vllm - 💡(How to fix) Fix [Feature]: Support TurboQuant KV quant [3 comments, 4 participants]

Fix Plan

To address the issue, we will implement a solution using the TurboQuant library.

• Install the required library: pip install turboquant-pytorch
• Import the library and initialize the model:

import torch
from turboquant_pytorch import TurboQuant

# Initialize the model
model = TurboQuant()

• Use the model for extreme compression:

# Compress the model
compressed_model = model.compress(model)

# Save the compressed model
torch.save(compressed_model, 'compressed_model.pth')

• Load and use the compressed model for inference:

# Load the compressed model
compressed_model = torch.load('compressed_model.pth')

# Use the compressed model for inference
output = compressed_model(input_data)

Verification

To verify that the fix worked, check the model's performance on a test dataset before and after compression. Compare the accuracy and latency of the original and compressed models.

Extra Tips

• Make sure to fine-tune the compression parameters for optimal results.
• Consider using other optimization techniques, such as quantization and pruning, in conjunction with TurboQuant for further performance improvements.