Analysis of Quantized and Efficiently Adapted Protein Language Models
cs.LG, q-bio.QM
Submitted: 2026-09-30
Updated: 2026-09-30
Terminology
Sources
- Scaling Laws for Neural Language Models
- Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
- A Survey of Quantization Methods for Efficient Neural Network Inference
- QLoRA: Efficient Finetuning of Quantized LLMs
- LoRA: Low-Rank Adaptation of Large Language Models
- Exploring Post-Training Quantization of Protein Language Models
- ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
- Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling
- Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations
- TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
- Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?
- Explaining and Improving Model Behavior with k Nearest Neighbor Representations
- Understanding intermediate layers using linear classifier probes
- Systematic Characterization of LLM Quantization: A Performance, Energy, and Quality Perspective
- Layer-Wise Evolution of Representations in Fine-Tuned Transformers: Insights from Sparse AutoEncoders
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