CVPR2024: Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models
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Updated
Jul 4, 2024 - Python
CVPR2024: Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models
Ultra-Sparse Adaptation of 1-Bit LLMs via XOR Patches
Unlock the potential of finetuning Large Language Models (LLMs). Learn from industry expert, and discover when to apply finetuning, data preparation techniques, and how to effectively train and evaluate LLMs.
[WACV 2024] MACP: Efficient Model Adaptation for Cooperative Perception.
Code for [ICML 2025] Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation
Companion code for the Manning book 'LLM Customization and Fine-Tuning.' Adapt open-weights LLMs end to end: prompting and RAG, LoRA/QLoRA, full SFT, distillation, and DPO/RLHF alignment.
This project demonstrates the application of transfer learning and fine-tuning techniques using a pre-trained ResNet50 convolutional neural network for custom image classification. The model is adapted to classify five different flower species, showcasing how pre-trained models can be efficiently customized for specific tasks with limited data.
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