#
EETQ

The EETQ library supports int8 per-channel weight-only quantization for NVIDIA GPUS. The high-performance GEMM and GEMV kernels are from FasterTransformer and TensorRT-LLM. It requires no calibration dataset and does not need to pre-quantize your model. Moreover, the accuracy degradation is negligible owing to the per-channel quantization.

Make sure you have eetq installed from the relase page

    pip install --no-cache-dir https://github.com/NetEase-FuXi/EETQ/releases/download/v1.0.0/EETQ-1.0.0+cu121+torch2.1.2-cp310-cp310-linux_x86_64.whl

or via the source code https://github.com/NetEase-FuXi/EETQ. EETQ requires CUDA capability <= 8.9 and >= 7.0

    git clone https://github.com/NetEase-FuXi/EETQ.git
    cd EETQ/
    git submodule update --init --recursive
    pip install .

An unquantized model can be quantized via “from_pretrained”.

    from transformers import AutoModelForCausalLM, EetqConfig
    path = "/path/to/model"
    quantization_config = EetqConfig("int8")
    model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", quantization_config=quantization_config)

A quantized model can be saved via “saved_pretrained” and be reused again via the “from_pretrained”.

    quant_path = "/path/to/save/quantized/model"
    model.save_pretrained(quant_path)
    model = AutoModelForCausalLM.from_pretrained(quant_path, device_map="auto")

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