6 papers
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
Prateek Yadav, Colin Raffel, Mohammed Muqeeth +6
The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to a particular domain or task. Model MoErging methods…
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization
Prateek Yadav, Leshem Choshen, Colin Raffel +1
Parameter-efficient fine-tuning (PEFT) techniques make it possible to efficiently adapt a language model to create "expert" models that specialize to new tasks or domains. Recent t…
Glider: Global and Local Instruction-Driven Expert Router
Pingzhi Li, Prateek Yadav, Jaehong Yoon +4
The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to particular domains. This has enabled the creation o…
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim +30
Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to…
RSQ: Learning from Important Tokens Leads to Better Quantized LLMs
Yi-Lin Sung, Prateek Yadav, Jialu Li +2
Layer-wise quantization is a key technique for efficiently compressing large models without expensive retraining. Previous methods typically quantize the weights of each layer by "…
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code
Taishi Nakamura, Mayank Mishra, Simone Tedeschi +42
Pretrained language models are an integral part of AI applications, but their high computational cost for training limits accessibility. Initiatives such as Bloom and StarCoder aim…