> > so http://github.com/karl3wm/httptransformer now has a resuming class > > i can load llama 405b and process 0.00002% of it on my tiny system, then > > reboot the system and start right off again at 0.00002% and process up to > > 0.00004% ! :D
> you can try this with `python test_nettensor.py` but I _do not recommend > doing it unless you are spending the time working on it_ because it is > incredibly inefficient. i do expect it to correctly complete prompts if let > run for weeks. of course the intent of his project was to implement models that provide for amortizable completion in much smaller time like deepseek or mistral or any ad-hoc setup providing for multi-token completion such as an assistant model [1] using it for llama 405b is just for fun 1: https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.generate.assistant_model note that you can probably implement this much more effectively than huggingface did by engaging logits and attention internals > > it doesn't do any paralleliz-- > > i tried it on google colab, but it actually ran at about the same speed > > because of the synchronous streaming nature -- > > so there's some interest in focusing on speeding up the network portion, > > the biggest bottleneck
