private versions of 11 different open source benchmarks. So things like pool queue or ANLI, where we've gone through and kind of cleaned up the data as much as possible by looking at all the ones that models get wrong or that are flagged for ambiguity and also our own kind of private reproductions of those where we've done like a kind of clean room black box, like, okay, this is what the data set is supposed to be. Here are some examples. Let's make our own version of this to make sure that there is no data contamination, etc. To make sure that we're actually, you know, not testing on train. And then I think a final thing that we're releasing there is around 450,000 human judgments about ambiguity and question quality, which we used in the process of cleaning these evaluations and we also hope will be helpful for other people training kind of similar models. And then the third thing is CARBS, our hyperparameter, our cost-aware hyperparameter optimizer, which was especially helpful for being able to experiment at much smaller scales and then scale those experiments up to the much larger scale kind of on the first try without having to retry it. You don't want to be training, you know, 10, 20 different 70B models. You really want to get these larger models SWYX [00:07:30]: right on the first try. JOSH [00:07:30]: And so the ability to kind of tune things very precisely and learn scaling laws, not just for, you know, the like data and flops, but also for learning rate and all the other hyperparameters and see like how should you scale these things up was extremely valuable to us as we were training the larger models. Yeah, that's a lot of stuff. SWYX [00:07:49]: Yeah, exactly. So there's a bunch of stuff JOSH [00:07:50]: we'll have to go through all of it. JONATHAN [00:07:52]: Yeah, I just want to throw in how excited I am about this. This is the stuff that nobody ever talks about. That is the difference between success and failure in this stuff. Like, can you get your cluster to run? Can you get software on your cluster? Can you figure out what broke? Because fault tolerance is still not really built into any of the fundamental primitives of training models. And so if something breaks, you have to go figure out what broke, your job stops, you have to restart your job. It is a nightmare just to get to the point where anything can train on the cluster. A basic MPI hello world that has the GPUs talk to each other is hard enough, let alone actually training a model, let alone getting good performance out of the GPUs, let alone actually getting a model that converges to anything interesting. There's so many levels of things you have to accomplish. This is the kind of stuff that matters. I think to a point that Josh made earlier, before we got on here, there are plenty of weights out there. Nobody's released this. JOSH [00:08:46]: Yeah, that was part of the motivation actually is that there are lots of other things that are complimentary, but I have not seen nearly as much discussion about some of these other things that we think are pretty important. I mean, in some sense, SWYX [00:08:56]: I'm very excited to have Jonathan on because this is a little bit, you're a bread and butter with Mosaic. And I think you've released some part with Composer. And I think it's just really interesting to see like a different take, basically a full stack take that's kind of open source today. JONATHAN [00:09:18]: Yeah, it's really kind of, it's been an ordeal to figure this out. And every time something changes, whether it's a new GPU or even a new driver update, you get new creative errors and new things go wrong. And, you know, we've dealt with the weirdest things from, you know, our InfiniBand cables getting stolen from the data center twice, like in boxes before they arrived at the data center. Like, you know, Porch Pirate basically had stolen our InfiniBand cables back when those were hard to come by. To like, you know, weird recalls of switches to like the strangest stuff has happened. I have my favorite GPU failures I've seen, like ones where the GPU doesn't fail, it has a correctable memory issue and the memory correction causes the GPU to become a straggler and hold up the whole job. Like weird stuff happens and figuring out how to not just identify all of that, but then eventually productize it, is in some sense, the entire story of Mosaic and now Databricks in terms of our ML offering. Really, the thing we offer is we have gone through this suffering and figured out how to even productize that. It has been a pain in the butt. SWYX [00:10:20]: Yeah, it's a lot of work. JOSH [00:10:20]: I think my favorite failure was GPU is just giving wrong math. Like if they give errors, great, because you can see the errors, but if they just give you the wrong math back, not so fun. SWYX [00:10:30]: When did they give you wrong math? JOSH [00:10:32]: Like literally you could just, you know, add two things. For example, the numbers come back. They're not the numbers that they're supposed to be. JONATHAN [00:10:40]: I think it's important to say at this stage, just because like it, I think it goes without saying for Josh and I, but it's worth saying here, this isn't to say that like anything is wrong with us. It's not like NVIDIA did a bad job or, you know, Mellanox did a bad job or the like the server builder, the data center operator, the cloud provider, like the million other parties that are involved in building this. We are running these insane chips that are huge and complicated and built on tiny transistors at insane frequencies with insane heat in data centers that for the most part, were not built remotely for this kind of power or heat and have been retrofitted for this. Like failures happen on a good day with normal CPUs. And this is not a good day and not a normal CPU for the most part. It's fun to joke about all the weird things we see. This is not to say anybody's done anything wrong. This is just kind of part and parcel of working on a massive cluster running at multiple megawatts of power at a time. SWYX [00:11:32]: It's crazy. Yeah. JONATHAN [00:11:33]: So optical cables, like all sorts, like everything. SWYX [00:11:37]: I'll take the opportunity to start going to the sort of infra piece. There's just like a description of the infra just to give people a sense of what we talk about when we talk about massive clusters. So I'm just going to read off the blog post here. This post is about one cluster that has 4,092 H100 GPUs spread across 511 computers. They use unified fabric manager nodes, which manage the infinite band network. And you talk a little bit about your networking. Is there anything unusual about this setup that you'll call out to people? JOSH [00:12:03]: Yeah, actually this particular cluster is a little bit non-standard. The normal, like vanilla setup for these large clusters as vanilla as it can be is what's normally like a 127 node cluster. So closer to like 1024 GPUs instead of 4,000. Here we have a larger cluster. As you start to get into the larger clusters, the networking becomes a little bit more custom. It's a little bit more, it's a little bit trickier. It's a little bit more difficult to get these things to all be able to talk to each other at the same speed. And so this has, in this particular case, this is a three tier network architecture instead of two tiers, kind of the normal one. So most of the clusters are a little bit smaller. As you get to even larger scales, then this becomes even much more complicated, SWYX [00:12:43]: much more expensive. JOSH [00:12:43]: So we chose this particular scale, kind of knowing our own workloads and kind of what we wanted to do. This was kind of the right size for us. But yeah, I think it's not exactly vanilla already. It's already getting into kind of the custom territory. SWYX [00:12:54]: So my understanding is that there, and is there any part of this that comes with the Voltage Park deal that you guys had? Is that part of the hardware that you got from the deal with them? JOSH [00:13:04]: Yeah, so we worked really closely with Voltage Park to set up all their clusters and infrastructure and everything and kind of decide even like what to order, how should the networking work? Like we were very involved in kind of the construction and bring up of this. And that's what this post is about, is about that process of like bringing up all these, there's like different clusters in different places of different scales. So in this particular post, we're talking about this one 4096 GPU, but there are other clusters that they have as well. And we were very closely involved with figuring out the exact architecture and kind of the trade-offs that go along with picking, you know, those exact components. You really don't want to like place the wrong order because it takes months to get it and it's very expensive. So yeah, we were happy to help out with that. JONATHAN [00:13:43]: And then your bit of good cables get stolen. SWYX [00:13:44]: Yeah, yeah, exactly. JOSH [00:13:47]: We wanted to make sure that we ended up with compute that would work for us and that would also work for their other customers. And so we kind of helped design something so that we would get exactly what we were looking for. We knew that these kinds of details would be super important and that getting down to the level of the hardware and like having these good scripts and everything was going to be a core part of like actually getting this to work. I'm very glad that we did that. I don't think that most companies kind of take that full stack approach, but for us, it certainly paid off. SWYX [00:14:12]: Yeah, it's basically sort of built to spec. It's interesting that relationship because you usually, for the rest of us who don't operate at your scale, we take whatever we can get from cloud providers, but you are basically co-designing from the single machine up. And you described that a little bit. Do you want to take us through the process that you described here? JOSH [00:14:27]: Yeah, so for the actual, like the blog post and kind of bringing these machines online. SWYX [00:14:32]: Yeah. JOSH [00:14:32]: So yeah, I think the process, as we have it broken down in the blog post, there's kind of a few different layers. First is like getting the individual machines to work at all and then getting the machines to actually be able to talk to each other. So getting the InfiniBand networking to work and then getting to a point where, you know, not just the machines are working and they can talk to each other, but everything is actually working correctly. There's a big gap between like it's working at all to it's working perfectly correctly. And then after you have all this stuff working perfectly correctly, nice and healthy, then now you get into kind of the software data, like training issues. And then after that, you're still not done. Like now, even once you're training at full speed, things are going to fail over time. Things are going to change. There's going to be new, you know, firmware updates. Like how do you kind of deal with this change and flux over time without going crazy SWYX [00:15:16]: and pulling your hair out, JOSH [00:15:16]: trying to like reproduce things or understand why there were regressions. And so there's a lot of work to kind of automate the infrastructure tooling as well. And kind of the first step, like bringing these things online in the first place, you know, you have hundreds of machines at this point. So you don't necessarily want to be like walking around with like a CD-ROM or a USB drive, like plugging it in with your keyboard, like hitting next, next, next on the OS install. That's not how this works. You do that for one machine. And then you use, we use this thing called Metal as a Service to bring up all the other machines. So it's a kind of server that can kind of install the operating system on these other machines. So most like when you're talking about these machines, like each machine is, you know, on the order of hundreds of thousands of dollars. So they usually come with a kind of out-of-band management interface as well. So they don't, they have their InfiniBand networking. They have their normal 100 gigabit per second Ethernet networking. These are like dual, redundant, et cetera. And then you also have this extra out-of-band management network. So you can log in and you can see like the boot screen or you can see the blue screen of death. You can like get in there and actually see what was wrong, which is pretty fun. And it makes it like possible to automate a lot of this work. So the beginning of that, and the blog post goes into much more detail about like exactly how we set these up and kind of the other errors that we ran into. When you're bringing these online, you'll definitely have failures. Even if they all worked in the factory, they get shipped, some parts come loose, something fails, something goes wrong. So when you're bringing them online, there'll be some that don't quite work for all sorts of reasons. As you start to be working with machines at this scale, like if something happens one in a thousand times, you're like pretty likely to see it. And so you can get pretty rare, weird things, especially since we had fairly early builds and fairly early versions of this hardware. Like these are some of the like first machines that were ever produced, some of the first GPUs. So you've got some extra special things there. We definitely worked with Dell, for example, on making fixes in the firmware level to be like, okay, like this thing is wrong. Like we need to update this at the firmware to like actually fix this particular thing. So we worked pretty closely with Dell and Nvidia. Yeah, that's what I'm saying. Like this stuff gets complicated. And the thing is like, you know, taking a step back, the whole reason we're doing this, right, is that we knew that this was going to be complicated. There would be these kinds of failures. And if we're just using, you know, AWS or some other cloud provider, these errors are still gonna be there and you're gonna have no way to know and no way to debug this and no way to diagnose what's going wrong. And so we would much rather be able to like call up Dell and say, hey, this isn't working. And they're like, yep, okay, cool. Let's debug it together. Oh, I see. Yeah, cool. We'll ship a firmware update and actually fix this for you. That was a much better experience than like, great, just magically fails. I guess we restart and hope that that machine goes away. Like that's not a very good place to be. So yeah, that's kind of the first place is getting to a place where like GPU training is working on your single node machines. You can observe stuff. We have tons of tooling around like, you know, Prometheus and all sorts of other tools for understanding what's going on in these machines because you don't want to be like logging into each one and looking at the temperature or something you really need to have tooling to collect all these metrics, et cetera. Unfortunately, all of the scripts that we have for this are like for this entire cluster and for all this infrastructure are a little bit like special purpose for our particular thing. So it's not that every script that we have, it's not that you can just like take this and plug this in. Even if we did open source all the tooling that we have, you'd still have to do like a lot of work to open source it. What we are releasing is as many of the things that we can that are going to be useful for other people. You're still going to have to have some way of kind of managing these things, making your own like logging aggregators, et cetera, et cetera. So that's kind of bringing them up to the like, you know, the single nodes that are working. From there, it goes into, I'm happy to keep going if you want. Well, I just want to leave the opportunity for John SWYX [00:18:53]: to comment if there's anything that's different from how he runs things. JONATHAN [00:18:57]: Oh, I mean, all I'll say is I'll endorse this and say this s**t is hard. Like this is really, really hard. And, you know, I have a special props to, you know, the folks in Vue because they were building this from the ground up. You know, at Databricks and at Mosaic, we typically work with cloud providers because some of this stuff is just, there's too much to handle. It's complicated. There's a lot to deal with. And this doesn't even get into things like physical security, you know, securing power if you're the data center operator. Like this gets infinitely complicated and you have to abstract somewhere. Like, you know, and then you get to the folks who are literally building their own custom chips and like, good God. SWYX [00:19:36]: Like, oh my God, that's, you know, JONATHAN [00:19:38]: if you're one of those folks, you're having, you know, pour one out for the infra people at some of the AI chip startups who are having a really, really interesting time right now. But this stuff is really hard. And I don't think we talk about it much because there's so many other things that are hard. But the other hard things, I think everybody's becoming pretty familiar with at this point. This is something that I don't think there's ever really been a comprehensive discussion of, at least not that I've seen. SWYX [00:20:00]: Yeah, so my impression is that you guys, Mosaic, have your own software for sort of spinning up and down machines, just like Imbue had to build. But Imbue probably, it sounds like Imbue, you guys went fuller stack. I don't know how to describe it. Like Mosaic is not working with Dell on like their firmware. JONATHAN [00:20:21]: No, no, we're typically working with like, you know, pick your cloud provider on their Dell firmware or what have you. Like, it's kind of, I think one of the things, I don't know, Josh, you can correct me on this. It's kind of impossible if you're doing training to not go all the way through the entire stack, regardless of what happens. Like somehow I'm still chatting with cloud providers about power contracts, even though the whole point of dealing with the cloud provider is not to have to think about power contracts. Somehow I'm still asking them about which InfiniBand provider they used this time to see if this is part of the bad batch of cables I encountered on that cloud provider or what have you. Or like, we're still talking about a firmware update from pick your provider. You can't not do this. It's convenient that they have data center staff who are worrying about what to send back to which provider when, and they have people who can go and wait for the InfiniBand cables so they don't get stolen outside. But, you know, it's kind of, it's impossible not to really go full stack if you're thinking about the infrastructure at all. I don't know, Josh, correct me. No, I think that's right. JOSH [00:21:17]: That's what we expected from the beginning as well, is that we would inevitably have to get into the details here. And I'm glad that we kind of just planned for it. I think it made it a lot easier from our perspective to have direct control over this. Instead of having to go to the cloud provider that goes to the data center, that goes to the supplier, we could just go direct to NVIDIA or Dell SWYX [00:21:37]: or the data center, JOSH [00:21:37]: whoever was responsible and be like, hey, this thing needs to change. And they're like, oh, okay. Yeah, that is our responsibility. Great, we can fix that. So it was just a lot easier for us to fix these bugs than if we had to go through an extra layer of email. SWYX [00:21:48]: Something we discussed in the pre-show was that you had a rule of thumb for your cluster of reliability. You say here in the post, by and large, you expect around 3% of your machines to break every week. So you're basically going to turn through all your machines in a year. JOSH [00:22:04]: As it says in the post. So that would be true if it was a uniform failure like that. But as it says in the post, it's usually these kind of problematic nodes. And to be clear, that is the number that we've heard from other people is like they're having about 3%. I don't think we're experiencing failure rates that are that high. I think ours is actually quite a bit lower than that, probably because we've taken the time to like dig into a large, maybe larger number than we should have of these failures and get to the root cause of it and be like, oh, okay, like that's exactly what's going wrong. SWYX [00:22:33]: How do we fix this? JOSH [00:22:33]: How do we prevent this from happening? How do we make automated checks for this so that if it does happen, it just goes back to whoever owns that particular part of the process and they can fix it immediately. SWYX [00:22:43]: And that's part of what you're also open sourcing, which is the health checks, right? You got the NIC health checks, GPU health check, this space health check, Docker D message. I don't know what that is. JOSH [00:22:52]: That one is just a lot of stuff. SWYX [00:22:54]: Yeah. JOSH [00:22:55]: That one is one where we realized that actually like when these machines boot, sometimes they wouldn't actually boot cleanly all the way. Or when they rebooted, they had problems that they didn't have when they were working before, which was kind of frustrating. Like usually if you restart your computer, SWYX [00:23:08]: it gets better. JOSH [00:23:08]: Here you restart. It did not get better. SWYX [00:23:10]: It got worse. JOSH [00:23:10]: That was very frustrating. So this health check looks at every particular line we've ever seen from the boot, like in D message, like every single log line that your computer emits SWYX [00:23:21]: and says like, JOSH [00:23:21]: have we ever seen this before? SWYX [00:23:23]: Is this expected? JOSH [00:23:23]: Is this in the right order? Or is there something out of place? If there's anything out of place, let me say, okay, great. Like now it goes into this, like longer, more triage list of like, all right, great. Like, is this acceptable? SWYX [00:23:33]: Should we flag this? JOSH [00:23:33]: Like, should someone take a look at this? So we're looking down at a very, very granular detail level, what's happening on these computers to make sure that nothing is out of place. And that's critical because without that, if you're running your training, as Jonathan said, and this thing is slow, like what are you supposed to do? Right? SWYX [00:23:49]: Like you really, JOSH [00:23:49]: you really want to be very certain that like all 4,000 of these GPUs are working like they're supposed to. SWYX [00:23:54]: We know that. JOSH [00:23:54]: And so if it's slow, it's because like we messed up the config or something else and not because of this earlier thing that's like really hard to detect in software later. JONATHAN [00:24:01]: Yeah. I think the, I'm just curious to ask, SWYX [00:24:03]: like, you know, JONATHAN [00:24:03]: suppose you were to set up another, let's say another H100 cluster and it were at a different data center. And instead of the vendor being Dell, it was super micro or what have you. How much of this would be repeatable? And how much of this would you have to redo? I, you know, I genuinely don't know. SWYX [00:24:18]: A decent amount. JOSH [00:24:19]: I think it would go a lot faster the second time. I think there's lots of learnings that we had. And also the blog post, SWYX [00:24:24]: you know, yes, JOSH [00:24:24]: we are releasing the health checks, releasing some scripts, but a lot of the valuable stuff is also in the blog post itself, in the details and kind of the, you know, the learnings that we've had and the sort of errors that we run into. We tried to as much as possible surface those to other people SWYX [00:24:36]: could learn from those JOSH [00:24:36]: and avoid the same mistakes or failures as well. But I think it would go a lot faster. SWYX [00:24:41]: Although, yes, JOSH [00:24:41]: there would certainly be some things that'd be a little bit different. I mean, there'd probably be different CPUs SWYX [00:24:46]: or whatever, JOSH [00:24:46]: but I think a lot of that stuff is less, SWYX [00:24:49]: it's less, JOSH [00:24:49]: that's the like, that's less variable. I think most of it would apply the second time around. Although I'm sure next time SWYX [00:24:56]: we're building one, JOSH [00:24:56]: it'll probably be, you know, at a scale that's 10x as big with a different chip or something like this. SWYX [00:25:00]: And then who knows? JOSH [00:25:01]: Yeah, with Kinect X8, JONATHAN [00:25:02]: that will have its own fun behavior and all that good stuff. Yeah. SWYX [00:25:06]: Perhaps there's something that people don't discuss about, and you don't even talk about this in the blog, but I always wonder is what is the timeline that's like kind of reasonable for this amount of work, at least the initial stages? And also what does the team composition look like for setting up a cluster, right? Like what are the mix of skills that you typically would require to get all this going? JOSH [00:25:27]: I'm, I can't really speak to typical. One thing I am very proud of is how much we accomplished with such a ridiculously small team. Like our infrastructure team is like, you know, fluctuates from week to week, depending on like how many things are on fire and how much we need to build. But it's like between like three and six people, like it's small. It's not like some huge team of like tons and tons of engineers. But those people are very, very good at what they do. And so that has allowed us to get a lot of mileage out of out of these things. I think it's not that we're building everything, right? It's not that three to six people build this whole thing. I definitely want to like, you know, say thanks very much to Dell and H5 and NVIDIA and the other people that have done a lot of the work, like to bring up this cluster, you know, with 4000 GPUs and three tier networking, networking architecture, you have 12,000 cables. So that's 24,000 things that need to be plugged in. Like that's just a lot of stuff to plug in, right? And you don't want to mess it up. Like each one needs to be done correctly. Like it's a little bit loose. Like it doesn't really work. SWYX [00:26:23]: If you break it, JOSH [00:26:23]: you need to replace it. Like there's a lot of work SWYX [00:26:26]: that goes into this. JOSH [00:26:27]: Yeah. SWYX [00:26:28]: And then, you know, JOSH [00:26:28]: that's just like that's it. That's if you were to do everything right the first time. SWYX [00:26:32]: And if you didn't JOSH [00:26:32]: have to fix anything. But inevitably, you know, you will have to replace something, which means like taking all the wires out, pulling the thing out, taking all the GPUs out, going and fixing some cable, putting it all back correctly, putting it back in, doing this every time. So there were a lot of people at Dell, NVIDIA and at H5 that all helped a ton with this stuff. I don't know the exact size of the Dell team. It also fluctuated over time. SWYX [00:26:55]: Yeah, excellent. And then, you know, you so you have all the hardware set up and now you're firing it up for a single node. There's a long description that you guys have about just like monitoring the MFU, right? And what each situation might look might be indicative of. One of the most interesting things to me that I saw from here is like, you know, if training immediately starts off at 60 to 80% MFU, something's wrong. SWYX [00:27:24]: But like, you know, like what what are like, you know, some anecdotes or, you know, notable scenarios here that you might you might call out as maybe counterintuitive or super interesting. JOSH [00:27:36]: There's just so many of them. I mean, one of them, which I think is probably pretty common, like common knowledge by this point. But like we did have a sort of like SWYX [00:27:46]: which one was this exactly? JOSH [00:27:47]: I think for the MFU, like gradually getting worse over time. I think that one, when we saw that the first time we were like, what the heck is going on? Like, why does it get just like a little bit worse? This is so strange. Like, what is it getting lazy or tired or something? Like, is it heat? Like what's going on? And in this particular case, it was memory fragmentation. Because you have hundreds of machines, they're doing garbage collection slightly different times. And then they get slightly further apart and slightly more and more jittered until eventually they're all happening kind of at random times. And just like really messing up each one of your steps. So you just turn off garbage collection and call it a day, basically, SWYX [00:28:20]: to be honest. JOSH [00:28:20]: There's other things you can do if you want to be a little bit more sophisticated about it. But you can also just manually JONATHAN [00:28:25]: have it all garbage collect on some interval. Like that's what we've done. We just have a garbage collection callback that just runs. But I've seen the exact same thing. JOSH [00:28:33]: Yeah, yeah, exactly. So I thought that one was kind of funny. And we did trace that one down and look and we did find the actual call. Like, again, this goes to like having good tools. So we had really good tools where we could look at a bunch of like actual traces in C and be like, OK, cool. This is the thing that's taking a lot of time. Or like, you know, this is the thing that doesn't quite line up here. Like, oh, I guess it's garbage collection. OK, cool. SWYX [00:28:52]: Interesting. JOSH [00:28:52]: Yeah, let's just try taking it off. SWYX [00:28:54]: OK, great. JOSH [00:28:54]: That's what it was. Now we can fix it. So for each of them, like basically bugs are not hard if you have good tools. But if you don't have good tools, bugs can be very, very hard. So similarly for like heat, another thing that we saw was like, oh, you know, the CPU is getting throttled. OK, well, it's easy to see if you're monitoring the CPU throttling or monitoring the heat. If you're not monitoring that, it's really hard to know why it's just suddenly one of them is going slower. I noticed also in the piece SWYX [00:29:17]: that you mentioned FSDP with 0.3. Actually, we met, I went to iClear and Guanhua from the DSP team was there presenting 0++. I was wondering if you want to make any call outs to, you know, particular open source or open library or open whatever implementation teams that were super helpful in your process. I think we ended up actually JOSH [00:29:39]: pulling from a whole bunch of different ones to pull things in into our own particular pipeline. So we use things from NVIDIA's, you know, Megatron stuff. We use stuff from probably DeepSpeed. I think we pulled in a bunch of different pieces from a bunch of different places. So it was really nice to see all these working open source like examples. I think I really appreciate all the effort that has gone into actually tuning these things because you can tune them, but it's a lot of work to like tune this stuff and do all this stuff from scratch. It's really nice to have like a working example. I think those are probably the two biggest ones, DeepSpeed and Megatron alone, but there are probably other ones as well. SWYX [00:30:13]: Is there a particular thing in the ecosystem where you would call out as like, you know, there should be something here that is open source, but like it's not really, it's like everyone kind of builds it on their own. I want to say something with the file system because everyone talks about the file system eventually. JOSH [00:30:28]: The file system actually was, SWYX [00:30:30]: I mean, we did something JOSH [00:30:31]: kind of dumb there. Like we have our own sort of local mirror so that we can, you know, like a crappy version of S3 SWYX [00:30:38]: that's local, JOSH [00:30:38]: but it's just a pretty simple script, right? SWYX [00:30:41]: Like I think we run like JOSH [00:30:41]: a little web server that just like serves files and then, you know, it can upload them SWYX [00:30:45]: and download them. JOSH [00:30:45]: Okay, great. And part of the reason we did that is that our internet connection SWYX [00:30:50]: in the beginning JOSH [00:30:50]: was not the like full speed SWYX [00:30:52]: one that we would JOSH [00:30:52]: eventually have. And so we are a little bit more kind of bottlenecked in terms of internet bandwidth. And so we had this. I think we looked at a bunch of services out there like Minio and some other ones, but a lot of these like come with a lot of extra overhead and maintenance. And since we already have so much infrastructure SWYX [00:31:09]: to deal with, JOSH [00:31:09]: we kind of didn't want to, you know, bring in a whole other like cloud provider, virtualize something, something. SWYX [00:31:14]: We just wanted something simple. JOSH [00:31:14]: So we went with that, which has been quite helpful. Like our tools SWYX [00:31:19]: are usually quite simple. JOSH [00:31:19]: It's like Bash and Python and SSH and Docker. Like we'd like to keep things simple so that's easier to debug, like less layers of infrastructure, less layers of abstraction, make it a lot easier to work with. Like we don't use Kubernetes, SWYX [00:31:30]: for example, JOSH [00:31:30]: and we just directly launch these things. And it's just been much easier to debug this way. One tool actually that does come into mind that I will call out is Kraken from Uber. That was great. We love that tool. We were a little bit skeptical. What is it? SWYX [00:31:44]: I'm sorry. Yeah. JOSH [00:31:45]: So Kraken is this, yeah, it's a distributed like Docker registry, basically, that uses BitTorrent to like transfer things between the machines in a sort of nice optimal way. Like in the very beginning, the naive way is like you have this one Docker registry, which was outside of the cluster. So every time we change an image, you know, there's many gigabytes that each of the 500 machines needs to download. SWYX [00:32:07]: So that just takes JOSH [00:32:07]: a really long time. So what this thing does is like just one of them downloads it and then like they all sort of broadcast all the pieces to each other. And it was just like a really nice, fast way of getting these images down. And it was very robust. SWYX [00:32:19]: Like there's a lot JOSH [00:32:19]: going on under the hood, but I think it's a pretty cool tool that we haven't really had any bugs with it at all. Amazing. SWYX [00:32:26]: Yeah. I mean, that's all my questions, I guess, for the info piece. I don't know if, John, you had something that you were sort of burning to ask or. JONATHAN [00:32:33]: No, all I can say is just same SWYX [00:32:36]: in a lot of places, like, you know, and they're done that JONATHAN [00:32:38]: seeing this plus one. I think the one big difference, you know, perhaps in philosophies is we've tried to basically standardize on as much commodity stuff as possible, just because, you know, I think the reason I asked about trying to do this SWYX [00:32:50]: on multiple different JONATHAN [00:32:50]: pieces of infrastructure is like, I think we're running on like six or seven different clouds right now. And everybody has done something slightly different. And my gosh, the little differences add up as you know, you've seen. And so, you know, SWYX [00:33:04]: our philosophy has been like, whatever the hell JONATHAN [00:33:05]: we can standardize, please let's standardize it. Like vanilla off the shelf FSDB. SWYX [00:33:10]: And like, you know, JONATHAN [00:33:10]: we wrote our own data loader, but we've tried to make that as much of a standard as we can across our infrastructure and in Databricks, because things just start