poutyne
nn
poutyne | nn | |
---|---|---|
1 | 26 | |
557 | 48,430 | |
0.2% | 3.7% | |
4.5 | 7.7 | |
10 months ago | about 1 month ago | |
Python | Jupyter Notebook | |
GNU Lesser General Public License v3.0 only | MIT License |
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poutyne
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[D] Looking for open source projects to contribute
Hi, I'm the author of Poutyne, a library that aims to simplify the use of PyTorch while keeping all its flexibility. Always looking for contributions. If you look in the issue on the Github repo, you'll few suggestions but I'm always looking for other ideas to improve the library.
nn
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Can't remember name of website that has explanations side-by-side with code
Hey are you talking about https://nn.labml.ai/ ?
- [D] Recent ML papers to implement from scratch
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[P] GPT-NeoX inference with LLM.int8() on 24GB GPU
Implementation & LM Eval Harness Results
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[P] Fine-tuned the GPT-Neox Model to Generate Quotes
Github: https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/neox
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Best resources to learn recent transformer papers and stay updated [D]
Regarding implementations this helps me: https://nn.labml.ai/
- Introductory papers to implement
- How to convert research papers to code?
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[D] How to convert papers to code?
Dunno if this is directly helpful, but this website has implementation with the math side by side https://nn.labml.ai/
- [D] Looking for open source projects to contribute
- Resource for papers explanation
What are some alternatives?
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
GFPGAN-for-Video-SR - A colab notebook for video super resolution using GFPGAN
general
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
habitat-sim - A flexible, high-performance 3D simulator for Embodied AI research.
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docarray - Represent, send, store and search multimodal data
ZoeDepth - Metric depth estimation from a single image
dataqa - Labelling platform for text using weak supervision.
onnx-simplifier - Simplify your onnx model
kaggle-environments
Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.