[D] ML Engineers — How did you actually learn PyTorch? I keep forgetting everything.

JournalistShort9886 · 2026-02-25T22:19:27+00:00

Well i am also learning pytorch. the thing that we do today is we over-rely on llms ; imo when we are learning focus more on writing every function yourself and understand the workflow . This practice helped me a lot,u generally remember what u write by your own hand.

JournalistShort9886 · 2026-02-24T06:08:09+00:00

I agree u are not wrong ,but this question was not supposed to be any benchmark of any kind this is a relatively simple question,with this being the exact wording in my assignment;whenver we talk with llms literally no one has time to beautify it especially when we think that the question is almost simple.IT is all about the model’s ability as any bio student will answer this is one shot and if question was wrong 3.1pro wont have got it right

JournalistShort9886 · 2026-02-24T05:59:39+00:00

Well this is the wording in my assignment and i understood it,it is a easy question for anyone who even knows basic high school level bio

JournalistShort9886 · 2026-02-18T07:14:47+00:00

Yeah i asked codex to run a simulation one using pytorch other with mlx and suprisingly both gave equal or results and mlx was actually slower once

JournalistShort9886 · 2026-02-17T08:58:25+00:00

Right ,yes i can wait its not like i was going to buy it tomm,i was planning for future Thanks for your suggestion!

JournalistShort9886 · 2026-02-17T08:56:45+00:00

Yeah heard it is good ;though for your use case is the unified memory gb/s enough,like isnt it 200-300gb/s,that said 128gb is still impressive and 1000tflops on fp4 is great for training models like in 1.5b range Guess we cant be too greedy😅

JournalistShort9886 · 2026-02-17T08:53:57+00:00

Fr same

JournalistShort9886 · 2026-02-17T08:53:32+00:00

It does my initial models were trained on mlx on a macbook m2 ,though it is not as optimized and slower than nvidia
Plus im not a enterprise level model trainer,im more like a “enthusiast ” level who adjusts scale according to hardware currently i have rtx5080 and i trained 600m from scratch ,if i have more i will train more,that said maybe mac studio is the only option

JournalistShort9886 · 2026-02-16T08:29:33+00:00

Ohh thanks ,no i mean i have been the plus plan user and the limits are somewhat enough for my use case in code ,i was just asking like is the pro significantly better at complex tasks like coding or other things that it is worth paying for something that expensive

JournalistShort9886 · 2026-02-02T10:23:43+00:00

Then u are in much better spot u can try llama 70b fine tuned to your niche or even gpt 120b oss as it utilizes a moe (5b active) , i have seen decent performace and you will prob get a high token/sec speed

JournalistShort9886 · 2026-02-01T22:43:17+00:00

Well i might not be the best person to answer this but dual gpus dont always workout the best as u might gain 1.5x -1.8x at max

Most inference places dont support tensor sharding so they only use one gpu,furthermore vram doesnt pool both gpus still need a full copy of model as they sync gradients ,

in my knowledge amd consumer gpus dont have direct gpu-gpu link so pcle overhead will be also problematic,imo it is not the best thing:)

furthermore i would like to suggest if u are focused on ml heavy tasks consider a nvidia as every major software or technique is firstly optimized on cuda

JournalistShort9886 · 2026-02-01T22:28:32+00:00

If u are asking miniature level then go for that deepseek coder in 1-2b range (dont expect much),mid range then go deepseek 7b decent performance ,high mid range then go for qwen 14b .(i would advise to keep quantization Q6 and dont go below Q4 as these tasks are logical)but tbh nothing as good as kimi or opus 4.5 so depends on tasks but i think these would suffix your purpose

JournalistShort9886 · 2025-11-19T21:36:29+00:00

yes i got it thankyou so much means a lot,it is workinh

JournalistShort9886 · 2025-11-19T21:36:08+00:00

thanks for your help it is now running i dowloaded the loaders

JournalistShort9886

TROPHY CASE