RobertCsordas / moeLinks
Official repository for the paper "Approximating Two-Layer Feedforward Networks for Efficient Transformers"
☆38Updated 5 months ago
Alternatives and similar repositories for moe
Users that are interested in moe are comparing it to the libraries listed below
Sorting:
- Triton Implementation of HyperAttention Algorithm☆48Updated last year
- Official repository for the paper "SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention"☆101Updated last year
- A repository for research on medium sized language models.☆78Updated last year
- GoldFinch and other hybrid transformer components☆45Updated last year
- ☆87Updated last year
- Using FlexAttention to compute attention with different masking patterns☆47Updated last year
- sigma-MoE layer☆20Updated last year
- Griffin MQA + Hawk Linear RNN Hybrid☆89Updated last year
- ☆81Updated last year
- ☆46Updated last year
- 32 times longer context window than vanilla Transformers and up to 4 times longer than memory efficient Transformers.☆48Updated 2 years ago
- Yet another random morning idea to be quickly tried and architecture shared if it works; to allow the transformer to pause for any amount…☆52Updated 2 years ago
- ☆48Updated last year
- ☆26Updated last year
- Demonstration that finetuning RoPE model on larger sequences than the pre-trained model adapts the model context limit☆62Updated 2 years ago
- Pytorch implementation of the PEER block from the paper, Mixture of A Million Experts, by Xu Owen He at Deepmind☆131Updated last week
- Official repository of paper "RNNs Are Not Transformers (Yet): The Key Bottleneck on In-context Retrieval"☆27Updated last year
- ☆45Updated 2 years ago
- Implementation of the paper: "Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention" from Google in pyTO…☆56Updated 3 weeks ago
- Here we will test various linear attention designs.☆61Updated last year
- ☆50Updated last year
- HGRN2: Gated Linear RNNs with State Expansion☆55Updated last year
- Implementation of "LM-Infinite: Simple On-the-Fly Length Generalization for Large Language Models"☆39Updated last year
- The source code of our work "Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models" [AISTATS …☆60Updated last year
- Token Omission Via Attention☆127Updated last year
- ☆56Updated last year
- ☆53Updated last year
- Minimum Description Length probing for neural network representations☆20Updated 9 months ago
- Collection of autoregressive model implementation☆86Updated 6 months ago
- ☆69Updated last year