cloneofsimo / minSAELinks
☆30Updated last year
Alternatives and similar repositories for minSAE
Users that are interested in minSAE are comparing it to the libraries listed below
Sorting:
- supporting pytorch FSDP for optimizers☆84Updated 11 months ago
- Supporting code for the blog post on modular manifolds.☆103Updated 2 months ago
- Simple implementation of muP, based on Spectral Condition for Feature Learning. The implementation is SGD only, dont use it for Adam☆85Updated last year
- Tiny re-implementation of MDM in style of LLaDA and nano-gpt speedrun☆57Updated 8 months ago
- WIP☆93Updated last year
- Focused on fast experimentation and simplicity☆75Updated 11 months ago
- Minimal (400 LOC) implementation Maximum (multi-node, FSDP) GPT training☆132Updated last year
- The simplest, fastest repository for training/finetuning medium-sized GPTs.☆174Updated 5 months ago
- research impl of Native Sparse Attention (2502.11089)☆63Updated 9 months ago
- ☆91Updated last year
- ☆19Updated 6 months ago
- DeMo: Decoupled Momentum Optimization☆197Updated last year
- ☆41Updated last month
- 📄Small Batch Size Training for Language Models☆64Updated last month
- H-Net Dynamic Hierarchical Architecture☆80Updated 2 months ago
- ☆68Updated last year
- ☆48Updated last month
- Efficient optimizers☆276Updated 3 weeks ago
- ☆224Updated last year
- Code accompanying the paper "Generalized Interpolating Discrete Diffusion"☆108Updated 5 months ago
- ☆53Updated last year
- Landing repository for the paper "Softpick: No Attention Sink, No Massive Activations with Rectified Softmax"☆85Updated 2 months ago
- Code for the paper "Function-Space Learning Rates"☆23Updated 5 months ago
- Mixture of A Million Experts☆50Updated last year
- ☆53Updated last year
- Accelerated First Order Parallel Associative Scan☆192Updated last year
- A basic pure pytorch implementation of flash attention☆16Updated last year
- Minimal (truly) muP implementation, consistent with TP4 and TP5 papers notation☆14Updated 6 months ago
- ☆34Updated last year
- Maximal Update Parametrization (μP) with Flax & Optax.☆16Updated last year