getting really complicated SWYX [00:33:18]: or like we use JONATHAN [00:33:18]: Kubernetes extensively because it at least gives us a uniform set of APIs. Like that's our hardware abstraction layer to a certain extent for everything else. So it's just, you know, a difference in philosophy there. But otherwise, like, yeah, this stuff is really, really hard. And I feel like we take for granted how much of this, you know, is done for us when you go and you just query chat GPT, for example. Like, oh my God, everything going on underneath that, you know, it's kind of a miracle that the machines boot up, let alone that you can like query a giant language model that's probably doing inference across multiple machines and was trained across thousands of machines. Like, you know, minor miracle. SWYX [00:33:54]: Yeah, it is an awesome amount of power that we invoke with a single API call that we take for granted these days. It's absurd. Yeah, I mean, like Kubernetes, like that point about Kubernetes, I will say as a former AWS employee, like it seems like it would be ideal for imbue to at some point make it more abstracted or agnostic because you're going to want to, you know, replicate your setup. We do have our own JOSH [00:34:19]: sort of replacement. It's just a much simpler version of Kubernetes. Kubernetes is really designed for running services, not for running experiments. Like that's not its like main architecture. And so for us, like we have everything that's like, cool, you're going to run an experiment. So you want it to run to completion, right? SWYX [00:34:34]: OK, great. JOSH [00:34:34]: Like the primitives are sort of built around a slightly different style. And that makes it a lot easier, like just a lot simpler to fit that the nature of like these machines are going to disappear. They will need to be rebooted for infrastructure upgrades. They will like something will happen to the GPUs. Failure is like baked into this as like a core part of our infrastructure. So it's not that we don't have an abstraction. It's that it's a sort of simpler, more tailored abstraction for the particular work that we're doing. JONATHAN [00:34:58]: Yeah, I think it all depends on what your goals are. And like, I think the challenge in a lot of the deep learning stuff right now is that people are trying to like, people often build things that are more complicated than necessary to get the job done. And the complication is the enemy of everything. You know, don't use a fancier parallelism strategy than you have to. Don't use a fancier set of libraries than you have to. SWYX [00:35:18]: Don't do anything JONATHAN [00:35:18]: that you don't have to do because it's hard enough as it is. Like, don't overcomplicate SWYX [00:35:23]: your own life. JONATHAN [00:35:23]: Don't try to bring in more tools or more fancy architecture tweaks if you absolutely don't have to. SWYX [00:35:29]: Like getting to the minimum JONATHAN [00:35:30]: necessary to get the job done. And it's really tempting to want to try to use everything. So like, I totally understand that one. SWYX [00:35:37]: I think the last piece I'll maybe call out is that I'm just going to weave this in just because I see the opportunity to do it. Are there any infrastructure shifts that need to be, that need to rise because of changing architecture? So I think, for example, SWYX [00:35:57]: you're announcing a dense model, a 70B dense model, whereas John just worked on DBRX and the image-to-text model, which presumably has different bottlenecks. JONATHAN [00:36:10]: That's correct for us. You know, we train both dense and mixture of expert models. The one we happened to, you know, kind of get permission to open source was a mixture of expert model. And those models are very demanding when it comes to network bandwidth, at least if you're training them in kind of FSTP 03 style, where there's just a lot of parameters getting shuffled back and forth. And your ratio of kind of compute to amount of data that you have to shuffle back and forth becomes a lot worse because you're now, you know, you're only using a fraction of the parameters for every token instead of all the parameters. And so we had to really push the envelope on getting all the stuff to the right places on time. And so actually the networking part of DBRX was the single hardest thing, I think, of the entire process. Just get MOE training, working at scale across a big cluster. We still managed to, I think, do it all with commodity parts, which was very exciting. You know, we were using FSTP and we eventually used HSTP so that we could have HSTP as a version of FSTP where you have multiple smaller replicas and you're doing data parallel within those replicas. And that helped a lot with network latency issues that we were running into just because we were transmitting so much data, you know, for every single part of the process. I think it actually, like, it was instructive for how Google designs their hardware and software together personally. Their training, as far as I understand, using kind of a 03 style of training and have been for a while. They also train mixture of expert models. TPUs have a very different network bandwidth to compute ratio. They have a lot more bandwidth just objectively. And TPUs per chip tend to be a little bit less compute intensive and have a little bit less memory. You know, it's just a different design choice. So the ratio of flops to bandwidth is very different. And that means that it's much easier for Google to be able to pull off SWYX [00:37:54]: some of this stuff. JONATHAN [00:37:54]: They also have interesting, you know, Torus style network architecture or Torus style, like, literal network architecture SWYX [00:38:00]: is not like the model, JONATHAN [00:38:00]: but the network. SWYX [00:38:02]: Is this the sort of block attention? I forgot what you call it. So this is just more or the, JONATHAN [00:38:07]: yeah, this is more, not the ring attention, but these are the ring all reduces. Like you have three different dimensions of rings because they kind of put you in these three dimensional Toruses from what I understand. And so like, you know, Google's infrastructure in some sense is kind of, I wouldn't say built for this, but maybe the way that Google trains models is built for a slightly different bit of infrastructure they have. And it's kind of neat to think about that. You know, as one thing that I think NVIDIA announced for, you know, for, for both the GH200 and the GB200 is this hybrid networking where you'll have blocks of NVLink network chips. I think for the GB200, I think it's like groups of 72 GPUs will all have NVLink to each other. So higher bandwidth, then you'll have normal networking of some kind, InfiniBand or Rocky or what have you between these blocks. And that's kind of a, you know, it's a change due to the fact that, you know, it's hard to build really high bandwidth networks over very large groups, but it is now a blocked networking. And you have to think about how you architect your model and your parallelism differently. You also have to think about fault tolerance differently because it now matters where you lose a GPU, whereas it didn't before. So, you know, it's, it's, it's just all really interesting and really fun speaking personally, but it's going to mean new nightmares when we all move to that generation and have to think about, you know, new versions of these problems. JOSH [00:39:20]: As you go up to larger scales, it gets quite different. Like right now, you know, if you're experiencing, let's say, for example, you experience a GPU failure every day, that's fine. SWYX [00:39:31]: Just restart. JOSH [00:39:31]: If you make your thing 24 times as big, now it's once an hour. Now it stops being quite as easy to just restart, right? So now you have to kind of break, like bake in this sort of redundancy that you didn't have before. So I think as you go up in scale, you end up running into like a lot of really interesting problems that also inform the, the actual like design. Yeah, I mean, as an orchestration guy, SWYX [00:39:52]: this is why I always emphasize like very cheap storage or very fast storage. So you can checkpoint more, but I don't think that's probably not the best solution to for fast, you know, training. JONATHAN [00:40:05]: Which works fine when you're doing language and then you move to vision or video. And then, you know, you have multi petabyte datasets SWYX [00:40:12]: and getting, you know, JONATHAN [00:40:13]: cheap, fast multi petabyte storage starts to bite. Like I've certainly encountered issues where the literal data center where my GPUs were did not have enough, you know, object store to fit the datasets that people wanted to bring into that data center from whichever users were, were trying to bring them in. And then you get to a whole SWYX [00:40:31]: different world of hurt JONATHAN [00:40:31]: where you have to keep your data in a different region because the region is just out of storage. So things get fun really fast. SWYX [00:40:39]: Speaking of vision, Josh, actually, you know, Embu is an agents company, but you're only, you're announcing a text-only model. What, where does, where does the vision side come in? JOSH [00:40:49]: I think we've actually done a lot of work in the past and people can see kind of our blog posts about sort of self-supervised learning and some other kind of vision-related stuff in the past as well. So we're very familiar with, with that stuff. But I think our main focus right now is on kind of, as we say, coding and reasoning. And there, there's certainly a visual component to some problems. But, you know, it's not necessarily required for all problems. And actually we found that for most of the kind of like code writing and, and reasoning problems that we care about, the visual part isn't really a huge important part of it. Sometimes if you really need to, you can maybe describe SWYX [00:41:24]: the thing. JOSH [00:41:24]: There are other like, you know, multimodal models that you can use off the shelf to sort of plug in for those particular pieces SWYX [00:41:30]: that you need, right? JOSH [00:41:30]: Like if something is driving a browser or whatever, like you can sometimes get away with not having to have that baked into the original model. So our folk were, you know, in a sense, we kind of do a lot across the stack. We're working on our own infrastructure and pre-training and RL and fine tuning and products and everything. But in another sense, we're very narrowly focused on the application side. So all of the stuff across the stack is kind of going toward a very particular purpose. And so that particular purpose right now doesn't really need vision. So we think that people are going to make all sorts of really cool image models SWYX [00:42:00]: like Jonathan, right? JOSH [00:42:00]: And all sorts of interesting multimodal models into the future. We'll let them go do that. That's great. We'll take advantage of that, partner with those people in the future. And right now we're really focused on kind of the core reasoning and coding capabilities and aspects of the model. SWYX [00:42:14]: I wanted to go into carbs since that's kind of the next layer of the stack. We talked about carbs in the first episode with Kanjin because you've actually had a blog post about it like a couple of years ago. Maybe let's introduce it. JONATHAN [00:42:26]: Has that been a couple of years now? JOSH [00:42:28]: No, it must have been at least one year. Hopefully it's not multiple years. SWYX [00:42:32]: Sorry, I'm counting AI time. Yeah, yeah. Yeah, I was going to say JONATHAN [00:42:35]: you're making me feel really old right now. SWYX [00:42:39]: I count everything before the generally intelligent rename as like, you know, prehistory. Yeah. And now sort of modernity, right? So I actually thought carbs was more about hyperparameter optimization in a sense of like sort of parameters, hyperparameter search. Whereas, you know, when you introduced it, especially in this blog post, it's more about scaling laws and predictability of like, are we sort of in the right ballpark before we scale things up? Maybe sort of recount the history of carbs. JOSH [00:43:10]: Yeah, so it really is a little bit of both. So carbs is, it's maybe a backronym, but it's for cost aware Pareto region Bayesian search. So this is about technically how it works, but carbs is like, you know, we like pastries and stuff. SWYX [00:43:26]: So great, why not? But the point is that JOSH [00:43:29]: it's a cost aware hyperparameter tuner. So most hyperparameter tuners, you kind of say, OK, here's this objective function. I want you to make this number as big as possible or as small as possible, whichever direction you want to go. So yeah, just go make this number, you know, as small as possible. OK, so it'll try a bunch of different SWYX [00:43:46]: hyperparameters, JOSH [00:43:46]: a bunch of different configurations SWYX [00:43:48]: to figure out, like, JOSH [00:43:48]: how do I tweak your network and architecture, et cetera, to get the kind of best performance I possibly can. That's usually saying, like, you know, almost all of these hyperparameter configurations are, let's say they're all going to use the same number of GPUs or the same number of nodes. SWYX [00:44:01]: So it's going to run JOSH [00:44:01]: for the same amount of time. SWYX [00:44:03]: So you can do that. JOSH [00:44:03]: You can get a number out and that's great. But what carbs does is it says, SWYX [00:44:07]: OK, actually, JOSH [00:44:07]: what if we relax that constraint? What if we say each of these different points, we're going to model how expensive it will be to sample this configuration. So if what if we train with just one one hundredth of the data? Like, how well can we do? SWYX [00:44:19]: What if we train JOSH [00:44:19]: with one tenth of the data? What if we train with all the data? That way you can understand, like, as we get more and more data, as we spend more and more compute, SWYX [00:44:26]: as we make a bigger JOSH [00:44:26]: and bigger network, how does performance change with these things that change? Like how expensive it is to even explore this data point. So by doing that, we can see the scaling laws for not just, you know, SWYX [00:44:36]: the scaling laws JOSH [00:44:36]: from like the, you know, Chantilla paper, the scaling laws for all parameters. We can see how does how does the number of layers change with this? How does the, you know, the learning rate change? How do the like, you know, various types of regularization change? So you can see these nice scaling laws. And as you're going across costs, like how should this be changing as you're scaling up your model? So that, coupled with the kind of metric that we chose, which is a very precise way of measuring performance, allowed us to really like hone in on parameters that worked really well SWYX [00:45:05]: and understand, like, JOSH [00:45:05]: how do we want to scale those up, especially as we're changing SWYX [00:45:08]: things about the network? JOSH [00:45:08]: Like one of the things that we did is we used a custom tokenizer. As we change this tokenizer, changes a bunch of other things about the model. So how should we scale up this entirely new tokenizer? Like no one has ever made a model this large with this tokenizer before. And so how do we want to SWYX [00:45:22]: change all these things? JOSH [00:45:22]: Harps kind of shows you, like, look, as you change these parameters, like these other ones are kind of dependent on this. SWYX [00:45:28]: Like this is the, these are JOSH [00:45:28]: the relationships between them. So you can better understand, like, OK, if I'm going to scale this up 10x or 100x, like, where do I want to be? I can only go so far. And so, you know, we did run, like, I think maybe it was like a 14b one or something SWYX [00:45:40]: like that to check. JOSH [00:45:41]: But and so we had a bunch of like 1b or 14b and then at 70b. I don't think we had a, I think we just did like one at 14b. So you can, we get to check that like, oh, is this on the curve? Like, is this where we expect? It was like right there. So then great, go on to the next one. Yeah, I mean, that makes a lot of sense. SWYX [00:45:56]: I wonder if, so one of the key questions, and correct me if I'm wrong, but like usually people do search or do their evals just based on loss. But you actually evaluate based on, you know, the sort of end state evals that people might expect, like HellaSwag and Lombata, whatever. What is the norm here? Is there a norm? JOSH [00:46:20]: Yeah, I don't know if there's a hundred percent. SWYX [00:46:21]: I don't know. I only see loss on most people's reports. JOSH [00:46:25]: I think it's easy to, like, loss is very nice because it's very precise. It will tell you, like, very fine grained differences between like really small changes in your hyperparameters or network architecture. Whereas, especially at the smaller scales, if you're looking at like accuracy, it's very noisy. Like it might be zero or a hundred or like, you know, fluctuating by like 10 or 20 percentage points, which makes it really hard to tell, like, did that change actually mean anything? So our loss is sort of a combination of these two. Instead of saying, like, let's just look at perplexity, we say, let's look at perplexity on the tasks that we care about for multiple choice questions effectively. SWYX [00:47:00]: So we're saying like, yes, JOSH [00:47:00]: this is formulated as a multiple choice question, and we're going to look at the, like, you know, the loss of perplexity for this particular answer token. And that ends up being something that's like both targeted to what you actually care about and also very precise. The nice thing about this though is that it's independent of the data that you train on. One thing that's annoying about perplexity or about loss is that as you change your data set, this is really obnoxious because now it fundamentally changes your loss, right? And so you can't tell, like, how do I tweak my data set? But because we have this held out evaluation data set where we're looking at perplexity, we can actually change the data mix. And so CARBs actually control what is the mix of data that we want to see, like how much code, you know, how much internet text, et cetera, in order to figure out what is the best optimal mix of data and we could do that because we have this other metric. So that was one of the things that was really, really helpful. SWYX [00:47:46]: I think there is a trend overall about changing data mix as training goes on. I don't know how, you know, we're deciding not to talk about data sets in this podcast, but what have you observed about the changing data mix question? JOSH [00:48:06]: We did some experiments SWYX [00:48:08]: and we've actually talked JOSH [00:48:08]: to a bunch of researchers who are doing work here as well SWYX [00:48:11]: and looking at kind of JOSH [00:48:12]: their experiments on this. And we were originally pretty hopeful because it sounds like something that should work and make sense, right? Like, oh, cool. Like maybe you would have your model, like learn the basic features SWYX [00:48:22]: and then over time, JOSH [00:48:22]: it could get really good at these complicated math problems or coding or something, right? But it just turns out that like, it's just not the way it works. Like we've done so many experiments and you can get like a tiny, tiny little boost from this, but it just is not like, it's just not the important thing, at least in the experiments that we've seen. So yeah, we've kind of, we're letting other people SWYX [00:48:40]: explore that more JOSH [00:48:40]: if they want, but that just doesn't seem like the most promising direction for us. JONATHAN [00:48:44]: We've had some surprisingly good luck with this. We just released a paper on it. The details matter a lot and it really matters what you're trying to do with the model. SWYX [00:48:53]: Yeah. JONATHAN [00:48:53]: But it's been quite effective for us depending on the setting. And certainly when we're thinking about domain-specific models, this helps a ton. You know, to a certain extent, you can always think of this as like early fine tuning. But yeah, I like, there've been little glimmers of this in the literature for years. Like especially, I think the Gemini 1.5 paper mentions this. And I don't remember whether the Llama 3 paper mentions this, SWYX [00:49:15]: but it's kind of, JONATHAN [00:49:16]: it's one of those, like people have different ways to get to these endpoints. SWYX [00:49:20]: I think, you know, JONATHAN [00:49:20]: there are the architectural tricks that each lab has to mitigate loss spikes or what have you. And everybody's got, you know, their own bag of tricks and it leads to kind of sometimes this contradictory information. It's not contradictory. People are just kind of exploring SWYX [00:49:33]: different parts of the space JONATHAN [00:49:33]: in some sense. And there are lots of ways to get a great model. But certainly for us within our config, and it seems like, I guess for the folks at Google, within kind of the part of the world they live in, changing the dataset has helped, but the details matter a lot. And it's really hard to get those details right for the reasons Josh, SWYX [00:49:48]: you know, just mentioned. JONATHAN [00:49:48]: Like there's a lot of search involved and you essentially have to make hard choices about SWYX [00:49:52]: what parts of the space JONATHAN [00:49:52]: you're going to search and which ones you're going to leave be. And so, you know, some people have done an amazing job. Like I think the, who is it? The Deep Seek folks have done an awesome job looking at like batch size warmup. And that's been really, really fruitful for them. You know, other people are looking really hard at things like data mix, but it just gets tricky to look at everything. JOSH [00:50:09]: Yeah, I think we've found that like we could get some things that looked like gains from datasets. But one of the things that I like about carbs is that when we applied carbs to like properly tune things, then a lot of those kind of evaporated. Whereas like, like if we just tune these other parameters, actually we can get almost the same gains without having to do this more complicated thing. So at least in the experiment and in the settings that we've, like in the particular metrics SWYX [00:50:34]: that we care about, JOSH [00:50:34]: we haven't seen these kind of like pan out or scale up in quite the same way. But not to rule it out. And I think you're right, Jonathan, SWYX [00:50:41]: that there probably are JOSH [00:50:41]: a lot of like details that go into like exactly what is the metric, exactly what is the dataset, exactly which, like what schedule are we using for this. And I certainly wouldn't rule it out working. SWYX [00:50:52]: Quick question about emergence. Doesn't emergence throw a spanner into a theory of carbs? Ah, so there is a paper JOSH [00:51:01]: of which I really liked and I think informed SWYX [00:51:05]: a little bit of how JOSH [00:51:05]: we thought about this, which is are emergent properties of language models a mirage? And I think if you look at that paper, it actually makes a relatively compelling case that in fact, you know, this emergent behavior that you're seeing is not really emergent behavior, but is really a function of the evaluation metrics that we're using. So if you look at accuracy as a metric, what's happening is that accuracy is actually going up continually over training, but it's in log scale. So it starts out at 0.001%, 0.1, 0.1, 10. SWYX [00:51:35]: Only when you're going JOSH [00:51:35]: between 10 and 90 do you see this happen, right? When you go from one in, you know, SWYX [00:51:40]: a thousand getting right JOSH [00:51:40]: to one in a thousand getting wrong, like there's many orders of magnitude happening here. SWYX [00:51:44]: So when you're looking JOSH [00:51:44]: at this in perplexity, then you just see this nice straight line. And so that's actually what carbs is exploiting. Like since we're, since our metric is in this kind of like perplexity log space, like you can see like, oh, it's just like getting better as you make it bigger in this nice, very predictable way. So that, and that is exactly what we saw. Like these things were really, really bad at, you know, predicting the multiple choice answer, just always guess A. OK, it's so terrible at it, but it was like learning to be less confident about that. SWYX [00:52:09]: Yeah. One trick I saw from one of the papers recently was just like, just randomize the order of the multiple choice questions. And if you, if, if, if they, if they over, if that hits the performance a lot, then they're just basically memorizing the test set, which makes a lot of sense. JONATHAN [00:52:28]: Yeah, this is, I, I mean, you know, I, I completely agree with what Josh said. SWYX [00:52:32]: I think the, you know, JONATHAN [00:52:32]: my bigger lesson is that anything can look however you want it to look. If you put it on a log scale to a certain extent and log, we love our log scales and deep learning for various reasons. Everything looks very clean on a log scale until everything looks very flat on a log scale. Um, I don't know. I like log scales always mix me up. That's, that's all I can say. SWYX [00:52:51]: Great. I think the, the last thing I was, I was going to mention on, uh, carbs. Oh, well, I mean, let's, let's just kind of go right into evals because I think that's going to be, uh, the, the sort of crowd favorite. Um, so carbs, we already mentioned, um, you know, leans heavily on, uh, the sort of end evals that we would typically eval LLMs on, except that you had to make your own. Um, there are a lot of documented problems with many of the common evals out there and you fixed all of them. It sounds like, I don't know JOSH [00:53:18]: about fixed all of them, but, uh, I think in the same way that we like to dig into the infrastructure and hardware and understand, like what actually is going SWYX [00:53:27]: wrong? JOSH [00:53:27]: Like what is the actual error on this machine with this GPU? SWYX [00:53:31]: And why did that happen? JOSH [00:53:31]: And how do we fix it? We take the same approach to the evaluations. So when we looked at the evaluations and actually looked at the data sets, you know, what we did is SWYX [00:53:39]: like, okay, if we're going JOSH [00:53:39]: to be, you know, evaluating natural language, understanding and reasoning, like, let's look at all the data sets that are out there. Let's actually look at a bunch of the examples and say, like, is this a good data set that we should use for evaluation? That's kind of how we selected the evaluation data set that we had. Uh, and then when we looked at the actual examples in there, we noticed like a lot of these are very messy. Like some of them messy SWYX [00:54:00]: to the point of like JOSH [00:54:00]: incoherence and some of the ones that we didn't choose. Uh, but even the ones that we chose, like people tried pretty hard on SWYX [00:54:06]: these data sets. JOSH [00:54:06]: They did try and clean them, but there's just a lot of data points in there and it's just easy to SWYX [00:54:10]: make mistakes. JOSH [00:54:10]: Right. And so, you know, it's not that they have a SWYX [00:54:13]: hundred people looking JOSH [00:54:13]: at every question, like that's just way too SWYX [00:54:15]: expensive. JOSH [00:54:15]: So you end up with questions that just don't make sense. SWYX [00:54:18]: Somebody didn't really JOSH [00:54:18]: see this. Somebody just clicked the wrong box for the answer. Uh, or the question makes sense in your head. When you write it, we've often seen this, it's not even like malice or SWYX [00:54:26]: incompetence. JOSH [00:54:26]: It's really just like, you know, you write this, SWYX [00:54:28]: you're ready. JOSH [00:54:28]: You're like, this makes SWYX [00:54:29]: sense to me. JOSH [00:54:29]: You show it to another person like that makes SWYX [00:54:31]: sense. JOSH [00:54:31]: You show it to a third SWYX [00:54:32]: person. JOSH [00:54:32]: They're like, this makes no sense at all. SWYX [00:54:34]: That's because you're JOSH [00:54:34]: kind of, you know, using a different meaning of SWYX [00:54:36]: the word. JOSH [00:54:36]: And then when they say that, you're like, Oh, SWYX [00:54:38]: wow, you're right. JOSH [00:54:38]: That is actually really confusing. It's easy for things to SWYX [00:54:41]: kind of make sense in JOSH [00:54:41]: our own head. So what we did for the evaluations is really dug into the details of each of these data sets and tried to ask, like, what makes a good SWYX [00:54:50]: question? JOSH [00:54:50]: What makes a good answer? SWYX [00:54:52]: Like, what does it mean JOSH [00:54:52]: for it to be ambiguous? We had a whole, like, SWYX [00:54:55]: we looked at lots of JOSH [00:54:55]: data, broke this down, asked lots of people SWYX [00:54:58]: about all these JOSH [00:54:58]: different questions to build a model of this and help us kind of clean these data sets. That was sort of one big piece of it. A second big piece was making sure that our data that we're training on is not data that we're testing on. So there we kind of took a step back and said, like, OK, well, let's just reproduce, you know, 500 to a thousand examples for every single one of these data sets ourselves. And just make sure that this data is definitely not in the, you know, the training set. So we did that. And then we're able to, like, now be confident about, like, our performance of our model and also performance of other open source and other closed source models. Yeah, there's a lot there. SWYX [00:55:33]: You had 11? I don't know how many data sets. I think so. One, two? Yeah. Any one you want to call out in particular to dive deeper on? Some of these are very famous, like HelloSwag, MitoGrand. Some are less famous, like Race. I don't know if... Race is a great data set. JOSH [00:55:50]: See that one? SWYX [00:55:51]: Yeah. Yeah. Just, you know, anything that's interesting you want on specific data sets? I think there are JOSH [00:55:57]: a few asterisks in there. You know, definitely read the whole paper SWYX [00:56:02]: as you're looking at JOSH [00:56:02]: some of these, like the GSM8K one is a little bit weird. I think one that was SWYX [00:56:06]: kind of funny, JOSH [00:56:06]: it was, like, low performance on ethics from some of the more recent models. I think that was a SWYX [00:56:11]: little bit funny JOSH [00:56:11]: because the models, you know, SWYX [00:56:13]: I think there was JOSH [00:56:13]: a reaction to, like, oh, no, like, you know, SWYX [00:56:16]: the models are saying JOSH [00:56:16]: bad things. SWYX [00:56:17]: And so they went way, JOSH [00:56:17]: way in the other direction. And now, like, on the ethics data set, SWYX [00:56:20]: it's always like, JOSH [00:56:20]: this is totally unethical, even though it's really fine. So they've just been tuned to, you know, make sure they don't make any PR disasters. SWYX [00:56:28]: I thought that was JOSH [00:56:28]: a little bit funny. Not to say that it's necessarily like a flaw of the model, but just kind of like, you know, political or tuning opinion. I think the main takeaway, I was just going to say SWYX [00:56:38]: the main takeaway JOSH [00:56:38]: for many of the, like, actual performance is, like, once you fix these ambiguous examples, a lot of these benchmarks are really saturated. Like, I think it's SWYX [00:56:48]: important to look at, JOSH [00:56:48]: like, you know, SWYX [00:56:50]: like when you're JOSH [00:56:50]: talking about performance on ANLI or race or pool queue or something, what you're really talking about is, like, performance on questions that make no sense. Like, it's just like, did it guess the answer in this, like, really weird scenario? Like, those are the ones that are left. SWYX [00:57:03]: Like, when you look JOSH [00:57:03]: at the performance on the ones that actually make sense to everyone, all the models agree. SWYX [00:57:07]: We agree, like, JOSH [00:57:07]: everyone's on the same page, which I think is kind of a really interesting result. SWYX [00:57:11]: The question then becomes, you know, what are the new, like, set of evals that would be like the next frontier that often embeds with it your idea of what reasoning is, because it's obviously you're super interested in reasoning. And yeah, I mean, like, where does this, where does the state of evals go from here? JOSH [00:57:30]: This work and this blog post is talking mostly about the public evaluations SWYX [00:57:34]: and the things JOSH [00:57:34]: that we can release. We do have our own internal evaluations. For example, one of them that we are releasing is the code understanding evaluation, which is about predicting, SWYX [00:57:44]: you know, JOSH [00:57:44]: what will this variable be or asking questions about code, et cetera. And that is one of the early benchmarks that we made that we can release. We can partly release it because we can generate an almost infinite amount of this data because these are programmatically generated. And so, you know, we're not really worried about there being like corruption in the kind of the training or test sets. So that makes it a little SWYX [00:58:03]: bit easier for us. JOSH [00:58:04]: But I think it's, you know, we have built other data sets as well that we can't release. Some of them, you know, SWYX [00:58:09]: for example, JOSH [00:58:09]: because they maybe use other open source code and so we can't redistribute it necessarily. Other ones, because, you know, that's, I think evaluations and data are like a core, important part of, you know, the business. And I think we take evaluations very seriously and are spending a lot of effort in terms of like, what exactly do we make as part of the evaluation set? How do you evaluate these things? We've done a lot of other stuff, you know, since these evaluations. But I think a lot around like code understanding for us, since that's our main focus. And it's a nice place to explore reasoning as well. SWYX [00:58:40]: It sounds like you talk a little bit about like code understanding as like sort of variable level, like sort of very micro context. Is there a sense of like larger code context as well? I don't know what I mean by that, by the way. It's mostly just like if I told the senior engineer to go look at a code base, they would understand at a broad level, the architecture, but also the design decisions and be able to tell me that. I don't know if that's useful or not, but I mean, that's useful to me as a, as someone who might be working with them. Yeah. JOSH [00:59:06]: This particular dataset is like the more low level code understanding, SWYX [00:59:10]: like just literally JOSH [00:59:10]: what happens in this code. And this is mostly because, you know, SWYX [00:59:13]: this is part of the JOSH [00:59:13]: carbs tuning metric, etc. SWYX [00:59:15]: Like we care about JOSH [00:59:15]: the low scale version SWYX [00:59:17]: of this as well. JOSH [00:59:17]: We want smaller scale models to be able to do something on this. And so that's kind of the focus for this. SWYX [00:59:22]: And hopefully this is more JOSH [00:59:22]: useful for other people. But yes, SWYX [00:59:25]: those other questions JOSH [00:59:25]: are also quite interesting. They get a lot harder to evaluate, like, is this a good architecture or not? Like you and I could probably debate for a while on, you know, different architectures. And so it becomes a lot trickier to do these evaluations as they become more realistic. So I think that's one