Sea-Snell / grokkingLinks
unofficial re-implementation of "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets"
☆83Updated 3 years ago
Alternatives and similar repositories for grokking
Users that are interested in grokking are comparing it to the libraries listed below
Sorting:
- Emergent world representations: Exploring a sequence model trained on a synthetic task☆198Updated 2 years ago
- Implementation of OpenAI's 'Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets' paper.☆41Updated 2 years ago
- Notebooks accompanying Anthropic's "Toy Models of Superposition" paper☆132Updated 3 years ago
- ☆83Updated 2 years ago
- Sparse and discrete interpretability tool for neural networks☆64Updated last year
- Neural Networks and the Chomsky Hierarchy☆211Updated last year
- ☆185Updated 2 years ago
- ☆73Updated 3 years ago
- [NeurIPS 2023] Learning Transformer Programs☆162Updated last year
- A MAD laboratory to improve AI architecture designs 🧪☆136Updated last year
- Omnigrok: Grokking Beyond Algorithmic Data☆62Updated 2 years ago
- Train very large language models in Jax.☆210Updated 2 years ago
- LoRA for arbitrary JAX models and functions☆143Updated last year
- Code Release for "Broken Neural Scaling Laws" (BNSL) paper☆59Updated 2 years ago
- nanoGPT-like codebase for LLM training☆114Updated 2 months ago
- ☆167Updated 2 years ago
- ☆241Updated last year
- Code associated to papers on superposition (in ML interpretability)☆35Updated 3 years ago
- ☆28Updated 2 years ago
- ☆53Updated last year
- Scaling scaling laws with board games.☆53Updated 2 years ago
- Code accompanying our paper "Feature Learning in Infinite-Width Neural Networks" (https://arxiv.org/abs/2011.14522)☆63Updated 4 years ago
- Materials for ConceptARC paper☆111Updated last year
- A centralized place for deep thinking code and experiments☆89Updated 2 years ago
- Language models scale reliably with over-training and on downstream tasks☆100Updated last year
- Latent Program Network (from the "Searching Latent Program Spaces" paper)☆107Updated last month
- Experiments and code to generate the GINC small-scale in-context learning dataset from "An Explanation for In-context Learning as Implici…☆106Updated 2 years ago
- ☆137Updated last year
- Unofficial but Efficient Implementation of "Mamba: Linear-Time Sequence Modeling with Selective State Spaces" in JAX☆92Updated last year
- Understand and test language model architectures on synthetic tasks.☆249Updated last week