#transformer-models tag

This Clever AI Hack Could Cut Processing Costs in Half | HackerNoon

Dynamic token allocation in transformer models can significantly increase computational efficiency.

11 months ago

Miscellaneous

Using Large Language Models for Zero-Shot Video Generation: A VideoPoet Case Study | HackerNoon

VideoPoet synthesizes high-quality videos using a transformer model that integrates multiple conditioning signals across various modalities.

This Clever AI Hack Could Cut Processing Costs in Half | HackerNoon

Dynamic token allocation in transformer models can significantly increase computational efficiency.

11 months ago

Miscellaneous

Using Large Language Models for Zero-Shot Video Generation: A VideoPoet Case Study | HackerNoon

VideoPoet synthesizes high-quality videos using a transformer model that integrates multiple conditioning signals across various modalities.

more#machine-learning

3 months ago

Miscellaneous

RNNs vs. Transformers: Innovations in Scalability and Efficiency | HackerNoon

RNNs can be efficiently scaled and trained, providing competitive alternatives to Transformer models for certain applications.

#memory-management

Data science

Evaluating the Performance of vLLM: How Did It Do? | HackerNoon

vLLM was tested using various Transformer-based large language models to evaluate its performance under load.

Miscellaneous

The Generation and Serving Procedures of Typical LLMs: A Quick Explanation | HackerNoon

Transformer-based language models use autoregressive approaches for token sequence probability modeling.

Miscellaneous

Batching Techniques for LLMs | HackerNoon

Batching improves compute utilization for LLMs, but naive strategies can cause delays and waste resources. Fine-grained batching techniques offer a solution.

Data science

Evaluating the Performance of vLLM: How Did It Do? | HackerNoon

vLLM was tested using various Transformer-based large language models to evaluate its performance under load.

Miscellaneous

The Generation and Serving Procedures of Typical LLMs: A Quick Explanation | HackerNoon

Transformer-based language models use autoregressive approaches for token sequence probability modeling.

Miscellaneous

Batching Techniques for LLMs | HackerNoon

Batching improves compute utilization for LLMs, but naive strategies can cause delays and waste resources. Fine-grained batching techniques offer a solution.

more#memory-management

Miscellaneous

Memory Challenges in LLM Serving: The Obstacles to Overcome | HackerNoon

LLM serving throughput is limited by GPU memory capacity, especially due to large KV cache demands.

9 months ago

Data science

Where does In-context Translation Happen in Large Language Models: Inference Efficiency | HackerNoon

Identifying task recognition in transformer models enables significant inference speed-ups.

fromTheregister

10 months ago

Etched scores $120M for an ASIC built for transformer models

Etched 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.