VITA-Group / Random-MoE-as-Dropout
[ICLR 2023] "Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers" by Tianlong Chen*, Zhenyu Zhang*, Ajay Jaiswal, Shiwei Liu, Zhangyang Wang
☆48Updated last year
Alternatives and similar repositories for Random-MoE-as-Dropout:
Users that are interested in Random-MoE-as-Dropout are comparing it to the libraries listed below
- A Closer Look into Mixture-of-Experts in Large Language Models☆41Updated 5 months ago
- The source code of "Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (EMNLP 2023)":☆34Updated 9 months ago
- Code for "Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes"☆27Updated 9 months ago
- ☆27Updated last year
- Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding (EMNLP 2023 Long)☆56Updated 3 months ago
- Long Context Extension and Generalization in LLMs☆40Updated 3 months ago
- [NeurIPS 2023] Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning☆29Updated last year
- ☆18Updated last year
- [NeurIPS 2024 Spotlight] Code and data for the paper "Finding Transformer Circuits with Edge Pruning".☆42Updated last month
- One Network, Many Masks: Towards More Parameter-Efficient Transfer Learning☆38Updated last year
- [ICML 2024] Junk DNA Hypothesis: A Task-Centric Angle of LLM Pre-trained Weights through Sparsity; Lu Yin*, Ajay Jaiswal*, Shiwei Liu, So…☆16Updated 7 months ago
- The this is the official implementation of "DAPE: Data-Adaptive Positional Encoding for Length Extrapolation"☆33Updated 3 months ago
- [ICML2024 Spotlight] Fine-Tuning Pre-trained Large Language Models Sparsely☆20Updated 6 months ago
- [NeurIPS-2024] 📈 Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies https://arxiv.org/abs/2407.13623☆75Updated 3 months ago
- ☆118Updated 5 months ago
- [EMNLP 2023 Main] Sparse Low-rank Adaptation of Pre-trained Language Models☆70Updated 10 months ago
- Repo for ACL2023 Findings paper "Emergent Modularity in Pre-trained Transformers"☆21Updated last year
- [NeurIPS 2024 Main Track] Code for the paper titled "Instruction Tuning With Loss Over Instructions"☆33Updated 7 months ago
- Code for paper "Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning"☆65Updated 11 months ago
- [EVA ICLR'23; LARA ICML'22] Efficient attention mechanisms via control variates, random features, and importance sampling☆80Updated last year
- ☆27Updated 2 months ago
- This pytorch package implements PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance (ICML 2022).☆43Updated 2 years ago
- Stick-breaking attention☆41Updated this week
- Official implementation of Bootstrapping Language Models via DPO Implicit Rewards☆41Updated 5 months ago
- "Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding" Zhenyu Zhang, Runjin Chen, Shiw…☆25Updated 8 months ago
- Code for "Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective"☆31Updated 8 months ago
- [ICML 2024] When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models☆28Updated 7 months ago
- This package implements THOR: Transformer with Stochastic Experts.☆61Updated 3 years ago
- [NeurIPS2024] Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging☆47Updated last month
- Official implementation of the paper: "A deeper look at depth pruning of LLMs"☆12Updated 5 months ago