shortgpt-lite
pytorch-lightning
| shortgpt-lite | pytorch-lightning | |
|---|---|---|
| 1 | 9 | |
| 5 | 27,064 | |
| - | 1.7% | |
| 4.3 | 9.9 | |
| 12 months ago | 5 days ago | |
| Dart | Python | |
| - | Apache License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
shortgpt-lite
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ShortGPT Lite
Windows and Linux releases available for the download. ShortGPT Lite GitHub repo
pytorch-lightning
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SB-1047 will stifle open-source AI and decrease safety
It's very easy to get started, right in your Terminal, no fees! No credit card at all.
And there are cloud providers like https://replicate.com/ and https://lightning.ai/ that will let you use your LLM via an API key just like you did with OpenAI if you need that.
You don't need OpenAI - nobody does.
- Lightning AI Studios – A persistent GPU cloud environment
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Como empezar con inteligencia artificial?
https://see.stanford.edu/Course/CS229 https://lightning.ai/ https://www.youtube.com/watch?v=00s9ireCnCw&t=57s https://towardsdatascience.com/
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Best practice for saving logits/activation values of model in PyTorch Lightning
I've been wondering on what is the recommended method of saving logits/activations using PyTorch Lightning. I've looked at Callbacks, Loggers and ModelHooks but none of the use-cases seem to be for this kind of activity (even if I were to create my own custom variants of each utility). The ModelCheckpoint Callback in its utility makes me feel like custom Callbacks would be the way to go but I'm not quite sure. This closed GitHub issue does address my issue to some extent.
- New to ML, which is easier to learn - Tensorflow or PyTorch?
- PyTorch Lightning – DL framework to train, deploy, and ship AI fast
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We just release a complete open-source solution for accelerating Stable Diffusion pretraining and fine-tuning!
Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase , lucidrains, Stable Diffusion, Lightning and Hugging Face. Thanks for open-sourcing!
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An elegant and strong PyTorch Trainer
For lightweight use, pytorch-lightning is too heavy, and its source code will be very difficult for beginners to read, at least for me.
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[D] Mixed Precision Training: Difference between BF16 and FP16
For the A100 GPU, theoretical performance is the same for FP16/BF16 and both rely on the same number of bits, meaning memory should be the same. However since it's quite newly added to PyTorch, performance seems to still be dependent on underlying operators used (pytorch lightning debugging in progress here).
What are some alternatives?
machine-learning-for-software-engineers - A complete daily plan for studying to become a machine learning engineer.
lnd - Lightning Network Daemon ⚡️
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Eclair - A scala implementation of the Lightning Network.
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/AutoGPT]
mmdetection - OpenMMLab Detection Toolbox and Benchmark
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
composer - Supercharge Your Model Training
umbrel - A beautiful home server OS for self-hosting with an app store. Buy a pre-built Umbrel Home with umbrelOS, or install on a Raspberry Pi 4, Pi 5, any Ubuntu/Debian system, or a VPS.
Keras - Deep Learning for humans
fastai - The fastai deep learning library
RTL - Ride The Lightning - A full function web browser app for LND, C-Lightning and Eclair