Tokens are the fundamental units that LLMs process. Instead of working with raw text (characters or whole words), LLMs convert input text into a sequence of numeric IDs called tokens using a ...

Test-time scaling (TTS) has emerged as a proven method to improve the performance of large language models in real-world applications by giving them extra compute cycles at inference time. However, ...

Token minimizing is the fastest way to lower LLM costs and latency. Learn practical techniques: prompt trimming, compaction, ...

Meta open-sourced Byte Latent Transformer (BLT), an LLM architecture that uses a learned dynamic scheme for processing patches of bytes instead of a tokenizer. This allows BLT models to match the ...