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+%global _empty_manifest_terminate_build 0
+Name:		python-labml-nn
+Version:	0.4.133
+Release:	1
+Summary:	🧑‍🏫 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit), optimizers (adam, radam, adabelief), gans(dcgan, cyclegan, stylegan2), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, diffusion, etc. 🧠
+License:	MIT License
+URL:		https://github.com/labmlai/annotated_deep_learning_paper_implementations
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ad/cd/0bc62f5b0208dbe8ed0c0fb2d7f548a6e7d6921665f8e0809b79a2d172ba/labml-nn-0.4.133.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-labml
+Requires:	python3-labml-helpers
+Requires:	python3-torch
+Requires:	python3-torchtext
+Requires:	python3-torchvision
+Requires:	python3-einops
+Requires:	python3-numpy
+Requires:	python3-fairscale
+
+%description
+[![Twitter](https://img.shields.io/twitter/follow/labmlai?style=social)](https://twitter.com/labmlai)
+[![Sponsor](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/labmlai)
+
+# [labml.ai Deep Learning Paper Implementations](https://nn.labml.ai/index.html)
+
+This is a collection of simple PyTorch implementations of
+neural networks and related algorithms.
+These implementations are documented with explanations,
+
+[The website](https://nn.labml.ai/index.html)
+renders these as side-by-side formatted notes.
+We believe these would help you understand these algorithms better.
+
+![Screenshot](https://nn.labml.ai/dqn-light.png)
+
+We are actively maintaining this repo and adding new 
+implementations almost weekly.
+[![Twitter](https://img.shields.io/twitter/follow/labmlai?style=social)](https://twitter.com/labmlai) for updates.
+
+## Paper Implementations
+
+#### ✨ [Transformers](https://nn.labml.ai/transformers/index.html)
+
+* [Multi-headed attention](https://nn.labml.ai/transformers/mha.html)
+* [Transformer building blocks](https://nn.labml.ai/transformers/models.html) 
+* [Transformer XL](https://nn.labml.ai/transformers/xl/index.html)
+    * [Relative multi-headed attention](https://nn.labml.ai/transformers/xl/relative_mha.html)
+* [Rotary Positional Embeddings](https://nn.labml.ai/transformers/rope/index.html)
+* [Attention with Linear Biases (ALiBi)](https://nn.labml.ai/transformers/alibi/index.html)
+* [RETRO](https://nn.labml.ai/transformers/retro/index.html)
+* [Compressive Transformer](https://nn.labml.ai/transformers/compressive/index.html)
+* [GPT Architecture](https://nn.labml.ai/transformers/gpt/index.html)
+* [GLU Variants](https://nn.labml.ai/transformers/glu_variants/simple.html)
+* [kNN-LM: Generalization through Memorization](https://nn.labml.ai/transformers/knn)
+* [Feedback Transformer](https://nn.labml.ai/transformers/feedback/index.html)
+* [Switch Transformer](https://nn.labml.ai/transformers/switch/index.html)
+* [Fast Weights Transformer](https://nn.labml.ai/transformers/fast_weights/index.html)
+* [FNet](https://nn.labml.ai/transformers/fnet/index.html)
+* [Attention Free Transformer](https://nn.labml.ai/transformers/aft/index.html)
+* [Masked Language Model](https://nn.labml.ai/transformers/mlm/index.html)
+* [MLP-Mixer: An all-MLP Architecture for Vision](https://nn.labml.ai/transformers/mlp_mixer/index.html)
+* [Pay Attention to MLPs (gMLP)](https://nn.labml.ai/transformers/gmlp/index.html)
+* [Vision Transformer (ViT)](https://nn.labml.ai/transformers/vit/index.html)
+* [Primer EZ](https://nn.labml.ai/transformers/primer_ez/index.html)
+* [Hourglass](https://nn.labml.ai/transformers/hour_glass/index.html)
+
+#### ✨ [Eleuther GPT-NeoX](https://nn.labml.ai/neox/index.html)
+* [Generate on a 48GB GPU](https://nn.labml.ai/neox/samples/generate.html)
+* [Finetune on two 48GB GPUs](https://nn.labml.ai/neox/samples/finetune.html)
+* [LLM.int8()](https://nn.labml.ai/neox/utils/llm_int8.html)
+
+#### ✨ [Diffusion models](https://nn.labml.ai/diffusion/index.html)
+
+* [Denoising Diffusion Probabilistic Models (DDPM)](https://nn.labml.ai/diffusion/ddpm/index.html)
+* [Denoising Diffusion Implicit Models (DDIM)](https://nn.labml.ai/diffusion/stable_diffusion/sampler/ddim.html)
+* [Latent Diffusion Models](https://nn.labml.ai/diffusion/stable_diffusion/latent_diffusion.html)
+* [Stable Diffusion](https://nn.labml.ai/diffusion/stable_diffusion/index.html)
+
