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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
| Date | Stars |
|---|---|
| 2026-07-24 | 67214 |
| 2026-07-25 | 67214 |
| 2026-07-28 | 67214 |
| 2026-07-30 | 67214 |
| 2026-07-31 | 67249 |
| 2026-08-01 | 67255 |
| 2026-08-02 | 67257 |
| 2026-08-03 | 67259 |
| 2026-08-04 | 67263 |
| 2026-08-05 | 67274 |
| 2026-08-06 | 67282 |
Today
+8 stars today
This week
+68 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.10%/day
[](https://twitter.com/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.
We are actively maintaining this repo and adding new
implementations almost weekly.
[](https://twitter.com/labmlai) for updates.
## Paper Implementations
#### ✨ [Transformers](https://nn.labml.ai/transformers/index.html)
* [JAX implementation](https://nn.labml.ai/transformers/jax_transformer/index.html)
* [Multi-headed attention](https://nn.labml.ai/transformers/mha.html)
* [Triton Flash Attention](https://nn.labml.ai/transformers/flash/index.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)
#### ✨ [Low-Rank Adaptation (LoRA)](https://nn.labml.ai/lora/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.htmExcerpt of 7,760 characters
Read on GitHub
vpj
767
18
Lakshith Nishshanke · @labmlai · Sri Lanka
17
Adithya S Narasinghe · @labmlai · Sri Lanka
4
Hongyou Fu · Shandong University · China
2
隔壁的泰山233 · @udacity
2
Kartik · NVIDIA · Germany
2
Ikko Eltociear Ashimine · Japan
2
Shaked Brody · Israel
2
Tatsuo Okubo · Chinese Institute for Brain Research, Beijing · China
2
Evan Han · Search OS · South Korea
2
Jialong Wu
1
1
Thanh Tran · JAIST
1
1
Nirmal Singhania · NIIT University · India
1
Nipun Wijerathne · Developer @labmlai · Sri Lanka
1
Nemo · Germany
1
Nail Ibrahimli · Netherlands
1
coder
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:11c6c94a1aa87db6, topic:deep-learning, topic:pytorch
matched fp:11c6c94a1aa87db6, topic:lora, readme:finetune, readme:lora
matched fp:11c6c94a1aa87db6, topic:gan, readme:stable diffusion, readme:latent diffusion
matched fp:11c6c94a1aa87db6, topic:reinforcement-learning, desc:reinforcement learning