of the things that we've been playing around with a lot, especially around like code generation. SWYX [00:59:44]: So if you're saying, JOSH [00:59:44]: you know, implement this function, okay, it can be kind of objective, but, you know, even MBPP, we've made our own internal version of this data set, right? SWYX [00:59:52]: Where we've taken like JOSH [00:59:52]: every single example SWYX [00:59:54]: and looked at it and been like, JOSH [00:59:54]: does this actually make sense? Like, what is the type signature? Like, can we remove all ambiguity, et cetera? SWYX [01:00:00]: So you basically like reviewed every single question on, I mean, that's impossible for like HelloSwag, right? Yeah, yeah. JOSH [01:00:05]: We didn't do that for HelloSwag, but this is for MBPP, which is only like a few hundred. So we just sat down and did it. Yeah. JONATHAN [01:00:12]: I'm so excited to get to look at this data set. Like this is such a resource for the community. I absolutely can't wait. We should probably do the, JOSH [01:00:19]: I don't know. I don't know if we were planning on doing the healed MBPP one, SWYX [01:00:23]: but hopefully we can do JOSH [01:00:23]: that one in the future. Did you look at SweetBench? SWYX [01:00:26]: It's the sort of hot new data set of the summer. JOSH [01:00:28]: Yeah, I've taken a quick look SWYX [01:00:29]: at SweetBench. JOSH [01:00:29]: It's really interesting. I like that it's a much more difficult kind of coding, code related task for bug fixing. I think it gets into some of these problems where it is a lot harder to evaluate these things once they get more realistic. Like we were looking at the AgentBench paper, I think just last week for our paper club and one of the things SWYX [01:00:49]: that we noticed JOSH [01:00:49]: is that actually like both of the examples in the appendix that are given as like traces where it got it right. This is actually not the right solution. And it's OK. You know, it's fine. Like it did make it past the test. That's what the metric is. SWYX [01:01:02]: That's what the benchmark JOSH [01:01:02]: is about, right? But like it just said, SWYX [01:01:05]: you know, like, JOSH [01:01:05]: you know, dot encode ASCII. Like, well, that's not the right way to do this. Like it just dropped all the other edge cases that you actually would have cared about in production for this thing. SWYX [01:01:14]: And there is like JOSH [01:01:14]: a better way of doing it. SWYX [01:01:16]: And you know, JOSH [01:01:16]: that's what the real golden patch was. But, you know, that's OK. But then how do you test all of that? SWYX [01:01:21]: Like as you start to do JOSH [01:01:21]: more realistic things, the test coverage, like getting test coverage over all possible ways of solving these bugs is really hard. Evaluation is the single JONATHAN [01:01:28]: hardest part of the whole thing. Like I spend a shocking amount of time just telling our customers SWYX [01:01:34]: we need to find a way JONATHAN [01:01:34]: to measure what you actually want out of the model before you should ever touch a GPU. And, you know, trying to convince my team and me to follow our own advice a lot of the time on that. And I think everybody like on the one hand, SWYX [01:01:46]: it's easy to laugh JONATHAN [01:01:46]: at the state of the evaluations that we have. None of them are good. Like if you go read these eval benchmarks, you'll always come away SWYX [01:01:52]: disappointed. JONATHAN [01:01:53]: And yet they've given us useful hills to climb. And we do seem to be making progress and measuring SWYX [01:01:58]: progress in the field. JONATHAN [01:01:58]: And I think anecdotally, models are getting better year to year. So I feel like people tend to go and get into one situation or the other, like evals don't matter. I'm just going to look at loss SWYX [01:02:07]: or like, you know, JONATHAN [01:02:08]: the evals matter a lot and they're all broken. So what do I do? And I think like a lot of things in deep learning, we have to make peace with just complete imperfection. Like the most successful scientists I see are the ones who are OK operating in a world SWYX [01:02:20]: where everything's JONATHAN [01:02:20]: going to be broken. SWYX [01:02:22]: And yet we can still JONATHAN [01:02:22]: cobble things together and make something SWYX [01:02:24]: interesting happen. JONATHAN [01:02:24]: I mean, we were just discussing that with literal infrastructure. And now we're all the way SWYX [01:02:28]: up to like, JONATHAN [01:02:28]: how do we measure whether a model performed a complex coding task correctly? And everything is broken. SWYX [01:02:34]: And yet we're still able JONATHAN [01:02:34]: to make huge amounts of forward progress. SWYX [01:02:36]: I think that's right, Jonathan. JOSH [01:02:38]: And that the challenge SWYX [01:02:40]: isn't necessarily JOSH [01:02:40]: making perfect evaluations. I think our blog post here is about going really into the weeds on these to figure out like, what does that look like? And I think one thing is like, you know, SWYX [01:02:49]: as you said, JOSH [01:02:49]: we have been able to make a lot of progress without making these perfect. SWYX [01:02:52]: That's great. JOSH [01:02:52]: You don't have to have perfect evaluations. And, you know, the more interesting work is the stuff that we can't necessarily publish about, which is the imperfect evaluations that we have for actual coding tasks, for example. SWYX [01:03:04]: Like, what does this JOSH [01:03:04]: really mean as a person? And there, as you said, it's much messier. SWYX [01:03:08]: So it's a lot harder JOSH [01:03:08]: to put it out and say like, hey, everybody use this because there's so many SWYX [01:03:12]: rough edges. JOSH [01:03:12]: It's so hard to like even say, oh, is this even the right task? Is this even the right way to do it? And there's a lot of judgment. SWYX [01:03:19]: There's a lot of intuition JOSH [01:03:19]: that it comes down to. But yeah, I think that's where it's critical to do SWYX [01:03:23]: if you actually want to JOSH [01:03:23]: make these systems work. JONATHAN [01:03:24]: Yeah, you have to make peace with with living in that in between. SWYX [01:03:28]: Yeah. JONATHAN [01:03:28]: And I think that in some sense, SWYX [01:03:30]: when I hire researchers, JONATHAN [01:03:30]: that's the number one quality I look for. Like, can they be at peace living in a house that is neither clean nor messy, SWYX [01:03:36]: but it's just kind of JONATHAN [01:03:36]: somewhere in between? And are they OK with that? Are they OK with a few dishes being out on the table and a few clothes SWYX [01:03:42]: being on the floor? JONATHAN [01:03:43]: Or will that drive them insane? Or will they just end up with all the clothes on the floor and like all the dishes out all the time? Like, it's kind of I'm looking for that perfect balance because, you know, we have to operate in this imperfect world. Like, yeah, go ahead and give me the perfect evaluation for programmers SWYX [01:03:58]: or for an LLM JONATHAN [01:03:58]: that is a program assistant tool. Like there is no perfect evaluation. But clearly we've made progress. And so the most important part SWYX [01:04:06]: is just are we JONATHAN [01:04:06]: climbing the right hills? And so this is why I'm so excited to see the ambiguity aspect of this. We often think we have more room to climb on these benchmarks. It turns out we don't. Or it turns out that actually we're climbing, getting good at the benchmark and not actually getting good at the task we care about underlying the benchmark anymore. SWYX [01:04:21]: Maybe the model, JONATHAN [01:04:21]: like this is the famous example where if you get 100% at MNIST, your model must be broken in some way because there are four examples mislabeled, you know, it's it's that all over again. Welcome to this. SWYX [01:04:33]: Yeah, it's the accidental canary canary in this. I think one thing that's JOSH [01:04:37]: actually really interesting about this also is that, yes, like the ambiguous examples are sort of, you know, not that great from the perspective of these particular tasks that we're evaluating. SWYX [01:04:46]: But actually, one thing JOSH [01:04:46]: that we're very interested in is ambiguity itself. Like, can we detect whether a task from a user is ambiguous or whether you've, you know, completed a task successfully? Like these are actually hard, messy problems, but are really important from like the user experience of using these models. I would much rather have a coding agent that will give me back a thing. And, you know, it's it's actually the code doesn't work like 10% less of the time than some other model, but it will tell me 100% of the time like when it's not sure. Like that's so much more useful if it can communicate like, I'm not really sure about this or maybe there's some errors here. Then just like, here's some code. I have no idea if it works. And so these kind of like, you know, detecting ambiguity and detecting correctness SWYX [01:05:25]: or uncertainty, JOSH [01:05:25]: I think are really interesting problems SWYX [01:05:27]: that we're really like JOSH [01:05:27]: digging into quite deeply. SWYX [01:05:29]: I want to touch on maybe a couple of hot topics in evals, maybe tangentially related, but we're on the evals train right now. So I'm just going to get on that. So ArcAGI, Francois Chollet's hot new thing, it's sort of my take on it is basically it's trying to measure reasoning through an abstract IQ test. Effectively, I noticed that you don't use it. There's a lot of community debate, pro and con about it. What are your thoughts on just more abstract reasoning and maybe ArcAGI specifically? JOSH [01:06:01]: I think we purposely stayed away from the very, like there's BigBench, for example, that has a lot of, I think, to me, feels sort of similar types of tasks that are like very unrealistic. Like, oh, you know, we have books of different colors and then you're going to shuffle them and like which book is furthest to the left or something like, OK, cool, I guess it's neat. It's neat, I think, for us to explore in terms of like an agent reasoning in a larger loop. And we do care about these types of evaluations there. The types of evaluations we're talking about in the blog post here are for getting at, like, does this model in a base model sense, is this working at all? There's no chain of thought in these evaluations. These are just like, go straight to the answer. Does this make sense? SWYX [01:06:42]: Like, is this a thing that JOSH [01:06:42]: you can answer very quickly? That's what we were selecting for with these evaluations. This is not to say that these are the only evaluations we have. I think the Arc ones are like a little bit too, probably, visual for us to really be able to integrate with. SWYX [01:06:56]: But I think some of the JOSH [01:06:56]: BigBench ones are... You can tokenize it. SWYX [01:06:59]: Yeah, but, you know, JOSH [01:07:00]: I think it's not really... I think you can spend a lot of time getting really good at these kinds of benchmarks without making, like, kind of more general purpose progress. And so I think we're a little bit leery of going too far in that direction. Similarly, like, coding competitions. Like, we do a lot of code generation, but we don't really do a lot on, like, code competition problems for the very, very hard ones. SWYX [01:07:20]: So I think you can go JOSH [01:07:20]: very far down that route SWYX [01:07:22]: and make something that's, like, JOSH [01:07:22]: really good at those problems, but not actually that useful as, like, a programmer day to day. SWYX [01:07:26]: Yeah. JONATHAN [01:07:27]: Take a different tactic, which is, like, at the end of the day at Databricks, I have 12,000 customers, or I think that's the latest number, all of whom are trying to do something with, you know, LLMs or AI or machine learning. And those things don't look like these tasks. I don't think I have a single customer that's asking to, you know, have AI solve abstract reasoning problems. Things are pretty, like, they can be ambiguous, SWYX [01:07:53]: they can be challenging, JONATHAN [01:07:53]: they can be really interesting, SWYX [01:07:55]: but none of them look quite like this. JONATHAN [01:07:56]: And so, you know, I think to Josh's point, like, it's really about asking, why are we doing this? Even if you're trying to build AGI, and that's not personally my purpose, and I, you know, Josh has much more interesting things to say about that than I do. I don't even know if this is the kind of intelligence I would get excited about or care about personally, or if I would consider, you know, to Josh's point, this to be the indicia of intelligence. SWYX [01:08:17]: It's neat. JONATHAN [01:08:17]: But, you know, for me, it's, like, more down to earth things, like having a model that can have a conversation with you about data SWYX [01:08:24]: that on the backend JONATHAN [01:08:24]: is running SQL queries on your literal data. That's a much more interesting task to me. That's something that really matters day to day for my customers and, you know, different perspectives, but, you know, I think Josh and I would probably say the same thing, SWYX [01:08:36]: even though I would, JONATHAN [01:08:36]: I'm guessing, I don't want to put words in your mouth. You would say that you're pursuing more general intelligence in your own way. And I would say that I'm very happy with narrow intelligence. Like, I'm very happy with my little SQL bot and building 12,000 of those because they're moving the needle for a lot of folks every day. JOSH [01:08:51]: Yeah, I think we're, you know, we're not as far away in our position as it might seem. I think we're also excited about, like, SWYX [01:08:58]: how do you actually JOSH [01:08:58]: make these things useful? And that does end up being pretty narrow. I think these other tasks can be interesting as, like, ways to explore these more abstract reasoning questions or like, OK, how could an agent actually work through this? But it's important to keep in mind that it's like a toy, not a real problem. It's like it's a scientific tool to tell us something about the models. SWYX [01:09:16]: It's not something we should JOSH [01:09:16]: be optimizing for necessarily. SWYX [01:09:18]: The one thing I'll point out is, you know, as a kid, I was graded into a gifted program based on my ability to solve these exact type of problems. And then I entered college based on my ability to solve SATs, which, again, have nothing to do with my college experience, but whatever. So, you know, we have a history in the humanity of doing correlated IQ tests to general capability. OK, so the two more, two more viral evals, and then, you know, I just want to be mindful of your time. Needle in a haystack, long context utilization. Oh, for the love of God. Something, well, OK, like, let's just assume that, you know, on our podcast, we've discussed the, you know, baseline problems with needle in a haystack, but just generally long context, right? It's a useful thing for agents. I assume. And it's something that, you know, it's out there. Like, we don't know, don't really know what the best way to utilize memory is. But like, I assume it's important, right? What I'll say is like, you know, JONATHAN [01:10:13]: I spend a lot of time thinking about RAG these days. And RAG, you know, in one sense, you know, the way that I think about RAG is it's the world's simplest agent. It is an agent that basically, you know, there's at least more than one thing happening in the process of building models, at least a system. If you give the model the ability to decide when it wants to retrieve data from a context or retrieve data from a database, then we're talking about an agent. So RAG kind of, I think, like toes that boundary really nicely. There are a lot of reasons why you do genuinely need a long context. Like, I don't think long contexts are problematic in and of themselves. I know there's some controversy even about that. I love the idea of doing like thousand shot tasks as an alternative to fine tuning. I love the idea of pulling in lots of data into the context. I love the idea of once you get in a multimodal land, you're just going to end up SWYX [01:10:54]: with giant context. JONATHAN [01:10:54]: It's kind of unavoidable. The flip side is I don't know of anyone who like is hiding a secret passphrase in a book and needs the model to find it. Needle in a haystack is, it's interesting. The challenge with long context to my mind, and Josh, SWYX [01:11:08]: I'm curious what you think, JONATHAN [01:11:08]: is simply that annotating long context evals is really hard and really expensive, you know, intrinsically, because you need someone to read 10,000 tokens or 100,000 tokens, or like you need someone to read a 1,000 page book or the equivalent thereof in order to measure those long context benchmarks. I don't know if a human could solve these tasks, let alone that a human could do this in any amount of time where you're willing to pay the money to get the data annotated. And so any long context eval SWYX [01:11:33]: has to, in some sense, JONATHAN [01:11:33]: be correct by construction. And you have to, you know, the, you have to know the answer before you've created the example. And needle in a haystack is kind of the simplest way SWYX [01:11:41]: of doing that. JONATHAN [01:11:41]: I think the problems of needle in a haystack are well known, you know, it doesn't measure anything real. You're not even testing the model's ability to holistically use the context just to identify one part of the context. So you can do some wacky things to your model, like quantize the hell out of the KV cache and still get needle in a haystack to work quite well because it's not trying to holistically take advantage SWYX [01:11:59]: of things. JONATHAN [01:12:00]: You know, I have some thoughts on things that I like more that are also still correct by construction. Like, I really like the idea of doing thousand shot tasks where you can look at the scaling as you go from 10 shot to 100 shot to thousand shot to fine tuning on that data instead. And I like that as a way to, you know, have something that's correct by construction, or at least where you have SWYX [01:12:19]: a nice baseline JONATHAN [01:12:19]: that you can compare to automatically. So I'm typically looking for like contexts that are situations where long context is one way to solve the task, but not the only way SWYX [01:12:28]: to solve the task. JONATHAN [01:12:28]: And we have some other strong baseline floating around personally. But yeah, needle in a haystack, not my favorite thing in the world, to say the least. JOSH [01:12:35]: Yeah, I mean, I agree with most of what Jonathan SWYX [01:12:38]: said, I think. JOSH [01:12:38]: I think one other thing that I will call out SWYX [01:12:40]: is that, you know, JOSH [01:12:40]: from like a coding application perspective, it's useful to have long context because the lazy thing of just like throw the whole repo in the context is like, SWYX [01:12:48]: OK, cool. JOSH [01:12:48]: Like, you know, you can just get started with that. But then in, you know, in real scenarios, you don't necessarily want to put the whole thing in there. You can have code bases SWYX [01:12:56]: that are bigger. JOSH [01:12:56]: You probably want to filter down to the stuff that's relevant anyway to not be confusing. Like you probably even if you did have a lot of context, SWYX [01:13:02]: you might want to sort it JOSH [01:13:02]: in some way to say this is more important than this other stuff. So and, you know, you don't want to wait for you don't want to be wasting all this time and compute SWYX [01:13:09]: on inference and like JOSH [01:13:09]: doesn't really matter. So, yeah, I don't know that it's the most important thing. SWYX [01:13:15]: I think people will find creative use cases. And like Jon said, I think the multimodality examples will naturally lend themselves to long context. Cool. And then one last one on just general sort of agent related capabilities that we didn't really talk about in the eval section is function calling and tool use. There's a recent trend, I think, basically led again by OpenAI on parallel function calling. There's always there's been a limit on how many tools you can call from four to now, I think, 128. And I think theoretically, Claude and Jem and I support a lot more. JOSH [01:13:49]: So just generally, SWYX [01:13:50]: how do you think about evaling tool use? Is that super important for you guys? We're thinking about it JOSH [01:13:55]: in a slightly different way, which is, yes, you can have this like hard coded list of tools. But if only you could have like this really large open set of like tools, maybe they would be like functions that you could call if only there was like a language or like a programming thing, like being able to write code. I think for us, it's like, well, look, if we can write code, like now you have all these tools accessible at the end of the day, SWYX [01:14:16]: like function calling JOSH [01:14:16]: is just a function invocation, like literally in code. I think our approach to this is like SWYX [01:14:21]: instead of worrying about JOSH [01:14:21]: like weird hard coded agents using tools, like let's just make them SWYX [01:14:25]: able to actually JOSH [01:14:25]: write code robustly and make that code work and be able to debug that code, know if that code is safe to run, like get really good at the like code writing and execution part of things, because that will open up the action space like far more than, you know, 128 tools, like just everything is at your fingertips, especially I think over the next few years, like we already have so many really good APIs. As we get better and better at writing code, we'll be able to make APIs to things that don't even have APIs today. That's kind of how we think about it is less as like a special purpose thing SWYX [01:14:52]: and more as like JOSH [01:14:52]: this is one of the reasons to focus on code. SWYX [01:14:55]: On my end, JONATHAN [01:14:55]: the way that I think about this is, you know, I think a lot about how models interact with data. SWYX [01:15:00]: And so for me, JONATHAN [01:15:00]: tool use is really a question of how do you take models SWYX [01:15:04]: that are really built JONATHAN [01:15:04]: for unstructured data SWYX [01:15:06]: and have them interact JONATHAN [01:15:06]: with structured data? So, you know, and I get the question a lot from my customers, SWYX [01:15:10]: like what do I do JONATHAN [01:15:10]: with tabular data? Or what do I do with like, you know, JSON? Or what do I do? I mean, you name it, like even what do I do SWYX [01:15:17]: with a PDF? JONATHAN [01:15:17]: Because PDF parsing is still an unsolved problem, even in 2024. And the answer, or even just the basic question SWYX [01:15:24]: of like, should I bother JONATHAN [01:15:24]: to structure my data anymore? Shouldn't I just toss the table? Shouldn't I flatten it SWYX [01:15:28]: and just throw it JONATHAN [01:15:28]: into the LLM context and like let the model SWYX [01:15:30]: figure it out? JONATHAN [01:15:30]: Answer is no. We've built all these fun APIs and fun languages SWYX [01:15:36]: and paradigms JONATHAN [01:15:36]: for dealing with structured data over the years. Just use them. SWYX [01:15:40]: Have your model use them. JONATHAN [01:15:40]: Train a model that can interact SWYX [01:15:42]: with these things JONATHAN [01:15:42]: in a meaningful way. Like text to SQL SWYX [01:15:45]: is still, JONATHAN [01:15:45]: or like having a model be able to make SQL calls in the backend is actually like one of the single SWYX [01:15:51]: most useful things JONATHAN [01:15:51]: for my customers. It sounds really boring. Models are really good at it. And it moves the needle day to day. SWYX [01:15:57]: So tool use for me JONATHAN [01:15:58]: really is that like, how do you just interact with structured data sources and take advantage of the fact that you have some SWYX [01:16:05]: prior knowledge JONATHAN [01:16:05]: about the structure of your data that an LLM would completely flatten away. In many ways, this is kind of one of the, one of my biggest frustrations with the fact that LLMs work well with code. We have decades and decades and decades SWYX [01:16:17]: of understanding JONATHAN [01:16:17]: about the structure and interpretation of programs. Like I think that's literally the name of a book on programming, if I remember right. And, you know, we have all this theory. We know everything there is to know about programming languages if they're well-formed languages and have the right properties. And yet when we have an LLM SWYX [01:16:31]: work with them, JONATHAN [01:16:31]: we literally just turn it into a token stream. SWYX [01:16:33]: Despite the fact that we know JONATHAN [01:16:34]: how to parse it. We know, you know, how to do all sorts of, you know, SWYX [01:16:38]: reference, you know, JONATHAN [01:16:38]: disambiguation and things like that. We're still just flattening it into a model and making the model relearn all of these things from scratch. And it frustrates SWYX [01:16:45]: the hell out of me. JONATHAN [01:16:45]: I don't have a better answer when it comes to code, but I really appreciate that with a lot of data sources that have structure to them. Tool uses and function calling SWYX [01:16:53]: are just, JONATHAN [01:16:53]: in my mind, SWYX [01:16:55]: So I think basically what you're saying is like code is the God tool for Jonathan. Like, you know, SQL is so much the right abstraction for accessing all this data. One thing I do spend a lot of time thinking about is for the stuff that doesn't fit in a SQL table, you know, is knowledge graphs the answer? I think a lot of people are exploring that and I think every now and then people get a bout of knowledge graph religion and then it kind of doesn't work out. So I wonder, I wonder what the end state is. Like, is this an idea where it's a mirage? Or is this the idea where it sometime is going to work? It's about having the right tools JOSH [01:17:27]: for the problems, right? Like as Jonathan was saying, SQL is sometimes definitely the right tool. Like you've got your, you know, order table or something and you want to know, you know, number of sales last month. Like you should be using SQL sum that column. OK, great. You're all set. Knowledge graphs also, SWYX [01:17:40]: you know, JOSH [01:17:40]: are sometimes the right tool for a particular problem. You have some like weird question about relationships between entities SWYX [01:17:46]: that are modeled JOSH [01:17:46]: on some particular ontology that you actually understand and it's like math to the real world. Great. Use a knowledge base. Like use a knowledge graph. This is fine. But I think in the real world, it gets a lot messier than like knowledge graph style of things where it's like, well, is there a relationship between these two nodes? Like, I don't know. SWYX [01:18:04]: Like, is are these JOSH [01:18:04]: two separate nodes? Like those kind of messy borders, I think, prevent it SWYX [01:18:08]: from being a tool JOSH [01:18:08]: that can like solve everything forever. And so I think it'll always be good for certain problems, just like SQL is good SWYX [01:18:14]: for certain problems. JOSH [01:18:14]: Like different abstractions are good for different problems. And yeah, I think this is why I'm excited about code. Like code lets you SWYX [01:18:20]: kind of pick the right, JOSH [01:18:20]: like let's use this library for this problem. SWYX [01:18:22]: Let's use this library JOSH [01:18:22]: for this other problem. JONATHAN [01:18:24]: I think Josh said it and you said it well, like code is kind of the God tool. It unlocks literally everything. The challenge for me is always like, SWYX [01:18:31]: you know, sometimes JONATHAN [01:18:31]: unlocking too much power can sometimes inconvenient things can happen. And so it's all about balancing that SWYX [01:18:37]: in some sense, JONATHAN [01:18:37]: language is the God tool. SWYX [01:18:39]: If only, you know, JONATHAN [01:18:39]: we knew how to interpret it all the time. So code is has the really nice property SWYX [01:18:44]: that at least you can JONATHAN [01:18:44]: always execute it. And sometimes you just literally want your model to be able to do SQL calls and nothing else. And setting those boundaries properly for the problem, SWYX [01:18:52]: I think is going to be, I think at least a lot of my customers JONATHAN [01:18:54]: are going to be thinking very hard about that. SWYX [01:18:56]: Like, should I give JONATHAN [01:18:56]: the model access to the web? SWYX [01:18:58]: Is that actually helpful JONATHAN [01:18:58]: for this problem? It sounds great to just like flip yes on all the tools. SWYX [01:19:02]: Is that actually going to mean JONATHAN [01:19:02]: I'm going to get better solutions to my problems? SWYX [01:19:04]: So I want to be mindful of time. I think that's basically our sort of recap of our discussion based on Imbue's releases today. I wanted to leave some time for what's next for both of you guys. Maybe Josh, as a guest of honor, you want to go first as to what happens next. JOSH [01:19:19]: We have these releases. We're happy to put these things out. I think there's a lot of stuff SWYX [01:19:22]: that we haven't released. JOSH [01:19:22]: Like, this is not the only thing we've been working on. Most of our actual focus has been on kind of coding and reasoning. In particular, like the things that we're excited about are can we make these things useful? Like Jonathan is saying, right? Like, it's not about toy problems. It's like, can we use these today in our day-to-day workflow and actually have them accelerate us? And I think we have some kind of internal product prototypes and things that we're excited about. And so we're excited to share more about this in the coming, you know, months to quarters as we get it to a place where like other people could maybe get value out of this as well. But that's kind of our real focus right now is like, how do you take these really cool capabilities that are out there that our models have, et cetera. And like, make sure that they're actually useful today for us, like when we're doing real work and then for other people as well. In particular, focused on generating code, understanding code, testing code, verifying it, like starting with the like robust creation of software. Excellent. SWYX [01:20:13]: Jonathan? JONATHAN [01:20:14]: I never like to talk too much about the future because I think you've heard this from me before. I like for us to speak SWYX [01:20:19]: through our work. JONATHAN [01:20:19]: And so I don't, I don't like to tease too much. Our mission is, to Josh's point, to make this stuff useful to 12,000 customers. And not a lot of that ends up making it into the public eye SWYX [01:20:30]: and not a lot of that JONATHAN [01:20:30]: ends up getting released open source. So for this kind of forum where really, you know, SWYX [01:20:34]: where we're talking JONATHAN [01:20:34]: to the community, I'm asking myself right now, like, you know, what exciting things SWYX [01:20:38]: are we going to have JONATHAN [01:20:38]: to offer the community in the next little while? I think the most exciting part is just we're writing a lot of blog posts right now. We're trying to share more and more of our science because I feel like SWYX [01:20:47]: we've been doing JONATHAN [01:20:47]: these big pushes to create these really giant models. SWYX [01:20:50]: I think, Josh, JONATHAN [01:20:50]: I'm sure you had SWYX [01:20:51]: the same experience. JONATHAN [01:20:51]: It's exhausting and all-consuming and you get to the end SWYX [01:20:54]: and you're like, JONATHAN [01:20:54]: oh, I have all this stuff SWYX [01:20:56]: I want to talk about. JONATHAN [01:20:56]: Now I need to find the time to talk about it now that I've survived this huge push. And we're definitely in that mode right now. So there's going to be a lot of that coming in in the next little while. And, you know, we're always cooking up fun new models. I think the real question is, you know, releasing models open source is not our day-to-day bread and butter. It's kind of a fun reward that we get to do sometimes when we have something really cool to share and a little bit of time and spare GPUs in our hands. But for the most part, SWYX [01:21:20]: everything is going JONATHAN [01:21:20]: toward customers. You know, I think the joke is Databricks has been 18 months away from IPO for five years. So I guess Databricks SWYX [01:21:26]: is 18 months away JONATHAN [01:21:26]: from IPO still. But 18 months away from IPO means there's a lot of pressure to deliver for customers. And we're going to keep working on that. But I think you'll see hopefully some cool, interesting things SWYX [01:21:36]: get dropped over the course JONATHAN [01:21:36]: of the summer and into the fall. We'll find out when we get there. SWYX [01:21:39]: I think that's the right way JONATHAN [01:21:39]: to put it. I know we were talking earlier about kind of Abracadabra and Alakazam. And all I'll say is that, you know, the DBRX small model that we still haven't released yet was called Abra. DBRX was called Kadabra. And there's a third Pokémon in that evolution. And that's all I'll say for now. Cool stuff kind of popping up sometimes on Chatbot Arena. And, you know, keep your eyes out. Yep. SWYX [01:21:59]: I'll leave the links and the hints in the show notes. That was a very fun way to leave some breadcrumbs for people to follow. Cool. I'll leave everything to sort of some calls to action. We're going to be releasing this next week. So I'll be deep in my conference, the AI Engineer World's Fair. So people can just go to AI.Engineer and livestream it. Do you guys have any other calls to action before you wrap? JOSH [01:22:20]: The only one is, you know, we're definitely hiring. So if you're interested in working on coding, reasoning, interested in working on all of this stuff, you know, from the ground up and really deeply understanding not just how does the hardware work, but how do the models work and also designing these, you know, systems to actually be useful for yourself day to day, come say hi. JONATHAN [01:22:36]: The only thing I'll say is, you know, and I like saying it these days, it feels like the field is so crowded and, you know, it requires so many resources to do impactful work. And, you know, on some days it feels like everything's been done or somebody else is doing everything before you can. At least I remember that feeling every single day of my PhD and even more so now. But I hope like what you heard from Josh today tells you there is so much enormously impactful work to do in the field. If only you take a step back and take a fresh look at some of these things and just talk about what you're doing. There's a huge amount left to do here and a huge amount of exciting work happening every day. And for those who are certainly feeling that exhaustion right now, and I count myself among those folks many days, it's refreshing to see these kinds of drops and see that there is so much more even in things that people feel like they understand how to set up a cluster. My God, you know, even in these evals that we think we understand, there is still more to understand and still more work to do. I hope everybody's keeping at it. SWYX [01:23:32]: All right. Keep on keeping on. Well, thanks so much for your time, guys. That was a great discussion and we'll put the links in the show notes for people to read more. Thanks. Thanks a bunch. JOSH [01:23:40]: Thank you so much. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