+#### ✨ [Generative Adversarial Networks](https://nn.labml.ai/gan/index.html)
+* [Original GAN](https://nn.labml.ai/gan/original/index.html)
+* [GAN with deep convolutional network](https://nn.labml.ai/gan/dcgan/index.html)
+* [Cycle GAN](https://nn.labml.ai/gan/cycle_gan/index.html)
+* [Wasserstein GAN](https://nn.labml.ai/gan/wasserstein/index.html)
+* [Wasserstein GAN with Gradient Penalty](https://nn.labml.ai/gan/wasserstein/gradient_penalty/index.html)
+* [StyleGAN 2](https://nn.labml.ai/gan/stylegan/index.html)
+
+#### ✨ [Recurrent Highway Networks](https://nn.labml.ai/recurrent_highway_networks/index.html)
+
+#### ✨ [LSTM](https://nn.labml.ai/lstm/index.html)
+
+#### ✨ [HyperNetworks - HyperLSTM](https://nn.labml.ai/hypernetworks/hyper_lstm.html)
+
+#### ✨ [ResNet](https://nn.labml.ai/resnet/index.html)
+
+#### ✨ [ConvMixer](https://nn.labml.ai/conv_mixer/index.html)
+
+#### ✨ [Capsule Networks](https://nn.labml.ai/capsule_networks/index.html)
+
+#### ✨ [U-Net](https://nn.labml.ai/unet/index.html)
+
+#### ✨ [Sketch RNN](https://nn.labml.ai/sketch_rnn/index.html)
+
+#### ✨ Graph Neural Networks
+
+* [Graph Attention Networks (GAT)](https://nn.labml.ai/graphs/gat/index.html)
+* [Graph Attention Networks v2 (GATv2)](https://nn.labml.ai/graphs/gatv2/index.html)
+
+#### ✨ [Counterfactual Regret Minimization (CFR)](https://nn.labml.ai/cfr/index.html)
+
+Solving games with incomplete information such as poker with CFR.
+
+* [Kuhn Poker](https://nn.labml.ai/cfr/kuhn/index.html)
+
+#### ✨ [Reinforcement Learning](https://nn.labml.ai/rl/index.html)
+* [Proximal Policy Optimization](https://nn.labml.ai/rl/ppo/index.html) with
+ [Generalized Advantage Estimation](https://nn.labml.ai/rl/ppo/gae.html)
+* [Deep Q Networks](https://nn.labml.ai/rl/dqn/index.html) with
+ with [Dueling Network](https://nn.labml.ai/rl/dqn/model.html),
+ [Prioritized Replay](https://nn.labml.ai/rl/dqn/replay_buffer.html)
+ and Double Q Network.
+
+#### ✨ [Optimizers](https://nn.labml.ai/optimizers/index.html)
+* [Adam](https://nn.labml.ai/optimizers/adam.html)
+* [AMSGrad](https://nn.labml.ai/optimizers/amsgrad.html)
+* [Adam Optimizer with warmup](https://nn.labml.ai/optimizers/adam_warmup.html)
+* [Noam Optimizer](https://nn.labml.ai/optimizers/noam.html)
+* [Rectified Adam Optimizer](https://nn.labml.ai/optimizers/radam.html)
+* [AdaBelief Optimizer](https://nn.labml.ai/optimizers/ada_belief.html)
+
+#### ✨ [Normalization Layers](https://nn.labml.ai/normalization/index.html)
+* [Batch Normalization](https://nn.labml.ai/normalization/batch_norm/index.html)
+* [Layer Normalization](https://nn.labml.ai/normalization/layer_norm/index.html)
+* [Instance Normalization](https://nn.labml.ai/normalization/instance_norm/index.html)
+* [Group Normalization](https://nn.labml.ai/normalization/group_norm/index.html)
+* [Weight Standardization](https://nn.labml.ai/normalization/weight_standardization/index.html)
+* [Batch-Channel Normalization](https://nn.labml.ai/normalization/batch_channel_norm/index.html)
+* [DeepNorm](https://nn.labml.ai/normalization/deep_norm/index.html)
+
+#### ✨ [Distillation](https://nn.labml.ai/distillation/index.html)
+
+#### ✨ [Adaptive Computation](https://nn.labml.ai/adaptive_computation/index.html)
+
+* [PonderNet](https://nn.labml.ai/adaptive_computation/ponder_net/index.html)
+
+#### ✨ [Uncertainty](https://nn.labml.ai/uncertainty/index.html)
+
+* [Evidential Deep Learning to Quantify Classification Uncertainty](https://nn.labml.ai/uncertainty/evidence/index.html)
+
+#### ✨ [Activations](https://nn.labml.ai/activations/index.html)
+
+* [Fuzzy Tiling Activations](https://nn.labml.ai/activations/fta/index.html)
+
+#### ✨ [Langauge Model Sampling Techniques](https://nn.labml.ai/sampling/index.html)
+* [Greedy Sampling](https://nn.labml.ai/sampling/greedy.html)
+* [Temperature Sampling](https://nn.labml.ai/sampling/temperature.html)
+* [Top-k Sampling](https://nn.labml.ai/sampling/top_k.html)
+* [Nucleus Sampling](https://nn.labml.ai/sampling/nucleus.html)
+
+#### ✨ [Scalable Training/Inference](https://nn.labml.ai/scaling/index.html)
+* [Zero3 memory optimizations](https://nn.labml.ai/scaling/zero3/index.html)
+
+## Highlighted Research Paper PDFs
+
+* [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2205.14135.pdf)
+* [Autoregressive Search Engines: Generating Substrings as Document Identifiers](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.10628.pdf)
