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Unit 1 Certification - AI Agent Fundamentals
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Kenneth Hamilton PRO
ZennyKenny
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Development and Ops for LLMs and CV.
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I've completed the first unit of the just-launched Hugging Face Agents Course. I would highly recommend it, even for experienced builders, because it is a great walkthrough of the smolagents library and toolkit.
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GradientBoostingClassifier is an algorithm supported by the Python SciKit library, and now you can quickly train an ML model using this powerful technique on any (viable) dataset in the Hugging Face Hub without a line of code.
Love finishing a project right when the late night starts to turn into the early morning: sklearn-docs/GradientBoostingClassifier
Long time listener, first time caller, but always pleased to contribute, even if only adjacently, to the power of SciKit.
Love finishing a project right when the late night starts to turn into the early morning: sklearn-docs/GradientBoostingClassifier
Long time listener, first time caller, but always pleased to contribute, even if only adjacently, to the power of SciKit.
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Open-R1: a fully open reproduction of DeepSeek-R1
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Make JetBrains IntelliJ Again! Very cool integration.
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🚩 Report: Ethical issue(s)
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We are reproducing the full DeepSeek R1 data and training pipeline so everybody can use their recipe. Instead of doing it in secret we can do it together in the open!
🧪 Step 1: replicate the R1-Distill models by distilling a high-quality reasoning corpus from DeepSeek-R1.
🧠 Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will involve curating new, large-scale datasets for math, reasoning, and code.
🔥 Step 3: show we can go from base model -> SFT -> RL via multi-stage training.
Follow along: https://github.com/huggingface/open-r1
🧪 Step 1: replicate the R1-Distill models by distilling a high-quality reasoning corpus from DeepSeek-R1.
🧠 Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will involve curating new, large-scale datasets for math, reasoning, and code.
🔥 Step 3: show we can go from base model -> SFT -> RL via multi-stage training.
Follow along: https://github.com/huggingface/open-r1
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