Kanjun Qiu

Co-founder & CEO

Imbue

Jonathan Frankle

Chief AI Scientist, Databricks

DatabricksMosaicMLMassachusetts Institute of TechnologyMIT

In this episode, Nathan sits down with Jonathan Frankle, Chief Scientist, and Abhi Venigalla, Research Scientist of MosaicML. They chat about Mosaic’s custom LLMs, the customers seeking Mosaic out and what their journeys and use cases look like, and exciting developments in Mosaic’s research: including their new inference platform, as well as Mosaic’s MPT-7B-65k+ storywriter model. RECOMMENDED PODCAST: The HR industry is at a crossroads. What will it take to construct the next generation of incredible businesses – and where can people leaders have the most business impact? Hosts Nolan Church and Kelli Dragovich have been through it all, the highs and the lows – IPOs, layoffs, executive turnover, board meetings, culture changes, and more. With a lineup of industry vets and experts, Nolan and Kelli break down the nitty-gritty details, trade offs, and dynamics of constructing high performing companies. Through unfiltered conversations that can only happen between seasoned practitioners, Kelli and Nolan dive deep into the kind of leadership-level strategy that often happens behind closed doors. Check out the first episode with the architect of Netflix’s culture deck Patty McCord. https://link.chtbl.com/hrheretics The Cognitive Revolution is a part of the Turpentine podcast network. To learn more: www.turpentine.co TIMESTAMPS: (00:00) Episode Preview (06:04) Mosaic’s business model (07:28) Who uses Mosaic’s custom LLMs? What does their data look like? (09:55)Mosaic’s use cases for custom LLMs (12:47) How much extraction and summarization was done by humans pre-LLMs? (15:28) Sponsor: Omneky (21:50) The journeys of Mosaic’s customers and would a Wendy’s LLM know about a Big Mac? (25:46) The curriculum model and fine-tuning (29:10) Language models in the life sciences (33:20) How raw can data be before it becomes a problem? (35:44) Using the output of bulk pre-training process vs additional after training (38:30) Redteaming as a service (39:40) Mosaic’s inference platform (41:53) Spending one cent on 20,000 tokens, how is that cent distributed? (46:00)) Selling compute on a dedicated capacity basis (47:30) Oracle and AWS (49:50) The storywriter model and 65,000 token window (54:35) The transition from finite parameters into infinite attention matrix LINKS: MosaicML: https://www.mosaicml.com/ MPT-7B Storywriter Model: https://huggingface.co/mosaicml/mpt-7b-storywriter TWITTER: @jefrankle (Jonathan) @abhi_venigalla (Abhi) @MosaicML (Mosaic) @CogRev_Podcast @labenz (Nathan) @eriktorenberg (Erik) SPONSORS: Shopify is the global commerce platform that helps you sell at every stage of your business. Shopify powers 10% of ALL eCommerce in the US. And Shopify's the global force behind Allbirds, Rothy's, and Brooklinen, and 1,000,000s of other entrepreneurs across 175 countries.From their all-in-one e-commerce platform, to their in-person POS system – wherever and whatever you're selling, Shopify's got you covered. With free Shopify Magic, sell more with less effort by whipping up captivating content that converts – from blog posts to product descriptions using AI. Sign up for $1/month trial period: https://shopify.com/cognitive Thank you Omneky for sponsoring The Cognitive Revolution. Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work, customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off. This show is produced by Turpentine: a network of podcasts, newsletters, and more, covering technology, business, and culture — all from the perspective of industry insiders and experts. We’re launching new shows every week, and we’re looking for industry-leading sponsors — if you think that might be you and your company, email us at erik@turpentine.co. Music Credit: MusicLM

Jonathan Frankle

Chief AI Scientist, Databricks

DatabricksMosaicMLMassachusetts Institute of TechnologyMIT

Abhi Venigalla

Research Scientist

MosaicML

MosaicML is a platform for training and deploying large AI models at scale. Explore their docs, check out their blog, and keep an eye on their open roles. Jonathan Frankle is the Chief Scientist at MosaicML and an incoming Assistant Professor of Computer Science at Harvard. Abhinav Venigalla is the NLP Architect at MosaicML. Today’s Lifeboat badge winner is singmotor for rescuing How to remove columns with too many missing values in Python from the dustbin of history. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Jonathan Frankle

