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mrdbourke
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Computer vision. Small on-device models. VLMs. High-quality tutorials.
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MARS results look great! Just started a training run with cmars, will report back
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We just dropped an LLM inside the SQL Console 🤯
The amazing, new Qwen/Qwen2.5-Coder-32B-Instruct model can now write SQL for any Hugging Face dataset ✨
It's 2025, you shouldn't be hand writing SQL! This is a big step in making it where anyone can do in depth analysis on a dataset. Let us know what you think 🤗
The amazing, new Qwen/Qwen2.5-Coder-32B-Instruct model can now write SQL for any Hugging Face dataset ✨
It's 2025, you shouldn't be hand writing SQL! This is a big step in making it where anyone can do in depth analysis on a dataset. Let us know what you think 🤗
Woah, looks like a good boost across most results. Been using torch.optim.adamw for months. Will try out a training run today with timm.optim.cadamw
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There's a new
New optimizers include:
* AdafactorBigVision -
* ADOPT -
* MARS -
* LaProp -
* Cautious Optimizers - a modification to all of the above, prefix with
I shared some caution comparisons in this model repo: rwightman/timm-optim-caution
For details, references, see the code: https://github.com/huggingface/pytorch-image-models/tree/main/timm/optim
timm release, v 1.0.12, with a focus on optimizers. The optimizer factory has been refactored, there's now a timm.optim.list_optimizers() and new way to register optimizers and their attributes. As always you can use an timm optimizer like a torch one, just replace torch.optim with timm.optimNew optimizers include:
* AdafactorBigVision -
adfactorbv* ADOPT -
adopt / adoptw (decoupled decay)* MARS -
mars* LaProp -
laprop* Cautious Optimizers - a modification to all of the above, prefix with
c as well as cadamw, cnadamw, csgdw, clamb, crmsproptf I shared some caution comparisons in this model repo: rwightman/timm-optim-caution
For details, references, see the code: https://github.com/huggingface/pytorch-image-models/tree/main/timm/optim
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