Principle:Mit han lab Llm awq NVILA Model Construction
| Knowledge Sources | |
|---|---|
| Domains | NLP, Model_Loading |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
Principle of constructing NVILA multimodal models by assembling LLM, tokenizer, and vision components with quantization support.
Description
NVILA model construction involves building the language model and tokenizer as separate components, then assembling them into a multimodal architecture. The process supports multiple quantization backends (QLlama, QMemLlama, FP8 variants) and handles context length extension via RoPE scaling. The tokenizer is augmented with media tokens and stop tokens are inferred from the chat template.
Usage
Apply this principle when deploying NVILA models with specific quantization configurations or custom tokenizer setups.
Theoretical Basis
The construction follows a Builder pattern: separate factory functions handle LLM instantiation (with quantization dispatch), tokenizer setup (with media token augmentation), and component assembly.