From the 1 of 22 linked papers with an AI index.
2 citations · 2 across the 10 of their papers we have counts for
6 papers · 1 filter
Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA
Mind Lab, :, Vin Bo +80
Macaron-V1 is an open agent-model family for experiential intelligence: learning from experience in real environments and continuing to learn after deployment. It is organized arou…
MinT: Managed Infrastructure for Training and Serving Millions of LLMs
Mind Lab, :, Song Cao +60
We present MindLab Toolkit (MinT), a managed infrastructure system for Low-Rank Adaptation (LoRA) post-training and online serving. MinT targets a setting where many trained polici…
Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routing
Kunfeng Lai, Zhenheng Tang, Xinglin Pan +7
Model merging aggregates Large Language Models (LLMs) finetuned on different tasks into a stronger one. However, parameter conflicts between models leads to performance degradation…
LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
Rui Pan, Xiang Liu, Shizhe Diao +4
The machine learning community has witnessed impressive advancements since large language models (LLMs) first appeared. Yet, their massive memory consumption has become a significa…
Plum: Prompt Learning using Metaheuristic
Rui Pan, Shuo Xing, Shizhe Diao +6
Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models. Special prompts, such as Chain-of-Thought, ha…
Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models
Peijie Dong, Lujun Li, Zhenheng Tang +4
Despite the remarkable capabilities, Large Language Models (LLMs) face deployment challenges due to their extensive size. Pruning methods drop a subset of weights to accelerate, bu…