Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning
Paper • 2310.20587 • Published • 18
Papers - Fine-tuning - LoRA
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MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data
Paper • 2304.08247 • Published • 2 -
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
Paper • 2311.03285 • Published • 30 -
WavLLM: Towards Robust and Adaptive Speech Large Language Model
Paper • 2404.00656 • Published • 11 -
OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data
Paper • 2404.12195 • Published • 12 -
OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
Paper • 2404.14619 • Published • 126 -
Stylus: Automatic Adapter Selection for Diffusion Models
Paper • 2404.18928 • Published • 15 -
LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report
Paper • 2405.00732 • Published • 122 -
In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering
Paper • 2311.06668 • Published • 5 -
A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA
Paper • 2312.03732 • Published • 12 -
CLEAR: Character Unlearning in Textual and Visual Modalities
Paper • 2410.18057 • Published • 209 -
LoRA vs Full Fine-tuning: An Illusion of Equivalence
Paper • 2410.21228 • Published • 3 -
Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems
Paper • 2408.16293 • Published • 27 -
AnglE-optimized Text Embeddings
Paper • 2309.12871 • Published • 3 -
No More Adam: Learning Rate Scaling at Initialization is All You Need
Paper • 2412.11768 • Published • 43