+* [Training Compute-Optimal Large Language Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.15556.pdf)
+* [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1910.02054.pdf)
+* [PaLM: Scaling Language Modeling with Pathways](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.02311.pdf)
+* [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/dall-e-2.pdf)
+* [STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.14465.pdf)
+* [Improving language models by retrieving from trillions of tokens](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2112.04426.pdf)
+* [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2003.08934.pdf)
+* [Attention Is All You Need](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1706.03762.pdf)
+* [Denoising Diffusion Probabilistic Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2006.11239.pdf)
+* [Primer: Searching for Efficient Transformers for Language Modeling](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2109.08668.pdf)
+* [On First-Order Meta-Learning Algorithms](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1803.02999.pdf)
+* [Learning Transferable Visual Models From Natural Language Supervision](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2103.00020.pdf)
+* [The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2109.02869.pdf)
+* [Meta-Gradient Reinforcement Learning](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1805.09801.pdf)
+* [ETA Prediction with Graph Neural Networks in Google Maps](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/google_maps_eta.pdf)
+* [PonderNet: Learning to Ponder](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/ponder_net.pdf)
+* [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/muzero.pdf)
+* [GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!)](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/gans_n_roses.pdf)
+* [An Image is Worth 16X16 Word: Transformers for Image Recognition at Scale](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/vit.pdf)
+* [Deep Residual Learning for Image Recognition](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/resnet.pdf)
+* [Distilling the Knowledge in a Neural Network](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/distillation.pdf)
+
+### Installation
+
+```bash
+pip install labml-nn
+```
+
+### Citing
+
+If you use this for academic research, please cite it using the following BibTeX entry.
+
+```bibtex
+@misc{labml,
+ author = {Varuna Jayasiri, Nipun Wijerathne},
+ title = {labml.ai Annotated Paper Implementations},
+ year = {2020},
+ url = {https://nn.labml.ai/},
+}
+```
+
+### Other Projects
+
+#### [🚀 Trending Research Papers](https://papers.labml.ai/)
+
+This shows the most popular research papers on social media. It also aggregates links to useful resources like paper explanations videos and discussions.
+
+
+#### [🧪 labml.ai/labml](https://github.com/labmlai/labml)
+
+This is a library that let's you monitor deep learning model training and hardware usage from your mobile phone. It also comes with a bunch of other tools to help write deep learning code efficiently.
+
+
+
+
+
+%package -n python3-labml-nn
+Summary:	🧑‍🏫 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit), optimizers (adam, radam, adabelief), gans(dcgan, cyclegan, stylegan2), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, diffusion, etc. 🧠
+Provides:	python-labml-nn
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-labml-nn
+[![Twitter](https://img.shields.io/twitter/follow/labmlai?style=social)](https://twitter.com/labmlai)
+[![Sponsor](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/labmlai)
+
+# [labml.ai Deep Learning Paper Implementations](https://nn.labml.ai/index.html)
+
+This is a collection of simple PyTorch implementations of
+neural networks and related algorithms.
+These implementations are documented with explanations,
+
+[The website](https://nn.labml.ai/index.html)
+renders these as side-by-side formatted notes.
+We believe these would help you understand these algorithms better.
+
+![Screenshot](https://nn.labml.ai/dqn-light.png)
+
+We are actively maintaining this repo and adding new 
+implementations almost weekly.
+[![Twitter](https://img.shields.io/twitter/follow/labmlai?style=social)](https://twitter.com/labmlai) for updates.
