cliang1453 / task-aware-distillationLinks
Less is More: Task-aware Layer-wise Distillation for Language Model Compression (ICML2023)
☆35Updated last year
Alternatives and similar repositories for task-aware-distillation
Users that are interested in task-aware-distillation are comparing it to the libraries listed below
Sorting:
- A family of efficient edge language models in 100M~1B sizes.☆15Updated 5 months ago
- [ICML 2024] Junk DNA Hypothesis: A Task-Centric Angle of LLM Pre-trained Weights through Sparsity; Lu Yin*, Ajay Jaiswal*, Shiwei Liu, So…☆16Updated 3 months ago
- [ACL 2023] Code for paper “Tailoring Instructions to Student’s Learning Levels Boosts Knowledge Distillation”(https://arxiv.org/abs/2305.…☆38Updated 2 years ago
- [ICLR 2025] When Attention Sink Emerges in Language Models: An Empirical View (Spotlight)☆107Updated last month
- A Sober Look at Language Model Reasoning☆81Updated last month
- Official Pytorch Implementation of Our Paper Accepted at ICLR 2024-- Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLM…☆49Updated last year
- [ICLR 2023] "Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers" by Tianlong Chen*, Zhenyu Zhang*, Ajay Jaiswal…☆53Updated 2 years ago
- [NeurIPS 2023] Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning☆31Updated 2 years ago
- Codes for Merging Large Language Models☆33Updated last year
- ☆28Updated last year
- BESA is a differentiable weight pruning technique for large language models.☆17Updated last year
- 🚀 LLaMA-MoE v2: Exploring Sparsity of LLaMA from Perspective of Mixture-of-Experts with Post-Training☆86Updated 8 months ago
- The source code of "Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (EMNLP 2023)":☆38Updated last year
- One Network, Many Masks: Towards More Parameter-Efficient Transfer Learning☆40Updated 2 years ago
- ☆30Updated last year
- TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models☆78Updated last year
- Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning☆34Updated 8 months ago
- SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Model https://arxiv.org/pdf/2411.02433☆28Updated 8 months ago
- This pytorch package implements PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance (ICML 2022).☆46Updated 2 years ago
- [NeurIPS 2024] Official code of $\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$☆45Updated 9 months ago
- Test-time-training on nearest neighbors for large language models☆45Updated last year
- Code accompanying the paper "Massive Activations in Large Language Models"☆174Updated last year
- [ICLR 2024 Spotlight] Code for the paper "Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy"☆89Updated last month
- [ICLR 2025] MiniPLM: Knowledge Distillation for Pre-Training Language Models☆53Updated 8 months ago
- ☆18Updated last week
- LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning☆33Updated last year
- Representation Surgery for Multi-Task Model Merging. ICML, 2024.☆46Updated 9 months ago
- [ICLR 2024] This is the repository for the paper titled "DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning"☆95Updated last year
- [EMNLP 2023, Main Conference] Sparse Low-rank Adaptation of Pre-trained Language Models☆80Updated last year
- [ICML 2024] SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models☆21Updated last year