11–20 of 71 posts
This would likely only get used for small finetuning jobs. It’s too slow for the scale of pretraining.
Earlier quoted context omitted.
It’s too slow for the scale of pretraining. There isn't really such a thing as 'too slow' as an objective fact though. It depends on how much patience and money for electricity you have. In AI image gen circles I see people complaining if a model takes more than 5s to generate an image, and other people on very limited hardware who happily wait half an hour per image. It's hard to make a judgement call about what 'to…
If it would take so long to train that the model will be obsolete before the training is finished that might be considered too long. With ML you can definitely hit a point where it is too slow for any practical purpose.
Earlier quoted context omitted.
If it would take so long to train that the model will be obsolete before the training is finished that might be considered too long. With ML you can definitely hit a point where it is too slow for any practical purpose.
Obsolete because of what? Because with limited hardware you’re never aiming for state of the art, and for fine-tuning, you don’t steer for too long anyway.
If your training time is measured in years or decades it probably won't be practical.
> MegaTrain stores parameters and optimizer states in host memory (CPU memory) and treats GPUs as transient compute engines. For each layer, we stream parameters in and compute gradients out, minimizing persistent device state This is pretty awesome. The only compute I have at home is an RTX 3080 with 10 GB of VRAM, so I struggle with training larger models (>40M, 50M params). I get OOM errors and have to optimize a…
In the past couple months there's been a kind of explosion in small-models that are occupying a niche in this kind of AI-transcoding space. What I'm hoping we're right on the cusp of achieving is a similar explosion in what I'd call tool-adaptation, where an LLM paired with some mostly-fixed suite of tools and problem cases can trade off some generality for a specialized (potentially hyper-specialized to the company or user) role.
The thing about more transcoding-related tasks is that they in general stay in sync with what the user of the device is actively doing, which will also typically be closely aligned with the capabilities of the user's hardware and what they want to do with their computer. So most people aren't being intentional about this kind of stuff right now, partly out of habit I think, because only just now does it make sense to think of personal computer as "stranded hardware" now that they can be steered/programmed somewhat autonomously.
I'm wondering if with the right approach to MoE on local devices (which local llms are heading towards) we could basically amortize the expensive hit from loading weights in and out of VRAM through some kind of extreme batch use case that users still find useful enough to be worth the latency. LoRa is already really useful for this but obviously sometimes you need more expertise/specialization than just a few layers' difference. Experimenting with this right now. It's the same basic principle as in the paper except less of a technical optimization and more workload optimization. Also it's literally the beginning of machine culture so that's kind of cool
> MegaTrain stores parameters and optimizer states in host memory (CPU memory) and treats GPUs as transient compute engines. For each layer, we stream parameters in and compute gradients out, minimizing persistent device state This is pretty awesome. The only compute I have at home is an RTX 3080 with 10 GB of VRAM, so I struggle with training larger models (>40M, 50M params). I get OOM errors and have to optimize a…
I'm on the same GPU, its intimidating to me if I even want to bother training anything at all. Do you mind sharing what kind of training you've done with that GPU? :)
This would likely only get used for small finetuning jobs. It’s too slow for the scale of pretraining.
It’s too slow for the scale of pretraining. There isn't really such a thing as 'too slow' as an objective fact though. It depends on how much patience and money for electricity you have. In AI image gen circles I see people complaining if a model takes more than 5s to generate an image, and other people on very limited hardware who happily wait half an hour per image. It's hard to make a judgement call about what 'to…