AI BriefingTransformers Supports GPTQ
Key point
Hugging Face integrates AutoGPTQ into Transformers to support 2-8 bit quantization of LLMs.
Details
Hugging Face has integrated the AutoGPTQ library into Transformers, supporting 2, 3, 4, and 8-bit precision quantization via the GPTQ algorithm.
GPTQ is a post-training quantization (PTQ) method that minimizes accuracy loss during 4-bit quantization while reducing memory usage by approximately 4x. It also lowers the weight bit width to reduce data communication time, which can be expected to improve inference speed.
Key supported features are as follows:
- Hardware: Support for Nvidia GPUs and RoCm-based AMD GPUs
- Workflow: Support for quantization via Optimum, model execution via Text-Generation-Inference, and fine-tuning of quantized models using PEFT
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