5 ms·

The new skill is programming, same as the old skill. To the extent these things are comprehensible, you understand them by writing programs: programs that train

The new skill is programming, same as the old skill. To the extent these things are comprehensible, you understand them by writing programs: programs that train them, programs that run inferenve, programs that analyze their behavior. You get the most out of LLMs by knowing how they work in detail. I had one view of what these things were and how they work, and a bunch of outcomes attached to that. And then I spent a bunch of time training language models in various ways and doing other related upstream and downstream work, and I had a different set of beliefs and outcomes attached to it. The second set of outcomes is much preferable. I know people really want there to be some different answer, but it remains the case that mastering a programming tool involves implemtenting such, to one degree or another. I've only done medium sophistication ML programming, and my understand is therefore kinda medium, but like compilers, even doing a medium one is the difference between getting good results from a high complexity one and guessing. Go train an LLM! How do you think Karpathy figured it out? The answer is on his blog!

pyman 1y ago
Saying the best way to understand LLMs is by building one is like saying the best way to understand compilers is by writing one. Technically true, but most people aren't interested in going that deep.
- hackable_sand 1y ago
  It's not that deep
- benreesman 1y ago
  I don't know, I've heard that meme too but it doesn't track with the number of cool compiler projects on GitHub or that frontpage HN, and while the LLM thing is a lot newer, you see a ton of useful/interesting stuff at the "an individual could do this on their weekends and it would mean they fundamentally know how all the pieces fit together" type stuff. There will always be a crowd that wants the "master XYZ in 72 hours with this ONE NEAT TRICK" course, and there will always be a..., uh, group of people serving that market need. But most people? Especially in a place like HN? I think most people know that getting buff involves going to the gym, especially in a place like this. I have a pretty high opinion of the typical person. We're all tempted by the "most people are stupid" meme, but that's because bad interactions are memorable, not because most people are stupid or lazy or whatever. Most people are very smart if they apply themselves, and most people will work very hard if the reward for doing so is reasonably clear. https://www.youtube.com/shorts/IQmOGlbdn8g https://www.youtube.com/shorts/IQmOGlbdn8g
- wickedsight 1y ago
  The best way to understand a car is to build a car. Hardly anyone is going to do that, but we still all use them quite well in our daily lives. In large part because the companies who build them spend time and effort to improve them and take away friction and complexity. If you want to be an F1 driver it's probably useful to understand almost every part of a car. If you're a delivery driver, it probably isn't, even if you use one 40+ hours a week.
  - benreesman 1y ago
    Your example / analogy is useful in the sense that its usually useful to establish the thought experiment with the boundary conditions. But in between someone commuting in a Toyota and an F1 driver are many, many people, the best example from inside the extremes is probably a car mechanic, and even there, there's the oil change place with the flat fee painted in the window, and the Koenigsberg dealership that orders the part from Europe. The guy who tunes those up can afford one himself. In the use case segment where just about anyone can do it with a few hours training, yeah, maybe that investment is zero instead of a week now. But I'm much more interested in the one where F1 cars break the sound barrier now.
  - eclecticfrank 1y ago
    It might make sense to split the car analogy into different users: 1. For the majority of regular users the best way to understand the car is to read the manual and use the car. 2. For F1 drivers the best way to understand the car is to consult with engineers and use the car. 3. For a mechanic / engineer the best way to understand the car is to build and use the car.
  - Davidzheng 1y ago
    yes except intelligence isn't like a car, there's no way to break the complicated emergent behaviors of these models into simple abstractions. you can understand a LLM by training one the same amount you can understand a brain by dissection.
    - LtWorf 1y ago
      I think making one would help you understand that they're not intelligent.
- deleted 1y ago
  [deleted]
Davidzheng 1y ago
I highly highly doubt that training a LLM like gpt-2 will help you use something the size of GPT-4. And I guess most people can't afford to train something like GPT-4. I trained some NNs back before the ChatGPT era, I don't think any of it helps in using Chatgpt/alternatives
- benreesman 1y ago
  With modern high-quality datasets and the plummeting H100 rental costs it is 100% a feasible undertaking for an individual to train a model with performance far closer to gpt-4-1106-preview than to gpt-2, in fact its difficult to train a model that performs as badly as gpt-2 without carefully selecting for datasets like OpenWebText with the explicit purpose of replicating runs of historical interest: modern datasets will do better than that by default. GPT-4 is a 1.75 terraweight MoE (the rumor has it) and that's probably pushing it for an individual's discretionary budget unless they're very well off, but you don't need to match that exactly to learn how these things fundamentally work. I think you underestimate how far the technology has come. torch.distributed works out of the box now, deepspeed and other strategies that are both data and model parallel are weekend projects to spin up on an 8xH100 SXM2 interconnected cluster that you can rent from Lambda Labs, HuggingFace has extreme quality curated datasets (the fineweb family I was alluding to from Karpathy's open stuff is stellar). In just about any version of this you come to understand how tokenizers work (which makes a whole class of failure modes go from baffling to intuitive), how models behave and get evaled after pretraining, after instruct training / SFT rounds, how convergence does and doesn't happen, how tool use and other special tokens get used (and why they are abundant). And no, doing all that doesn't make Opus 4 completely obvious in all aspects. But its about 1000x more effective as a learning technique than doing prompt engineer astrology. Opus 4 is still a bit mysterious if you don't work at a frontier lab, there's very interesting stuff going on there and I'm squarely speculating how some of that works if I make claims about it. Models that look and act a lot like GPT-4 while having dramatically lower parameter counts are just completely understood in open source now. The more advanced ones require resources of a startup rather than an individual, but you don't need to eval the same as 1106 to take all the mystery out of how it works. The "holy shit" models are like 3-4 generations old now.

