ai-agent

GPTQModel: Faster AI

Name: GPTQModel: Faster AI
Author: ModelCloud

Compress AI models with GPTQModel, a toolkit for startup founders, with 1.2k+ GitHub stars.

1,202 stars192 forksPythonQuality 8/10Updated 7/9/2026100% free · open source

What it does

GPTQModel compresses large language models to reduce their size and improve deployment efficiency on various hardware platforms, including Nvidia, AMD, Intel, and Apple CPUs and GPUs.

Install / run

pip install git+https://github.com/ModelCloud/GPTQModel.git

When to use it

•When deploying AI models on edge devices with limited storage or computational resources
•When reducing the latency and cost of inference in cloud-based AI applications
•When optimizing AI model size and speed for real-time customer interactions, such as chatbots or voice assistants

Quick start

1Prepare your model by converting it to the Hugging Face format using the `transformers` library
2Create a configuration file (e.g., `quantization_config.json`) specifying the model, hardware accelerator, and desired level of quantization
3Run the quantization process using the command `python -m gptqmodel.quantize --model <your_model_name> --config quantization_config.json`
4Evaluate the performance of the quantized model using the `python -m gptqmodel.evaluate` command
5Fine-tune the quantized model as needed to achieve the desired balance between accuracy and efficiency

Ready-to-paste prompt

python -m gptqmodel.quantize --model gpt2 --config quantization_config.json --hardware accelerator=nvidia

Heads up: Ensure you have the correct version of the `torch` and `transformers` libraries installed, as GPTQModel relies on these dependencies for model quantization and hardware acceleration

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