+
+## Paper Implementations
+
+#### ✨ [Transformers](https://nn.labml.ai/transformers/index.html)
+
+* [Multi-headed attention](https://nn.labml.ai/transformers/mha.html)
+* [Transformer building blocks](https://nn.labml.ai/transformers/models.html) 
+* [Transformer XL](https://nn.labml.ai/transformers/xl/index.html)
+    * [Relative multi-headed attention](https://nn.labml.ai/transformers/xl/relative_mha.html)
+* [Rotary Positional Embeddings](https://nn.labml.ai/transformers/rope/index.html)
+* [Attention with Linear Biases (ALiBi)](https://nn.labml.ai/transformers/alibi/index.html)
+* [RETRO](https://nn.labml.ai/transformers/retro/index.html)
+* [Compressive Transformer](https://nn.labml.ai/transformers/compressive/index.html)
+* [GPT Architecture](https://nn.labml.ai/transformers/gpt/index.html)
+* [GLU Variants](https://nn.labml.ai/transformers/glu_variants/simple.html)
+* [kNN-LM: Generalization through Memorization](https://nn.labml.ai/transformers/knn)
+* [Feedback Transformer](https://nn.labml.ai/transformers/feedback/index.html)
+* [Switch Transformer](https://nn.labml.ai/transformers/switch/index.html)
+* [Fast Weights Transformer](https://nn.labml.ai/transformers/fast_weights/index.html)
+* [FNet](https://nn.labml.ai/transformers/fnet/index.html)
+* [Attention Free Transformer](https://nn.labml.ai/transformers/aft/index.html)
+* [Masked Language Model](https://nn.labml.ai/transformers/mlm/index.html)
+* [MLP-Mixer: An all-MLP Architecture for Vision](https://nn.labml.ai/transformers/mlp_mixer/index.html)
+* [Pay Attention to MLPs (gMLP)](https://nn.labml.ai/transformers/gmlp/index.html)
+* [Vision Transformer (ViT)](https://nn.labml.ai/transformers/vit/index.html)
+* [Primer EZ](https://nn.labml.ai/transformers/primer_ez/index.html)
+* [Hourglass](https://nn.labml.ai/transformers/hour_glass/index.html)
+
+#### ✨ [Eleuther GPT-NeoX](https://nn.labml.ai/neox/index.html)
+* [Generate on a 48GB GPU](https://nn.labml.ai/neox/samples/generate.html)
+* [Finetune on two 48GB GPUs](https://nn.labml.ai/neox/samples/finetune.html)
+* [LLM.int8()](https://nn.labml.ai/neox/utils/llm_int8.html)
+
+#### ✨ [Diffusion models](https://nn.labml.ai/diffusion/index.html)
+
+* [Denoising Diffusion Probabilistic Models (DDPM)](https://nn.labml.ai/diffusion/ddpm/index.html)
+* [Denoising Diffusion Implicit Models (DDIM)](https://nn.labml.ai/diffusion/stable_diffusion/sampler/ddim.html)
+* [Latent Diffusion Models](https://nn.labml.ai/diffusion/stable_diffusion/latent_diffusion.html)
+* [Stable Diffusion](https://nn.labml.ai/diffusion/stable_diffusion/index.html)
+
+#### ✨ [Generative Adversarial Networks](https://nn.labml.ai/gan/index.html)
+* [Original GAN](https://nn.labml.ai/gan/original/index.html)
+* [GAN with deep convolutional network](https://nn.labml.ai/gan/dcgan/index.html)
+* [Cycle GAN](https://nn.labml.ai/gan/cycle_gan/index.html)
+* [Wasserstein GAN](https://nn.labml.ai/gan/wasserstein/index.html)
+* [Wasserstein GAN with Gradient Penalty](https://nn.labml.ai/gan/wasserstein/gradient_penalty/index.html)
+* [StyleGAN 2](https://nn.labml.ai/gan/stylegan/index.html)
+
+#### ✨ [Recurrent Highway Networks](https://nn.labml.ai/recurrent_highway_networks/index.html)
+
+#### ✨ [LSTM](https://nn.labml.ai/lstm/index.html)
+
+#### ✨ [HyperNetworks - HyperLSTM](https://nn.labml.ai/hypernetworks/hyper_lstm.html)
+
+#### ✨ [ResNet](https://nn.labml.ai/resnet/index.html)
+
+#### ✨ [ConvMixer](https://nn.labml.ai/conv_mixer/index.html)
+
+#### ✨ [Capsule Networks](https://nn.labml.ai/capsule_networks/index.html)
+
+#### ✨ [U-Net](https://nn.labml.ai/unet/index.html)
+
+#### ✨ [Sketch RNN](https://nn.labml.ai/sketch_rnn/index.html)
+
+#### ✨ Graph Neural Networks
+
+* [Graph Attention Networks (GAT)](https://nn.labml.ai/graphs/gat/index.html)
+* [Graph Attention Networks v2 (GATv2)](https://nn.labml.ai/graphs/gatv2/index.html)
+
+#### ✨ [Counterfactual Regret Minimization (CFR)](https://nn.labml.ai/cfr/index.html)
+
+Solving games with incomplete information such as poker with CFR.
