Discover how AnLLMs perform across various benchmarks, including OpenBookQA and BoolQ.

Discover the training procedure for AnLLMs using the RedPajama-Data-1TSample dataset, featuring 850,000 samples and 1 billion tokens.

Explore the implementation of AnLLM, featuring two strategies for anchor token selection: using punctuation marks and introducing a new token.

Discover how anchor tokens can optimize the inference process in LLMs by enhancing the keys/values caching mechanism.

Introducing anchor-based attention masks that optimize self-attention in LLMs by controlling how tokens access information across sequences.

Explore the inner workings of decoder-only transformers in LLMs, focusing on self-attention networks and the challenges of keys/values caching during inference.

Discover how our research extends in-context learning by introducing anchor tokens for sequence compression in LLMs.

Learn how anchor-based LLMs compress sequence information to reduce memory demand and boost inference speed.

Anchor-based LLMs show 3.5x speed gains, improve model scalability, and optimize real-time inference by reducing key-value cache lengths in translation tasks.

Learn how Anchor-based LLMs with AnSAN reduce memory use by 99% and boost inference speed by 3.5x.

This analysis explores the flexibility and memory efficiency of AnLLM models in the German-to-English translation task.

The results in this section highlight how AnLLM-EP and AnLLM-AC models achieve strong accuracy across various benchmarks.

10/11/2024: Top 5 stories on the HackerNoon homepage!

Explore the training and testing data settings for AnLLM models, including examples of tokenization and evaluation methods using HuggingFace datasets.