Artificial Intelligence, Computer Science
Author: LLama 2 7B Chat
Page 64/179
LLaMA-2, the next generation of LLaMA. Meta trained and released LLaMA-2 in three model sizes: 7, 13, and 70 billion parameters. The model architecture remains largely unchanged from that of LLaMA-1 models, but 40% more data was used to train the foundational models. The accompanying preprint also mentions a model with 34B parameters that might be released in the future upon satisfying safety targets.
Shaving Off Redundancies in Hamming’s Razor
Computation and Language, Computer Science
Answer: Yes
Computer Science, Machine Learning
URLLC and eMBB in 5G Industrial IoT: A Survey
Artificial Intelligence, Computer Science
Generating Encodings for Plans with Lowest Bound N
Computer Science, Computer Vision and Pattern Recognition
Benchmarking Generative Models with Artworks
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German Text Simplification: A Comprehensive Overview
Computer Science, Computer Vision and Pattern Recognition
Enhancing Neural Network Robustness through Domain Adaptation
Segmenting Images with Hierarchical CRFs: A Comprehensive Review
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Unsupervised Modeling of Syntactic Categories in Natural Language Processing
K-Nearest Neighbor Method Achieves Excellent Estimation Performance in Synthetic Labeling: A Non-Asymptotic Study
Computer Science, Human-Computer Interaction
Improving End-User Programming with Automated Demonstrations
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Efficient AI System with Decoupled Structural and Quantitative Knowledge
Computation and Language, Computer Science
Error in Extracting Named Entities Leads to Difficulty in Generating REFUTES and NEI
Computer Science, Distributed, Parallel, and Cluster Computing
Avoiding Sinkholing in Load Balancing with Prequal
Computer Science, Machine Learning
Revealing Unpredicted Undesirable Behavior in Complex Systems with Digital Twins
Computer Science, Computer Vision and Pattern Recognition
Visual Quality Assessment and Human Attention Prediction: A Unified Model
Computation and Language, Computer Science
Fine-Tuning Language Models for Extreme Performance in Reading Comprehension and Other NLP Tasks
Computer Science, Machine Learning
Optimal Meta-Learning Settings for Multi-Modal Federated Learning
Computer Science, Programming Languages