Language
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AI
TensorRT-LLM: A Comprehensive Guide to Optimizing Large Language Model Inference for Maximum Performance
As the demand for large language models (LLMs) continues to rise, ensuring fast, efficient, and scalable inference has become more…
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AI
EAGLE: Exploring the Design Space for Multimodal Large Language Models with a Mixture of Encoders
The ability to accurately interpret complex visual information is a crucial focus of multimodal large language models (MLLMs). Recent work…
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AI
AI Language Showdown: Comparing the Performance of C++, Python, Java, and Rust
The choice of programming language in Artificial Intelligence (AI) development plays a vital role in determining the efficiency and success…
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AI
Jamba: AI21 Labs’ New Hybrid Transformer-Mamba Language Model
Language models have witnessed rapid developments, with Transformer-based architectures leading the way in natural language processing. However, as models grow…
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AI
Improving Retrieval Augmented Language Models: Self-Reasoning and Adaptive Augmentation for Conversational Systems
Large language models often struggle with delivering precise and current information, particularly in complex knowledge-based tasks. To overcome these hurdles,…
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AI
SGLang: Efficient Execution of Structured Language Model Programs
Large language models (LLMs) are increasingly utilized for complex tasks requiring multiple generation calls, advanced prompting techniques, control flow, and…
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AI
Tracking Large Language Models (LLM) with MLflow : A Complete Guide
As Large Language Models (LLMs) grow in complexity and size, tracking their performance, experimentation, and implementations becomes increasingly challenging. This…
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