https://pyvideo.org/2024-07-08T00:00:00+00:002024-07-08T00:00:00+00:002024-07-08T00:00:00+00:00Benjamin Bossantag:pyvideo.org,2024-07-08:/europython-2024/fine-tuning-large-models-on-local-hardware.html

<h3>Description</h3><p>[EuroPython 2024 — Forum Hall on 2024-07-11]</p> <p>Fine-tuning large models on local hardware by Benjamin Bossan</p> <p><a class="reference external" href="https://ep2024.europython.eu/session/fine-tuning-large-models-on-local-hardware">https://ep2024.europython.eu/session/fine-tuning-large-models-on-local-hardware</a></p> <p>Fine-tuning big neural nets like Large Language Models (LLMs) has traditionally been prohibitive due to high hardware requirements. However, Parameter-Efficient Fine-Tuning (PEFT) and quantization enable the training of …</p>

<h3>Description</h3><p>[EuroPython 2024 — Forum Hall on 2024-07-11]</p> <p>Fine-tuning large models on local hardware by Benjamin Bossan</p> <p><a class="reference external" href="https://ep2024.europython.eu/session/fine-tuning-large-models-on-local-hardware">https://ep2024.europython.eu/session/fine-tuning-large-models-on-local-hardware</a></p> <p>Fine-tuning big neural nets like Large Language Models (LLMs) has traditionally been prohibitive due to high hardware requirements. However, Parameter-Efficient Fine-Tuning (PEFT) and quantization enable the training of large models on modest hardware. Thanks to the PEFT library and the Hugging Face ecosystem, these techniques are now accessible to a broad audience.</p> <p>Expect to learn:</p> <ul class="simple"> <li>what the challenges are of fine-tuning large models</li> <li>what solutions have been proposed and how they work</li> <li>practical examples of applying the PEFT library</li> </ul> <p>---</p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License: <a class="reference external" href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a></p> 2019-10-10T00:00:00+00:002019-10-10T00:00:00+00:00Benjamin Bossantag:pyvideo.org,2019-10-10:/pydata-berlin-2019/skorch-a-scikit-learn-compatible-neural-network-library.html

<h3>Description</h3><p>Speaker: Benjamin Bossan</p> <p>Track:PyData This talk is about the open source package [skorch](https://github.com/skorch-dev/skorch), a wrapper library that allows you to combine the best of sklearn and PyTorch. It covers when it makes sense to use skorch and highlights interesting features.</p> <p>Recorded at the …</p>

<h3>Description</h3><p>Speaker: Benjamin Bossan</p> <p>Track:PyData This talk is about the open source package [skorch](https://github.com/skorch-dev/skorch), a wrapper library that allows you to combine the best of sklearn and PyTorch. It covers when it makes sense to use skorch and highlights interesting features.</p> <p>Recorded at the PyConDE &amp; PyData Berlin 2019 conference. <a class="reference external" href="https://pycon.de">https://pycon.de</a></p> <p>More details at the conference page: <a class="reference external" href="https://de.pycon.org/program/NMXSE7">https://de.pycon.org/program/NMXSE7</a> Twitter: <a class="reference external" href="https://twitter.com/pydataberlin">https://twitter.com/pydataberlin</a> Twitter: <a class="reference external" href="https://twitter.com/pyconde">https://twitter.com/pyconde</a></p>