+
+* [Kuhn Poker](https://nn.labml.ai/cfr/kuhn/index.html)
+
+#### ✨ [Reinforcement Learning](https://nn.labml.ai/rl/index.html)
+* [Proximal Policy Optimization](https://nn.labml.ai/rl/ppo/index.html) with
+ [Generalized Advantage Estimation](https://nn.labml.ai/rl/ppo/gae.html)
+* [Deep Q Networks](https://nn.labml.ai/rl/dqn/index.html) with
+ with [Dueling Network](https://nn.labml.ai/rl/dqn/model.html),
+ [Prioritized Replay](https://nn.labml.ai/rl/dqn/replay_buffer.html)
+ and Double Q Network.
+
+#### ✨ [Optimizers](https://nn.labml.ai/optimizers/index.html)
+* [Adam](https://nn.labml.ai/optimizers/adam.html)
+* [AMSGrad](https://nn.labml.ai/optimizers/amsgrad.html)
+* [Adam Optimizer with warmup](https://nn.labml.ai/optimizers/adam_warmup.html)
+* [Noam Optimizer](https://nn.labml.ai/optimizers/noam.html)
+* [Rectified Adam Optimizer](https://nn.labml.ai/optimizers/radam.html)
+* [AdaBelief Optimizer](https://nn.labml.ai/optimizers/ada_belief.html)
+
+#### ✨ [Normalization Layers](https://nn.labml.ai/normalization/index.html)
+* [Batch Normalization](https://nn.labml.ai/normalization/batch_norm/index.html)
+* [Layer Normalization](https://nn.labml.ai/normalization/layer_norm/index.html)
+* [Instance Normalization](https://nn.labml.ai/normalization/instance_norm/index.html)
+* [Group Normalization](https://nn.labml.ai/normalization/group_norm/index.html)
+* [Weight Standardization](https://nn.labml.ai/normalization/weight_standardization/index.html)
+* [Batch-Channel Normalization](https://nn.labml.ai/normalization/batch_channel_norm/index.html)
+* [DeepNorm](https://nn.labml.ai/normalization/deep_norm/index.html)
+
+#### ✨ [Distillation](https://nn.labml.ai/distillation/index.html)
+
+#### ✨ [Adaptive Computation](https://nn.labml.ai/adaptive_computation/index.html)
+
+* [PonderNet](https://nn.labml.ai/adaptive_computation/ponder_net/index.html)
+
+#### ✨ [Uncertainty](https://nn.labml.ai/uncertainty/index.html)
+
+* [Evidential Deep Learning to Quantify Classification Uncertainty](https://nn.labml.ai/uncertainty/evidence/index.html)
+
+#### ✨ [Activations](https://nn.labml.ai/activations/index.html)
+
+* [Fuzzy Tiling Activations](https://nn.labml.ai/activations/fta/index.html)
+
+#### ✨ [Langauge Model Sampling Techniques](https://nn.labml.ai/sampling/index.html)
+* [Greedy Sampling](https://nn.labml.ai/sampling/greedy.html)
+* [Temperature Sampling](https://nn.labml.ai/sampling/temperature.html)
+* [Top-k Sampling](https://nn.labml.ai/sampling/top_k.html)
+* [Nucleus Sampling](https://nn.labml.ai/sampling/nucleus.html)
+
+#### ✨ [Scalable Training/Inference](https://nn.labml.ai/scaling/index.html)
+* [Zero3 memory optimizations](https://nn.labml.ai/scaling/zero3/index.html)
+
+## Highlighted Research Paper PDFs
+
+* [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2205.14135.pdf)
+* [Autoregressive Search Engines: Generating Substrings as Document Identifiers](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.10628.pdf)
+* [Training Compute-Optimal Large Language Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.15556.pdf)
+* [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1910.02054.pdf)
+* [PaLM: Scaling Language Modeling with Pathways](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.02311.pdf)
+* [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/dall-e-2.pdf)
+* [STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.14465.pdf)
+* [Improving language models by retrieving from trillions of tokens](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2112.04426.pdf)
+* [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2003.08934.pdf)
+* [Attention Is All You Need](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1706.03762.pdf)
+* [Denoising Diffusion Probabilistic Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2006.11239.pdf)
+* [Primer: Searching for Efficient Transformers for Language Modeling](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2109.08668.pdf)
+* [On First-Order Meta-Learning Algorithms](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1803.02999.pdf)
+* [Learning Transferable Visual Models From Natural Language Supervision](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2103.00020.pdf)
+* [The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2109.02869.pdf)
+* [Meta-Gradient Reinforcement Learning](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1805.09801.pdf)
+* [ETA Prediction with Graph Neural Networks in Google Maps](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/google_maps_eta.pdf)
+* [PonderNet: Learning to Ponder](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/ponder_net.pdf)
+* [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/muzero.pdf)
+* [GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!)](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/gans_n_roses.pdf)
+* [An Image is Worth 16X16 Word: Transformers for Image Recognition at Scale](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/vit.pdf)
+* [Deep Residual Learning for Image Recognition](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/resnet.pdf)
+* [Distilling the Knowledge in a Neural Network](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/distillation.pdf)
+
+### Installation
+
+```bash
+pip install labml-nn
+```
+
+### Citing
+
+If you use this for academic research, please cite it using the following BibTeX entry.