pyman 1y ago

Saying the best way to understand LLMs is by building one is like saying the best way to understand compilers is by writing one. Technically true, but most people aren't interested in going that deep.

hackable_sand 1y ago
It's not that deep
benreesman 1y ago
I don't know, I've heard that meme too but it doesn't track with the number of cool compiler projects on GitHub or that frontpage HN, and while the LLM thing is a lot newer, you see a ton of useful/interesting stuff at the "an individual could do this on their weekends and it would mean they fundamentally know how all the pieces fit together" type stuff. There will always be a crowd that wants the "master XYZ in 72 hours with this ONE NEAT TRICK" course, and there will always be a..., uh, group of people serving that market need. But most people? Especially in a place like HN? I think most people know that getting buff involves going to the gym, especially in a place like this. I have a pretty high opinion of the typical person. We're all tempted by the "most people are stupid" meme, but that's because bad interactions are memorable, not because most people are stupid or lazy or whatever. Most people are very smart if they apply themselves, and most people will work very hard if the reward for doing so is reasonably clear. https://www.youtube.com/shorts/IQmOGlbdn8g https://www.youtube.com/shorts/IQmOGlbdn8g
wickedsight 1y ago
The best way to understand a car is to build a car. Hardly anyone is going to do that, but we still all use them quite well in our daily lives. In large part because the companies who build them spend time and effort to improve them and take away friction and complexity. If you want to be an F1 driver it's probably useful to understand almost every part of a car. If you're a delivery driver, it probably isn't, even if you use one 40+ hours a week.
- benreesman 1y ago
  Your example / analogy is useful in the sense that its usually useful to establish the thought experiment with the boundary conditions. But in between someone commuting in a Toyota and an F1 driver are many, many people, the best example from inside the extremes is probably a car mechanic, and even there, there's the oil change place with the flat fee painted in the window, and the Koenigsberg dealership that orders the part from Europe. The guy who tunes those up can afford one himself. In the use case segment where just about anyone can do it with a few hours training, yeah, maybe that investment is zero instead of a week now. But I'm much more interested in the one where F1 cars break the sound barrier now.
- eclecticfrank 1y ago
  It might make sense to split the car analogy into different users: 1. For the majority of regular users the best way to understand the car is to read the manual and use the car. 2. For F1 drivers the best way to understand the car is to consult with engineers and use the car. 3. For a mechanic / engineer the best way to understand the car is to build and use the car.
- Davidzheng 1y ago
  yes except intelligence isn't like a car, there's no way to break the complicated emergent behaviors of these models into simple abstractions. you can understand a LLM by training one the same amount you can understand a brain by dissection.
  - LtWorf 1y ago
    I think making one would help you understand that they're not intelligent.
deleted 1y ago
[deleted]

Davidzheng 1y ago

I highly highly doubt that training a LLM like gpt-2 will help you use something the size of GPT-4. And I guess most people can't afford to train something like GPT-4. I trained some NNs back before the ChatGPT era, I don't think any of it helps in using Chatgpt/alternatives

benreesman 1y ago
With modern high-quality datasets and the plummeting H100 rental costs it is 100% a feasible undertaking for an individual to train a model with performance far closer to gpt-4-1106-preview than to gpt-2, in fact its difficult to train a model that performs as badly as gpt-2 without carefully selecting for datasets like OpenWebText with the explicit purpose of replicating runs of historical interest: modern datasets will do better than that by default. GPT-4 is a 1.75 terraweight MoE (the rumor has it) and that's probably pushing it for an individual's discretionary budget unless they're very well off, but you don't need to match that exactly to learn how these things fundamentally work. I think you underestimate how far the technology has come. torch.distributed works out of the box now, deepspeed and other strategies that are both data and model parallel are weekend projects to spin up on an 8xH100 SXM2 interconnected cluster that you can rent from Lambda Labs, HuggingFace has extreme quality curated datasets (the fineweb family I was alluding to from Karpathy's open stuff is stellar). In just about any version of this you come to understand how tokenizers work (which makes a whole class of failure modes go from baffling to intuitive), how models behave and get evaled after pretraining, after instruct training / SFT rounds, how convergence does and doesn't happen, how tool use and other special tokens get used (and why they are abundant). And no, doing all that doesn't make Opus 4 completely obvious in all aspects. But its about 1000x more effective as a learning technique than doing prompt engineer astrology. Opus 4 is still a bit mysterious if you don't work at a frontier lab, there's very interesting stuff going on there and I'm squarely speculating how some of that works if I make claims about it. Models that look and act a lot like GPT-4 while having dramatically lower parameter counts are just completely understood in open source now. The more advanced ones require resources of a startup rather than an individual, but you don't need to eval the same as 1106 to take all the mystery out of how it works. The "holy shit" models are like 3-4 generations old now.