New AI Method Lets Models Decide What to Think About | HackerNoonMixture-of-Depths Transformers improve efficiency in transformer architectures by dynamically allocating computational resources.
This Clever AI Hack Could Cut Processing Costs in Half | HackerNoonDynamic token allocation in transformer models can significantly increase computational efficiency.
Using Large Language Models for Zero-Shot Video Generation: A VideoPoet Case Study | HackerNoonVideoPoet synthesizes high-quality videos using a transformer model that integrates multiple conditioning signals across various modalities.
Leveraging the Transformer Architecture for Music Recommendation on YouTubeTransformers can enhance music recommendations by understanding user actions within context, addressing current system limitations in predicting evolving preferences.
Deep Learning Architecture: Naive Retrieval-Augmented Generation(RAG)Naive RAG simplifies data retrieval and generation processes through indexing, retrieving, and generating, optimizing response accuracy for user queries.
New AI Method Lets Models Decide What to Think About | HackerNoonMixture-of-Depths Transformers improve efficiency in transformer architectures by dynamically allocating computational resources.
This Clever AI Hack Could Cut Processing Costs in Half | HackerNoonDynamic token allocation in transformer models can significantly increase computational efficiency.
Using Large Language Models for Zero-Shot Video Generation: A VideoPoet Case Study | HackerNoonVideoPoet synthesizes high-quality videos using a transformer model that integrates multiple conditioning signals across various modalities.
Leveraging the Transformer Architecture for Music Recommendation on YouTubeTransformers can enhance music recommendations by understanding user actions within context, addressing current system limitations in predicting evolving preferences.
Deep Learning Architecture: Naive Retrieval-Augmented Generation(RAG)Naive RAG simplifies data retrieval and generation processes through indexing, retrieving, and generating, optimizing response accuracy for user queries.
RNNs vs. Transformers: Innovations in Scalability and Efficiency | HackerNoonRNNs can be efficiently scaled and trained, providing competitive alternatives to Transformer models for certain applications.
Evaluating the Performance of vLLM: How Did It Do? | HackerNoonvLLM was tested using various Transformer-based large language models to evaluate its performance under load.
The Generation and Serving Procedures of Typical LLMs: A Quick Explanation | HackerNoonTransformer-based language models use autoregressive approaches for token sequence probability modeling.
Batching Techniques for LLMs | HackerNoonBatching improves compute utilization for LLMs, but naive strategies can cause delays and waste resources. Fine-grained batching techniques offer a solution.
Evaluating the Performance of vLLM: How Did It Do? | HackerNoonvLLM was tested using various Transformer-based large language models to evaluate its performance under load.
The Generation and Serving Procedures of Typical LLMs: A Quick Explanation | HackerNoonTransformer-based language models use autoregressive approaches for token sequence probability modeling.
Batching Techniques for LLMs | HackerNoonBatching improves compute utilization for LLMs, but naive strategies can cause delays and waste resources. Fine-grained batching techniques offer a solution.
Memory Challenges in LLM Serving: The Obstacles to Overcome | HackerNoonLLM serving throughput is limited by GPU memory capacity, especially due to large KV cache demands.
Where does In-context Translation Happen in Large Language Models: Characterising Redundancy in Laye | HackerNoonCritical layers in pre-trained transformers are essential for task execution and locating specific tasks, impacting overall model performance.
Quantum Computers Can Run Powerful AI That Works like the BrainTransformers are a key component in driving the AI boom, with the potential to be run on quantum computers for even greater advancements.
Where does In-context Translation Happen in Large Language Models: Characterising Redundancy in Laye | HackerNoonCritical layers in pre-trained transformers are essential for task execution and locating specific tasks, impacting overall model performance.
Quantum Computers Can Run Powerful AI That Works like the BrainTransformers are a key component in driving the AI boom, with the potential to be run on quantum computers for even greater advancements.
TTT models might be the next frontier in generative AI | TechCrunchEfficiency challenge of transformers due to increasing power demand is pushing for new architectures like test-time training models (TTT) as a potential solution.
Where does In-context Translation Happen in Large Language Models: Inference Efficiency | HackerNoonIdentifying task recognition in transformer models enables significant inference speed-ups.
TTT models might be the next frontier in generative AI | TechCrunchEfficiency challenge of transformers due to increasing power demand is pushing for new architectures like test-time training models (TTT) as a potential solution.
Where does In-context Translation Happen in Large Language Models: Inference Efficiency | HackerNoonIdentifying task recognition in transformer models enables significant inference speed-ups.
Researchers jimmy OpenAI's and Google's closed modelsResearchers discovered an attack on AI services to reveal hidden parts of transformer models through API queries.The attack can expose the embedding projection layer of black box models, costing from a few dollars to several thousand depending on model size.
Etched scores $120M for an ASIC built for transformer modelsEtched is developing an inference chip, Sohu, specialized in serving transformer models, claiming a 20x performance advantage over Nvidia's H100 by focusing on a specific type of AI model.
Etched is building an AI chip that only runs one type of model | TechCrunchGenerative AI companies are seeking alternative chip providers to challenge dominant players like Nvidia.