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Ajiboye Toluwalase
Ajiboye
AI & ML interests
LLMs , and Optical AIs
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Introducing Skops liked a model 6 days ago
Falconsai/nsfw_image_detectionOrganizations
upvoted a changelog 1 day ago
commented on Introducing Skops 6 days ago
Great intro post — one thing that tripped me up as a newcomer: the code examples here (pickle.dump()/pickle.load()) made me think skops itself was somehow making pickle safe to load. It took me a while to realize this post is really about hub_utils and card (repo management, model cards) and is serialization-format-agnostic — the actual secure serialization work (skops.io.dump/load, which avoids pickle entirely using a zip + JSON schema format) came later and isn't mentioned here at all.
Might be worth adding a note pointing to the skops.io secure serialization docs, since this is the first result people find when searching "skops," and reading it in isolation gives the impression pickle is still the recommended approach. Thanks for the library either way!
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Introducing Skops
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reacted to PhysiQuanty's post with 🔥 2 months ago
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❗ Dating apps do not allow us to control the profiles suggested to us based on our mutual search criteria ❗
🧬 If you want to see if your soulmate has already existed, I have published a dataset of 59k anonymized public profiles
SpiceeChat/OkCupid-59k-Anonymized-Profiles
Are you looking for a female ML engineer who is looking for a male ML engineer and you can't find it on the apps ?
You need to look for her, but more importantly, she needs to look for you.
Personally, I'm looking for a physicist I'm encountering the same problem. I can't find it
My answer : Paradox of choice of dating apps solved by patent ⚡ WO2026082672 ⚡
https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2026082672
J'ai du breveté pour te trouver et on se trouvera bientôt !
🧬 If you want to see if your soulmate has already existed, I have published a dataset of 59k anonymized public profiles
SpiceeChat/OkCupid-59k-Anonymized-Profiles
Are you looking for a female ML engineer who is looking for a male ML engineer and you can't find it on the apps ?
You need to look for her, but more importantly, she needs to look for you.
Personally, I'm looking for a physicist I'm encountering the same problem. I can't find it
My answer : Paradox of choice of dating apps solved by patent ⚡ WO2026082672 ⚡
https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2026082672
J'ai du breveté pour te trouver et on se trouvera bientôt !
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reacted to IlyasMoutawwakil's post with 🔥 6 months ago
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After 2 months of refinement, I'm happy to announce that a lot of Transformers' modeling code is now significantly more torch-compile & export-friendly 🔥
Why it had to be done 👇
PyTorch's Dynamo compiler is increasingly becoming the default interoperability layer for ML systems. Anything that relies on torch.export or torch.compile, from model optimization to cross-framework integrations, benefits directly when models can be captured as a single dynamo-traced graph !
Transformers models are now easier to:
⚙️ Compile end-to-end with torch.compile backends
📦 Export reliably via torch.export and torch.onnx.export
🚀 Deploy to ONNX / ONNX Runtime, Intel Corporation's OpenVINO, NVIDIA AutoDeploy (TRT-LLM), AMD's Quark, Meta's Executorch and more hardware-specific runtimes.
This work aims at unblocking entire TorchDynamo-based toolchains that rely on exporting Transformers across runtimes and accelerators.
We are doubling down on Transformers commitment to be a first-class citizen of the PyTorch ecosystem, more exportable, more optimizable, and easier to deploy everywhere.
There are definitely some edge-cases that we still haven't addressed so don't hesitate to try compiling / exporting your favorite transformers and to open issues / PRs.
PR in the comments ! More updates coming coming soon !
Why it had to be done 👇
PyTorch's Dynamo compiler is increasingly becoming the default interoperability layer for ML systems. Anything that relies on torch.export or torch.compile, from model optimization to cross-framework integrations, benefits directly when models can be captured as a single dynamo-traced graph !
Transformers models are now easier to:
⚙️ Compile end-to-end with torch.compile backends
📦 Export reliably via torch.export and torch.onnx.export
🚀 Deploy to ONNX / ONNX Runtime, Intel Corporation's OpenVINO, NVIDIA AutoDeploy (TRT-LLM), AMD's Quark, Meta's Executorch and more hardware-specific runtimes.
This work aims at unblocking entire TorchDynamo-based toolchains that rely on exporting Transformers across runtimes and accelerators.
We are doubling down on Transformers commitment to be a first-class citizen of the PyTorch ecosystem, more exportable, more optimizable, and easier to deploy everywhere.
There are definitely some edge-cases that we still haven't addressed so don't hesitate to try compiling / exporting your favorite transformers and to open issues / PRs.
PR in the comments ! More updates coming coming soon !
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reacted to SelmaNajih001's post with 👍 10 months ago
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Introducing a Hugging Face Tutorial on Regression
While Hugging Face offers extensive tutorials on classification and NLP tasks, there is very little guidance on performing regression tasks with Transformers.
In my latest article, I provide a step-by-step guide to running regression using Hugging Face, applying it to financial news data to predict stock returns.
In this tutorial, you will learn how to:
-Prepare and preprocess textual and numerical data for regression
-Configure a Transformer model for regression tasks
-Apply the model to real-world financial datasets with fully reproducible code
Read the full article here: https://huggingface.co/blog/SelmaNajih001/how-to-run-a-regression-using-hugging-face
The dataset used: SelmaNajih001/FinancialClassification
While Hugging Face offers extensive tutorials on classification and NLP tasks, there is very little guidance on performing regression tasks with Transformers.
In my latest article, I provide a step-by-step guide to running regression using Hugging Face, applying it to financial news data to predict stock returns.
In this tutorial, you will learn how to:
-Prepare and preprocess textual and numerical data for regression
-Configure a Transformer model for regression tasks
-Apply the model to real-world financial datasets with fully reproducible code
Read the full article here: https://huggingface.co/blog/SelmaNajih001/how-to-run-a-regression-using-hugging-face
The dataset used: SelmaNajih001/FinancialClassification
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