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

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+
+# Optimum Quanto
+
+[Quanto](https://github.com/huggingface/optimum-quanto) is a PyTorch quantization backend for [Optimum](https://huggingface.co/docs/optimum/index). It features linear quantization for weights (float8, int8, int4, int2) with accuracy very similar to full-precision models. Quanto is compatible with any model modality and device, making it simple to use regardless of hardware.
+
+Quanto is also compatible with [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for faster generation.
+
+Install Quanto with the following command.
+
+```bash
+pip install optimum-quanto accelerate transformers
+```
+
+Quantize a model by creating a [`QuantoConfig`] and specifying the `weights` parameter to quantize to. This works for any model in any modality as long as it contains [torch.nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) layers.
+
+> [!TIP]
+> The Transformers integration only supports weight quantization. Use the Quanto library directly if you need activation quantization, calibration, or QAT.
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer, QuantoConfig
+
+quant_config = QuantoConfig(weights="int8")
+model = transformers.AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-3.1-8B", 
+    dtype="auto", 
+    device_map="auto", 
+    quantization_config=quant_config
+)
+```
+
+## torch.compile
+
+Wrap a Quanto model with [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for faster generation.
+
+```py
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, QuantoConfig
+
+quant_config = QuantoConfig(weights="int8")
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+  "openai/whisper-large-v2",
+  dtype="auto",
+  device_map="auto",
+  quantization_config=quant_config
+)
+
+model = torch.compile(model)
+```
+
+## Resources
+
+Read the [Quanto: a PyTorch quantization backend for Optimum](https://huggingface.co/blog/quanto-introduction) blog post to learn more about the library design and benchmarks.
+
+For more hands-on examples, take a look at the Quanto [notebook](https://colab.research.google.com/drive/16CXfVmtdQvciSh9BopZUDYcmXCDpvgrT?usp=sharing).