+
+```bibtex
+@misc{labml,
+ author = {Varuna Jayasiri, Nipun Wijerathne},
+ title = {labml.ai Annotated Paper Implementations},
+ year = {2020},
+ url = {https://nn.labml.ai/},
+}
+```
+
+### Other Projects
+
+#### [🚀 Trending Research Papers](https://papers.labml.ai/)
+
+This shows the most popular research papers on social media. It also aggregates links to useful resources like paper explanations videos and discussions.
+
+
+#### [🧪 labml.ai/labml](https://github.com/labmlai/labml)
+
+This is a library that let's you monitor deep learning model training and hardware usage from your mobile phone. It also comes with a bunch of other tools to help write deep learning code efficiently.
+
+
+
+
+
+%package help
+Summary:	Development documents and examples for labml-nn
+Provides:	python3-labml-nn-doc
+%description help
+[![Twitter](https://img.shields.io/twitter/follow/labmlai?style=social)](https://twitter.com/labmlai)
+[![Sponsor](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/labmlai)
+
+# [labml.ai Deep Learning Paper Implementations](https://nn.labml.ai/index.html)
+
+This is a collection of simple PyTorch implementations of
+neural networks and related algorithms.
+These implementations are documented with explanations,
+
+[The website](https://nn.labml.ai/index.html)
+renders these as side-by-side formatted notes.
+We believe these would help you understand these algorithms better.
+
+![Screenshot](https://nn.labml.ai/dqn-light.png)
+
+We are actively maintaining this repo and adding new 
+implementations almost weekly.
+[![Twitter](https://img.shields.io/twitter/follow/labmlai?style=social)](https://twitter.com/labmlai) for updates.
+
+## Paper Implementations
+
+#### ✨ [Transformers](https://nn.labml.ai/transformers/index.html)
+
+* [Multi-headed attention](https://nn.labml.ai/transformers/mha.html)
+* [Transformer building blocks](https://nn.labml.ai/transformers/models.html) 
+* [Transformer XL](https://nn.labml.ai/transformers/xl/index.html)
+    * [Relative multi-headed attention](https://nn.labml.ai/transformers/xl/relative_mha.html)
+* [Rotary Positional Embeddings](https://nn.labml.ai/transformers/rope/index.html)
+* [Attention with Linear Biases (ALiBi)](https://nn.labml.ai/transformers/alibi/index.html)
+* [RETRO](https://nn.labml.ai/transformers/retro/index.html)
+* [Compressive Transformer](https://nn.labml.ai/transformers/compressive/index.html)
+* [GPT Architecture](https://nn.labml.ai/transformers/gpt/index.html)
+* [GLU Variants](https://nn.labml.ai/transformers/glu_variants/simple.html)
+* [kNN-LM: Generalization through Memorization](https://nn.labml.ai/transformers/knn)
+* [Feedback Transformer](https://nn.labml.ai/transformers/feedback/index.html)
+* [Switch Transformer](https://nn.labml.ai/transformers/switch/index.html)
+* [Fast Weights Transformer](https://nn.labml.ai/transformers/fast_weights/index.html)
+* [FNet](https://nn.labml.ai/transformers/fnet/index.html)
+* [Attention Free Transformer](https://nn.labml.ai/transformers/aft/index.html)
+* [Masked Language Model](https://nn.labml.ai/transformers/mlm/index.html)
