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View Code? Open in Web Editor NEWResources for skilling up in AI alignment research engineering. Covers basics of deep learning, mechanistic interpretability, and RL.
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missaishagurung pranavgade20 dlorell aprillion johny-b felhof artkpv jbloomaus firstuserhere jfaina bilal-chughtai eugleo notrichardren shahofblah znd4 codrutalugoj tbroadley atagade tjennerjahn tommybark samadamday zelithezealot nch0w xt0r3 ziyuewang25 euanong fxrcode nmoran poppingtonic alex-garvey ckkissane ajmeek jjallaire iyanjo wendlerc dviggiano alexserra98 bryce13950 tteodorescu0 alitair alan-cooney jparklev shenzhouhong akshatabhat17 speediedan connor-henderson shreyasrajesh0308 williamhackspeare jmague phylliida emrysguo therealmolf jamescampbell57 huntera-ai jmsdao mehmetcanbudak j-muthu maksymdel pidongg ramesharun codeboy5 hiikate adulaney ayush1801 michelle-viotti coelingh mbalesni littlefive5 ttb-git dtbinh jasonlim131arena_2.0's Issues
TransformerBlock link no working
The above link is present inside section [1.2], under Parameters and Activations and is not working, it shows invalid encoded code. Can you please fix it?
execise.ipynbs need Lightning in colab
In exercises 1.1 and 1.2 (and probably more) running in colab results in :
`ModuleNotFoundError: No module named 'pytorch_lightning'
This can be fixed by including:
if IN_COLAB:
# Install packages
%pip install pytorch-lightning
I could't find the exercise.ipynb files in the repo, if you can point me towards them I'll also gladly submit a PR.
P.S.: Thanks for providing this awesome content! :)
Over resource limits on Streamlit Cloud
Hey there 👋 Just wanted to let you know that your app on Streamlit Cloud deployed from this repo has gone over its resource limits. Access to the app is temporarily limited. Visit the app to see more details and possible solutions.
When running on mac, not all tensors reside on same device (example below)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, mps:0 and cpu!
examples:
tokens = t.cat([tokens, next_token[None, None]], dim=-1)
in def load_gpt2_test(cls, gpt2_layer, input):
comparison = t.isclose(output, reference_output, atol=1e-4, rtol=1e-3)
in LayerNorm:
return residual* self.w + self.b
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