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The sequential generation of tokens where each step processes a single token based on previous context.
Distinct from Incremental Audio Token Decoding: Distinct from audio or design token decoding; this is the standard LLM auto-regressive decoding step.
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tiny-llm is a large language model inference engine and transformer model implementation. It serves as a quantized model runtime and paged key-value cache manager, providing a specialized inference stack optimized for Apple Silicon. The system distinguishes itself through high-throughput execution techniques, including continuous batching and paged attention. It utilizes a paged memory system to eliminate fragmentation during token generation and employs on-the-fly dequantization of compressed weights to reduce the memory footprint during matrix multiplication. The project covers a broad ran
Generates tokens sequentially by processing an initial prompt followed by iterative single-token steps.