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Found 8 papers, 3 papers with code
Llama 3 Meets MoE: Efficient Upcycling
Scaling large language models (LLMs) significantly improves performance but comes with prohibitive computational costs.
Ranked #3 on
Multi-task Language Understanding
on MMLU
(using extra training data)
Upcycling Large Language Models into Mixture of Experts
Upcycling pre-trained dense language models into sparse mixture-of-experts (MoE) models is an efficient approach to increase the model capacity of already trained models.
LLM Pruning and Distillation in Practice: The Minitron Approach
We present a comprehensive report on compressing the Llama 3. 1 8B and Mistral NeMo 12B models to 4B and 8B parameters, respectively, using pruning and distillation.
Nemotron-4 340B Technical Report
We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward.
NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment
Aligning Large Language Models (LLMs) with human values and preferences is essential for making them helpful and safe.
Nemotron-4 15B Technical Report
We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens.
Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching
Cross-device user matching is a critical problem in numerous domains, including advertising, recommender systems, and cybersecurity.
3D Object Detection on Point Clouds using Local Ground-aware and Adaptive Representation of scenes' surface
A novel, adaptive ground-aware, and cost-effective 3D Object Detection pipeline is proposed.
