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

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+
+# SpQR
+
+The [SpQR](https://hf.co/papers/2306.03078) quantization algorithm involves a 16x16 tiled bi-level group 3-bit quantization structure with sparse outliers.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/spqr-diagram.png">
+</div>
+
+> [!TIP]
+> To quantize a model with SpQR, refer to the [Vahe1994/SpQR](https://github.com/Vahe1994/SpQR) repository.
+
+Load a SpQR-quantized model with [`~PreTrainedModel.from_pretrained`].
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+quantized_model = AutoModelForCausalLM.from_pretrained(
+    "elvircrn/Llama-2-7b-SPQR-3Bit-16x16-red_pajama-hf",
+    dtype=torch.half,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("elvircrn/Llama-2-7b-SPQR-3Bit-16x16-red_pajama-hf")
+```