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Advanced Quantization Algorithm for LLMs

read original get Quantization Toolkit for AI → more articles
Why This Matters

AutoRound represents a significant advancement in quantization technology for large language and vision-language models, enabling high accuracy at ultra-low bit widths with minimal tuning. Its broad ecosystem compatibility and efficient processing make it a valuable tool for deploying powerful models more cost-effectively and efficiently, benefiting both industry developers and end-users. This innovation can accelerate the deployment of large models in resource-constrained environments, fostering wider adoption and innovation in AI applications.

Key Takeaways

English | 简体中文

User Guide | 用户指南

🚀 What is AutoRound?

AutoRound is an advanced quantization toolkit designed for Large Language Models (LLMs) and Vision-Language Models (VLMs). It achieves high accuracy at ultra-low bit widths (2–4 bits) with minimal tuning by leveraging sign-gradient descent and providing broad hardware compatibility. See our papers SignRoundV1 and SignRoundV2 for more details. For usage instructions, please refer to the User Guide.

🆕 What's New

✨ Key Features

✅ Superior Accuracy Delivers strong performance even at 2–3 bits example models, with leading results at 4 bits benchmark.

✅ Ecosystem Integration Seamlessly works with Transformers, vLLM, SGLang and more.

✅ Multiple Formats Export Support AutoRound, AutoAWQ, AutoGPTQ, and GGUF for maximum compatibility. Details are shown in export formats

✅ Fast Mixed Bits/Dtypes Scheme Generation Automatically configure in minutes, with about 1.1X-1.5X the model’s BF16 RAM size as overhead. Accuracy results and user guide.

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