Model Compilation

Overview

Model Compiler provides the LLiMa command-line tool llima-compile to compile models from Hugging Face safetensors, GGUF files, or pre-quantized compressed tensor models (GPTQ/AWQ):

llima-compile [options] <model_path>

When you run this command, the tool handles the entire compilation pipeline including calibration, quantization, and code generation. The pipeline consists of several stages that differ slightly depending on the input format:

For HuggingFace Models:

1. DEVKIT - Generate runtime orchestration scripts
2. ONNX - Convert model to ONNX intermediate representation
3. QUANTIZE - Quantize model weights and calibrate
4. COMPILE - Compile to Modalix machine code

For GGUF Models:

1. DEVKIT - Generate runtime orchestration scripts
2. MODEL_SDK_DIRECT - Convert GGUF directly to ModelSDK format (quantization already applied)
3. COMPILE - Compile to Modalix machine code

For Pre-quantized Compressed Tensor Models (GPTQ/AWQ):

1. DEVKIT - Generate runtime orchestration scripts
2. SOURCE_TO_QUANT - Convert compressed tensor model directly to ModelSDK format
3. COMPILE - Compile to Modalix machine code

note

Compressed tensor models are safetensor models pre-quantized with llm-compressor (e.g. GPTQ or AWQ). Supported for LLMs only. A GPU is recommended for the quantization step.

You can run individual stages using --onnx, --quantize, --model_sdk, --compile, or --devkit flags if needed.

The compilation process generates the following directory structure in your output directory:

output_directory/
├── onnx_files/                # ONNX intermediate files (HF models only)
│   └── ...
└── sima_files/                # Compiled model files
    ├── devkit/                # Python runtime orchestration files
    │   ├── tokenizer.json
    │   ├── vlm_config.json
    │   └── ...
    ├── mpk/                   # MPK archives with compiled binaries
    │   ├── layer_0.tar.gz
    │   └── ...
    ├── npy_files/             # LoRA adapter weights (only when compiled with LoRA)
    │   ├── <adapter_name>/
    │   │   └── *.npy
    │   └── ...
    └── ...

Command-Line Arguments

The llima-compile tool accepts various arguments to customize the compilation process. The following tables describe the available options:

Argument	Description
model_path	Input model path (HuggingFace directory, GGUF file, or pre-quantized compressed tensor directory).
-o, --output	Output directory for compiled files. Defaults to the model name.
-c, --configuration_file	Python script to configure precision per layer (e.g., for mixed-precision).
--max_num_tokens	Max context length. Default: 1024.
--resume	Resume interrupted builds by skipping existing files.
-j, --jobs	Number of parallel compilation jobs. Default: Number of physical CPU cores.
--log_level	Logging level (DEBUG, INFO, WARNING, ERROR). Default: WARNING.
--input_height	Input image height in pixels. Required for Siglip2 and Qwen-VL based models.
--input_width	Input image width in pixels. Required for Siglip2 and Qwen-VL based models.

Advanced Argument	Description
--language_group_size	Batch size for parallel token processing during prefill. Larger values (e.g., 256) can improve TTFT for large input prompts, but can decrease TTFT for smaller input prompts. Default: 128.
--future_token_mask_size	Mask size for reusing compiled models across token positions. Larger values reduce number of compiled binary files, but may reduce TPS. Default: 128.
--enable_filter_sharing	Reduces DRAM usage by sharing weights between group and single models. Useful for 16GB Modalix boards. Note: only effective when both group and single models use the same precision. Required when compiling with LoRA.
--quantize_embeddings	Reduces memory consumption through embedding quantization. May result in a loss of accuracy.
--return_logits	Return logits at the last layer output (needed for model evaluator).
--lora_name	Name for the LoRA adapter being compiled alongside the base model.
--lora_path	Path to the LoRA adapter directory to compile with the base model.

Configuration File

The configuration file allows customizing compilation on a per-layer basis, enabling mixed-precision compilation and selective layer compilation.

