Mlc_llm_compile

As explained in Domain-Specific Small Language Models

MLC LLM’s command-line utility for compiling a model library according to a generated chat configuration and target device.

mlc_llm_compile builds a platform-specific model library from a generated mlc-chat-config.json file. It targets a device such as an NVIDIA GPU and writes the compiled library to a chosen output path, completing the transition from converted model files to deployable inference artifacts. section 11.3

Use the command after model preparation:

• Ensure the model directory contains mlc-chat-config.json, parameter shards, tokenizer files, and related conversion artifacts. section 11.3
• Create an output directory for compiled libraries. section 11.3
• Pass the chat-configuration path, target device, and output library path to mlc_llm compile. section 11.3
• Load the resulting library with MLCEngine for OpenAI-compatible synchronous or streaming chat completions, or serve it through MLC LLM’s REST API. section 11.3 section 11.5

Compiling for CUDA

mlc_llm compile ./dist/RedPajama-INCITE-Instruct-3B-v1-q4f16_1-MLC/mlc-chat-config.json --device cuda -o dist/libs/RedPajama-INCITE-Instruct-3B-v1-q4f16_1-cuda.so section 11.3

Figure 11.3 The end-to-end workflow for MLC LLM, from model conversion to deployment on the supported platforms’ native runtimes

Use the end-to-end workflow to situate compilation between model conversion, configuration generation, and deployment on supported runtimes. section 11.3

How Domain-Specific Small Language Models covers it

How deep it goes

Defines required inputs and outputs with a CUDA example, but defers operating-system-specific compilation details to official documentation.

Examples

Uses a quantized RedPajama model, mlc-chat-config.json, a CUDA target, and a generated shared-library output.

What it assumes

Assumes converted weights and generated chat configuration, while showing required model shards, tokenizer files, and output-directory preparation.

What you can do after

Operate/configure MLC LLM deployment by compiling a model library and supplying it to MLCEngine or REST serving.

How it connects

Connects model conversion, quantization, chat configuration, MLCEngine, and REST API serving.

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