What indeed can GPT models do in chemistry? A comprehensive benchmark on eight tasks Paper • 2305.18365 • Published May 27, 2023 • 4
O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson? Paper • 2411.16489 • Published about 1 month ago • 40
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 • 60
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 • 53
Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models Paper • 2406.13542 • Published Jun 19 • 16