Chief AI Scientist, Databricks

DatabricksMosaicMLMassachusetts Institute of TechnologyMIT

Abhinav Venigalla

NLP Architect

MosaicML

We are excited to be the first podcast in the world to release an in-depth interview on the new SOTA in commercially licensed open source models - MosiacML MPT-7B! The Latent Space crew will be at the NYC Lux AI Summit next week, and have two meetups in June. As usual, all events are on the Community page! We are also inviting beta testers for the upcoming AI for Engineers course. See you soon! One of GPT3’s biggest limitations is context length - you can only send it up to 4000 tokens (3k words, 6 pages) before it throws a hard error, requiring you to bring in LangChain and other retrieval techniques to process long documents and prompts. But MosaicML recently open sourced MPT-7B, the newest addition to their Foundation Series, with context length going up to 84,000 tokens (63k words, 126 pages): This transformer model, trained from scratch on 1 trillion tokens of text and code (compared to 300B for Pythia and OpenLLaMA, and 800B for StableLM), matches the quality of LLaMA-7B. It was trained on the MosaicML platform in 9.5 days on 440 GPUs with no human intervention, costing approximately $200,000. Unlike many open models, MPT-7B is licensed for commercial use and it’s optimized for fast training and inference through FlashAttention and FasterTransformer. They also released 3 finetuned models starting from the base MPT-7B: * MPT-7B-Instruct: finetuned on dolly_hhrlhf, a dataset built on top of dolly-5k (see our Dolly episode for more details). * MPT-7B-Chat: finetuned on the ShareGPT-Vicuna, HC3, Alpaca, Helpful and Harmless, and Evol-Instruct datasets. * MPT-7B-StoryWriter-65k+: it was finetuned with a context length of 65k tokens on a filtered fiction subset of the books3 dataset. While 65k is the advertised size, the team has gotten up to 84k tokens in response when running on a single node A100-80GB GPUs. ALiBi is the dark magic that makes this possible. Turns out The Great Gatsby is only about 68k tokens, so the team used the model to create new epilogues for it! On top of the model checkpoints, the team also open-sourced the entire codebase for pretraining, finetuning, and evaluating MPT via their new MosaicML LLM Foundry. The table we showed above was created using LLM Foundry in-context-learning eval framework itself! In this episode, we chatted with the leads of MPT-7B at Mosaic: Jonathan Frankle, Chief Scientist, and Abhinav Venigalla, Research Scientist who spearheaded the MPT-7B training run. We talked about some of the innovations they’ve brought into the training process to remove the need for 2am on-call PagerDutys, why the LLM dataset mix is such an important yet dark art, and why some of the traditional multiple-choice benchmarks might not be very helpful for the type of technology we are building. Show Notes * Introducing MPT-7B * Cerebras * Lottery Ticket Hypothesis * Hazy Research * ALiBi * Flash Attention * FasterTransformer * List of naughty words for C4 https://twitter.com/code_star/status/1661386844250963972 * What is Sparsity? * Hungry Hungry Hippos * BF16 FP p.s. yes, MPT-7B really is codenamed LLongboi! Timestamps * Introductions [00:00:00] * Intro to Mosaic [00:03:20] * Training and Creating the Models [00:05:45] * Data Choices and the Importance of Repetition [00:08:45] * The Central Question: What Mix of Data Sets Should You Use? [00:10:00] * Evaluation Challenges of LLMs [0:13:00] * Flash Attention [00:16:00] * Fine-tuning for Creativity [00:19:50] * Open Source Licenses and Ethical Considerations [00:23:00] * Training Stability Enhancement [00:25:15] * Data Readiness & Training Preparation [00:30:00] * Dynamic Real-time Model Evaluation [00:34:00] * Open Science for Affordable AI Research [00:36:00] * The Open Approach [00:40:15] * The Future of Mosaic [00:44:11] * Speed and Efficiency [00:48:01] * Trends and Transformers [00:54:00] * Lightning Round and Closing [1:00:55] Transcript Alessio: [00:00:00] Hey everyone. Welcome to the Latent Space podcast. This is Alessio partner and CTO-in-Residence at Decibel Partners. I'm joined by my co-host, Swyx, writer and editor of Latent Space. Swyx: Hey, and today we have Jonathan and Abhi from Mosaic ML. Welcome to our studio. Jonathan: Guys thank you so much for having us. Thanks so much. Swyx: How's it feel? Jonathan: Honestly, I've been doing a lot of podcasts during the pandemic, and it has not been the same. Swyx: No, not the same actually. So you have on your bio that you're primarily based in Boston, Jonathan: New York. New York, yeah. My Twitter bio was a probability distribution over locations. Swyx: Exactly, exactly. So I DMd you because I was obviously very interested in MPT-7B and DMd you, I was like, for the 0.2% of the time that you're in San Francisco, can you come please come to a podcast studio and you're like, I'm there next week. Jonathan: Yeah, it worked out perfectly.  Swyx: We're really lucky to have you, I'll read off a few intros that people should know about you and then you can fill in the blanks. So Jonathan, you did your BS and MS at Princeton in programming languages and then found your way into ML for your PhD at MiT where you made a real splash with the lottery ticket hypothesis in 2018, which people can check up on. I think you've done a few podcasts about it over the years, which has been highly influential, and we'll talk about sparse models at Mosaic. You have also had some side [00:01:30] quest. You taught programming for lawyers and you did some law and privacy stuff in, in DC and also did some cryptography stuff. Um, and you've been an assistant professor at Harvard before earning your PhD. Jonathan:  I've yet to start. Swyx: You, you yet to start. Okay. But you just got your PhD. Jonathan:. I technically just got my PhD. I was at Mosaic which delayed my defense by about two years. It was, I was at 99% done for two years. Got the job at Harvard, Mosaic started, and I had better things to do than write my dissertation for two years.  Swyx: You know, you know, this is very out of order. Jonathan: Like, oh, completely out of order, completely backwards. Go talk to my advisor about that. He's also an advisor at Mosaic and has been from the beginning. And, you know, go talk to him about finishing on time. Swyx: Great, great, great. And just to fill it out, Abhi, you did your BS and MS and MIT, you were a researcher at Cerebras, and you're now a research scientist at Mosaic. Just before we go into Mosaic stuff, I'm actually very curious about Cereus and, uh, just that, that space in general. Um, what are they doing that people should know about? Abhinav: Yeah, absolutely. Um, I think the biggest thing about CEREUS is that they're really building, you know, kind of the NextGen computing platform beyond, like GPUs. Um, they're trying to build a system that uses an entire wafer, you know, rather than cutting up a wafer into smaller chips and trying to train a model on that entire system, or actually more recently on many such wafers. Um, so it's, and it's really extraordinary. I think it's like the first time ever that kind of wafer scale computing has ever really worked. And so it's a really exciting time to be there, trying to figure out how we can map ML workloads to work, um, on a much, much bigger chip. Swyx: And do you use like [00:03:00] a different programming language or framework to do that? Or is that like.. Abhinav: Yeah, so I mean, things have changed a bit since I was there. I think, um, you can actually run just normal tensor flow and pie torch on there. Um, so they've built a kind of software stack that compiles it down. So it actually just kind of works naturally. But yeah. Jonathan : Compiled versions of Python is a hot topic at the moment with Mojo as well.  Swyx: And then Mosaic, you, you spearheaded the MPT-7B effort. INTRO TO MOSAIC [00:03:20] Abhinav: Uh, yeah. Yeah, so it's kind of like, it's been maybe six months, 12 months in the making. We kind of started working on LMs sort of back in the summer of last year. Um, and then we came with this blog post where we kind of profiled a lot of LMs and saw, hey, the cost of training is actually a lot lower than what people might think. Um, and then since then, you know, being inspired by kind of, you know, meta’s release, so the LLaMA models and lots of other open source work, we kind of started working towards, well, what if we were to release a really good kind of 7 billion parameter model? And that's what MPT is.  Alessio:You know, we mentioned some of the podcasts you had done, Jonathan, I think in one of them you mentioned Mosaic was not planning on building a  model and releasing and obviously you eventually did. So what are some of the things that got you there that maybe obviously LLaMA you mentioned was an inspiration. You now have both the training and like inference products that you offer. Was this more of a research challenge in a way, uh, that you wanted to do? Or how did the idea come to be? Jonathan: I think there were a couple of things. So we still don't have a first class model. We're not an open AI where, you know, our businesses come to use our one great model. Our business is built around customers creating their own models. But at the end of the day, if customers are gonna create their own models, we have to have the tools to help them do that, and to have the tools to help them do that and know that they work we have to create our own models to start.  We have to know that we can do something great if customers are gonna do something great. And one too many people may have challenged me on Twitter about the fact that, you know, mosaic claims all these amazing numbers, but, you know, I believe not to, you know, call out Ross Whiteman here, but, you know, I believe he said at some point, you know, show us the pudding. Um, and so Ross, you know, please let me know how the pudding tastes. But in all seriousness, like I think there is something, this is a demo in some sense. This is to say we did this in 9.5 days for a really reasonable cost, straight through 200, an intervention. 200 K. Yep. Um, you can do this too. Swyx: Uh, and just to reference the numbers that you're putting out, this is the, the last year you were making a lot of noise for trading GPT 3 under 450 K, which is your, your initial estimate. Um, and then it went down to a 100 K and stable diffusion 160 k going down to less than 50 K as well. Jonathan: So I will be careful about that 100 K number. That's certainly the challenge I've given Abhi to hit. Oh, I wouldn't make the promise that we’ve hit yet, but you know, it's certainly a target that we have. And I, you know, Abhi may kill me for saying this. I don't think it's crazy.  TRAINING AND CREATING THE MODELS [00:05:45]  Swyx: So we definitely want to get into like estimation math, right? Like what, what needs to happen for those big order magnitude changes to in, in infrastructure costs. But, uh, let's kind of stick to the MPT-7B story. Yeah. Tell us everything. Like you have, uh, three different models. One of them. State of the art essentially on context length. Let's talk about the process of training them, the, uh, the decisions that you made. Um, I can go into, you know, individual details, but I just wanna let you let you rip. Abhinav: Yeah, so I mean, I think, uh, we started off with the base model, which is kind of for all practical purposes, a recreation of LLaMA 7B. Um, so it's a 7 billion perimeter model trained on the trillion tokens. Um, and our goal was like, you know, we should do it efficiently. We should be able to do it like, kind of hands free so we don't have to babysit the runs as they're doing them. And it could be kind of a, a launching point for these fine tune models and those fine tune models, you know, on, on the one hand they're kind of really fun for the community, like the story writer model, which has like a 65,000 length context window and you can even kind of extrapolate beyond that. Um, but they're, they're also kind of just tr inspirations really. So you could kind of start with an MPT-7B base and then build your own custom, you know, downstream. If you want a long context code model, you could do that with our platform. If you wanted one that was for a particular language, you could do that too. But yeah, so we picked kind of the three variance chat and instruct and story writer just kind of like inspirations looking at what people were doing in the community today. Yeah.  Alessio: And what's the beginning of the math to come up with? You know, how many tokens you wanna turn it on? How many parameters do you want in a bottle? 7 billion and 30 billion seem to be kind of like two of the magic numbers going around right now.  Abhinav: Yeah, definitely. Definitely. Yeah, I think like there's sort of these scaling laws which kind of tell you how to best spend your training compute if that's all you cared about. So if you wanna spend $200,000 exactly in the most efficient way, there'd be a recipe for doing that. Um, and that we usually go by the Chinchilla laws. Now for these models, we actually didn't quite do that because we wanted to make sure that people could actually run these at home and that they [00:07:30] were good for inference. So we trained them kind of beyond those chinchilla points so that we're almost over-training them. I think there's like a joke going on online that they're like long boy and that that came up internally because we were training them for really, really long durations. So that 7B model, the chinchilla point might be 140 billion tokens. Instead, we trained a trillion, so almost seven times longer than you normally would. Swyx: So longboi was the code name. So is it, is it the trading method? Is it the scaling law that you're trying to coin or is it the code name for the 64 billion? Jonathan: Uh, 64. It was just an internal joke for the, for training on way more tokens than you would via chinchilla. Okay. Um, we can coin it long boy and it, it really stuck, but just to, you know, long boys filled with two ELs at the beginning. Yeah. Cause you know, we wanted the lLLaMA thing in there as well.  Jonathan: Yeah, yeah, yeah. Our darn CEO we have to rein him in that guy, you know, you can't, yeah. I'm gonna take away his Twitter password at some point. Um, but you know, he had to let that one out publicly. And then I believe there was a YouTube video where someone happened to see it mentioned before the model came out and called it the Long G boy or something like that. Like, so you know, now it's out there in the world. It's out there. It's like Sydnee can't put it back in Swyx: There's a beautiful picture which I think Naveen tweeted out, which, um, shows a long boy on a whiteboard. Jonathan: That was the origin of Long Boy. In fact, the legs of the lLLaMA were the two Ls and the long boy. DATA CHOICES AND THE IMPORTANCE OF REPETITION [00:08:45] Swyx: Well, talk to me about your data choices, right? Like this is your passion project. Like what can you tell us about it? Jonathan: Yeah, I think Abhi wanted to kill me by the end for trying to use all the GPUs on data and none of them on actually training the model.  Um, at the end of the day, We know that you need to train these models and [00:09:00] lots of data, but there are a bunch of things we don't know. Number one is what kinds of different data sources matter. The other is how much does repetition really matter? And really kind of repetition can be broken down into how much does quality versus quantity matter. Suppose I had the world's best 10 billion tokens of data. Would it be better to train on that a hundred times or better to train on a trillion tokens of low quality, fresh data? And obviously there's, there's a middle point in between. That's probably the sweet spot. But how do you even know what good quality data is? And. So, yeah, this is, nobody knows, and I think the more time I spent, we have a whole data team, so me and several other people, the more time that we spent on this, you know, I came away thinking, gosh, we know nothing. Gosh, if I were back in academia right now, I would definitely go and, you know, write a paper about this because I have no idea what's going on. Swyx: You would write a paper about it. I'm interested in such a paper. I haven't come across any that exists. Could you frame the central question of such a paper? THE CENTRAL QUESTION: WHAT MIX OF DATA SETS SHOULD YOU USE? [00:10:00] Jonathan: Yeah. The central question is what mix of data sets should you use? Okay. Actually I've, you know, you had mentioned my law school stuff. I went back to Georgetown Law where I used to teach, um, in the midst of creating this model, and I actually sat down with a class of law students and asked them, I gave them our exact data sets, our data mixes, um, like how many tokens we had, and I said, Create the best data set for your model. Knowing they knew nothing about large language models, they just know that data goes in and it's going to affect the behavior. Um, and I was like, create a mix and they basically covered all the different trade-offs. Um, you probably want a lot of English language [00:10:30] text to start with. You get that from the web, but do you want it to be multilingual? If so, you're gonna have a lot less English text. Maybe it'll be worse. Do you wanna have code in there? There are all these beliefs that code leads to models being better at logical reasoning, of which I've seen zero evidence. Rep. It's not, um, I mean, really made a great code model, but code models leading to better chain of thought reasoning on the part of language or code being in the training set leading to better chain of thought reasoning. People claim this all the time, but I've still never seen any real evidence beyond that. You know, one of the generations of the GPT three model started supposedly from Code Da Vinci. Yes. And so there's a belief that, you know, maybe that helped. But again, no evidence. You know, there's a belief that spending a lot of time on good sources like Wikipedia is good for the model. Again, no evidence. At the end of the day, we tried a bunch of different data mixes and the answer was that there are some that are better or worse than others. We did find that the pile, for example, was a really solid data mix, but you know, there were stronger data mixes by our evaluation metrics. And I'll get back to the evaluation question in a minute cuz that's a really important one. This data set called c4, which is what the original T five model was trained on, is weirdly good. And everybody, when I posted on this on Twitter, like Stella Beaterman from Luther mentioned this, I think someone else mentioned this as well. C4 does really well in the metrics and we have no idea why we de-duplicated it against our evaluation set. So it's not like it memorized the data, it is just one web scrape from 2019. If you actually look at the T five paper and see how it was pre-processed, it looks very silly. Mm-hmm. They removed anything that had the word JavaScript in it because they didn't want to get like no JavaScript [00:12:00] warnings. They removed anything with curly braces cuz they didn't wanna get JavaScript in it. They looked at this list of bad words, um, and removed anything that had those bad words. If you actually look at the list of bad words, words like gay are on that list. And so there's, you know, it is a very problematic, you know, list of words, but that was the cleaning that leads to a data set that seems to be unbeatable. So that to me says that we know nothing about data. We, in fact used a data set called mc four as well, which is they supposedly did the same pre-processing of C4 just on more web calls. The English portion is much worse than C4 for reasons that completely escape us. So in the midst of all that, Basically I set two criteria. One was I wanted to be at least as good as mc four English, like make sure that we're not making things actively worse. And mc four English is a nice step up over other stuff that's out there. And two was to go all in on diversity after that, making sure that we had some code, we had some scientific papers, we had Wikipedia, because people are gonna use this model for all sorts of different purposes. But I think the most important thing, and I'm guessing abhi had a million opinions on this, is you're only as good as your evaluation. And we don't know how to evaluate models for the kind of generation we ask them to do. So past a certain point, you have to kinda shrug and say, well, my evaluation's not even measuring what I care about. Mm-hmm. So let me just make reasonable choices.  EVALUATION CHALLENGES OF LLMs [0:13:00] Swyx: So you're saying MMLU, big bench, that kind of stuff is not. Convincing for you Jonathan: A lot of this stuff is you've got two kinds of tasks. Some of these are more of multiple choice style tasks where there is a right answer. Um, either you ask the model to spit out A, B, C, or D or you know, and if you're more [00:13:30] sophisticated, you look at the perplexity of each possible answer and pick the one that the model is most likely to generate. But we don't ask these models to do multiple choice questions. We ask them to do open-ended generation. There are also open-ended generation tasks like summarization. You compare using things like a blue score or a rouge score, which are known to be very bad ways of comparing text. At the end of the day, there are a lot of great summaries of a paper. There are a lot of great ways to do open form generation, and so humans are, to some extent, the gold standard. Humans are very expensive. It turns out we can't put them into our eval pipeline and just have the humans look at our model every, you know, 10 minutes? Not yet. Not yet. Maybe soon. Um, are you volunteering Abhi? Abhinav: I, I, I just know we have a great eval team who's, uh, who's helping us build new metrics. So if they're listening, Jonathan:  But it's, you know, evaluation of large language models is incredibly hard and I don't think any of these metrics really truly capture. What we expect from the models in practice. Swyx: Yeah. And we might draw wrong conclusions. There's been a debate recently about the emergence phenomenon, whether or not it's a mirage, right? I don't know if you guys have opinions about that process.  Abhinav: Yeah, I think I've seen like this paper and all and all, even just kind of plots from different people where like, well maybe it's just a artifact of power, like log scaling or metrics or, you know, we're meshing accuracy, which is this a very like harsh zero one thing. Yeah. Rather than kind of something more continuous. But yeah, similar to what Jonathan was saying about evals. Like there there's one issue of like you just like our diversity of eval metrics, like when we put these models up, even like the chat ones, the instruct ones, people are using 'em for such a variety of tasks. There's just almost no way we get ahead of time, like measuring individual dimensions. And then also particularly like, you know, at the 7B scale, [00:15:00] um, these models still are not super great yet at the really hard tasks, like some of the hardest tasks in MMLU and stuff. So sometimes they're barely scoring like the above kind of random chance, you know, like on really, really hard tasks. So potentially as we. You know, aim for higher and higher quality models. Some of these things will be more useful to us. But we kind of had to develop MPT 7B kind of flying a little bit blind on, on what we knew it was coming out and just going off of like, you know, a small set of common sensor reasoning tasks. And of course, you know, just comparing, you know, those metrics versus other open source models.  Alessio: I think fast training in inference was like one of the goals, right? So there's always the trade off between doing the hardest thing and like. Doing all the other things quickly. Abhinav: Yeah, absolutely. Yeah, I mean, I think like, you know, even at the 7B scale, you know, uh, people are trying to run these things on CPUs at home. You know, people are trying to port these to their phones, basically prioritizing the fact that the small scale would lead to our adoption. That was like a big, um, big thing going on.  Alessio: Yeah. and you mentioned, um, flash attention and faster transformer as like two of the core things. Can you maybe explain some of the benefits and maybe why other models don't use it? FLASH ATTENTION [00:16:00] Abhinav: Yeah, absolutely. So flash attention is this basically faster implementation of full attention. Um, it's like a mathematical equivalent developed by like actually some of our collaborators, uh, at Stanford. Uh, the hazy research. Hazy research, yeah, exactly. Jonathan: What is, what, what, what's the name hazy research mean? Abhinav: I actually have no idea. Swyx: I have no clue. All these labs have fun names. I always like the stories behind them. Abhinav: Yeah, absolutely. We really, really liked flash attention. We, I think, had to integrate into repo even as [00:16:30] as early as September of last year. And it really just helps, you know, with training speed and also inference speed and we kind of bake that into model architecture. And this is kind of unique amongst all the other hugging face models you see out there. So ours actually, you can toggle between normal torch attention, which will work anywhere and flash attention, which will work on GPUs right out of the box. And that way I think you get almost like a 2x speed up at training time and somewhere between like 50% to a hundred percent speed up at inference time as well. So again, this is just like, we really, really wanted people to use these and like, feel like an improvement and we, we have the team to, to help deliver that.  Swyx: Another part, um, of your choices was alibi position, encodings, which people are very interested in, maybe a lot of people just, uh, to sort of take in, in coatings as, as a given. But there's actually a lot of active research and honestly, it's a lot of, um, it's very opaque as well. Like people don't know how to evaluate encodings, including position encodings, but may, may, could you explain, um, alibi and, um, your choice? Abhinav: Yeah, for sure. The alibi and uh, kind of flash attention thing all kind of goes together in interesting ways. And even with training stability too. What alibi does really is that it eliminates the need to have positional embeddings in your model. Where previously, if you're a token position one, you have a particular embedding that you add, and you can't really go beyond your max position, which usually is like about 2000. With alibies, they get rid of that. Instead, just add a bias to the attention map itself. That's kind of like this slope. And if at inference time you wanna go much, much larger, they just kind of stretch that slope out to a longer, longer number of positions. And because the slope is kind of continuous and you can interpret it, it all works out now. Now one of [00:18:00] the, the funny things we found is like with flash attention, it saved so much memory and like improved performance so much that even as early as I kind of last year, like we were profiling models with, with very long context lines up to like, you know, the 65 k that you seen in release, we just never really got around to using it cuz we didn't really know what we might use it for. And also it's very hard to train stably. So we started experimenting with alibi integration, then we suddenly found that, oh wow, stability improves dramatically and now we can actually work together with alibi in a long context lens. That's how we got to like our story writer model where we can stably train these models out to very, very long context lenses and, and use them performantly. Jonathan: Yeah. Swyx: And it's also why you don't have a firm number. Most people now have a firm number on the context line. Now you're just like, eh, 65 to 85 Abhinav: Oh yeah, there's, there's a, there's a big age to be 64 K or 65 k. 65 k plus. Swyx: Just do powers of twos. So 64 isn't, you know.  Jonathan: Right, right. Yeah. Yeah. But we could, I mean, technically the context length is infinite. If you give me enough memory, um, you know, we can just keep going forever. We had a debate over what number to say is the longest that we could handle. We picked 84 cakes. It's the longest I expect people to see easily in practice. But, you know, we played around for even longer than that and I don't see why we couldn't go longer. Swyx: Yeah. Um, and so for those who haven't read the blog posts, you put the Great Gatsby in there and, uh, asked it to write an epilogue, which seemed pretty impressive. Jonathan: Yeah. There are a bunch of epilogues floating around internally at Mosaic. Yeah. That wasn't my favorite. I think we all have our own favorites. Yeah. But there are a bunch of really, really good ones. There was one where, you know, it's Gatsby's funeral and then Nick starts talking to Gatsby's Ghost, and Gatsby's father shows up and, you know, then he's [00:19:30] at the police station with Tom. It was very plot heavy, like this is what comes next. And a bunch of that were just very Fitzgerald-esque, like, you know, beautiful writing. Um, but it was cool to just see that Wow, the model seemed to actually be working with. You know, all this input. Yeah, yeah. Like it's, it's exciting. You can think of a lot of things you could do with that kind of context length. FINE-TUNING FOR CREATIVITY [00:19:50] Swyx: Is there a trick to fine tuning for a creative task rather than, um, factual task? Jonathan: I don't know what that is, but probably, yeah, I think, you know, the person, um, Alex who did this, he did fine tune the model explicitly on books. The goal was to try to get a model that was really a story writer. But, you know, beyond that, I'm not entirely sure. Actually, it's a great question. Well, no, I'll ask you back. How would you measure that?  