-
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Paper • 2403.03507 • Published • 180 -
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
Paper • 2205.05638 • Published • 3 -
The Power of Scale for Parameter-Efficient Prompt Tuning
Paper • 2104.08691 • Published • 8 -
In-Context Learning Demonstration Selection via Influence Analysis
Paper • 2402.11750 • Published • 2
Collections
Discover the best community collections!
Collections including paper arxiv:2403.03507
-
Scaling Instruction-Finetuned Language Models
Paper • 2210.11416 • Published • 5 -
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 132 -
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Paper • 2403.05530 • Published • 51 -
Yi: Open Foundation Models by 01.AI
Paper • 2403.04652 • Published • 59
-
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Paper • 2403.03507 • Published • 180 -
Mixture-of-Subspaces in Low-Rank Adaptation
Paper • 2406.11909 • Published • 3 -
Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients
Paper • 2406.17660 • Published • 5
-
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Paper • 2403.03507 • Published • 180 -
RAFT: Adapting Language Model to Domain Specific RAG
Paper • 2403.10131 • Published • 65 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 58 -
InternLM2 Technical Report
Paper • 2403.17297 • Published • 26
-
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 50 -
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Paper • 2403.03507 • Published • 180 -
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Paper • 2402.04291 • Published • 48 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 574