LLM inference consists of two distinct phases, and the compiler generates optimized models for each:

• Prefill (Group models): Processes the input prompt in batches using language_group_size (e.g., 128 tokens at once). This phase determines TTFT (Time To First Token) and is optimized for throughput.
• Decode (Single-token models): Generates output tokens one at a time autoregressively. This phase determines TPS (Tokens Per Second) and is optimized for low-latency generation.

Because these phases have different performance characteristics, you can apply different quantization strategies to each using the is_group flag in the configuration function.

Input Parameters

The get_layer_configuration function is called for each layer and receives:

• model_properties: Dictionary with {"num_hidden_layers": int}

• layer: Dictionary with:

  • "part": Layer type - "PRE", "CACHE", "POST", or "VISION"
  • "is_group": True for batch processing layers, False for single-token layers
  • "index": Layer index (0 to num_hidden_layers-1)

Return Values

The function returns a dictionary with:

• "precision": Quantization level (required)

  • "BF16": Full precision - best quality, largest size, slowest
  • "A_BF16_W_INT8": Medium quantization - good quality, moderate size
  • "A_BF16_W_INT4": High quantization - acceptable quality, smallest size, fastest

• "compile": Set to False to skip compiling this layer (optional, default: True)

• "lora": LoRA mode for this layer (optional, default: "LORA_DISABLED")

  • "LORA_DISABLED": No LoRA support for this layer. This is the default when no configuration file is provided, resulting in a standard model with no adapter overhead.
  • "LORA_BRANCH": Compiles parallel LoRA branches with zero weights alongside the base model. Adapter weights are loaded from .npy files at runtime, enabling dynamic switching between adapters without restarting the model. Use this mode when you need to swap adapters on the fly.
  • "LORA_MERGED": LoRA weights are merged into the base model weights at runtime. The adapter becomes permanently active for the session with no ability to switch or remove it. Use this mode when you always want the adapter applied and do not need dynamic switching.

note

Best Practice: Use INT8 (A_BF16_W_INT8) for group layers to maintain quality during prefill, INT4 (A_BF16_W_INT4) for single-token layers for fast generation, and BF16 for vision encoders to preserve image understanding quality. For most models, this configuration provides the optimal balance between model accuracy, throughput, and memory usage.

Examples

Example 1: Compiling a Simple LLM

Compile a Llama model, downloaded from Hugging Face, with default settings:

sima-user@docker-image-id:/home/docker$ hf download meta-llama/Llama-3.2-3B --local-dir Llama-3.2-3B-Instruct
sima-user@docker-image-id:/home/docker$ llima-compile Llama-3.2-3B-Instruct -o Llama-3.2-3B-Instruct_out

This will:

• Use default BF16 precision for all layers
• Set context length to 1024 tokens
• Output to Llama-3.2-3B-Instruct_out directory

Example 2: Compiling with Custom Context Length

sima-user@docker-image-id:/home/docker$ hf download meta-llama/Llama-3.2-3B --local-dir Llama-3.2-3B-Instruct
sima-user@docker-image-id:/home/docker$ llima-compile --max_num_tokens 4096 Llama-3.2-3B-Instruct -o Llama-3.2-3B-Instruct_out

This will:

• Use default BF16 precision for all layers
• Set context length to 4096 tokens
• Output to Llama-3.2-3B-Instruct_out directory

Example 3: Compiling Gemma 3 VLM with Mixed Precision

For complex models like Gemma 3 VLM, you may need to specify different precisions for different layers (e.g., keeping the vision encoder in BF16).