Swyx: Uh, God, human feedback is the solve to all things. Um, I think there is a labeling question, right? Uh, in computer vision, we had a really, really good episode with Robo Flow on the segment. Anything model where you, you actually start human feedback on like very, I think it's something like 0.5% of the, the overall, uh, final, uh, uh, labels that you had. But then you sort augment them and then you, you fully automate them, um, which I think could be applied to text. It seems intuitive and probably people like snorkel have already raised ahead on this stuff, but I just haven't seen this applied in the language domain yet. Jonathan: It, I mean there are a lot of things that seem like they make a lot of sense in machine learning that never work and a lot of things that make zero sense that seem to work. So, you know, I've given up trying to even predict. Yeah, yeah. Until I see the data or try it, I just kind shg my shoulders and you know, you hope for the best. Bring data or else, right? Yeah, [00:21:00] exactly. Yeah, yeah, yeah. Alessio: The fine tuning of books. Books three is like one of the big data sets and there was the whole. Twitter thing about trade comments and like, you know, you know, I used to be a community moderator@agenius.com and we've run into a lot of things is, well, if you're explaining lyrics, do you have the right to redistribute the lyrics? I know you ended up changing the license on the model from a commercial use Permitted. Swyx: Yeah let's let them. I'm not sure they did.  Jonathan: So we flipped it for about a couple hours.  Swyx: Um, okay. Can we, can we introduce the story from the start Just for people who are under the loop.  Jonathan: Yeah. So I can tell the story very simply. So, you know, the book three data set does contain a lot of books. And it is, you know, as I discovered, um, it is a data set that provokes very strong feelings from a lot of folks. Um, that was one, one guy from one person in particular, in fact. Um, and that's about it. But it turns out one person who wants a lot of attention can, you know, get enough attention that we're talking about it now. And so we had a, we had a discussion internally after that conversation and we talked about flipping the license and, you know, very late at night I thought, you know, maybe it's a good thing to do. And decided, you know, actually probably better to just, you know, Stan Pat's license is still Apache too. And one of the conversations we had was kind of, we hadn't thought about this cuz we had our heads down, but the Hollywood writer Strike took place basically the moment we released the model. Mm-hmm. Um, we were releasing a model that could do AI generated creative content. And that is one of the big sticking points during the strike. Oh, the optics are not good. So the optics aren't good and that's not what we want to convey. This is really, this is a demo of the ability to do really long sequence lengths and. Boy, you know, [00:22:30] that's, that's not timing that we appreciated. And so we talked a lot internally that night about like, oh, we've had time to read the news. We've had time to take a breath. We don't really love this. Came to the conclusion that it's better to just leave it as it is now and learn the lesson for the future. But certainly that was one of my takeaways is this stuff, you know, there's a societal context around this that it's easy to forget when you're in the trenches just trying to get the model to train. And you know, in hindsight, you know, I might've gone with a different thing than a story writer. I might've gone with, you know, coder because we seem to have no problem putting programmers out of work with these models. Swyx: Oh yeah. Please, please, you know, take away this stuff from me. OPEN SOURCE LICENSES AND ETHICAL CONSIDERATIONS [00:23:00] Jonathan: Right. You know, so it's, I think, you know, really. The copyright concerns I leave to the lawyers. Um, that's really, if I learned one thing teaching at a law school, it was that I'm not a lawyer and all this stuff is a little complicated, especially open source licenses were not designed for this kind of world. They were designed for a world of forcing people to be more open, not forcing people to be more closed. And I think, you know, that was part of the impetus here, was to try to use licenses to make things more closed. Um, which is, I think, against the grain of the open source ethos. So that struck me as a little bit strange, but I think the most important part is, you know, we wanna be thoughtful and we wanna do the right thing. And in that case, you know, I hope with all that interesting licensing fund you saw, we're trying to be really thoughtful about this and it's hard. I learned a lot from that experience.  Swyx: There’s also, I think, an open question of fair use, right? Is training on words of fair use because you don't have a monopoly on words, but some certain arrangements of words you do. And who is to say how much is memorization by a model versus actually learning and internalizing and then. Sometimes happening to land at the right, the [00:24:00] same result. Jonathan: And if I've learned one lesson, I'm not gonna be the person to answer that question. Right, exactly. And so my position is, you know, we will try to make this stuff open and available. Yeah. And, you know, let the community make decisions about what they are or aren't comfortable using. Um, and at the end of the day, you know, it still strikes me as a little bit weird that someone is trying to use these open source licenses to, you know, to close the ecosystem and not to make things more open. That's very much against the ethos of why these licenses were created. Swyx: So the official mosaic position, I guess is like, before you use TC MPC 7B for anything commercial, check your own lawyers now trust our lawyers, not mosaic’s lawyers. Jonathan: Yeah, okay. Yeah. I'm, you know, our lawyers are not your lawyers. Exactly. And, you know, make the best decision for yourself. We've tried to be respectful of the content creators and, you know, at the end of the day, This is complicated. And this is something that is a new law. It's a new law. It's a new law that hasn't been established yet. Um, but it's a place where we're gonna continue to try to do the right thing. Um, and it's, I think, one of the commenters, you know, I really appreciated this said, you know, well, they're trying to do the right thing, but nobody knows what the right thing is to even do, you know, the, I guess the, the most right thing would've been to literally not release a model at all. But I don't think that would've been the best thing for the community either. Swyx: Cool.Well, thanks. Well handled. Uh, we had to cover it, just cause Jonathan:  Oh, yes, no worries. A big piece of news. It's been on my mind a lot. TRAINING STABILITY ENHANCEMENT [00:25:15] Swyx: Yeah. Yeah. Well, you've been very thoughtful about it. Okay. So a lot of these other ideas in terms of architecture, flash, attention, alibi, and the other data sets were contributions from the rest of the let's just call it open community of, of machine learning advancements. Uh, but Mosaic in [00:25:30] particular had some stability improvements to mitigate loss spikes, quote unquote, uh, which, uh, I, I took to mean, uh, your existing set of tools, uh, maybe we just co kind of covered that. I don't wanna sort of put words in your mouth, but when you say things like, uh, please enjoy my empty logbook. How much of an oversell is that? How much, you know, how much is that marketing versus how much is that reality? Abhinav: Oh yeah. That, that one's real. Yeah. It's like fully end-to-end. Um, and I think. Swyx: So maybe like what, what specific features of Mosaic malibu? Abhinav: Totally, totally. Yeah. I think I'll break it into two parts. One is like training stability, right? Knowing that your model's gonna basically get to the end of the training without loss spikes. Um, and I think, you know, at the 7B scale, you know, for some models like it ha it's not that big of a deal. As you train for longer and longer durations, we found that it's trickier and trickier to avoid these lost spikes. And so we actually spent a long time figuring out, you know, what can we do about our initialization, about our optimizers, about the architecture that basically prevents these lost spikes. And you know, even in our training run, if you zoom in, you'll see small intermittent spikes, but they recover within a few hundred steps. And so that's kind of the magical bit. Our line is one of defenses we recover from Las Vegas, like just naturally, right? Mm-hmm. Our line two defense was that we used determinism and basically really smart resumption strategies so that if something catastrophic happened, we can resume very quickly, like a few batches before. And apply some of these like, uh, interventions. So we had these kinds of preparations, like a plan B, but we didn't have to use them at all for MPT 7B training. So, that was kind of like a lucky break. And the third part of like basically getting all the way to the empty law book is having the right training infrastructure.[00:27:00] So this is basically what, like is, one of the big selling points of the platform is that when you try to train these models on hundreds of GPUs, not many people outside, you know, like deep industry research owners, but the GPUs fail like a lot. Um, I would say like almost once every thousand a 100 days. So for us on like a big 512 cluster every two days, basically the run will fail. Um, and this is either due to GPUs, like falling off the bus, like that's, that's a real error we see, or kind of networking failures or something like that. And so in those situations, what people have normally done is they'll have an on-call team that's just sitting round the clock, 24-7 on slack, once something goes wrong. And if then they'll basically like to try to inspect the cluster, take nodes out that are broken, restart it, and it's a huge pain. Like we ourselves did this for a few months. And as a result of that, because we're building such a platform, we basically step by step automated every single one of those processes. So now when a run fails, we have this automatic kind of watch talk that's watching. It'll basically stop the job. Test the nodes cord in anyone's that are broken and relaunch it. And because our software's all deterministic has fast resumption stuff, it just continues on gracefully. So within that log you can see sometimes I think maybe at like 2:00 AM or something, the run failed and within a few minutes it's back up and running and all of us are just sleeping peacefully. Jonathan: I do wanna say that was hard one. Mm-hmm. Um, certainly this is not how things were going, you know, many months ago, hardware failures we had on calls who were, you know, getting up at two in the morning to, you know, figure out which node had died for what reason, restart the job, have to cord the node. [00:28:30] Um, we were seeing catastrophic loss spikes really frequently, even at the 7B scale that we're just completely derailing runs. And so this was step by step just ratcheting our way there. As Abhi said, to the point where, Many models are training at the moment and I'm sitting here in the studio and not worrying one bit about whether the runs are gonna continue. Yeah.  Swyx: I'm, I'm not so much of a data center hardware kind of guy, but isn't there existing software to do this for CPUs and like, what's different about this domain? Does this question make sense at all? Jonathan: Yeah, so when I think about, like, I think back to all the Google fault tolerance papers I read, you know, as an undergrad or grad student mm-hmm. About, you know, building distributed systems. A lot of it is that, you know, Each CPU is doing, say, an individual unit of work. You've got a database that's distributed across your cluster. You wanna make sure that one CPU failing can't, or one machine failing can't, you know, delete data. So you, you replicate it. You know, you have protocols like Paxos where you're literally, you've got state machines that are replicated with, you know, with leaders and backups and things like that. And in this case, you were performing one giant computation where you cannot afford to lose any node. If you lose a node, you lose model state. If you lose a node, you can't continue. It may be that, that in the future we actually, you know, create new versions of a lot of our distributed training libraries that do have backups and where data is replicated so that if you lose a node, you can detect what node you've lost and just continue training without having to stop the run, you know? Pull from a checkpoint. Yeah. Restart again on different hardware. But for now, we're certainly in a world where if anything dies, that's the end of the run and you have to go back and recover from it. [00:30:00] DATA READINESS & TRAINING PREPARATION [00:30:00] Abhinav: Yeah. Like I think a big part, a big word there is like synchronous data pluralism, right? So like, we're basically saying that on every step, every GP is gonna do some work. They're gonna stay in sync with each other and average their, their gradients and continue. Now that there are algorithmic techniques to get around this, like you could say, oh, if a GP dies, just forget about it. All the data that's gonna see, we'll just forget about it. We're not gonna train on it. But, we don't like to do that currently because, um, it makes us give up determinism, stuff like that. Maybe in the future, as you go to extreme scales, we'll start looking at some of those methods. But at the current time it's like, we want determinism. We wanted to have a run that we could perfectly replicate if we needed to. And it was, the goal is figure out how to run it on a big cluster without humans having to babysit it. Babysit it.  Alessio: So as you mentioned, these models are kind of the starting point for a lot of your customers To start, you have a. Inference product. You have a training product. You previously had a composer product that is now kind of not rolled into, but you have like a super set of it, which is like the LLM foundry. How are you seeing that change, you know, like from the usual LOP stack and like how people train things before versus now they're starting from, you know, one of these MPT models and coming from there. Like worship teams think about as they come to you and start their journey. Jonathan: So I think there's a key distinction to make here, which is, you know, when you say starting from MPT models, you can mean two things. One is actually starting from one of our checkpoints, which I think very few of our customers are actually going to do, and one is starting from our configuration. You can look at our friends at Rep for that, where, you know, MPT was in progress when Refl [00:31:30] came to us and said, Hey, we need a 3 billion parameter model by next week on all of our data. We're like, well, here you go. This is what we're doing, and if it's good enough for us, um, hopefully it's good enough for you. And that's basically the message we wanna send to our customers. MPT is basically clearing a path all the way through where they know that they can come bring their data, they can use our training infrastructure, they can use all of our amazing orchestration and other tools that abhi just mentioned, for fault tolerance. They can use Composer, which is, you know, still at the heart of our stack. And then the l l M Foundry is really the specific model configuration. They can come in and they know that thing is gonna train well because we've already done it multiple times.  Swyx: Let's dig in a little bit more on what should people have ready before they come talk to you? So data architecture, eval that they're looking, etc. Abhinav: Yeah, I, I mean, I think we'll accept customers at any kind of stage in their pipeline. You know, like I'd say science, there's archetypes of people who have built products around like some of these API companies and reach a stage or maturity level where it's like we want our own custom models now, either for the purpose of reducing cost, right? Like our inference services. Quite a bit cheaper than using APIs or because they want some kind of customization that you can't really get from the other API providers. I'd say the most important things to have before training a big model. You know, you wanna have good eval metrics, you know, some kind of score that you can track as you're training your models and scaling up, they can tell you you're progressing. And it's really funny, like a lot of times customers will be really excited about training the models, right? It's really fun to like launch shelves on hundreds of gfs, just all around. It's super fun. But then they'll be like, but wait, what are we gonna measure? Not just the training loss, right? I mean, it's gotta be more than that.[00:33:00] So eval metrics is like a, it's a good pre-req also, you know, your data, you know, either coming with your own pre-training or fine-tune data and having like a strategy to clean it or we can help clean it too. I think we're, we're building a lot of tooling around that. And I think once you have those two kinds of inputs and sort of the budget that you want, we can pretty much walk you through the rest of it, right? Like that's kind of what we do. Recently we helped build CR FM's model for biomedical language a while back.  Jonathan: Um, we can. That's the center of research for foundation models.  Abhi: Exactly, exactly. Jonathan: Spelling it out for people. Of course. Abhinav: No, absolutely. Yeah, yeah. No, you've done more of these than I have. Um, I think, uh, basically it's sort of, we can help you figure out what model I should train to scale up so that when I go for my big run company, your here run, it's, uh, it's predictable. You can feel confident that it's gonna work, and you'll kind of know what quality you're gonna get out before you have to spend like a few hundred thousand dollars. DYNAMIC REAL-TIME MODEL EVALUATION [00:34:00] Alessio: The rap Reza from rap was on the podcast last week and, uh, they had human eval and then that, uh, I'm Jon Eval, which is like vibe based.  Jonathan: And I, I do think the vibe based eval cannot be, you know, underrated really at the, I mean, at the end of the day we, we did stop our models and do vibe checks and we did, as we monitor our models, one of our evals was we just had a bunch of prompts and we would watch the answers as the model trained and see if they changed cuz honestly, You know, I don't really believe in any of these eval metrics to capture what we care about. Mm-hmm. But when you ask it, uh, you know, I don't know. I think one of our prompts was to suggest games for a three-year-old and a seven-year-old. That would be fun to play. Like that was a lot more [00:34:30] valuable to me personally, to see how that answer evolved and changed over the course of training. So, you know, and human eval, just to clarify for folks, human human eval is an automated evaluation metric. There's no humans in it at all. There's no humans in it at all. It's really badly named. I got so confused the first time that someone brought that to me and I was like, no, we're not bringing humans in. It's like, no, it's, it's automated. They just called it a bad name and there's only a hundred cents on it or something. Abhinav: Yeah. Yeah. And, and it's for code specifically, right? Jonathan: Yeah. Yeah. It's very weird. It's a, it's a weird, confusing name that I hate, but you know, when other metrics are called hella swag, like, you know, you do it, just gotta roll with it at this point.  Swyx: You're doing live evals now. So one, one of the tweets that I saw from you was that it is, uh, important that you do it paralyzed. Uh, maybe you kind of wanna explain, uh, what, what you guys did. Abhinav: Yeah, for sure. So with LLM Foundry, there's many pieces to it. There's obviously the core training piece, but there's also, you know, tools for evaluation of models. And we've kind of had one of the, I think it's like the, the fastest like evaluation framework. Um, basically it's multi GPU compatible. It runs with Composer, it can support really, really big models. So basically our framework runs so fast that even Azure models are training. We can run these metrics live during the training. So like if you have a dashboard like weights and biases, you kind of watch all these evil metrics. We have, like, 15 or 20 of them honestly, that we track during the run and add negligible overhead. So we can actually watch as our models go and feel confident. Like, it's not like we wait until the very last day to, to test if the models good or not Jonathan: That's amazing. Yeah. I love that we've gotten this far into the conversation. We still haven't talked about efficiency and speed. Those are usually our two watch words at Mosaic, which is, you know, that's great. That says that we're [00:36:00] doing a lot of other cool stuff, but at the end of the day, um, you know, Cost comes first. If you can't afford it, it doesn't matter. And so, you know, getting things down cheap enough that, you know, we can monitor in real time, getting things down cheap enough that we can even do it in the first place. That's the basis for everything we do. OPEN SCIENCE FOR AFFORDABLE AI RESEARCH [00:36:00] Alessio: Do you think a lot of the questions that we have around, you know, what data sets we should use and things like that are just because training was so expensive before that, we just haven't run enough experiments to figure that out. And is that one of your goals is trying to make it cheaper so that we can actually get the answers? Jonathan: Yeah, that's a big part of my personal conviction for being here. I think I'm, I'm still in my heart, the second year grad student who was jealous of all his friends who had GPUs and he didn't, and I couldn't train any models except in my laptop. And that, I mean, the lottery ticket experiments began on my laptop that I had to beg for one K 80 so that I could run amist. And I'm still that person deep down in my heart. And I'm a believer that, you know, if we wanna do science and really understand these systems and understand how to make them work well, understand how they behave, understand what makes them safe and reliable. We need to make it cheap enough that we can actually do science, and science involves running dozens of experiments. When I finally, you know, cleaned out my g c s bucket from my PhD, I deleted a million model checkpoints. I'm not kidding. There were over a million model checkpoints. That is the kind of science we need, you know, that's just what it takes. In the same way that if you're in a biology lab, you don't just grow one cell and say like, eh, the drug seems to work on that cell. Like, there's a lot more science you have to do before you really know. Abhinav: Yeah. And I think one of the special things about Mosaic's kind of [00:37:30] position as well is that we have such, so many customers all trying to train models that basically we have the incentive to like to devote all these resources and time to do this science. Because when we learn which pieces actually work, which ones don't, we get to help many, many people, right? And so that kind of aggregation process I think is really important for us. I remember way back there was a paper about Google that basically would investigate batch sizes or something like that. And it was this paper that must have cost a few million dollars during all the experience. And it was just like, wow, what a, what a benefit to the whole community. Now, like now we all get to learn from that and we get, we get to save. We don't have to spend those millions of dollars anymore. So I think, um, kind of mosaical science, like the insights we get on, on data, on pre-screening architecture, on all these different things, um, that's why customers come to us. Swyx: Yeah, you guys did some really good stuff on PubMed, G B T as well. That's the first time I heard of you. Of you. And that's also published to the community. Abhinav: Yeah, that one was really fun. We were like, well, no one's really trained, like fully from scratch domain specific models before. Like, what if we just did a biomed one? Would it still work? And, uh, yeah, I'd be really excited. That did, um, we'll probably have some follow up soon, I think, later this summer. Jonathan: Yeah. Yes. Stay tuned on that. Um, but I, I will say just in general, it's a really important value for us to be open in some sense. We have no incentive not to be open. You know, we make our money off of helping people train better. There's no cost to us in sharing what we learn with the community. Cuz really at the end of the day, we make our money off of those custom models and great infrastructure and, and putting all the pieces together. That's honestly where the Mosaic name came from. Not off of like, oh, we've got, you know, this one cool secret trick [00:39:00] that we won't tell you, or, you know, closing up. I sometimes, you know, in the past couple weeks I've talked to my friends at places like Brain or, you know, what used to be Brain Now Google DeepMind. Oh, I R I P Brain. Yeah. R i p Brian. I spent a lot of time there and it was really a formative time for me. Um, so I miss it, but. You know, I kind of feel like we're one of the biggest open research labs left in industry, which is a very sad state of affairs because we're not very big. Um, but at least can you say how big the team is actually? Yeah. We were about 15 researchers, so we're, we're tiny compared to, you know, the huge army of researchers I remember at Brain or at fair, at Deep Mind back, you know, when I was there during their heydays. Um, you know, but everybody else is kind of, you know, closed up and isn't saying very much anymore. Yeah. And we're gonna keep talking and we're gonna keep sharing and, you know, we will try to be that vanguard to the best of our ability. We're very small and I, I can't promise we're gonna do what those labs used to do in terms of scale or quantity of research, but we will share what we learn and we will try to create resources for the community. Um, I, I dunno, I just, I believe in openness fundamentally. I'm an academic at heart and it's sad to me to watch that go away from a lot of the big labs.  THE OPEN APPROACH [00:40:15] Alessio: We just had a live pod about the, you know, open AI snow mode, uh, post that came out and it was one of the first time I really dove into Laura and some of the this new technologies, like how are you thinking about what it's gonna take for like the open approach to really work? Obviously today, GPT four is still, you know, part of like that state-of-the-art model for a [00:40:30] lot of tasks. Do you think some of the innovation and kind of returning methods that we have today are enough if enough people like you guys are like running these, these research groups that are open? Or do you think we still need a step function improvement there? Jonathan: I think one important point here is the idea of coexistence. I think when you look at, I don't know who won Linux or Windows, the answer is yes. Microsoft bought GitHub and has a Windows subsystem for Linux. Linux runs a huge number of our servers and Microsoft is still a wildly profitable company. Probably the most successful tech company right now. So who won open source or closed source? Yes. Um, and I think that's a similar world that we're gonna be in here where, you know, it's gonna be different things for different purposes. I would not run Linux on my laptop personally cuz I like connecting to wifi and printing things. But I wouldn't run Windows on one of my surfers. And so I do think what we're seeing with a lot of our customers is, do they choose opening IR mosaic? Yes. There's a purpose for each of these. You have to send your data off to somebody else with open eyes models. That's a risk. GPT four is amazing and I would never promise someone that if they come to Mosaic, they're gonna get a GPT four quality model. That's way beyond our means and not what we're trying to do anyway. But there's also a whole world for, you know, domain specific models, context specific models that are really specialized, proprietary, trained on your own data that can do things that you could never do with one of these big models. You can customize in crazy ways like G B T four is not gonna hit 65 K context length for a very long time, cuz they've already trained that [00:42:00] model and you know, they haven't even released the 32 K version yet. So we can, you know, we can do things differently, you know, by being flexible. So I think the answer to all this is yes. But we can't see the open source ecosystem disappear. And that's the scariest thing for me. I hear a lot of talk in academia about, you know, whatever happened to that academic research on this field called information retrieval? Well, in 1999 it disappeared. Why? Because Google came along and who cares about information retrieval research when you know you have a Google Scale, you know, Web Scale database. So you know, there's a balance here. We need to have both.  