+* [MLP-Mixer: An all-MLP Architecture for Vision](https://nn.labml.ai/transformers/mlp_mixer/index.html)
+* [Pay Attention to MLPs (gMLP)](https://nn.labml.ai/transformers/gmlp/index.html)
+* [Vision Transformer (ViT)](https://nn.labml.ai/transformers/vit/index.html)
+* [Primer EZ](https://nn.labml.ai/transformers/primer_ez/index.html)
+* [Hourglass](https://nn.labml.ai/transformers/hour_glass/index.html)
+
+#### ✨ [Eleuther GPT-NeoX](https://nn.labml.ai/neox/index.html)
+* [Generate on a 48GB GPU](https://nn.labml.ai/neox/samples/generate.html)
+* [Finetune on two 48GB GPUs](https://nn.labml.ai/neox/samples/finetune.html)
+* [LLM.int8()](https://nn.labml.ai/neox/utils/llm_int8.html)
+
+#### ✨ [Diffusion models](https://nn.labml.ai/diffusion/index.html)
+
+* [Denoising Diffusion Probabilistic Models (DDPM)](https://nn.labml.ai/diffusion/ddpm/index.html)
+* [Denoising Diffusion Implicit Models (DDIM)](https://nn.labml.ai/diffusion/stable_diffusion/sampler/ddim.html)
+* [Latent Diffusion Models](https://nn.labml.ai/diffusion/stable_diffusion/latent_diffusion.html)
+* [Stable Diffusion](https://nn.labml.ai/diffusion/stable_diffusion/index.html)
+
+#### ✨ [Generative Adversarial Networks](https://nn.labml.ai/gan/index.html)
+* [Original GAN](https://nn.labml.ai/gan/original/index.html)
+* [GAN with deep convolutional network](https://nn.labml.ai/gan/dcgan/index.html)
+* [Cycle GAN](https://nn.labml.ai/gan/cycle_gan/index.html)
+* [Wasserstein GAN](https://nn.labml.ai/gan/wasserstein/index.html)
+* [Wasserstein GAN with Gradient Penalty](https://nn.labml.ai/gan/wasserstein/gradient_penalty/index.html)
+* [StyleGAN 2](https://nn.labml.ai/gan/stylegan/index.html)
+
+#### ✨ [Recurrent Highway Networks](https://nn.labml.ai/recurrent_highway_networks/index.html)
+
+#### ✨ [LSTM](https://nn.labml.ai/lstm/index.html)
+
+#### ✨ [HyperNetworks - HyperLSTM](https://nn.labml.ai/hypernetworks/hyper_lstm.html)
+
+#### ✨ [ResNet](https://nn.labml.ai/resnet/index.html)
+
+#### ✨ [ConvMixer](https://nn.labml.ai/conv_mixer/index.html)
+
+#### ✨ [Capsule Networks](https://nn.labml.ai/capsule_networks/index.html)
+
+#### ✨ [U-Net](https://nn.labml.ai/unet/index.html)
+
+#### ✨ [Sketch RNN](https://nn.labml.ai/sketch_rnn/index.html)
+
+#### ✨ Graph Neural Networks
+
+* [Graph Attention Networks (GAT)](https://nn.labml.ai/graphs/gat/index.html)
+* [Graph Attention Networks v2 (GATv2)](https://nn.labml.ai/graphs/gatv2/index.html)
+
+#### ✨ [Counterfactual Regret Minimization (CFR)](https://nn.labml.ai/cfr/index.html)
+
+Solving games with incomplete information such as poker with CFR.
+
+* [Kuhn Poker](https://nn.labml.ai/cfr/kuhn/index.html)
+
+#### ✨ [Reinforcement Learning](https://nn.labml.ai/rl/index.html)
+* [Proximal Policy Optimization](https://nn.labml.ai/rl/ppo/index.html) with
+ [Generalized Advantage Estimation](https://nn.labml.ai/rl/ppo/gae.html)
+* [Deep Q Networks](https://nn.labml.ai/rl/dqn/index.html) with
+ with [Dueling Network](https://nn.labml.ai/rl/dqn/model.html),
+ [Prioritized Replay](https://nn.labml.ai/rl/dqn/replay_buffer.html)
+ and Double Q Network.
