Compress Your Model

LLM Compressor provides a straightforward way to compress your models using various optimization techniques. This guide walks you through the process of compressing a model with different quantization methods.

Prerequisites

Before you begin, ensure that your environment meets the following prerequisites: - Operating System: Linux (recommended for GPU support) - Python Version: 3.9 or newer - Available GPU: For optimal performance, it's recommended to use a GPU. LLM Compressor supports the latest PyTorch and CUDA versions for compatibility with NVIDIA GPUs.

Select a Model and Dataset

Before you start compressing, select the model you'd like to compress and a calibration dataset that is representative of your use case. LLM Compressor supports a variety of models and integrates natively with Hugging Face Transformers and Model Hub, so a great starting point is to use a model from the Hugging Face Model Hub. LLM Compressor also supports many datasets from the Hugging Face Datasets library, making it easy to find a suitable dataset for calibration.

For this guide, we'll use the TinyLlama model and the open_platypus dataset for calibration. You can replace these with your own model and dataset as needed.

Select a Quantization Method and Scheme

LLM Compressor supports several quantization methods and schemes, each with its own strengths and weaknesses. The choice of method and scheme will depend on your specific use case, hardware capabilities, and chosen trade-offs between model size, speed, and accuracy.

Supported compression schemes include quantization into W4A16, W8A8‑INT8, and W8A8‑FP8 formats, and sparsification. For a more detailed overview of available quantization schemes, see Compression Schemes.

Compression schemes use quantization methods including the following:

Method	Description	Accuracy Recovery vs. Time
GPTQ	Utilizes second-order layer-wise optimizations to prioritize important weights/activations and enables updates to remaining weights	High accuracy recovery but more expensive/slower to run
AWQ	Uses channelwise scaling to better preserve important outliers in weights and activations	Better accuracy recovery with faster runtime than GPTQ
SmoothQuant	Smooths outliers in activations by folding them into weights, ensuring better accuracy for weight and activation quantized models	Good accuracy recovery with minimal calibration time; composable with other methods
Round-To-Nearest (RTN)	Simple quantization technique that rounds each value to the nearest representable level in the target precision.	Provides moderate accuracy recovery in most scenarios. Computationally cheap and fast to implement, making it suitable for real-time or resource-constrained environments.

For this guide, we'll use GPTQ composed with SmoothQuant to create an INT W8A8 quantized model. This combination provides a good balance for performance, accuracy, and compatability across a wide range of hardware.

Apply the Recipe

LLM Compressor provides the oneshot API for simple and straightforward model compression. This API allows you to apply a predefined recipe to your model and dataset, making it easy to get started with compression. To apply what we discussed above, we'll import the necessary modifiers and create a recipe to apply to our model and dataset:

from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor import oneshot

recipe = [
    SmoothQuantModifier(smoothing_strength=0.8),
    GPTQModifier(scheme="W8A8", targets="Linear", ignore=["lm_head"]),
]
oneshot(
    model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    dataset="open_platypus",
    recipe=recipe,
    output_dir="TinyLlama-1.1B-Chat-v1.0-INT8",
    max_seq_length=2048,
    num_calibration_samples=512,
)

When you run the above code, the compressed model is saved to the specified output directory: TinyLlama-1.1B-Chat-v1.0-INT8. You can then load this model using the Hugging Face Transformers library or vLLM for inference and testing.