Power-LM Collection Dense & MoE LLMs trained with power learning rate scheduler. • 4 items • Updated Oct 17 • 15
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models Paper • 2403.13372 • Published Mar 20 • 62
MURI: High-Quality Instruction Tuning Datasets for Low-Resource Languages via Reverse Instructions Paper • 2409.12958 • Published Sep 19 • 7
Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale Paper • 2409.17115 • Published Sep 25 • 59
Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization Paper • 2409.12903 • Published Sep 19 • 21
Training Language Models to Self-Correct via Reinforcement Learning Paper • 2409.12917 • Published Sep 19 • 135
Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler Paper • 2408.13359 • Published Aug 23 • 22
Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations Paper • 2403.09704 • Published Mar 8 • 31
LongVILA: Scaling Long-Context Visual Language Models for Long Videos Paper • 2408.10188 • Published Aug 19 • 51
Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters Paper • 2406.05955 • Published Jun 10 • 22
Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies Paper • 2407.13623 • Published Jul 18 • 52
Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models Paper • 2406.13542 • Published Jun 19 • 16
SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark Paper • 2402.05138 • Published Feb 6 • 2
Model Merging Collection Model Merging is a very popular technique nowadays in LLM. Here is a chronological list of papers on the space that will help you get started with it! • 30 items • Updated Jun 12 • 217
Teaching Large Language Models to Reason with Reinforcement Learning Paper • 2403.04642 • Published Mar 7 • 46