Welcome to our comprehensive guide on utilizing SqueezeLLM, a cutting-edge post-training quantization framework! This allows you to deploy large language models (LLMs) with remarkable efficiency by leveraging a novel method known as Dense-and-Sparse Quantization. If you’re looking to serve more extensive models without sacrificing performance, you’ve come to the right place!

What is SqueezeLLM?

At its core, SqueezeLLM aims to make deploying LLMs easier by reducing their hefty memory requirements. The process involves a sophisticated technique where weight matrices are split into two parts: a dense component that can undergo heavy quantization and a sparse component that preserves crucial details. This ensures that while the model consumes less memory, its accuracy and quality remain intact or even improved!

Installation Guide

Let’s get you set up! Follow these steps to install SqueezeLLM:

1. Create a conda environment:

conda create --name sqllm python=3.9 -y
conda activate sqllm

2. Clone and install the dependencies:

git clone https://github.com/SqueezeAILab/SqueezeLLM
cd SqueezeLLM
pip install -e .

3. Setup CUDA:

cd squeezeLLM
python setup_cuda.py install

From-Scratch Quantization

To quantize your own models, simply follow the procedure available in this link.

Supported Models

SqueezeLLM currently supports various models, including:

• LLaMA (7B, 13B, 30B, 65B)
• LLaMA-2 (7B, 13B)
• Instruction-tuned Vicuna (7B, 13B)
• XGen (7B with 8k sequence length)
• OPT (1.3B to 30B)

For each model, we support both 3-bit and 4-bit quantized variants.

Understanding the Dense-and-Sparse Approach

Imagine a large library filled with countless books (these represent the models) housed on shelves (the weight matrices). If you reduce the number of books by summarizing (quantizing) them, it’s easier to maintain, but you risk losing vital information. SqueezeLLM changes the game by categorizing books into two sections: essentials (dense) that can be summarized without losing meaning and rare, special editions (sparse) that require a careful preservation method. This allows us to retain the richness of information while easing storage burdens.

Running the Models

After setting up, you can benchmark and evaluate the models. Here’s how to do it:

Benchmarking 3-bit Models

Use the following command:

CUDA_VISIBLE_DEVICES=0 python llama.py model_path c4 --wbits 3 --load sq-llama-7b-w3-s0.pt --benchmark 128 --check --torch_profile

When using sparse models, append the –include_sparse flag:

CUDA_VISIBLE_DEVICES=0 python llama.py model_path c4 --wbits 3 --load sq-llama-7b-w3-s5.pt --include_sparse --benchmark 128 --check --torch_profile

Troubleshooting

If you encounter issues while using SqueezeLLM, consider the following troubleshooting ideas:

• Ensure all dependencies are correctly installed.
• Verify your CUDA version matches the requirements.
• Check for compatibility issues with specific models.
• Refer to the project documentation for additional support.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.