+
+#### ✨ [Optimizers](https://nn.labml.ai/optimizers/index.html)
+* [Adam](https://nn.labml.ai/optimizers/adam.html)
+* [AMSGrad](https://nn.labml.ai/optimizers/amsgrad.html)
+* [Adam Optimizer with warmup](https://nn.labml.ai/optimizers/adam_warmup.html)
+* [Noam Optimizer](https://nn.labml.ai/optimizers/noam.html)
+* [Rectified Adam Optimizer](https://nn.labml.ai/optimizers/radam.html)
+* [AdaBelief Optimizer](https://nn.labml.ai/optimizers/ada_belief.html)
+
+#### ✨ [Normalization Layers](https://nn.labml.ai/normalization/index.html)
+* [Batch Normalization](https://nn.labml.ai/normalization/batch_norm/index.html)
+* [Layer Normalization](https://nn.labml.ai/normalization/layer_norm/index.html)
+* [Instance Normalization](https://nn.labml.ai/normalization/instance_norm/index.html)
+* [Group Normalization](https://nn.labml.ai/normalization/group_norm/index.html)
+* [Weight Standardization](https://nn.labml.ai/normalization/weight_standardization/index.html)
+* [Batch-Channel Normalization](https://nn.labml.ai/normalization/batch_channel_norm/index.html)
+* [DeepNorm](https://nn.labml.ai/normalization/deep_norm/index.html)
+
+#### ✨ [Distillation](https://nn.labml.ai/distillation/index.html)
+
+#### ✨ [Adaptive Computation](https://nn.labml.ai/adaptive_computation/index.html)
+
+* [PonderNet](https://nn.labml.ai/adaptive_computation/ponder_net/index.html)
+
+#### ✨ [Uncertainty](https://nn.labml.ai/uncertainty/index.html)
+
+* [Evidential Deep Learning to Quantify Classification Uncertainty](https://nn.labml.ai/uncertainty/evidence/index.html)
+
+#### ✨ [Activations](https://nn.labml.ai/activations/index.html)
+
+* [Fuzzy Tiling Activations](https://nn.labml.ai/activations/fta/index.html)
+
+#### ✨ [Langauge Model Sampling Techniques](https://nn.labml.ai/sampling/index.html)
+* [Greedy Sampling](https://nn.labml.ai/sampling/greedy.html)
+* [Temperature Sampling](https://nn.labml.ai/sampling/temperature.html)
+* [Top-k Sampling](https://nn.labml.ai/sampling/top_k.html)
+* [Nucleus Sampling](https://nn.labml.ai/sampling/nucleus.html)
+
+#### ✨ [Scalable Training/Inference](https://nn.labml.ai/scaling/index.html)
+* [Zero3 memory optimizations](https://nn.labml.ai/scaling/zero3/index.html)
+
+## Highlighted Research Paper PDFs
+
+* [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2205.14135.pdf)
+* [Autoregressive Search Engines: Generating Substrings as Document Identifiers](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.10628.pdf)
+* [Training Compute-Optimal Large Language Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.15556.pdf)
+* [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1910.02054.pdf)
+* [PaLM: Scaling Language Modeling with Pathways](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.02311.pdf)
+* [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/dall-e-2.pdf)
+* [STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.14465.pdf)
+* [Improving language models by retrieving from trillions of tokens](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2112.04426.pdf)
+* [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2003.08934.pdf)
+* [Attention Is All You Need](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1706.03762.pdf)
+* [Denoising Diffusion Probabilistic Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2006.11239.pdf)
+* [Primer: Searching for Efficient Transformers for Language Modeling](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2109.08668.pdf)
+* [On First-Order Meta-Learning Algorithms](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1803.02999.pdf)
+* [Learning Transferable Visual Models From Natural Language Supervision](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2103.00020.pdf)
+* [The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2109.02869.pdf)
+* [Meta-Gradient Reinforcement Learning](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1805.09801.pdf)
+* [ETA Prediction with Graph Neural Networks in Google Maps](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/google_maps_eta.pdf)
+* [PonderNet: Learning to Ponder](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/ponder_net.pdf)
+* [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/muzero.pdf)
+* [GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!)](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/gans_n_roses.pdf)
+* [An Image is Worth 16X16 Word: Transformers for Image Recognition at Scale](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/vit.pdf)
+* [Deep Residual Learning for Image Recognition](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/resnet.pdf)
+* [Distilling the Knowledge in a Neural Network](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/distillation.pdf)
+
+### Installation
+
+```bash
+pip install labml-nn
+```
+
+### Citing
+
+If you use this for academic research, please cite it using the following BibTeX entry.
+
+```bibtex
+@misc{labml,
+ author = {Varuna Jayasiri, Nipun Wijerathne},
+ title = {labml.ai Annotated Paper Implementations},
+ year = {2020},
+ url = {https://nn.labml.ai/},
+}
+```
+
+### Other Projects
+
+#### [🚀 Trending Research Papers](https://papers.labml.ai/)
+
+This shows the most popular research papers on social media. It also aggregates links to useful resources like paper explanations videos and discussions.
+
+
+#### [🧪 labml.ai/labml](https://github.com/labmlai/labml)
+
+This is a library that let's you monitor deep learning model training and hardware usage from your mobile phone. It also comes with a bunch of other tools to help write deep learning code efficiently.
+
+
+
+
+
+%prep
+%autosetup -n labml-nn-0.4.133
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-labml-nn -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.133-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..1dcf9b1
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+e1124ae4c482e61124bedd25287abe7f  labml-nn-0.4.133.tar.gz