VITA-Group / Junk_DNA_Hypothesis
[ICML 2024] Junk DNA Hypothesis: A Task-Centric Angle of LLM Pre-trained Weights through Sparsity; Lu Yin*, Ajay Jaiswal*, Shiwei Liu, Souvik Kundu, Zhangyang Wang
☆16Updated 7 months ago
Alternatives and similar repositories for Junk_DNA_Hypothesis:
Users that are interested in Junk_DNA_Hypothesis are comparing it to the libraries listed below
- ☆27Updated 2 months ago
- Long Context Extension and Generalization in LLMs☆40Updated 4 months ago
- [ICLR 2023] "Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers" by Tianlong Chen*, Zhenyu Zhang*, Ajay Jaiswal…☆48Updated last year
- [ICML‘2024] "LoCoCo: Dropping In Convolutions for Long Context Compression", Ruisi Cai, Yuandong Tian, Zhangyang Wang, Beidi Chen☆16Updated 4 months ago
- Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding (EMNLP 2023 Long)☆56Updated 4 months ago
- Codebase for decoding compressed trust.☆22Updated 8 months ago
- Revisiting Efficient Training Algorithms For Transformer-based Language Models (NeurIPS 2023)☆79Updated last year
- Preprint: Asymmetry in Low-Rank Adapters of Foundation Models☆31Updated 11 months ago
- Code for "Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes"☆27Updated 10 months ago
- About Code for the paper "NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models" (EMNLP…☆13Updated last year
- ☆31Updated last year
- ☆16Updated 2 months ago
- [NeurIPS 2024 Spotlight] Code and data for the paper "Finding Transformer Circuits with Edge Pruning".☆45Updated last month
- ☆49Updated last year
- Is In-Context Learning Sufficient for Instruction Following in LLMs? [ICLR 2025]☆29Updated last week
- A Kernel-Based View of Language Model Fine-Tuning https://arxiv.org/abs/2210.05643☆74Updated last year
- ☆16Updated 6 months ago
- Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).☆58Updated 3 years ago
- Code for paper "Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning"☆65Updated last year
- Stick-breaking attention☆41Updated 2 weeks ago
- ☆11Updated 5 months ago
- Official Pytorch Implementation of Our Paper Accepted at ICLR 2024-- Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLM…☆38Updated 9 months ago
- ☆26Updated last year
- [SafeGenAi @ NeurIPS 2024] Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates☆68Updated 3 months ago
- Official implementation of Bootstrapping Language Models via DPO Implicit Rewards☆42Updated 6 months ago
- Test-time-training on nearest neighbors for large language models☆37Updated 9 months ago
- ☆78Updated 10 months ago
- [NeurIPS-2024] 📈 Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies https://arxiv.org/abs/2407.13623☆76Updated 4 months ago
- [ICML2024 Spotlight] Fine-Tuning Pre-trained Large Language Models Sparsely☆20Updated 7 months ago
- Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning [ICML 2024]☆17Updated 8 months ago