Swyx: I wanna applaud you, Elaine. We'll maybe edit it a little like crowd applause, uh, line. Cuz I, I think that, um, that is something that as a research community, as people interested in progress, we need to see these things instead of just, uh, seeing marketing papers from the advertising GPT 4. Jonathan: Yeah. I, I think I, you know, to get on my soapbox for 10 more seconds. Go ahead. When I talk to policymakers about, you know, the AI ecosystem, the usual fear that I bring up is, Innovation will slow because of lack of openness. I've been complaining about this for years and it's finally happened. Hmm. Why is Google sharing, you know, these papers? Why is Open AI sharing these papers? There are a lot of reasons. You know, I have my own beliefs, but it's not something we should take for granted that everybody's sharing the work that they do and it turns out well, I think we took it for granted for a while and now it's gone. I think it's gonna slow down the pace of progress. In a lot of cases, each of these labs has a bit of a monoculture and being able to pass ideas [00:43:30] back and forth was a lot of what kept, you know, scientific progress moving. So it's imperative not just, you know, for the open source community and for academia, but for the progress of technology. That we have a vibrant open source research community. THE FUTURE OF MOSAIC [00:44:11] Swyx: There’s a preview of the ecosystem and commentary that we're, we're gonna do. But I wanna close out some stuff on Mosaic. You launched a bunch of stuff this month. A lot of stuff, uh, actually was, I was listening to you on Gradient descent, uh, and other podcasts we know and love. Uh, and you said you also said you were not gonna do inference and, and, and last week you were like, here's Mosaic ML inference. Oops. So maybe just a, at a high level, what was Mosaic ml and like, what is it growing into? Like how do you conceptualize this?  Jonathan: Yeah, and I will say gradient, when graded dissent was recorded, we weren't doing inference and had no plans to do it. It took a little while for the podcast to get out. Um, in the meantime, basically, you know, one thing I've learned at a startup, and I'm sure abhi can comment on this as well, focus is the most important thing. We have done our best work when we've been focused on doing one thing really well and our worst work when we've tried to do lots of things. Yeah. So, We don't want to do inference, we don't want to have had to do inference. Um, and at the end of the day, our customers were begging us to do it because they wanted a good way to serve the models and they liked our ecosystem. And so in some sense, we got dragged into it kicking and screaming. We're very excited to have a product. We're going to put our best foot forward and make something really truly amazing. But there is, you know, that's something that we were reluctant to do. You know, our customers convinced us it would be good for our business. It's been wonderful for business and we are gonna put everything into this, but you know, back when grading dissent came out, I [00:45:00] was thinking like, or when we recorded it or focused, oh God, like focus is the most important thing. I've learned that the hard way multiple times that Mosaic, abhi can tell you like, you know, I've made a lot of mistakes on not focusing enough. Um, boy inference, that's a whole second thing, and a whole different animal from training. And at the end of the day, when we founded the company, our belief was that inference was relatively well served at that time. There were a lot of great inference companies out there. Um, training was not well served, especially efficient training. And we had something to add there. I think we've discovered that as the nature of the models have changed, the nature of what we had to add to inference changed a lot and there became an opportunity for us to contribute something. But that was not the plan. But now we do wanna be the place that people come when they wanna train these big, complex, difficult models and know that it's gonna go right the first time and they're gonna have something they can servee right away. Um, you know, really the rep example of, you know, with 10 days to go saying, Hey, can you please train that model? And, you know, three or four days later the model was trained and we were just having fun doing interesting, fine tuning work in it for the rest of the 10 days, you know. That also requires good inference.  Swyx: That’s true, that's true. Like, so running evals and, and fine tuning. I'm just putting my business hat on and you know, and Alessio as well, like, uh, I've actually had fights with potential co-founders about this on the primary business. Almost like being training, right? Like essentially a one-time cost. Jonathan: Who told you it was a one time cost? What, who, who told you that? Swyx: No, no, no, no. Correct me.  Jonathan: Yeah. Yeah. Let me correct you in two ways. Um, as our CEO Navine would say, if he were here, when you create version 1.0 of your software, do you then fire all the engineers? Of [00:46:30] course not. You never, like, MPT has a thousand different things we wanted to do that we never got to. So, you know, there will be future models. Abhinav: And, and the data that's been trained on is also changing over time too, right? If you wanna ask anything about, I guess like May of 2023, we'll have to retrain it further and so on. Right? And I think this is especially true for customers who run like the kind of things that need to be up to date on world knowledge. So I, I think like, you know, the other thing I would say too is that, The malls we have today are certainly not the best malls we'll ever produce. Right. They're gonna get smaller, they're gonna get faster, they're gonna get cheaper, they're gonna get lower latency, they're gonna get higher quality. Right? And so you always want the next gen version of MPT and the one after that and one after that. There's a reason that even the GPT series goes three, four, and we know there's gonna be a five. Right? Um, so I I I also don't see as a, as a one-time cost. Jonathan: Yeah. Yeah. And I, if you wanna cite a stat on this, there are very, very few stats floating around on training versus inference cost. Mm-hmm. One is this blog post from I think David Patterson at Google, um, on the energy usage of ML at Google. And they break down and say three fifths of energy over the previous three years. I think this 2022 article was for inference, and two fifths were for training. And so actually that, you know, this is Google, which is serving models to billions of users. They're probably the most inference heavy place in the world. It's only a two fifth, three fifth breakdown, and that's energy training. Hardware is probably more expensive because it has fancier networking. That could be a 50 50 cost breakdown. And that's Google for a lot of other folks. It's gonna be weighed even more heavily, in favor of training. SPEED AND EFFICIENCY [00:48:01] Swyx: Amazing answer. Well, thanks. Uh, we can, we can touch on a little bit [00:48:00] on, uh, efficiency and speed because we, we, uh, didn't mention about that. So right now people spend between three to 10 days. You, you spend 10 days on, on mpc, seven rep spend three days. What's feasible? What's what Do you wanna get it down to? Abhinav: Oh, for, for these original models? Yeah. Yeah. So I think, um, this is probably one of the most exciting years, I think for training efficiency, just generally speaking, because we have the, the combination of a couple things, like one is like this next generation of hardware, like the H 100 s coming out from Nvidia, which on their own should be like, at least like a two x improvement or they 100 s on top of that, there's also a new floating point format f P eight, um, which could also deliver that alone. Does it? Yes. Yeah. Yeah. How, what, why? Oh, the f p thing? Yeah. Yeah. So basically what's happening is that, you know, when we do all of our math, like in the models matrix, multiplication, math, we do it in a particular precision. We started off in 32 bit precision a few years ago, and then in video came with 16 bit, and over the course of several years, we've all figured out how to do 16 bit training and that basically, you know, due to the harder requirements like. Increase the throughput by two x, reduce the cost by two x. That's about to happen again with FBA eight, like starting this year. And with Mosaic, you know, we've already started profiling L L M training with f p eight on H 100 s. We're seeing really, really good improvements there. And so you're gonna see a huge cost reduction this year just from this hardware fact alone. On top of that, you know, there's a lot of architectural applications. We're looking at ways to introduce some forms of sparsity, not necessarily like the, the, the super unstructured sparsity like lottery ticket. Um, which not that I'm sure I'm really happy to talk about. Um, but, but, um, are there ways of doing, like you [00:49:30] gating or like, kind of like m moe style architectures? So, you know, I think originally, you know, what was like 500 k. I think to try and train a Jeep, the equality model, if at the end of the year we could get that down to a hundred k, that would be fantastic. Swyx: That is this year's type of thing.  Jonathan: Not, not, like, that's not a pie in the sky thing. Okay. It is not, it's not a place we are now, but I think it is a, you know, I don't think more than a year in the future these days, cuz it's impossible. I think that is very much a 2023 thing. Yeah. Yeah. Okay. And hold me to that later this year. Swyx: G PT three for a hundred K, let's go. Um, and then also stable diffusion originally reported to be 600 K. Uh, you guys can get it done for under 50. Anything different about image models that we should image, to text? Jonathan: Um, I mean I think the, the most important part in all this is, you know, it took us a while to get 50 down by almost seven x. That was our original kind of proof of concept project for Mosaic. You know, just at the beginning to show like, you know, we can even do this and our investors should give us more money. But what I love about newer models that come out is they're always really slow. We haven't figured out how to optimize them yet. And so there's so much work to be done. So getting, you know, in that case, I guess from the cost you mentioned like a 12 x cost reduction in stable diffusion. Mm-hmm. Honestly it was a lot easier than getting a seven X for RESNET 50 an image net or a three X for Burt, cuz the architecture was much newer and there were a lot of inefficiencies to improve. Um, you know, I'm guessing that's gonna continue to be the case as we lean toward the bleeding edge and try to, you know, push the bleeding edge. I hope that, you know, in some sense you'll see smaller speed ups from us because the new models will come from us and they'll already be fast. Alessio: So that's making existing [00:51:00] things better with the, the long boy, the 60 5K context window, uh, you've doubled instead of the r. There was the R M T a couple weeks ago that had a possible 1 million. Uh, that's the unlimited former thing that came out last week, which is theoretically limitless context. What should people think about trade offs? Implications? You mentioned memories kind of start to become one of the bounds. Yeah. What's the right number? Like is it based on the customer's needs? Like how would you advise customers and startups who might be building their own models? Jonathan: It's all contextual. You know, there's a lot of buzz coming for long contexts lately with a lot of these papers. None of them are exact. In terms of the way that they're doing attention. And so there's, you know, to some extent there's an approximation or a trade off between doing some kind of inexact or approximate or hierarchical or, you know, non quadratic attention versus doing it explicitly correctly the quadratic way. I'm a big fan of approximation, so I'm eager to dig into these papers. If I've learned one thing from writing and reading papers, it's to believe nothing until I've implemented it myself. And we've certainly been let down many, many, many times at Mosaic by papers that look very promising until we implement them and realize, you know, here's how they cook the books on their data. Here's, you know, the one big caveat that didn't show up in the paper. So I look at a lot of this with skepticism until, you know, I believe nothing until I re-implement it. And in general, I'm rewarded for doing that because, you know, a lot of this stuff doesn't end up working quite as well in practice. This is promised in a paper, the [00:52:30] incentives just aren't there, which is part of the reason we went with just pure quadratic attention here. Like it's known to work. We didn't have to make an approximation. There's no asterisk or caveat. This was in some sense a sheer force of will by our amazing engineers. Alessio: So people want super long context because, you know, they wanna feed more documents and right now people do it with embeddings and feed them into the context window. How do you kind of see that changing? Are we gonna get to a point where like, you know, maybe it's 60 4k, maybe it's 120 k, where it's like, okay. You know, semantic search and embeddings are gonna work better than just running a million parameters, like a million token context window. Jonathan: Do, do you wanna say the famous thing about 64 K? Does somebody wanna say that, that statement, the, you know, the 64 K is all you'll ever need? The Bill Gates statement about Rams. Swyx: Andre Kaparthi actually made that comparison before that, uh, context is essentially Ram, Jonathan: if I get quoted here saying 60 4K is all you need, I will be wrong. We have no idea. People are gonna get ambitious. Yes. Um, GPT four has probably taken an image and turning it into a bunch of tokens and plugging it in. I'm guessing each image is worth a hell of a lot of tokens. Um, maybe that's not a thousand words. Not a thousand words, but, you know, probably a thousand words worth of tokens, if not even more so. Maybe that's the reason they did 32 k. Maybe, you know, who knows? Maybe we'll wanna put videos in these models. Like every time that we say, ah, that isn't that model big enough, somebody just gets more ambitious. Who knows?  TRENDS AND TRANSFORMERS [00:54:00] Swyx: Right? Um, you've famously made one. [00:54:00] Countertrend, uh, bet, which is, uh, you, you're actually betting that, uh, transformers will stick around for a long time.  Jonathan: How is that counter trend?  Swyx: Counter trend is in, you just said, a lot of things won't last. Right. A lot of things will get replaced, uh, really easily, but Jonathan: transformers will stick around. I mean, look at the history here. How long did the Convolutional neural network stick around for? Oh wait. They're still here and vision Transformers still haven't replaced them. Mm-hmm. How long did r and n stick around for? Decades. And, you know, they're still alive and kicking in a bunch of different places, so, you know. The fundamental architecture improvements are really hard to come by. I can't wait to collect from Sasha on that bet. Abhinav: I, I think a lot of your bet hinges on what counts as attention, right. Swyx: Wait, what do you mean? Well, how, how can that change? Oh, because it'll be approximated. Abhinav: Well, I suppose if, if we ever replace like the Qk multiplication, something that looks sort of like it, I, I wonder who, who, who comes out on top here. Jonathan: Yeah. I mean at the end of the day is a feed forward network, you know, that's fully connected, just a transformer with very simple attention. Mm-hmm. Um, so Sasha better be very generous to me cause it's possible that could change, but at the end of the day, we're still doing Transformers the way, you know, Vaswani had all intended back six years ago now, so, I don't know, things. Six years is a pretty long time. What's another four years at this point? Alessio: Yeah. What do you think will replace it if you lose Ben? What do you think? You would've lost  it time? Jonathan: If I knew that I'd be working on it. Abhinav:  I think it's gonna be just like MLPs, you know, that's the only, that's the only way we can go, I think at this point, because Thelp, I, I dunno. Oh, just basically down to, to um, to linear layers.[00:55:30] Oh, mostly the percepts. Exactly. Got, yeah. Yeah. Yeah. Cuz the architecture's been stripped, simplified so much at this point. I think, uh, there's very little left other than like some linear layers, some like residual connections and, and of course the attention, um, dot product. Jonathan: But you're assuming things will get simpler, maybe things will get more complicated. Swyx: Yeah, there's some buzz about like, the hippo models. Hungry, hungry hippos. Jonathan: I, I mean there's always buzz about something, um, you know, that's not to dismiss this work or any other work, but there's always buzz about something. I tend to wait a little bit to see if things stand the test of time for like two weeks. Um, at this point, it used to be, you know, a year, but now it's down to two weeks. Oh. But you know, I'm. I don't know. I don't like to follow the hype. I like to see what sticks around, what people actually manage to build off of.  Swyx: I have a follow up question actually on that. Uh, what's a, what's an egregiously overrated paper that once you actually looked into it fell apart completely? Jonathan: I'm not going down that path. Okay. I, you know, I even, even though I think there are papers that, you know, did not hold up under scrutiny, I don't think any of this was out of malice. And so I don't wanna go down that path.  Alessio: Yeah. I know you already talked about your focus on open research. Are you mostly gonna focus on open models or are there also, are you working on configurations that are more just for your customers and private, like, what percentage of your time are you focusing on, on open work? Jonathan: It's a little fuzzy. I mean, I think at the end of the day you have to ask what is the point of our business? Our business is not just to train a bunch of open models and give them to the world. That would, our VCs probably wouldn't be very happy if that were the case. The open [00:57:00] models serve our business because they're demos. A demo does not mean we give away everything. Um, a demo does not mean every single thing we do is shared with the world, but. We do have a business imperative to share with the world, which I kind of like. That was part of the design of the company, was making sure we had an imperative to do science and an imperative to share. But we are still a company and we do have to make money, but it would be a disaster for our business if we didn't share. And that's by design from the start. So, you know, there's certainly going to be some work that we do that is for our customers only, but by and large for anything that we wanna advertise to customers, there has to be something that is meaningful and useful that's out there in the world. Otherwise we can't convince people that we have it.  Abhinav: Yeah, I think like this, our recent inference product also makes the decision easier for us, right? So even since these open malls like we've developed so far, um, you can actually like, you know, uh, query them on our inference api, like our starter tier, and we basically charge like a, a per token fee. Very, very similar to the other API fighters. So there are pathways by which, you know, like even the open mall we provide for free still end up like helping our business out, right? You can customize them, deploy them on our, on our platform, and that way we, we still make money off of them. Alessio: Do you wanna jump into the landing ground? Anything else that you guys wanna cover that we didn't get to? Jonathan: This has been great. These are great questions.  Swyx: Do you want to dish on why Sparsity is not a focus for Mosaic? Jonathan: Um, I can just say that, you know, sparsity is not a focus for Mosaic and I am definitely over lottery tickets when I give my mosaic talk. The first slide is a, you know, a circle with a slash through it over a lottery ticket. [00:58:30] Um, and anyone who mentions lottery tickets, I ask to leave the room. Um, cuz you know there's other work out there. But Abhi, please feel free to dish on sparsity. Abhinav: Yeah, I, I think it really comes down to the fact that we don't have hardware yet that can accelerate it. Right? Or at least it's been mostly true for a long period of time. So the kinds of sparsity that the lottery check was working on was like if you put random zeros in the, in the weights, you know, and basically we found basically the fast year is that yes, you can turn most of the weights to zeros and the model still does kind of work, but there's no hardware out there that can take a matrix with a bunch of zeros and one without and make it go fast. Now, the one caveat for this, and this is gonna sound like a bit of advertisement, is, is Cereus actually, and they've been, since the beginning, they've built that architecture for Sparsity and they've actually published some research papers just earlier this year showing that yes, they really can train with Sparsity and get, this is, uh, sparse. U P T. Exactly. Yeah, exactly right. So, the final missing piece is really like, okay, we have the science to show you can train with sparse models, you know, from initialization even, or, or close initialization. Um, the last piece is just, is there a piece of hardware that actually speeds it up and gives you a cost savings? In which case, like the, the field is wide open.  Jonathan: The other big challenge here is that if you want to make sparsity go fast in general right now on standard hardware, you do need it to be structured in various ways. And any incremental amount of structure that you force on the sparsity dramatically reduces the quality of the resulting model that you get up to the point where if you remove just, you know, entire neurons from the model, you're just making the layers smaller and that really hurts the quality of the model. So these models, steel is all you need. These models love unstructured [01:00:00] sparsity. Um, and yeah, if there were a chip and a software package that made it really, really easy to accelerate it, I bet we would be doing it at Mosaic right now.  Alessio: This is like Sarah Hooker's point with the hardware lottery post, talking about lotteries. Absolutely. Where you know, if you don't have the right hardware, some models, architectures just can't emerge quickly enough. Abhinav: This there, there's like an invariance to think of, which is that today's popular models always run fast on today's hardware. Like this, this has to be true. Mm-hmm. Right? Like there's no such thing as a popular model that runs slow cuz no one would've developed it. Yeah. Um, so it's kind of like with the new architectures, right? If there's new hardware that can do sparsity, you have to co-evolve like a new architecture that works with it. And then those two pair together really well. Transformers and GPUs are like a match made in heaven.  Jonathan: How would say transformers and GPUs are a match made in heaven. Yeah. And we're lucky that they work on GPUs, but the folks at Google D designed them for TPUs cuz TPUs and R and Ns were not a match made in heaven. LIGHTNING ROUND AND CLOSING [1:00:55] Alessio: All right, we have three questions. One is on acceleration, one on exploration, and then just a takeaway for the audience. And you can, you know, either of you can start and the other can finish. So the first one is, what has already happened in AI That thought would take much longer than it has? Abhinav: Do you have an answer, Jon?  Jonathan: Yeah, I have answer everything. Um, you know, I, I remember when GPT two came out and I looked at that and went, eh, you know, that doesn't seem very exciting. And gosh, it's already 1.5 billion parameters. You know, they can't possibly keep getting better as they make it bigger. And then GPT three came out and I was like, eh, it's slightly better at [01:01:30] generating text. Yeah, who cares? And you know, I've been wrong again and again and again. That. Next token prediction, making things big can produce useful models. To be fair, pretty much all of us were wrong about that. So I can't take that precisely on myself. Otherwise, Google, Facebook and Microsoft Research would all have had killer large language models way before opening I ever got the chance to do it. Um, opening I made a very strange bet and it happened to work out very well. But yeah, diffusion models, like they're pretty stupid at the end of the day and they produce beautiful images, it’s astounding. Abhinav: Yeah, I think my, my answer is gonna be like the, the chatbots at scale, like idea, like basically I thought it would be quite a while before, you know, like hundreds of millions of people will be talking to AI models for a large portion of the data, but now there's many startups and companies not, not just open with chat pt, but, but you know, like character and others where, um, it, it's really astounding, like how many people are actually developing like emotional connections to these, to these AI models. And I don't think I was. Would've predicted that like September, October of last year. But you know, the inflection point of the last six months has been really surprising. Swyx: I haven't actually tried any of these models, but I, I don't know. It seems like a very educational thing. It's like, oh, talk to Genius can, but like that's a very educational use case. Right? Right. Like what, what do you think they're using for, I guess, emotional support? Abhinav: Well, yes. I mean, I think some of them are sort of like, yeah, like either for emotional support or honestly just friends and stuff. Right. I mean, I think like, you know, loneliness mental health is a really a big problem everywhere. And so the most interesting I think I've found is that if you go to the subreddits, you know, for those communities and you see like how they [01:03:00] talk about and think about their like AI friends and like these characters, it's, it's, it's like out of a science fiction book, like I would never expect this to be like reality. Swyx: Yeah. What do you think are the most interesting unsolved questions in ai? Abhinav: I'm really interested in seeing how far down we can go in terms of precision and, and stuff like that. Particularly similar to the BF16 FP thing.  Swyx: Okay. Um, there's also like just quantizing until like it's two bits. Abhinav: Yeah, exactly. Like, or even like down to analog or something like that. Because our brains obviously are not running on digital logic and stuff and so, you know, how many orders of magnitude do we have remaining in kind of like just these um, things and I wonder if some of these problems just get easier with scale. Like there have been sort of hints in some papers that, you know, it becomes easier to quantize or easier to prune as it gets bigger and bigger. So maybe as we, almost as a natural consequence of a scaling up over the next few years, will we just naturally become easier and easier to just start going to like four bits or two that are even binary leg weights. Jonathan: I want to know how small we can go in a different way. I just want to know how efficient we can make it to get models that are this good. That was my research question for my entire PhD lottery tickets were one way to get at that. That's now kind of the research question I'm chasing at Mosaic in a sense. I, you know, open ai has shown us that there is one path to getting these incredible capabilities that is scale. I hope that's not the only path. I hope there are lots of ways of getting there. There's better modeling, there are better algorithms. I hate the neuroscience metaphors, but in some sense, our existence and our brains are, you know, evidence that there is at least one other way to get to these kinds of incredible capabilities that doesn't require, you know, [01:04:30] a trillion parameters and megawatts and megawatts and gazillions of dollars. So, you know, I do wonder how small we can go? Is there another path to get to these capabilities without having to do it this way? If it's there, I hope we find it at Mosaic. Swyx: Yeah my, my favorite fact is something on the order of the human brain runs on 30 watts of energy, and so we are, we're doing like dozens of orders of magnitude off on that one. Abhinav: I, I don't think you can get like one gpu, one different. Yeah. Alessio: If there’s one message you want everyone. To remember when thinking about this thing. There's a lot of, you know, fear mongering. There's a lot of messaging being spread around, like, what should people think about in ai? What should be top of mind for them? Jonathan: I'll go for it. Which is, you know, stay balanced. They're the people who really feed into the hype or who, you know, eat up the hype. They're the people who are, you know, big pessimists or react very strongly against the hype, or to some extent are in denial. Stay balanced, embrace the fact that we've built extraordinarily useful tools. Um, but we haven't built a g I and you know, personally, I don't think we're anywhere close to that. You know, so stay balanced and follow the science. I think that's really, that's what we try to do around Mosaic. We try to focus on what's useful to people, what will, you know, hopefully make the world a better place. We try our best on that, but especially, you know, how we can follow the science and use data to be our guide, not just, you know, talk a lot, you know, try to talk through our work instead. Abhinav: And I would also say just kinda like research done in the open. I think like, you know, there's no computing with the, the open community, [01:06:00] right? Just in volume, the number of like, kind of eyeballs you basically have, like looking at your models at the, even at the problems with the models, at ways we improve them. Um, I just think, you know, yeah, research done in the open. It will, it will be the way forward, both to keep our models safe and to bely, like examine the consequences of these AI models like in the world. Alessio: Awesome. Thank you so much guys for coming on. Swyx: and thanks for keeping AI open.  Abhinav: Thank you for having us.  Jonathan: Yeah. Thank you so much for having us. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

