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transformers/docs/source/en/quantization/eetq.md

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+
+# EETQ
+
+The [Easy & Efficient Quantization for Transformers (EETQ)](https://github.com/NetEase-FuXi/EETQ) library supports int8 weight-only per-channel quantization for NVIDIA GPUs. It uses high-performance GEMM and GEMV kernels from [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) and [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM). The attention layer is optimized with [FlashAttention2](https://github.com/Dao-AILab/flash-attention). No calibration dataset is required, and the model doesn't need to be pre-quantized. Accuracy degradation is negligible owing to the per-channel quantization.
+
+EETQ further supports fine-tuning with [PEFT](https://huggingface.co/docs/peft).
+
+Install EETQ from the [release page](https://github.com/NetEase-FuXi/EETQ/releases) or [source code](https://github.com/NetEase-FuXi/EETQ). CUDA 11.4+ is required for EETQ.
+
+<hfoptions id="install">
+<hfoption id="release page">
+
+```bash
+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
+```
+
+</hfoption>
+<hfoption id="source code">
+
+```bash
+git clone https://github.com/NetEase-FuXi/EETQ.git
+cd EETQ/
+git submodule update --init --recursive
+pip install .
+```
+
+</hfoption>
+</hfoptions>
+
+Quantize a model on-the-fly by defining the quantization data type in [`EetqConfig`].
+
+```py
+from transformers import AutoModelForCausalLM, EetqConfig
+
+quantization_config = EetqConfig("int8")
+model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-3.1-8B",
+    dtype="auto",
+    device_map="auto",
+    quantization_config=quantization_config
+)
+```
+
+Save the quantized model with [`~PreTrainedModel.save_pretrained`] so it can be reused again with [`~PreTrainedModel.from_pretrained`].
+
+```py
+quant_path = "/path/to/save/quantized/model"
+model.save_pretrained(quant_path)
+model = AutoModelForCausalLM.from_pretrained(quant_path, device_map="auto")
+```