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HuggingFace Introduces Quanto: A Python Quantization Toolkit to Reduce the Computational and Memory Costs of Evaluating Deep Learning Models
HuggingFace Researchers have introduced Quanto to address the challenge of optimizing deep learning models for deployment on resource-constrained devices, such as mobile phones and embedded systems. Quanto uses low-precision data types like 8-bit integers (int8) to reduce computational and memory costs, making it easier to deploy large language models on such devices.
Key Features of Quanto:
- Python library designed to simplify the quantization process for PyTorch models
- Support for eager mode quantization, deployment on various devices, and automatic insertion of quantization and dequantization steps within the model workflow
- Streamlines the quantization workflow by providing a simple API for quantizing PyTorch models
- Automates tasks such as inserting quantization and dequantization stubs, handling functional operations, and quantizing specific modules
- Supports int8 weights and activations and int2, int4, and float8, providing flexibility in the quantization process
- Integration with the Hugging Face transformers library for seamless quantization of transformer models
Quanto is a beneficial tool for optimizing deep learning models for deployment on devices with limited resources, as it demonstrates promising reductions in model size and gains in inference speed.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- HuggingFace Introduces Quanto: A Python Quantization Toolkit to Reduce the Computational and Memory Costs of Evaluating Deep Learning Models
- MarkTechPost
- Twitter – @itinaicom