Jonathan Frankle

Chief AI Scientist, Databricks

DatabricksMosaicMLMassachusetts Institute of TechnologyMIT

Abhinav Venigalla

NLP Architect

MosaicML

Jonathan Frankle, Chief Scientist at MosaicML and Assistant Professor of Computer Science at Harvard University, joins us on this episode. With comprehensive infrastructure and software tools, MosaicML aims to help businesses train complex machine-learning models using their own proprietary data. We discuss: - Details of Jonathan’s Ph.D. dissertation which explores his “Lottery Ticket Hypothesis.” - The role of neural network pruning and how it impacts the performance of ML models. - Why transformers will be the go-to way to train NLP models for the foreseeable future. - Why the process of speeding up neural net learning is both scientific and artisanal. - What MosaicML does, and how it approaches working with clients. - The challenges for developing AGI. - Details around ML training policy and ethics. - Why data brings the magic to customized ML models. - The many use cases for companies looking to build customized AI models. Jonathan Frankle - https://www.linkedin.com/in/jfrankle/ Resources: - https://mosaicml.com/ - The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation. #OCR #DeepLearning #AI #Modeling #ML

Jonathan Frankle

Chief AI Scientist, Databricks

DatabricksMosaicMLMassachusetts Institute of TechnologyMIT

In this episode of Machine Learning Street Talk, we chat with Jonathan Frankle, author of The Lottery Ticket Hypothesis. Frankle has continued researching Sparse Neural Networks, Pruning, and Lottery Tickets leading to some really exciting follow-on papers! This chat discusses some of these papers such as Linear Mode Connectivity, Comparing and Rewinding and Fine-tuning in Neural Network Pruning, and more (full list of papers linked below). We also chat about how Jonathan got into Deep Learning research, his Information Diet, and work on developing Technology Policy for Artificial Intelligence! This was a really fun chat, I hope you enjoy listening to it and learn something from it! Thanks for watching and please subscribe! Huge thanks to everyone on r/MachineLearning who asked questions! Paper Links discussed in the chat: The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks: https://arxiv.org/abs/1803.03635 Linear Mode Connectivity and the Lottery Ticket Hypothesis: https://arxiv.org/abs/1912.05671 Dissecting Pruned Neural Networks: https://arxiv.org/abs/1907.00262 Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs: https://arxiv.org/abs/2003.00152 What is the State of Neural Network Pruning? https://arxiv.org/abs/2003.03033 The Early Phase of Neural Network Training: https://arxiv.org/abs/2002.10365 Comparing Rewinding and Fine-tuning in Neural Network Pruning: https://arxiv.org/abs/2003.02389 (Also Mentioned) Block-Sparse GPU Kernels: https://openai.com/blog/block-sparse-gpu-kernels/ Balanced Sparsity for Efficient DNN Inference on GPU: https://arxiv.org/pdf/1811.00206.pdf Playing the Lottery with Rewards and Multiple Languages: Lottery Tickets in RL and NLP: https://arxiv.org/pdf/1906.02768.pdf r/MachineLearning question list: https://www.reddit.com/r/MachineLearning/comments/g9jqe0/d_lottery_ticket_hypothesis_ask_the_author_a/ (edited) #machinelearning #deeplearning

Jonathan Frankle

Chief AI Scientist, Databricks

DatabricksMosaicMLMassachusetts Institute of TechnologyMIT

We spoke with Jonathan Frankle, a PhD student at MIT and coauthor of a seminal paper outlining a technique, known as the “The Lottery Ticket,” hypothesis that promises to help advance our understanding of why neural networks, and deep learning, works so well.

Jonathan Frankle

Chief AI Scientist, Databricks

DatabricksMosaicMLMassachusetts Institute of TechnologyMIT