1. Download the model:

   sima-user@docker-image-id:/home/docker$ hf download simaai/gemma3-siglip448 --local-dir gemma-3-model

2. Create a configuration file (e.g., config.py):

   def get_layer_configuration(model_properties, layer):
       # Keep vision encoder in full precision
       if layer["part"] == "VISION":
           precision = "BF16"
       # Use INT8 for batch processing layers (better quality)
       elif layer["is_group"]:
           precision = "A_BF16_W_INT8"
       # Use INT4 for single-token layers (smaller size)
       else:
           precision = "A_BF16_W_INT4"
       return {"precision": precision}

3. Run the compiler:

   sima-user@docker-image-id:/home/docker$ llima-compile -c config.py --max_num_tokens 2048 gemma-3-model -o gemma-3-model_out

Example 4: Advanced Configuration

Mixed precision with layer-specific control:

def get_layer_configuration(model_properties, layer):
    # Skip compiling certain cache layers
    if layer["part"] == "CACHE" and layer["index"] > 20:
        return {"compile": False}

    # Higher precision for early layers
    if layer["index"] < 4:
        return {"precision": "BF16"}

    # Standard quantization for middle layers
    return {"precision": "A_BF16_W_INT8"}

Example 5: Compiling an LLM with LoRA

LoRA (Low-Rank Adaptation) allows a base model to be fine-tuned and the adapter to be dynamically applied or removed at runtime without recompiling the base model. The base model is compiled with parallel LoRA branches (initialized to zero), and the adapter weights are compiled separately into .npy files that are loaded on demand.

note

--enable_filter_sharing is required when compiling with LoRA. LoRA branches are always compiled in INT8 even if INT4 is specified, for better accuracy.

1. Download the base model and LoRA adapter:

   sima-user@docker-image-id:/home/docker$ hf download meta-llama/Llama-3.2-3B-Instruct --local-dir Llama-3.2-3B-Instruct
   sima-user@docker-image-id:/home/docker$ hf download <org>/<lora-adapter> --local-dir my-lora

2. Create a configuration file (e.g., lora_config.py):

   The lora key controls LoRA mode per layer. Use "LORA_BRANCH" to enable dynamic switching at runtime.

   def get_layer_configuration(model_properties, layer):
       if layer["is_group"]:
           return {"precision": "A_BF16_W_INT8", "compile": True, "lora": "LORA_BRANCH"}
       else:
           return {"precision": "A_BF16_W_INT4", "compile": True, "lora": "LORA_BRANCH"}

3. Compile the base model with the LoRA adapter:

   sima-user@docker-image-id:/home/docker$ llima-compile Llama-3.2-3B-Instruct \
       --lora_name my_adapter \
       --lora_path my-lora \
       --enable_filter_sharing \
       -c lora_config.py \
       -o Llama-3.2-3B-lora-out

   This compiles the base model with one LoRA branch and automatically compiles the adapter weights into Llama-3.2-3B-lora-out/sima_files/npy_files/my_adapter/.

   Multiple adapters can be compiled in the same step by repeating --lora_name and --lora_path:

   sima-user@docker-image-id:/home/docker$ llima-compile Llama-3.2-3B-Instruct \
       --lora_name my_adapter_A --lora_path my-lora_A \
       --lora_name my_adapter_B --lora_path my-lora_B \
       --enable_filter_sharing \
       -c lora_config.py \
       -o Llama-3.2-3B-lora-out

4. To add more adapters without recompiling the base model, use llima-compile-lora for each additional adapter:

   sima-user@docker-image-id:/home/docker$ llima-compile-lora Llama-3.2-3B-Instruct ./lora-c \
       -w Llama-3.2-3B-lora-out/sima_files/mpk \
       -o Llama-3.2-3B-lora-out/sima_files/npy_files/adapter_c

   llima-compile-lora arguments

   Argument	Description
   base_path	Path to the original base model directory (HuggingFace format).
   lora_path	Path to the LoRA adapter directory to compile.
   -w, --weight_map_path	Required. Path to the mpk/ folder from the base model compilation. Contains the weight maps needed to compile the adapter.
   -o, --output	Output directory for the compiled adapter .npy files. Defaults to the adapter directory name.